a survey on localization techniques for wireless networks

Journal of the Chinese Institute of Engineers, Vol. 29, No. 7, pp. 1125-1148 (2006)

1125

A SURVEY ON LOCALIZATION TECHNIQUES FOR WIRELESS NETWORKS (Invited) Santosh Pandey and Prathima Agrawal*

ABSTRACT Wireless networks have displaced the well established and widely deployed wired communication networks of the past. Tetherless access and new services offered to mobile users contribute to the popularity of these networks. Thus users have access from many locations and can roam ubiquitously. The knowledge of the physical location of mobile user devices, such as phones, laptops and PDAs, is important in several applications such as network planning, location based services, law enforcement and for improving network performance. A device’s location is usually estimated by monitoring a distance dependent parameter such as wireless signal strength from a base station whose location is known. In practical deployments, signal strength varies with time and its relationship to distance is not well defined. This makes location estimation difficult. Many location estimation or localization schemes have been proposed for networks adopting a variety of wireless technologies. This paper reviews a broad class of localization schemes that are differentiated by the fundamental techniques adopted for distance estimation, indoor vs. outdoor environments, relative cost and accuracy of the resulting estimates and ease of deployment. The paper exposes many challenges that remain and elaborates on several future research problems that need to be solved. Key Words: location estimation, survey, localization factors, localization classification, infrastructure network, ad hoc network, sensor network.

I. INTRODUCTION Today’s wireless applications like cellular phone services or television broadcast are a part of the day to day life of many people. Wireless communication has evolved immensely from the time it was first implemented. The ease of setting up a wireless network, tetherless communication and low cost of deployment (as compared to a wired network) are some of the key reasons for its popularity. Also, the reliability of wireless communication has improved significantly and is reflected in its application to areas such as police radio, military communication, and disaster recovery services. This reliability is not only reflected in such public safety applications, but also in many civilian applications. Currently, many people *Corresponding author. (Email: [email protected]) The authors are with the Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.

have wireless cellular phones as their primary means of contact. Similarly, it is common to carry out a secure transaction over the Internet through a wireless local area network (WLAN). City wide WLAN deployments are planned in many metropolitan areas. The above examples indicate the widespread implementation of wireless networks, making wireless communication a technology that is available “anytime” and “anywhere”. Wireless communication, thus, is no longer a luxury but a necessity. Almost all wireless networks support user mobility which makes it possible for users to connect and roam within a large area called coverage region. The coverage region is the physical area from where the wireless network can be accessed. The freedom to connect from anywhere within the coverage region along with the seamless mobility between neighboring base stations, makes it possible for users to communicate tetherlessly. However, this also makes it difficult to trace the user’s physical location. The physical location information

1126

Journal of the Chinese Institute of Engineers, Vol. 29, No. 7 (2006)

of the wireless user may be very important in some wireless network deployments. For example, the current cellular phone networks are required to implement the E911 (enhanced 911, http://www.fcc.gov/ 911/enhanced/) service in the near future. Thus the E911 user location can be obtained when he/she makes an emergency 911 call. There are manyother examples (discussed later in section II) where location information is desired. The process of estimating the physical location of a wireless device is called localization. Such location is estimated relative to a reference location. In order to explain the localization process we develop a framework as shown in Fig. 1. The wireless device whose location is to be estimated is called localization node (LN), while the network entity with known location is called the localization base station (LBS). For example, in a wireless local area network (WLAN), the LNs may be devices such as laptops and PDAs, while the LBS may be the WiFi access points (APs). For a sensor network the regular nodes whose locations are to be estimated are referred as LNs while the ‘anchor nodes’ (special nodes whose locations are known) are referred to as LBSs. Usually, LN is a low-cost simple device, while LBS is comparatively expensive with more resources. We now explain localization using the above framework. Localization is usually carried out by measuring certain distance dependent parameters of the wireless radio link between the LN and several LBSs. This parameter may be measured at either the LN or LBS. One such parameter is signal strength (SS) of the received signals. Recall that the SS varies inversely with square of the distance in free space. The time taken for the signal to travel between LN and LBS is proportional to the distance between them and can also be used as the parameter for localization. It is referred as the time of arrival (ToA). Other distance related parameters are explained in Section III. In an ideal environment, the distance between LN and LBS can be accurately estimated by using such relationships between the measured parameter and distance. Considering circular transmission range, the location of LN would hence be the intersection of arcs from different LBSs with radius equal to the respective distance estimates. In practical deployments the measured value of the parameter varies with time at a given location and its relation with distance is not well defined. This makes the process of localization complicated. The time variation of the parameter values are caused by factors such as change in the environment (wind, temperature, movement of people), fading, and mobility. Specifically, SS is affected by wireless channel fading and ToA is affected by the delay caused by processing queues at the source or destination.

LBS2 LBS1 Localization node (LN) Localization base station (LBS) LBS3 Fig. 1 Localization framework

Usually the distance between the transmitter and receiver is estimated from the measured parameter values using a pre-defined model (for example the inverse square law model for SS in free space). For cases where such models are not well defined (as in the case of SS in an indoor environment), a location lookup table (LLT) is created to map the measured parameter values to different locations. The LLT can be manually generated with significant effort. Moreover, it can be rendered useless after some time due to the dynamics of the wireless environment. So the challenge is to construct the lookup table intelligently on a demand basis. Accurate localization is difficult to achieve, yet it may be important in some situations, to obtain a good estimate of a wireless device location. For example, government requirements of E911, would necessitate localization of cellular phone users. In many wireless networks, location of a wireless device is required for its functionality. For example, in a sensor network a geographical location based routing would require the location of devices to setup route paths for packets (Ko and Vaidya, 2000). In general, the choice of underlying physical technology, measured parameter, estimation technique, etc affect the accuracy and cost of localization. Due to such factors and other challenges (discussed in details in Section VI), localization is still considered a difficult and challenging problem in the research community. As an example of a localization system consider an indoor WLAN deployment. Generally for indoor WLAN, an initial ‘training’/’off-line’ phase is carried out to generate an LLT. In this phase, the parameter under consideration is measured at various locations throughout the deployment site. These measurements may be stored in the form of a table (location lookup table) which contains the parameter values at different locations. Hence, a radio map (mapping between parameter value and location) of the deployment site is created. After the training phase, the ‘real-time’/’online’ phase is implemented.

S. Pandey and P. Agrawal: A Survey on Localization Techniques for Wireless Networks

In this phase, the location of the wireless device is estimated based on the current measured value of the parameter and the LLT. A simple table lookup may be carried out, wherein the estimated location is the location in the LLT corresponding to the parameter entry with least mean square error to the current measured parameter value. The issues such as construction and maintenance of LLT, choosing the parameter, estimation technique and physical layer technology influence the choice of localization scheme for this case. RADAR (Bahl and Padmanabhan, 2000), LEASE (Ganu et al., 2004), and TRaVarSeL (Anjum et al., 2005) are examples of localization schemes for WLAN networks. Many localization schemes such as (Capkun et al., 2005), ROPE (Lazos et al., 2005), and (Pandey et al., 2005) have been proposed recently. These schemes use different choice of parameters and are suitable for different networks. This paper discusses the various localization schemes proposed for different wireless networks such as WLAN and cellular network. The objective of this paper is to provide a framework for understanding existing and future localization schemes. First, the localization schemes are classified into various categories, based on the underlying physical layer technology, measured parameter, construction of LLT, and estimation techniques. Next, the characteristics of a localization scheme are studied based on the characteristics of its implementation in each of the categories. For example, localization schemes may be classified as Ultrasound (US) (Priyantha et al., 2000) or RF (Capkun et al., 2001) based schemes depending on the underlying physical layer technology used for localization. In this case, US and RF are the implementations for the category corresponding to physical layer technology. RF operates at much higher frequency than US and hence has a higher time resolution requirement for ToA schemes. This results in higher accuracy but more expensive hardware for RFbased localization schemes as compared to US-based schemes. Similarly other characteristics of localization schemes can be studied based on such classification. The paper is organized as follows. Location based applications are described in Section II to indicate the motivation for performing localization in a wireless network. The classification of localization schemes into appropriate categories is provided in Section III. Based on this classification, some of the key localization schemes are described in Section IV. The factors that influence the decision of choosing a localization scheme is outlined in Section V. Finally, the paper concludes with a discussion of research issues related to the localization problem in Section VI.

1127

II. LOCATION BASED APPLICATIONS As wireless networks have gained popularity, many location based services are being deployed. Localization plays a key role in ubiquitous computing which refers to the embedding of computers “everywhere” and also enable their easy interaction with people. Different services can be offered based on the location of the user (Hightower and Borriello, 2001). For example, the nearest printer may be selected to print the user’s document. In an enterprise network, access to the network or certain security files can be restricted to users present only in specific locations. Such location based access filtering and authentication simplifies the key distribution and management in a large enterpise (Faria and Cheriton, 2004). Localization also plays a key role in the emerging mobile services. Mobile services such as location specific advertising, location sensitive information service, and tracking services are proposed (Zeimpekis et al., 2003). Location information is also important in network planning, especially to cellular network deployments wherein extensive drive-test is carried out to plan and maintain the base station parameters. Instead of such drivetests, the location and performance of the user devices can be used as a low-cost alternative for network maintenance and planning (IST-2000-25382-CELLO, 2004). Similar planning tools such as AirMagnet (http://www. airmagnet.com/) and WiSE (http://www.belllabs. com/org/wireless/wisext.html) are available for WLAN networks. Sensor networks are deployed to collect/sense the data from a large region. In many cases, such as intruder detection, the location of the data is as important as the data itself. Asset management (also known as fleet management) relates to the management of the assets such as personnel and vehicles of a large company (for example, workers and trucks of a construction company). In this case, location of the various entities is required to efficiently track and manage the company resources. Thus localization is important in these upcoming areas such as ubiquitous computing, enterprise networks, mobile services, network planning, sensor networks, and asset management. We now look into specific applications in some key areas. The most prominent amongst the localization techniques is the global positioning system (GPS) (Guard, 1996). The GPS receiver estimates its location (latitude, longitude and altitude) based on satellite broadcasts (details in Section IV). Although, GPS is widely used in outdoor environments, it is not suitable for indoor or cluttered environments. Also, incorporating GPS receivers would be costly (in terms of expense, power consumption and resources) for standard laptops and sensor nodes. GPS system for

1128


civilian use is not very secure and can be easily compromised (Kuhn, 2004). Hence GPS is not favorable for many applications. However, it is simple and is a widely used navigational tool in cars, planes and ships. Today, some cars are equipped with GPS systems that support services such as locating the nearest gas station. The number of cellular phone users in USA was 158.722 million in 2003 (http://www.cia.gov/cia/publications/factbook/geos/us.html). Today, this has increased significantly and is gradually replacing a large number of wired phones. The FCC Wireless E911 mandates that location information of the subscribers should be available at Public Safety Answering Points when the subscriber makes a 911 emergency call from his/her cell phone. Thus, localization facility would be mandatory in cellular networks shortly. Currently, GPS is used for handset based implementation (example, Sprint) while cellular network based ToA techniques are used for network based implementations (example Cingular). The current cellular phone standards namely 2.5G and 3G support multimedia data transfer between users and the network. This has developed a market for content providers who sell games, music, and videos to cellular users (Zeimpekis et al., 2003). Amongst these content providers are companies that provide location based services. For example, OpenMotion (http://www.openmotion.com/corp/news 10 6 04.asp) provides advertisement of local merchants to cellular phone users based on their current location. Location information can also be used by cellular companies to implement location based services such as location based billing and indicating nearest prepaid card retailer or help center to its subscribers. A project supported by European Commission called ‘CELLO: Cellular network optimisation based on mobile location’ (http://www.telecom.ece.ntua.gr/ cello/) studies the application of location of mobile users in cellular networks (IST-2000-25382-CELLO, 2004). It investigates the use of mobile user location for network management and planning (recording drop call location), adaptive coverage system (intelligent network capacity allocation) and intelligent location aided handover of mobile users. The widespread deployment of wireless network in enterprise and commercial establishments has also accelerated the development of location based services for these private networks. The main goal of these services is to utilize the location information in order to increase the user efficiency or customer satisfaction. For example, PanGo Networks is working on a wireless location based asset-tracking project for Rockford Memorial Hospital (http://www. pangonetworks.com/News Events/News/10 19%20Networld.htm). The project aims at locating

and tracking equipment in order to reduce its operating cost and increase the efficiency in the workplace. For achieving this, each equipment is tagged with a small 802.11 radio device and Wi-Fi is used as an underlying technology to estimate its location. The tags send updated location information to a central database whenever the equipment is moved. The project also plans to extend the technology to monitor Alzheimer’s patients and infants within the hospital premises. Another area of increasing interest is location based access control, wherein a user is granted access to network resources based on his/her location. In such a case, secure localization schemes would make it possible to implement organizational policies that restrict access to network resources, such as database access or higher bandwidth, based on the location of the wireless user. These organizational policies could include guidelines to track malicious users within the organization’s physical perimeter or to restrict the locations from where users can access confidential material. In (Faria and Cheriton, 2004) the authors describe a key less authentication scheme for network access using wireless user’s location. Location based access control can also facilitate applications such as the interactive dance club (Hromin et al., 2003) where it is necessary to determine that a user is in “valid” locations before allowing her to communicate (interact) with the system. Localization is an integral part of most sensor networks where the data collected is mapped to its originating physical location. For example consider ZebraNet (Juang et al., 2002), a sensor network deployed to monitor the migration of zebras. Here the sensor nodes were strapped on zebras to take periodic measurement of their location and relevant biometric data. A GPS receiver is used in this case due to the sparse nature of deployment. The location data was used to study the migration patterns of zebras. Similarly sensor networks for habitat monitoring (Szewczyk et al., 2004), health monitoring, and intruder detection have prominent uses of location information. Many aspects of sensor/ad-hoc networks, such as location-based routing (Ko and Vaidya, 2000), and data aggregation use localization. The above examples indicate the importance of localization and the current focus in developing efficient localization solution for different wireless networks. As pointed out earlier, several localization schemes exist. In the following section, we classify them into different categories for better understanding. III. CLASSIFICATION OF LOCALIZATION SCHEMES The task of localization can either be integrated


1129

Localization

Parameter

Area of deployment

PHY

Wide Area

Signal strength

Localizing entity Lookup table

Deterministic

Time of arrival

Local Area

Security

Estimation technique

Probabilistic

Secure

Open Network

RF Ad Hoc

Measurement Connectivity Ultrasound

Infrared

Client

Agent Area of arrival

Model

Fig. 2 Localization Classification

with the communications network or can be achieved by an independent network. An example of the former is RADAR (Bahl and Padmanabhan, 2000) and the later is GPS. In RADAR, the underlying communication network (802.11 WLAN) is used for localization as location of the user is estimated based on his/ her 802.11 SS value. HTA (Venkatraman and Caffery, 2004), HORUS (Youssef and Agrawala, 2005) and DV-Hop (Nagpal et al., 2003) are examples of such schems. However, for a cellular network user localized via GPS technology, the communication network is the cellular network while GPS is the independent localization network. Other examples of such dedicated localization system as GPS, are Active Badge (Want et al., 1992), BAT (Ward et al., 1997) and SpotOn (Hightower et al., 2001), (Hightower et al., 2000). For such cases we are interested only in the localization network. In general, considering the framework developed in Section I, procedure for localization can be summarized as: 1) Physical layer (PHY) and a physical parameter that varies with distance, are chosen for the localization scheme. The location of a wireless device (LN) is estimated based on the measured values of the physical parameter. The parameter is related to the radio link between the LN and LBS and can be measured by either entity. 2) The relationship between the physical parameter values and the various locations in the deployment site has to be determined prior to the actual deployment of the localization scheme. Such a

relationship is stored in a lookup table (location lookup table), which is constructed by actual measurements or by using existing parameter models. 3) After building the location lookup table, the localization scheme is deployed. The current measured physical parameter values of a LN are used to estimate LN’s location from the LLT. We create several categories of localization such as area of deployment, type of physical layer and measured parameter, based on the above steps. These categories are shown in Fig. 2. Various implementation options are possible in each category. They are shown as branches of the respective category in the figure. For example, different types of physical layers, such as infrared (IR), ultrasound (US), or RF, can be chosen. Localization schemes are classified based on such implementations for each category. Active Badge (Want et al., 1992), Cricket (Priyantha et al., 2000) and RADAR (Bahl and Padmanabhan, 2000) are examples of the schemes classified as IR-based, US-based and RF-based localization schemes for the ‘type of physical layer’ category. The description of different implementations in all categories as shown in Fig. 2, follows next. 1. Area of Deployment Localization schemes for wireless networks of different deployment area are predominantly different. This is due to the differences in network topology, number of users, and available resources for such networks. Depending on the deployment area and type of wireless network, the localization scheme may be

1130


Fig. 3 Localization schemes for different area of deployment

classified into wide area localization, local area localization and ad-hoc localization as shown in Fig. 3.

by predominantly indoor implementation throughout an enterprise or commercial establishment, relatively cheap LBS, and lower area of coverage as compared to a wide area network. The schemes such as Active Badge (Want et al., 1992), BAT (Ward et al., 1997), Nibble (Castro et al., 2001) are examples of LAL. Also, WLAN is a popular example where LAL such as (Bahl and Padmanabhan, 2000; Krishnan et al., 2004; Pandey et al., 2005) may be applied. For WLAN, the wireless access points act as LBSs while devices such as laptops and PDAs are the LNs. Due to the indoor deployment, GPS is not used. Localization is usually carried out based on a distance varying parameter (such as received signal strength). The main challenge in this form of localization is to overcome the error due to cluttered indoor environment, multipath and movement of people.

(i) Wide Area Localization (WAL) (iii) Ad Hoc Localization (AHL) Wide area localization (WAL) is characterized by predominantly outdoor deployment, expensive longrange LBS and no power constraint at the LBS. Localization in cellular networks is an example of wide area localization. Usually, GPS is used to localize the LN (accuracy 6-12 meters (Garmin Ltd., 2005)). However, the inherent cellular network infrastructure can also be used for this purpose (Caffrey and Stber, 1998; Sakagam et al., 1992; Laitinen et al., 2001; Swales et al., 1999; Najar et al., 2004; Venkatraman and Caffery, 2004; Chen, 1999). In fact, for the E911 requirement in cellular networks, some carriers (Cingular and T-mobile) have adopted ToA as the localization technique. The time of arrival of the cellular phone signal to multiple base stations is recorded. The location of phone is estimated using readings from atleast three base stations. In this case LBSs are the base stations and LNs are the cellular phones. Multiple or hybrid localization schemes may be employed to obtain higher accuracy (Venkatraman and Caffery, 2004). For example, in assisted GPS (AGPS), the correction signals are transmitted by cellular base stations to aid GPS localization of the GPS enabled phone. The estimated location may be reported back to the base station and is shown to have higher accuracy compared to normal GPS (Djuknic and Richton, 2001; Moeglein and Krasner, 1998). Note that some of the deployment area may consist of indoor environment or non line of sight (NLOS) regions (no direct path to LBS). In these cases, the localization accuracy usually falls (Venkatraman and Caffery, 2004). The challenge in this case is to maintain the accuracy of localization throughout the entire deployment site. (ii) Local Area Localization (LAL) Local area localization (LAL) is characterized

Ad hoc/sensor networks are power constrained and may be heterogeneous in nature. Examples of AHL are SeRLoc (Lazos and Poovendran, 2004), DVHop (Nagpal et al., 2003), APIT (He et al., 2003), SPA (Capkun et al., 2001), and AHLoS (Savvides et al., 2001). The algorithmic requirements of AHLs are low-power consumption, lower computation and communication costs. In wireless sensor networks (WSN), usually node locations are determined relative to location of reference nodes called ‘anchor nodes’. The anchor nodes may be mobile (Corke et al., 2003; Sichitiu and Ramadurai, 2004) or stationary (He et al., 2003; Lazos and Poovendran, 2004). For AHL, sensor nodes such as (Crossbow Technology Inc., 1999; Smart Dust, 2005) or tags such as (Hightower et al., 2001; Hightower et al., 2000) correspond to the LN while the anchor node corresponds to the LBS. 2. PHY: The Physical Layer The underlying wireless technology used for localization directly influences its performance. The physical layer (PHY) of the localization scheme is defined by this underlying technology. PHY deals with the various physical layer specifications such as operating frequency, bandwidth, modulation technique, transmission power level and antenna diversity. The frequency spectrum, over which the localization scheme is deployed, directly influences its performance. Various localization techniques have been based on different physical spectrum such as infrared (IR) (Want et al., 1992), ultrasound (US) (Priyantha et al., 2000; Ward et al., 1997; Savvides et al., 2001; Dutta and Bergbrieter, 2003; Whitehouse, 2002) and radio frequency (RF) (Guard, 1996; Bahl and Padmanabhan, 2000; Caffrey and Stber,

1131


Table 1 PHY parameters IR

US

RF

Frequency (Hz)

3000 G

20 K - 40 K

3 K - 300 G

Speed in air (m/s)

3 × 10

343 (20°C)

3 × 10 8

Technologies

IrSimple (www.irda.org)

SONAR Medical imaging

802.11 (WiFi) Bluetooth UWB

8

1998). Note that technologies using same frequency spectrum may have different PHY specifications and hence result in different localization performances. For example, Bluetooth and 802.11 occupy ISM band but have different PHY related parameters such as transmission power and modulation techniques. Also, as reported by (Gwon et al., 2004) the performance of Bluetooth is slightly better than that of 802.11. This is due to the lower transmission power of Bluetooth which limits its tranmission range. However, amongst all the parameters of PHY, operating frequency plays the most prominent role in defining localization characteristics and is considered for the purpose of classification in this paper. Recall that, Frequency (f) = 1/Time Period (T). Thus at higher f, period T is small and a finer time resolution is offered. This indicates that for higher frequencies, ToA may be the preferred for higher accuracy localization. Also, from Friis free space equation (Rappaport, 2001), Received Power ∝ ( λ ) d

(1)

where, wavelength λ ∝ 1/f and d is the distance between the transmitter and receiver. Thus attenuation of the signals increases with frequency. In general, higher frequency leads to higher localization accuracy but shorter range. Some of the key parameter values for different frequency spectrums are tabulated in Table 1. (i) Infrared (IR) Active Badge (Want et al., 1992), one of the earliest localization schemes, used IR. In this case, LN would be an IR badge that would periodically transmit unique ID. Since IR is limited to line of sight transmission, each room has an IR receiver which would relay the received IDs to a central server. The server then maps LN’s location to the corresponding room. The localization schemes based on IR usually have lower accuracy and are affected by sunlight. Also, it is not scalable for large deployment areas. Hence IR is rarely used for localization. (ii) Ultrasound (US) US can also be used for localization. Ultrasound

signals can propagate through the walls and ToA can be accurately measured using existing inexpensive devices. ToA provides more robust location estimate as compared to SS measurements (discussed later) and is generally adopted for US. In Cricket (Priyantha et al., 2000), LN uses ToA of US signals from various LBS to estimate its location. As RF travels much faster than US, an RF signal is used by LBS to indicate the start of US transmission in Cricket. An LN measures the time difference between the reception of RF and US signals from a LBS. This difference is the ToA for US signals from LBS to LN. Using the LBS location information (broadcasted in the initial RF transmission), LN estimates its location using US ToA from different LBSs. (iii) Radio Frequency (RF) RF is commonly used by most communication networks. As integrated location estimation systems are less expensive than their dedicated counterparts, localization based on this underlying RF technology of communication network, lowers deployment cost. The RF localization uses received SS measurements (example, RADAR (Bahl and Padmanabhan, 2000)) or time of arrival (example, GPS). Time of arrival for RF signals requires precision time resolution (order of nanoseconds) which increases hardware cost. It should be noted that as higher frequencies are used, most surfaces act as reflectors and multipath effect predominates. Hence RF based ToA techniques are preferred for large capital, open area deployments (example, (Caffrey and Stber, 1998)). However, RF based (3-10 GHz) ultra wide band (UWB) has a nanosecond precision clock and is inexpensive. It can inherently estimate ToA due to its tight transmitter receiver synchronization (Fontana et al., 2003). An RF based ToA secure localization technique for AHL (using UWB) is described in (Capkun and Hubaux, 2005). 3. Measurement Parameter Location of a wireless device (LN) is estimated by measuring a location dependent parameter of the radio link between the LN and multiple LBSs.

1132


Various localization schemes use different parameters such as signal strength (SS) (Laitinen et al., 2001; Bahl and Padmanabhan, 2000; Gwon et al., 2004; Krishnan et al., 2004; Tao et al., 2003; Castro et al., 2001; Youssef and Agrawala, 2005; Madigan et al., 2005), time of arrival (ToA) (Moeglein and Krasner, 1998; Najar et al., 2004; Priyantha et al., 2000; Ward et al., 1997; Capkun et al., 2001; Savvides et al., 2001; Dutta and Bergbrieter, 2003), connectivity (Cnn) (Anjum et al., 2005; Pandey et al., 2005; Want et al., 1992; Nagpal et al., 2003; He et al., 2003) and angle of arrival (AoA) (Lazos and Poovendran, 2004; Sakagam et al., 1992; Swaleset al., 1999). These parameters are indicated in Fig. 4. The parameters SS, ToA and Cnn are distance dependent parameters while AoA depends on the orientation. Usually, the parameter is used to estimate distance/orientation between LN and various LBS. This is further used to estimate location of LN using different location estimation techniques (described later). Although, measurements in the figure are shown with respect to LN, these can be carried out at LBS too. (i) Signal Strength (SS) In practical indoor/outdoor environments, SS varies inversely with distance d (Rappaport, 2001), SS ∝ 1n d

(2)

where n, is the path loss exponent (2 for free space). SS is measured by monitoring the received signals from a wireless interface connected to a client device like a laptop or PDA. SS is preferred for low-cost simple localization and is hence frequently used in LAL. In LAL schemes such as Nibble (Castro et al., 2001), LN measures SS at various locations with respect to multiple LBS to construct the LLT during the ‘training phase’. During subsequent visit of LN at the location, current SS readings are compared with the values in LLT to estimate LN’s current location. Alternative techniques to construct LLT are described later. The dynamics of outdoor environment along with (usually) low received SS values, limits its usage for WAL and AHL. Also, a more reliable and accurate GPS system is available for WAL. (ii) Time of Arrival (ToA) The time taken for wireless signals (or packets) to travel from transmitter to receiver at distance d (time of arrival (ToA)) is given by, ToA = dv

(3)

where, v is the velocity of the wireless signal. At

dmax = transmission range of LBS d = distance between LBS and LN dmax LBS d, ToA AoA

SS

LN d < dmax, Cnn = 1 Fig. 4 Various measurement parameter

higher frequency of transmission, v approaches the speed of light (ref Table 1) and a high resolution clock may be required to measure ToA accurately. For example, a clock resolution of 1 1sec would limit the distance resolution to 300 m for RF transmissions or 343 1 m for US. As pointed out earlier, due to the cost of such a high resolution clock, US based ToA schemes (such as (Priyantha et al., 2000; Ward et al., 1997; Savvides et al., 2001 and Dutta and Bergbrieter, 2003)) are preferred for LAL and AHL than RF based ToA schemes (such as (Capkun et al., 2001; Guard, 1996 and Najar et al., 2004)). For example, a GPS receiver (LN) estimates distance using ToA estimates from different satellites (LBS). Note that LN is in tight time synchronization with the satellites. The exact location of each satellite is also broadcasted. The location of LN is then estimated using multilateration (described later). This parameter is more robust in a multipath environment, since it estimates time based on the shortest received path (usually direct path). Note that time of arrival may require accurate time synchronization between the transmitter and receiver (as in UWB and GPS). This may be avoided by considering return time of flight (RToF). In this case, the receiver retransmits the signal back to transmitter. The transmitter can then calculate the ToA as half the delay between its transmitted and received signal (the return time of flight). This parameter is affected by the latency in receiver response which may be due to processing queue at the receiver. Variation of the time of arrival parameter, such as time difference of arrival (TDoA) are also proposed in (Capkun et al., 2005) and (Larder, 2001). The TDoA considers the difference in time of the signal received at different LBSs. The LBSs have to be time synchronized with each other. This parameter is preferred in an asymmetric network such as WAL, where LBSs are highly resourceful, while LNs are resource constrained. (iii) Connectivity (Cnn) Connectivity indicates that LN is within the

1133


-75

Access Point

x

-65

-90

-65

-75

-75

-90

-75

-90

x -65

Actual measured Extrapolated 90 200 m 60 150 m

-7 5

Access Point

-90

120

Boundary

-90

150

100 m 50 m

Boundary 180

30 0

-90

210

330 240

300 270

(a) Indoor boundary at 100 mW

(b) Indoor boundary at 1 mW

(c) Outdoor boundary at 1 mW

FIg. 5 Transmission range boundary (received signal strength = 90 dBm) at different transmission powers

transmission range of LBS. It can be given as, Cnn = Id < d max

(4)

where I x is the indicator function which has the value 1 when x is true and 0 otherwise. Note that d max represents the transmission range which may not be circular in practical environment. This can be seen from the measured indoor transmission boundary with 100mW and 1mW transmission power as shown in Figs. 5(a) and 5(b), respectively. The measured outdoor transmission boundary is relatively circular as seen from Fig. 5(c). The location of the LN may be estimated based on its connectivity to LBSs by finding the region where the transmission range of these LBSs overlap. This method incurs minimal processing/communication overhead but results in lower localization accuracy compared to other schemes. It has been proposed for ad-hoc/sensor networks where light weight, energy efficient protocols are used for localization (Shang et al., 2003; Doherty et al., 2001). This method is also favorable for secure localization schemes such as TRaVarSeL (Anjum et al., 2005). In this case the connectivity of LN to LBSs is verified by using nonce (random number). An LBS transmits different nonces at different power levels. An LN will be able to receive only some of these nonces corresponding to power levels for which its connectivity is maintained. LN collects such nonces from different LBSs and transmits them to a central server. The central server can then estimate the LN’s location based on the set of nonces received by LN. (iv) Angle of Arrival (AoA) Recent developments in directional antenna technology permits discriminatory resolution of angle

of arrival (AoA) for a given transmission. Thus the location of the wireless device may be estimated based on the angle of arrival measurements from different LBSs. However, the AoA measurement in any practical setup is strongly affected by multipaths (Sakagam et al., 1992). Since the AoA estimate would be of the strongest received path, it may lead to an erroneous estimate of the transmitter direction due to multipaths. Thus, it performs better for an open environment than an indoor or cluttered environment (urban areas). This parameter may be favorable for low-cost, low-accuracy deployments. Cellular phone base stations, where directional antennas are already in use for sectorization, can use AoA without incurring extra cost of deployment. 4. Location Lookup Table In most practical environments the variation of a physical parameter such as SS with distance, is not well defined. The physical parameter values have to be mapped onto the various locations within the deployment site prior to the actual localization process. The resultant map may be stored in a lookup table, LLT. The LLT would contain the statistics (such as mean, median, standard deviation) of the parameter at various locations. Each location may be represented as a row in the LLT. (i) Measurement Based LLT may be constructed and updatedin different ways. The most common way to construct an LLT is to measure the parameter at various locations within the deployment site (for example, (Anjum et al., 2005; Laitinen et al., 2001)). The parameter values at the remaining locations are interpolated from these

1134


measured values. Such maps have been built in many indoor localization schemes where SS is the parameter of localization (Bahl and Padmanabhan, 2000; Gwon et al., 2004; Tao et al., 2003; Castro et al., 2001; Youssef and Agrawala, 2005). This is a very time consuming and tedious process. Also due to the time variation of SS, the LLT may have to be updated frequently (Pandey et al., 2005). This adds on to the cost of deployment and maintenance of the localization scheme. In (Haeberlen et al., 2004) the time required to build the lookup table to cover an area of 135,178 sq. ft was 28 man-hours. (512 cells, 2.7 minute per cell). However, a minimum of one minute per cell was recommended for data collection, reducing the total time for data collection to less than half, which is nevertheless considerable. The authors also propose a calibration function to update the LLT for different WLAN cards and to relate time varying effects using a small number of new measurements. The time variation can also be accommodated using environmental profiling (Bahl et al., 2000). In this case, the SS LLTs for different environmental conditions (such as peak hours, nights etc) are measured. Using fixed reference measurements (APs monitoring SS of neighboring AP), an appropriate LLT may be chosen for current localization process. (ii) Agent Based In order to frequently update an LLT at low cost, schemes such as LEASE (Krishnan et al., 2004), client assisted data collection (Pandey et al., 2005) and DAIR (Bahl et al., 2005) have been proposed. In these schemes, the LLT is built using agents without extensive manual measurements. The agents are deployed at various locations and LLT is generated from the readings of these agents. This avoids the laborious task of manual measurement. However the initial cost of the deployment may increase. The agents may be low cost devices (Krishnan et al., 2004) or trusted users within the network (Pandey et al., 2005) or desktops with wireless interfaces (Bahl et al., 2005). This scheme is favorable for high-initial cost deployments requiring low maintenance cost such as in enterprise networks.

et al., 1997; Bahl and Padmanabhan, 2000; Capkun et al., 2001; Savvides et al., 2001; Dutta and Bergbrieter, 2003; Guard, 1996; Moeglein and Krasner, 1998; Najar et al., 2004; Venkatraman and Caffery, 2004; Chen, 1999). For example, ToA can be used to analytically calculate the distance between LN and LBS analytically as v × ToA 1 (from Eq. 3). The model parameters may be estimated empirically for a specific deployment site. For example, indoor path loss models are used to estimate the SS based LLT for indoor WLAN deployments. For example in (Bahl and Padmanabhan, 2000) a Wall Attenuation Factor (WAF) model is considered. The received signal power, P(d), at distance d is given by, P(d) = P(d 0) – 10nlog 10( d ) – nW × WAF nW < C C × WAF nW ≥ C d0

(5) where, P(d0) is the received signal power at some reference distance d0 , n is the path loss exponent, nW is the number of walls between transmitter and receiver and C is the threshold number of walls considered in the model. WAF is the attenuation due to each wall which is estimated empirically by a small number of measurements at the eployment site. Thus, instead of maintaining an actual table mapping the parameter values to different locations, only the model variables need to be maintained. Note that agents may be deployed in order to update the model parameters (Djuknic and Richton, 2001). Although, such LLT generation approach is generally less expensive than the previous schemes, the accuracy of the resultant LLT may be lower (Bahl and Padmanabhan, 2000). Due to the resource constraint nature of sensor network and dynamic environment of the deployment site, the LLT in AHL are usually based on simple models with predefined parameters (Capkun et al., 2001; Savvides et al., 2001; Dutta and Bergbrieter, 2003). Such implementation is also preferred in WAL due to the large area of deployment (Guard, 1996; Venkatraman and Caffery, 2004; Najar et al., 2004; Moeglein and Krasner, 1998; Chen, 1999). 5. Estimation Technique

(iii) Model Based The LLT may also be generated by limited or no actual measurements of the parameter. An analytical/emphirical model defining parameter variation with distance may be used to estimate the parameter values at any location (Priyantha et al., 2000; Ward 1

The localization scheme may be deployed after generating an LLT. The location of LN is then estimated based on the current measured values of the parameter and the LLT. As pointed out earlier, multiple measurements of the parameter are carried out at a fixed location to account for the time variations.

For practical deployments, the model may be more complicated as it may incorporate secondary effects such as doppler shift and network latency


Using an estimation technique, the physical location in LLT having parameter values ‘closest’ to the current measured values is chosen as the current location of LN. Both deterministic (Najar et al., 2004; Venkatraman and Caffery, 2004; Chen, 1999; Anjum et al., 2005; Priyantha et al., 2000; Ward et al., 1997; Want et al., 1992; Bahl and Padmanabhan, 2000) and probabilistic (Tao et al., 2003; Castro et al., 2001; Youssef and Agrawala, 2005; Madigan et al., 2005) estimation techniques may be applied.

1135

d2

d1

O

d3 d4

(i) Deterministic Estimation For deterministic estimation, statistical parameters such as mean (Bahl and Padmanabhan, 2000) or median (Li et al., 2005) are used for robust estimation. The most commonly used technique in schemes such as (Bahl and Padmanabhan, 2000 and Capkun et al., 2001), is multilateration as shown in Fig. 6. For two (three) dimensional localization, the distance of LN from three (four) non-collinear LBSs will be required. The distance from different LBS may be obtained by measurement of distance related parameters. In this case, distances of LN from multiple LBSs (represented as d1 to d4 from 4 LBS respectively) are used to estimate its location. The problem of localization is thus translated to a geometrical problem of finding a location satisfying the distance criteria. Assuming circular transmission range, the location of LN would be the intersection of circles of radii d1 to d4 from respective LBS. Due to the practical measurement errors in distance estimates, the intersection results in the shaded area rather than a single point. In order to reduce this estimated region and obtain finer resolution, estimation methods such as average of K-nearest neighbors and smallest polygon may be carried out (Gwon et al., 2004). Multilateration using three LBS is called trilateration. Similarly, angle measurements (AoA parameter) from three different LBS may be considered. In this case, angle geometry is used to estimate user location (triangulation). Least square estimation (LSE) may also be used for multilateration when LLTs are available. The measured parameter statistic with respect to each LBS is represented as a vector MP = , where MPj represents the measured parameter for the jth LBS. The current measured parameter vector is compared to similar vectors of each location present in LLT. The LLT parameters for ith location is represented as LP i = , where I varies from 1 to m for the m locations (rows of LLT) and n LBS are considered in each LPi. When LSE is considered, the estimated location would be the kth entry in LLT (LP k ) corresponding to,

LBS

LN

Fig. 6 Multilateration j=n

LPk = min ( Σ (MPj – LPji) 2) . arg i j=1

(6)

LSE is commonly used for LLT based schemes especially for SS based techniques in LAL such as (Pandey et al., 2005 and Krishnan et al., 2004). A variation of LSE such as Prioritize Maximum Power is also proposed in (Hatami and Pahlavan, 2004). In this case, MP and LP i’s are sorted in descending order. Only the set of locations in LLT which have LP i that follow the same order as MP are considered. LSE is then carried out over this reduced set of LP i . (ii) Probabilistic Estimation Consider the distribution of 100 SS readings collected at 4 different locations as shown in Fig. 7. Each of the distributions is unique (statistically different). LN’s location can thus be estimated based on the distribution of parameter at different locations. In such cases, probabilistic estimation such as Bayesian estimation techniques are commonly used. The probability distribution function, pdf, of SS at each of these locations can be chosen as a nonparametric distribution based on the histogram or it may be approximated to a normal distribution (Haeberlen et al., 2004). In this case, the LPi for LLT would represent the pdf of the parameter values (such as SS) at the ith location from different LBS. During ‘online’ phase, the current distribution of parameter (MP) would be measured. Assuming all locations are equally likely, the estimated location LP k can be obtained by maximum likelihood test, LPk = min (P( MP LPi)) . arg i

(7)

Such probabilistic estimation is used for many localization schemes such as (Youssef and Agrawala, 2005; Castro et al., 2001; Ladd et al., 2004; Madigan et al., 2005). The difference of SS between different

1136

50 45 40 35 30 25 20 15 10 5 0 170 175 180 185 190 195 200 205 210

Number of readings (%)



50 45 40 35 30 25 20 15 10 5 0 170 175 180 185 190 195 200 205 210

50 45 40 35 30 25 20 15 10 5 0 170 175 180 185 190 195 200 205 210 SS

SS (b) Location 2



SS (a) Location 1

50 45 40 35 30 25 20 15 10 5 0 170 175 180 185 190 195 200 205 210 SS

(c) Location 3

(d) Location 4

Fig. 7 Signal strength histogram at different location

LBS may be used in order to provide a robust location estimation against malicious users who may vary their transmission power level (Tao et al., 2003). Since probabilistic estimation considers higher order statistics of parameter of localization, they are more robust against outliers (erroneous values) in the measured parameter. Aliasing in localization occurs when two largely separated locations have similar parameter distribution or statistics (such as mean SS). In this case, the erroneous location may be selected due to the inability to distinguish parameter values for their respective LLT entries. Probabilistic techniques have also been used as a post-processing step to improve localization estimate, track users and avoid aliasing. The movement of user can be modeled as a hidden Markov model. Thus, the previous location estimates affect the current probability of user being at a given location. The use of such models improve localization accuracy (Haeberlen et al., 2004; Castro et al., 2001) by avoiding any sudden change in current location estimate that may be caused due to aliasing or other errors.

out by the client device (LN) or by the network (comprising of the several LBS). (i) Client For the client-based localization (Priyantha et al., 2000; Capkun et al., 2001; Guard, 1996; Youssef and Agrawala, 2005; Nagpal et al., 2003; Castro et al., 2001; Lazos and Poovendran, 2004; He et al., 2003; Savvides et al., 2001), the client (LN) itself determines its location by monitoring signals from different LBSs. In this case, the LBS acts as a beaconing device which may send out time stamped packets along with its location. The LN measures the parameter of localization (such as SS) for the messages transmitted from different LBSs. It then determines its location by using these measurements and the knowledge of all LBSs location (available at LN). This scheme is highly desirable where user privacy is an important consideration as the network has no knowledge of the users location. (ii) Network

6. Localizing Entity The location estimation process can be carried

For network-based localization (Biswas and Ye, 2004; Madigan et al., 2005; Capkun and Hubaux,


2005; Ward et al., 1997; Want et al., 1992; Bahl and Padmanabhan, 2000; Gwon et al., 2004; Krishnan et al., 2004; Tao et al., 2003; Sakagam et al., 1992; Laitinen et al., 2001), the network would determine LN’s location by measuring the parameter of localization for the signals from LN at several LBSs. For example, ToA of LN packets can be measured at various LBS. The location of LN can be determined based on these measured values and the location of the LBS known by the network. This scheme is desirable where the security of localization is an important consideration. For the above schemes, the client and network may share the estimated location information with each other. For a case with limited privacy and security concerns, the network and client may assist each other to improve the accuracy of localization (Chen, 1999; Djuknic and Richton, 2001; Moeglein and Krasner, 1998; Krishnan et al., 2004).

1137

and Poovendran, 2004; Capkun and Hubaux, 2005; Liu et al., 2005; Sastry et al., 2003; Li et al., 2005) This is also an important consideration in sensor networks, which may be deployed for military purposes. Secure localization is carried out by various means such as encryption and nonces (random number). These schemes usually require more resources (in terms of computation, bandwidth, and energy) than open schemes. IV. STUDYING LOCALIZATION SCHEMES

7. Security Consideration

The classification of localization schemes in the previous section gives us a useful tool to understand and analyze various existing localization schemes. As an example, we describe a localization scheme for WAL, LAL and AHL by using the classification in the previous section. Note that the characteristics of the localization schemes are explained using the characteristics of the respective implementation in different categories.

(i) Open

1. WAL Schemes

Many localization schemes such as (Bahl and Padmanabhan, 2000; Guard, 1996; Want et al., 1992; Ward et al., 1997; Sakagam et al., 1992; Laitinen et al., 2001; Savvides et al., 2001; Biswas and Ye, 2004; Priyantha et al., 2003) can be considered as an open system, wherein the devices could easily spoof their location to various location. For example, an LN may transmit at different power levels and hence induce an erroneous measurement of SS at different LBS (Anjum et al., 2005; Tao et al., 2003). This may result in location spoofing by malicious users in the network. Other attacks on localization scheme are also possible. For example, an attacker may disrupt the localization scheme using fake LBS, replaying the LBS messages at one location somewhere else, or creating wormhole (dedicated data link between attackers across the network) to disrupt localization in AHL (Lazos and Poovendran, 2004; Capkun and Hubaux, 2005).

The schemes in WAL are mainly classified based on the localization entity (IST-2000-25382-CELLO, 2001). The client based localization such as GPS (Guard, 1996) is preferred for higher accuracy, faster localization and for user applications such as navigation. The network based (Sakagamet al., 1992) is preferred when network management functions such as automatic coverage control (IST-2000-25382CELLO, 2004) and location based billing are considered. Although many schemes (Sakagam et al., 1992; Laitinen et al., 2001; Swales et al., 1999; Larder, 2001; Moeglein and Krasner, 1998; Najar et al., 2004; Venkatraman and Caffery, 2004; Chen, 1999) are proposed for cellular systems, GPS is the most popular amongst all the WAL schemes. We describe GPS in details.

(ii) Secure However, in order to deploy location based services of commercial value, location spoofing would be undesirable. Also, any attacks on a localization scheme, wherein the user may be lead to believe that he is in an erroneous location by rogue LBSs should be prevented. Secure localization is a localization scheme that is resilient to such attacks. In recent developments, the secure localization is gaining considerable attention (Anjum et al., 2005; Tao et al., 2003; Lazos et al., 2005; Pandey et al., 2005; Lazos

Global Positioning System (GPS) GPS is primarily a WAL but has been used in AHL (Juang et al., 2002). It can also be extended to indoor environments (with large spaces) using artificial satellites called ‘pseudolites’ (Kee et al., 2000). Since direct GPS signals are difficult to receive in an indoor environment, the pseudolites transmit GPS signals that enable GPS location estimation with a regular GPS receiver. Although many flavors of GPS exist, the Standard Positioning Service GPS scheme (Guard, 1996) is considered in this section. Note that similar satellite based location systems such as Galileo (European Satellite Navigation System) (http://europa.eu.int/comm/dgs/energy transport/

1138


galileo/intro/index en.htm.) are under development. GPS comprises of 24 satellites (21 active) which revolve round the earth at an altitude of about 12,000 miles once every 12 hours. For the framework described in this paper, the satellites act as LBSs while the GPS receiver is a LN as shown in Fig. 8. The satellites broadcast their exact location to the GPS receivers by transmitting “almanac” (approximate orbit information for all satellites) and “ephemeris” (precise satellite orbit information for short sections) data. LN measures ToA from different satellites to estimate its distance from different LBSs. It identifies each satellite by the pseudorandom (PN) code that is unique to each satellite. The PN code between a transmitter and receiver is synchronized prior to any data exchange. Also, the receiver clock bias has to be accounted for in order to obtain accurate ToA measurements. In other words, the receiver and satellites have to be time synchronized. The receiver may choose any four appropriate satellites or all 10 of the satellites in view (‘all-in-view’ scheme) to estimate its location. Using the time of arrival information from atleast four satellites, the receiver can estimate its location in 3-D space (longitude, latitude and altitude). The accuracy of GPS systems is about 6-12 meters (Garmin Ltd., 2005). GPS is a dedicated localization system and can be used in conjunction with any wireless network (example, cellular network) or as an independent scheme to determine location using GPS receivers. We now derive various characteristics of the GPS system by classifying it using the categories in Section III. GPS is a RF based scheme that operates in L1 band (1575.42 MHz) 2. This high operating frequency gives rise to many multipaths in a cluttered environment and prohibits GPS signal propagation to building interiors. Thus GPS usage is limited to open outdoor environments. GPS uses the ToA parameter to estimate distance between LBS and LN. Thus, time synchronization between the receiver and satellites is necessary. The time of arrival is robust against multipath in case of availability of direct path. This again indicates that GPS would provide inaccurate results in a cluttered or indoor environments where direct path to satellite is blocked. The LLT can be considered to be generated via a model based on Eq. 3. For accurate localization, the receiver complexity increases as it would have to accommodate changes in model variables. For Precise Positioning Service, PPS, (Guard, 1996), the signals transmitted by satellites on L2 (1227.60 MHz) are used to measure ionosphere delay. A sophisticate PPS receiver can use this delay measurement to improve the location estimate accuracy. This 2

LBS2

LBS1

GPS Receiver (LN) Satellites (LBS) LBS3 Fig. 8 Localization framework for GPS

complexity in the receiver can be avoided by use of a Differential GPS (DGPS) (Guard, 1996) system. In this case an additional reference signal from a terrestrial DGPS transmitter is used to correct location estimates. A similar system called assisted GPS (AGPS) is proposed for cellular networks (Djuknic and Richton, 2001). Here the correction is transmitted by the cellular base station. The resultant accuracy after correction is 1-5 meters (Garmin Ltd., 2005). The estimation technique used in GPS is multilateration. As this technique is affected by outliers (erroneous estimates), many satellites (‘all-inview’ mode) may be considered for location estimation. This reduces the dependence of estimation on outliers from any single satellite. The location is estimated at LN and thus it is a client-based localization scheme that provides user privacy. The satellites convert PN codes (P-code) to an encrypted code (P(Y)-code) before transmission. This is used in the PPS scheme to avoid spoofing of satellite signals by malicious users. Thus GPS may be considered a secure localization scheme for military applications. However, civilian GPS signals use only P-code and can be easily compromised since these codes are not very strong. The receiver can be deceived by an attacker transmitting fake GPS reference signals (Kuhn, 2004). The GPS receiver itself can be tampered with to display false location. This can be avoided using tamper proof hardware for GPS receiver. However, this too cannot guarantee security (Anderson and Kuhn, 1996). Thus civilian GPS is insecure and considered as an open system. 2. LAL Schemes There have been many localization schemes proposed for wireless networks. These schemes are typically based on the features of the underlying physical layer. For example various schemes based

L2 band (1227.60 MHz) is also used for a higher resoluation Precise Positioning Service (PPS) GPS receiver in military application.

1139


on ultrasound (Priyantha et al., 2000), infrared (Want et al., 1992), Bluetooth (Gwon et al., 2004), 802.11 RF networks (Ganu et al., 2004; Gwon et al., 2004; Bahl and Padmanabhan, 2000; Ladd et al., 2004) have been proposed. These schemes infer the location of users by measuring various parameters such as SS (Ganu et al., 2004; Gwon et al., 2004; Bahl and Padmanabhan, 2000; Ladd et al., 2004; Madigan et al., 2005; Youssef and Agrawala, 2005), and ToA (Capkun and Hubaux, 2005). Some of these schemes are client based schemes (Ganu et al., 2004; Priyantha et al., 2000) while the others are network based (Castro et al., 2001; Bahl and Padmanabhan, 2000). Note that the former approach might be preferred when user privacy concerns are important. Schemes for secure localization in 802.11 networks are addressed in (Pandey et al., 2005; Tao et al., 2003). Some of the LAL schemes such as Ekahau (http:// www.ekahau.com/) are commercially available localization schemes for WLAN. TRaVarSeL (Anjum et al., 2005) is described as an example of a LAL scheme. Transmission Range Variation Based Secure Localization (TRaVarSeL): TRaVarSeL (Anjum et al., 2005), is a LAL scheme developed for deploying inexpensive secure localization for WLAN. For the framework in this paper, the APs of TRaVarSeL act as LBS while user devices are the LNs. This scheme exploits the property of current access points (AP) that enables an AP to transmit at different power levels. Use of a different power level will result in a different transmission range for the AP. The proposed scheme assumes that each location in the system under consideration is within the maximum transmission range of multiple APs. It also assumes an 802.11i framework for secure transmission between LN and LBS. An AP (LBS) in the system securely transmits unique messages at the different power levels to an LN. Each message comprises of a nonce (random number), timestamp, AP identifier and power level, encrypted using a key K by a central entity (CE). An LN at any location will receive a unique set of messages from multiple APs. For example consider 3 APs transmitting at 3 power level as shown in Fig. 9. The messages Nij transmitted from ith AP at jth power level. Thus the user in the shaded region will receive the set fN12, N13, N22, N23, N33g of messages from different APs. The user device is expected to securely transmit back the messages received to CE. The CE can decrypt all the messages using the key K and hence determine LN’s location. The characteristics of TRaVarSeL can also be studied using the classification in the previous section. TRaVarSeL can be

C3 LBS LN

C2 N12 C1 AP1

C3 C2

AP2 C1 C3

C1

C2

Fig. 9 Three access points with 3 transmission power levels

deployed over wireless networks whose LBS can vary their transmission range. The scheme is described for 802.11 networks and can thus be considered as LAL. The application of TRaVarSeL for AHL is discussed in (Anjum et al., 2005). TRaVarSeL works in the RF band. Since the localization scheme is developed as an overlay over the existing 802.11 network (also in RF band) the cost of deployment is lower than a dedicated localization scheme such as GPS. Localization in TRaVarSeL is based on the set of received messages at different locations. Thus, the parameter of localization is connectivity with the LBSs at various power levels. In order to account for time variation a ‘k out of N’ scheme is employed. Here the message is split into N sub messages for transmission. This message is said to be successfully received by the LN if it receives any k of the N sub messages. An LLT is generated by measurement for TRaVarSeL. However, an agent based method is proposed for enterprise WLAN deployments where the client devices can be used to generate and update the LLT. Due to such agent based approach, the initial deployment cost of generating LLT may be high but it can be updated inexpensively. The location is estimated by comparing the current received subset of messages to the one in the lookup table. Multilateration with LSE is the proposed estimation technique. The estimated location is the one where the message set is close to the received message set. It should be noted that outliers in the measured data may be a result of attacks on the system and hence should be monitored. TRaVarSeL targets secure localization and is deployed as a network-based scheme. As the localization of each user is estimated by the network, the privacy of the user is compromised. Thus, TRaVarSeL may be deployed for networks where user

1140


privacy is not an important consideration. The security in TRaVarSeL is based on the transmission of nonces in the messages, that are known only by the network. The messages are proposed to be transmitted in a secure manner using encryption key K and 802.11i session keys. Thus the scheme may be deployed for commercial networks where the network requires protection against location spoofing. The scheme is implemented using 802.11i and hence any attacks from rogue APs can be avoided. Hence, TRaVarSeL is a secure localization scheme. 3. AHL Schemes The problem of localization for AHL is completely different. In this case, many LNs may not have a direct radio link to the LBS. Thus neighboring LNs collaborate with each other to estimate their respective locations. The proposed schemes for AHL may be broadly divided into range-free and range-based techniques. Range free techniques use AoA or Conn as measured parameters to estimate the locations of the LNs without the knowledge of exact pointto- point distance estimates (He et al., 2003; Bulusu et al., 2000; Nagpal, 1999; Shang et al., 2003; Lazos and Poovendran, 2004). On the other hand range-based techniques use techniques such as SS (Bahl and Padmanabhan, 2000; Hightower et al., 2000; Girod and Estrin, 2001; Savarese et al., 2001), ToA (Savvides et al., 2001; Capkun et al., 2001; Priyantha et al., 2003) and RToF using RF or US to estimate the distance of neighboring nodes, i.e., nodes within communication range of each other. Location estimates can be improved by combining the range-free and rangebased methods of localization, as shown in (Chintalapudi et al., 2004). Centralized network based implementation of localization algorithms are reported in (Shang et al., 2003; Doherty et al., 2001; Biswas and Ye, 2004), while (He et al., 2003; Savarese et al., 2001; Chintalapudi et al., 2004) discuss client based schemes that can be implemented in a distributed manner. Recently, a condition for localization in a sensor network was developed in (Goldenberg et al., 2005). A sufficient condition for unique localization of an LN requires that the LN should belong to a triconnected redundantly rigid subgraph ontaining three LBS3. The techniques discussed above consider no mobility or limited mobility of LN (Capkun et al., 2001). Many previous solutions would fail when mobility is taken into consideration, such as beacon placement through smart deployment in (Goldenberg 3

et al., 2005). A method of maintaining an updated location estimate of mobile LN is proposed by periodic beaconing using anchor nodes (He et al., 2003). As reported in (Whitehouse, 2002), different distance estimation schemes vary in energy consumption and accuracy of distance estimation. It reports that the energy required for RF SS measurements is half of US ToA measurements. However, the later technique results in 10 times more accurate distance estimates than the former. (Biswas and Ye, 2004) and (Pandey et al., 2005) consider heterogeneous networks with different accuracy of distance estimation techniques amongst neighboring LNs. Secure localization for sensor networks is dealt with in (Lazos and Poovendran, 2004; Capkun and Hubaux, 2005; Capkun et al., 2005; Lazos et al., 2005; Liu et al., 2005; Li et al., 2005; Anjum et al., 2005 and Sastry et al., 2003). DV-Hop DV-Hop is an AHL scheme. The DV-Hop based location is proposed in (Nicolescu and Nath, 2001) and (Nicolescu and Nath, 2003). This is a rangefree localization scheme that uses Cnn as the parameter for localization. The anchor nodes (LBS) broadcast a beacon to their neighbors. This beacon consist of the LBS location (X i; Y i), and the hop count (hi ) (set as 1). The nodes (LN) that receive the beacon update the value to hop count by 1 and retransmit the beacon. Beacons from same LBS with higher hop counts are dropped by LNs. This selective flooding of beacons allows the LN to obtain its lowest hop count from different LBS in the network. The nodes (LN) in the network maintain a table of X i ; Y i , min h i for each LBS beacon it receives. Each LBS calculates the correction that approximates the distance value of each hop. The correction (c i) for the ith LBS is calculated as, Σ j (Xi – X j) 2(Y i – Y j) 2 ci = ,i≠j (8) Σ jh j where hj represents the hop count of beacon received from jth LBS. Consider the configuration shown in Fig. 10 where 3 LBS are considered. The hop count of LBS2 and LBS3 at LBS1 is 2 and 3 respectively. Thus c 1 = 10 + 15 = 5. Similarly c 2 = 4.75 and c 3 = 2+3 4.67. This correction factor is broadcast by selective flooding such that only the first c i’s are considered and subsequent c i ’s are dropped. Thus the c i ’s from the nearest LBS are used to calibrate LN’s location.

A connected graph which remains connected after deleting any two vertices (and incident edges) is called triconnected graph. A rigid graph does not allow any deformation other than global rotation, translation, and reflection. A redundantly rigid graph is a rigid graph that remains rigid after the removal of any single edge


V. DECIDING FACTORS FOR SELECTING A LOCALIZATION SCHEME

LBS2 10

1141

m m 13

LBS1

15 m LBS3 LN Fig. 10 DV-Hop scheme with 3 anchor nodes

Using the table and the received correction factor each LN can estimate its location using multilateration. DV-Hop uses the same technology for localization as communication (RF) and requires no additional devices are required on LN. This reduces the cost of LN, which is an important consideration in sensor networks. Also, it uses Conn as the measured parameter and hence is simple and consumes low energy. However, this results in lower accuracy as compared to using SS distance estimates (“Euclidean” technique in (Nicolescu and Nath, 2003)). The LLT is said to be model based with the model parameter being the correction factor. The scheme would be sensitive to the error in model parameter which is higher for non-uniform topologies. Multilateration is used for estimation as the distances from different LBSs are used to estimate LN location. As the LNs estimate their own location, it is a client based scheme. However, the network does assist in the localization process by calculating correction factors for LNs. The scheme is not secure (Open) as fake LBSs may be introduced by the attacker to disrupt the scheme. Also, the attacker may introduce LNs to report incorrect beacons to its neighbors or drop legitimate beacons from LBS. Localization schemes can be classified with respect to different categories as shown in Table 2. The accuracy is reported as the average (avg), median or some percentile error in distance. For example, the accuracy reported by DCM scheme is 90 m (90%), which indicates that the estimated locations were within 90 m of the actual location for 90% of the measurements. Some of the accuracies reported are under certain conditions that are noted along with the accuracy. The details of these conditions may be obtained from the respective references. The accuracy for AHL is not reported since most related results are simulation based. The table may be used as a reference to study a localization scheme using the classification of previous section.

In this section we describe the various factors that would effect the choice of localization scheme. We discuss the selection process by considering a typical commercial, enterprise and military deployment. However, it should be pointed out that the specific requirements will vary on a case by case basis. 1. Cost of Deployment This is one of the most important factors for commercial deployments. The cost of localization constitutes the initial deployment cost along with the maintenance cost. It may include the expense of the LBS deployment, user device upgrade and setup, training, and constructing and maintaining an LLT. For a low cost deployment, it may be desirable to overlay the localization scheme over an existing network. This avoids the cost of setting up a dedicated localization system. A simple SS based scheme may be favorable. The cost of deployment is of higher importance for commercial and enterprise deployments. Schemes such as DAIR (Bahl et al., 2005) and TRaVarSeL (Anjum et al., 2005) aim towards such low cost deployments. 2. Required Accuracy We described earlier that the choice of PHY, measured parameter, generation of LLT and estimation technique, influence the accuracy of localization. The accuracy requirement would affect the selection for implementation schemes in the categories. For example, in order to obtain high accuracy, the ToA may be selected. Generally for military deployments there is a high accuracy constraint. Higher accuracy is required in military network installations such as sensor network deployed for intrusion detection. However, for commercial networks, which may use localization to send advertisements from neighboring shops, the required accuracy may not be very high. It should be noted that, (Krishnakumar and Krishnan, 2005) showed that the accuracy of SS based localization has an analytical bound. Specifically, it shows that for a given confidence level, the location lies in an elliptical uncertainty region. This region decreases with increase in the propagation constant, number of LBSs and decrease in the distance between LN and LBS. Horus (Youssef and Agrawala, 2005) focuses on improving the accuracy by using autoregressive models to reduce SS variance. Similarly for AGPS (Djuknic and Richton, 2001), LN uses additional corrections from cellular base stations to reduce ToA errors and improve its GPS location estimates.

1142


Table 2 Classification of some localization schemes *Accuracy in AHL schemes are not reported, since no uniform comparison could be found Localization

Are of

Parameter of

Lookup

Estimation

Localizing

localization

table

techinque

entity

ToA

Model

Deterministic

Client

PHY Scheme

deployment

GPS (Guard, 1996)

WAL

(Sakagami et al., 1992) DCM (Laitinen et al., 2001) TeleSentinel (Swales et al., 1999) Cursor (Larder, 2001) SnapTrack (Moeglein and Krasner, 1998) (Najar et al., 2004)

WAL WAL WAL WAL WAL WAL

RF (L1, L2 band) RF RF RF (TDMA) RF (GSM) RF

Security

AoA Any

Model Deterministic Measurement Deterministic

Accuracy

Open 1-6 m (avg) (civilian)

Network Network

Open Open

AoA

Model

Deterministic

Network

Open

TDoA

Agent

Deterministic

Network

Open

ToA

Model

Deterministic

Client

ToA

Model

Deterministic

Network

ToA and AoA ToA

Model

Deterministic

Network

Model

Deterministic

Client

200 m (avg) 90 m (90%) urban 89 m (90%)

125 m (67%) urban Open 4 m (68%) open outdoor Open 60 m (90%) urban Open 225 m (90%) NLOS Open 125 m (avg) 50% NLOS Secure room

HTA(Venkatraman and Caffery, 2004) (Chen, 1999)

WAL

RF (UMTS) RF

WAL

RF

TRaVarSeL (Anjum et al., 2005; Pandey et al., 2005) Cricket (Priyantha et al., 2000) BAT (Ward et al., 1997) Active Badge (Want et al., 1992) RADAR (Bahl and Padmanabhan, 2000) SELFLOC (Gwon et al., 2004)

LAL

RF (802.11)

Cnn

Agent

Deterministic

Network

LAL

US

ToA

Model

Deterministic

Client

Open

1 feet (99)%

LAL LAL

US IR

ToA Cnn

Model Agent

Deterministic Deterministic

Network Network

Open Open

8 cm (95%) room

LAL

RF (802.11) RF (802.11 and) Bluetooth) RF (802.11)

SS

Measurement Deterministic Model Measurement Deterministic

Network

Open

Network

Open

2.94 m (50%) 4.3 m (50%) 1.6 m (avg 2W2B case

Deterministic

Network

Open

SS

Measurement Probabilitstic

Network

Secure

10.1 feet (median) 38 sniffers 2 m (61%)

SS


Client

Open

room (80%)

SS


Client

Open

LAL

LEASE (Krishnan et al., 2004)

LAL

Difference method (Tao et al., 2003) Nibble (Castro et al., 2001) HORUS (Youssef and Agrawala, 2005) (Madigan et al., 2005)

LAL

SPINE (Capkun and Hubaux, 2005) SeRLoc (Lazos and Poovendran, 2004) DV-Hop (Nicolescu and Nath, 2001) APIT (He et al., 2003) SPA (Capkun et al., 2001) AHLoS (Savvides et al., 2001) (Biswas and Ye, 2004) AFL (Priyantha et al., 2003) MobiLoc (Dutta and Bergbrieter, 2003)

SS

SS

Agent

SS

Model

Probabilitstic

Network

Open

86-132 cm (90%) 20 feet

AHL

RF (802.11) RF (802.11) RF (802.11) RF (802,11) Any

ToA

Model

Deterministic

Network

Secure

*

AHL

Any

AoA

Model

Deterministic

Client

Secure

*

AHL

RF

Cnn

Model

Deterministic

Client

Open

*

AHL AHL AHL

RF RF US

Cnn ToA ToA

Model Model Model

Deterministic Deterministic Deterministic

Client Client Client

Open Open Open

* * *

AHL AHL AHl

Any Any US

Any Any ToA

Model Model Model

Deterministic Deterministic Deterministic

Network Client Client

Open Open Open

* * *

LAL LAL LAL


3. Resource Requirements and Computational Complexity Resource requirements of wireless networks vary considerably depending on the type of network where localization is deployed. For example, sensor networks demand a low complexity, low energy scheme. The resources that the localization scheme can utilize can affect the performance of the scheme. For example, SELFLOC (Gwon et al., 2004) combines the result of multiple location estimation schemes to improve accuracy. It shows that if Bluetooth and 802.11 networks are available, then the accuracy of the location estimates can be improved by considering both technologies for localization. An improvement of 40%70% is reported when 2 Bluetooth and 2 802.11 APs are used with SELFLOC instead of 4 802.11 AP. Network entities such as LBSs usually have more resources than the LNs. Thus computationally intensive task should be dedicated to LBS rather than LNs. For example, in a cellular network deployment the base stations have much more resources than the power-constraint client. In a commercial wireless deployment, the available resources and complexity are important considerations. This is to lower the deployment cost and accommodate low performance client devices. 4. Effect on Underlying Network The underlying communication network may incur some overhead cost due to localization. This is not a serious issue when dedicated schemes such as GPS are utilized. However, it has to considered for localization schemes such as (Bahl and Padmanabhan, 2000; He et al., 2003 and Venkatraman and Caffery, 2004) that are deployed over the existing communication network. The additional latency, data traffic and processing for localization may effect the performance of the underlying network. It may reduce throughput, disrupt client connection, limit user mobility, or induce delay in user connectivity. For example, localization related messages in (Anjum et al., 2005; Capkun and Hubaux, 2005 and Lazos and Poovendran, 2004) would reduce the throughput. Similarly, location based authentication (Anjum et al., 2005) and (Faria and Cheriton, 2004) would delay user connectivity. Such effects should be understood before selecting a localization scheme. For example, in military application, this factor would be of high importance, due to the critical nature of communications and the mobile operation requirements. 5. Security and Privacy An attacker or intruder in a system would try to

1143

gain access to restricted network resources in order to disrupt the system or obtain confidential data. Since anonymity is highly desirable in his case, an attacker would try to disrupt the localization scheme or spoof his location. Similarly he may try to fool the localization scheme to access some location based services. Location based service such as accessing high security (proprietary) data within a room, may be deployed in an enterprise. Secure localization would provide an additional level of security that would prevent malicious users (who may have compromised the authentication scheme) from accessing the data. Similarly (Faria and Cheriton, 2004) describes localization for authentication, where the user is granted network access within a predefined area only. Depending on the level of security required, secure localization schemes may be selected (Anjum et al., 2005; Capkun and Hubaux, 2005). Privacy is an important issue while localizing users. This is primarily the case in WAL and LAL. However, requirement for security and privacy lead to conflicting design issues. The users may not like to disclose their location to the network or other users. Since localization is carried out by measuring physical layer parameters (such as SS), user (location) privacy may be compromised even for a secure encrypted network. For example, packet transmissions in 802.11i are encrypted but the MAC address of the source are openly transmitted. This MAC address may be used along with a SS based scheme to determine the location of a user without his consent. For a military deployment, where privacy may be a very important issue, localization scheme may be deployed using spread spectrum technology. These signals would appear as noise for all receivers except intended recipients. User privacy is generally realized by using client-based localization schemes (Priyantha et al., 2000). 6. Type of Environment The type of environment affects the choice of physical layer technology, LLT generation and estimation technique. IR is avoided in outdoor environment or non line of sight operations. The environment may be static in an indoor WLAN deployment or dynamic for a mobile ad-hoc network. A mobile ad-hoc network for a military battalion would require easy setup of localization in every new area they camp. For a commercial deployment, such as a mall, the dynamic nature of environment (due to movement and density of people) may necessitate frequent updates for LLT. These cases would favor the deployment of schemes with easy lookup table generation and hence a model based LLT may be utilized. The summary of factors influencing a typical

1144


Table 3 Factors influencing typical deployments Factos

Commercial Enterprise Military

Cost of deployment

high

medium

low

Required accuracy

low

medium

high

Resources and computational complexity

high

medium

low

Effect on underlying network

low

medium

high

Security

medium

medium

high

Privacy

high

medium

medium

Environment type

low

low

hith

commercial, enterprise and military deployments is shown in Table 3. As pointed out before, a typical case is considered for these deployments and the specific requirements may vary for different cases. However, by considering the requirements for localization for the various deployment scenarios, an appropriate choice can be made from the available alternatives described in section III. There may be some interdependence between factors such as cost of deployment and required accuracy. These relations should be considered while deciding the localization requirements. VI. DISCUSSION AND FUTURE WORK The evolution in wireless network may necessitate the development of novel techniques for the purpose of localization. However, even for the current wireless networks the issue of localization is considered as an open problem. Some of the issues in localization are discussed below.

depletion in energy at various LBS. It may not always be possible to deploy LBS optimally, the improvement in accuracy by deploying more number of suboptimally placed LBS needs to be investigated. Alternately, mobile LBS (Corke et al., 2003; Sichitiu and Ramadurai, 2004) or LBS-free schemes (Priyantha et al., 2003) may be used. 2. Mobility The mobility of a wireless user makes it very difficult to develop an accurate localization scheme. The value of the measured parameter may change during measurement for a mobile LN. For example, the ToA for a cellular user in a vehicle will be effected by his velocity. Localization latency would play an important role in localization process for mobile LN. The issues also include choosing appropriate parameter, building lookup tables to incorporate mobility and improving accuracy for a mobile LN. 3. LLT Generation As pointed out earlier, the measurement based LLT are expensive to build and maintain. In this case, building and updating the LLT at low cost, selecting training points to construct a measurement based LLT and generate radio map from it are the issues to be considered. Models may be used for LLT generation and hence reduce the deployment cost. However, obtaining accurate models and corresponding model parameter values is challenging. In order to maintain the accuracy and reduce deployment cost, agent based techniques such as (Krishnan et al., 2004; Pandey et al., 2005; Bahl et al., 2005) are proposed. In this case, issues such as the density and placement of agents are to be considered. 4. Security

1. LBS placement The localization of any wireless device in a network depends on the distribution and locations of the LBS. For the same number of LBS the accuracy and the resolution of localization varies with the placement of these LBS (Krishnan et al., 2004; Pandey et al., 2005). Also the accuracy of localization may not improve with the increase in the number of LBS. In many cases such as (Capkun and Hubaux, 2005; Anjum et al., 2005), the LBS configuration requirements differs from the normal network coverage problems. Thus a planned optimal LBS deployment is needed. Also, the optimal LBS deployment may depend on the localization scheme. In sensor networks such planned deployment may not be viable and may be invalidated with the

Secure localization schemes that provide accurate location estimates have not been completely developed. Attacks such as wormhole attacks, message replay and spoofing, and transmission power variations have be shown to affect the localization schemes (Anjum et al., 2005; Lazos and Poovendran, 2004; Tao et al., 2003). The challenges in this case are to obtain localization technique robust against attackers, preventing attackers from spoofing location and making the secure localization scheme light-weight to avoid excessive overhead on underlying network. 5. Heterogeneous Networks Currently one of the focus of wireless technology industry is towards convergence. Many wireless


devices are equipped with multiple interface to talk to different homogeneous/ hetrogeneous networks at the same time. For example, consider the current trend of modern PDAs which have a cellular, 802.11, bluetooth and infrared interface (example, Nokia 9500). Some of these devices are also equipped with GPS receiver. If localization is currently available over all the interface then it may not be advisable to use all of them since additional errors may be introduced and power consumption would increase. For example, if the PDA is in an outdoor environment only GPS and cellular capabilities can be used for localization, whereas for indoor environment only Bluetooth and 802.11 may be used. Thus, incorporating multiple interfaces of LN and choosing appropriate interfaces for localization have to be considered in heterogeneous networks. Also, an universal index of localization accuracy has to be developed in order to assign different weights for the estimates from different interfaces. This may depend on the inherent accuracy of localization from corresponding interface. The localization results should also be standardized so that it can be used across various networks. 6. Incorporating New Technologies Localization techniques may have to be modified to suit the new technologies. For example, for a time hopped ultrawide band (TH-UWB) communications the localization functionality can be fused with communication (http://www.timedomain.com/ techdev/communications.html). Here the time delay between the transmitted and received pulse (pulse width 1ns) can be resolved due to the high resolution clock. This delay estimate is required in order to synchronize the receiver with the transmitter and hence retrieve the transmitted signals. However, the delay estimate (ToA) also enables the estimation of distance and hence the location of the receiver with respect to multiple transmitters. Thus the cost of localization (in terms of processing and setup) for a UWB network is reduced when ToA based techniques are used. Similarly as other new technologies are unraveled, different aspects of the technology should be investigated in order to develop an appropriate localization scheme. 7. Using Location Information An important issue for localization relates to its application. For example, the location information has been shown to (theoretically) improve the throughput of network (Nadeem et al., 2005). Secure localization can be used to physically locate an attacker in the network and hence make the network defence

1145

pro-active. Location based services should be incorporated to increase the usability of localization. The organization of the location information of various LN is challenging for large networks. Also, the potential of location based services and their application to network performance improvement and network security are yet to be completely realized. Other issues such as effect on network performance, the maximum number of simultaneous localization request, maintaining and displaying the LN locations, incorporating location based services also have to be addressed. VII. CONCLUSIONS In this paper, localization schemes are classified using a number of criteria. The characteristics of various localization schemes are studied using this classification. The factors affecting the choice of localization scheme have also been discussed. A localization scheme should be able to estimate the user’s location with desired accuracy without tedious setup. Network components should be able to exchange localization information and utilize it to deploy location based services. Several issues outlined in this paper need to be addressed before a widespread deployment of location based services can be realized. ACKNOWLEDGMENTS The authors would like to thank Farooq Anjum and Byungsuk Kim for their valuable inputs. REFERENCES Anderson, R., and Kuhn, M., 1996, “Tamper Resistancea Cautionary Note,” Second USENIX Workshop on Electronic Commerce Proceedings, pp. 1-11. Anjum, F., Pandey, S., and Agrawal, P., 2005, “Travarseltransmission Range Variation Based Secure Localization,” Technical Paper, Available: http://www.eng.auburn.edu/pandesg/pub/ TRaVarSeL.pdf. Anjum, F., Pandey, S., Kim, B., and Agrawal, P., 2005, “Secure Localization in Sensor Networks Using Transmission Range Variation,” IEEE International Conference Mobile Adhoc and Sensor Systems Conference, pp. 195-203. Bahl, P., and Padmanabhan, V. N., 2000, “RADAR: An In-Building Rf-Nased User Location and Tracking System,” Proceedings of IEEE INFOCOM, Vol. 2, pp. 775-784. Bahl, P., Padmanabhan, V., and Balachandran, A., 2000, “Enhancements to the RADAR User Location and Tracking System,” Technical Report. Bahl, P., Padhye, J., Ravindranath, L., Singh, M.,

1146


Wolman, A., and Zill, B., 2005, “DAIR: A Framework for Managing Enterprise Wireless Networks Using Desktop Infrastructure,” HotnetsIV. Biswas, P., and Ye, Y., 2004, “Semidefinite Programming for Ad Hoc Wireless Sensor Network Localization,” Third International Symposium on Information Processing in Sensor Networks, pp. 46-54. Bulusu, N., Heidemann, J., and Estrin, D., 2000, “GPS-Less Low Cost Outdoor Localization for Very Small Devices,” IEEE Personal Communications Magazine, Vol. 7, No. 5, pp. 28-34. Caffrey, J., and Stber, G., 1998, “Overview of Radiolocation in Cdma Cellular System,” IEEE Communications Magazine, Vol. 36, No. 4, pp. 38-45. Castro, P., Chiu, P., Kremenek, T., and Muntz, R., 2001, “A Probabilistic Room Location Service for Wireless Networked Environments,” Proceedings of the 3rd International Conference on Ubiquitous Computing, pp. 18 - 34. Capkun, S., Srivastava, M., Cagalj, M., and Hubaux, J., 2005, “Securing Positioning with Covert Base Stations,” NESL-UCLA Technical Report. Capkun, S., and Hubaux, J., 2005, “Secure Positioning of Wireless Devices with Application to Sensor Networks,” Proceedings of IEEE INFOCOM, Vol. 3, pp. 1917-1928. Capkun, S., Hamdi, M., and Hubaux, J. P., 2001, “GPSfree Positioning in Mobile Ad-Hoc Networks,” Proceedings of the Hawaii International Conference on System Sciences. Chen, P. -C., 1999, “A Cellular Based Mobile Location Tracking System,” IEEE Vehicular Technology Conference, Vol. 3, pp. 1979-1983. Chintalapudi, K. K., Dhariwal, A., Govindan, R., and Sukhatme, G., 2004, “Adhoc Localization Using Ranging and Sectoring,” Proceedings of IEEE INFOCOM, Vol. 4, pp. 2662-2672. Corke, P., Peterson, R., and Rus, D., 2003, “Networked Robots: Flying Robot Navigation Using a sensor net,” Proceedings of 11th ISRR. Crossbow Technology Inc., 1999, Mica2 Datasheet, Available:http://www.xbow.com/Products/Product pdf files/Wireless pdf/6020-0042-05 A MICA2.pdf. Djuknic, G. M., and Richton, R. E., 2001, “Geolocation and Assisted GPS,” IEEE Computer, Vol. 34, No. 2, pp. 123-125. Doherty, L, Ghaoui, L. E., and Pister, K., 2001, “Convex Position Estimation in Wireless Sensor Networks,” Proceedings of IEEE INFOCOM, Vol. 3, pp. 1655-1663. Dutta, P., and Bergbrieter, S., 2003, “Mobiloc: Mobility Enhanced Localization,” Technical Report, UC Berkeley, CA, USA. Faria, D. B., and Cheriton, D. R., 2004, “No Long-term

Secrets: Locationbased Security in Overprovisioned Wireless Lans,” ACM HOTNETSIII. Fontana, R., Richley, E., and Barney, J., 2003, “Commercialization of an Ultra Wideband Precision Asset Location System,” IEEE Conference on Ultra Wideband Systems and Technologies, pp. 369-373. Galileo, http://europa.eu.int/comm/dgs/energy transport/galileo/intro/index en.htm. Ganu, S., A. Krishnakumar, S., and Krishnan, P., 2004, “Infrastructure-Based Location Estimation in wlan Networks,” IEEE Wireless Communications and Networking Conference (WCNC 2004), Vol. 1, pp. 465-470. Garmin Ltd., 2005, “Gps Guide for Beginners,” Technical Report Part No. 190-00224-00. Girod, L., and Estrin, D., 2001, “Robust Range Estimation Using Acoustic and Multimodal Sensing,” IEEE International Conference on Intelligent Robots and Systems, Vol. 3, pp. 1312-1320. Goldenberg, D., Krishnamurthy, A., Maness, W., Yang, Y. R., Young, A., Morse, A. S., Savvides, A., and Anderson, B., 2005, “Network Localization in Partially Localizable Networks,” Proceedings of IEEE INFOCOM, Vol. 1, pp. 313-326. Guard, U. C., 1996, “Navstar GPS User Equipment Introduction (Public Release Version),” Technical Report. Gwon, Y., Jain, R., and Kawahara, T., 2004, “Robust Indoor Location Estimation of Stationary and Mobile Users,” Proceedings of IEEE INFOCOM, Vol. 2, pp. 1032-1043. Haeberlen, A., Rudys, A., Flannery, E., Wallach, D. S., Ladd, A., M., and Kavraki, L. E., 2004, “Practical Robust Localization Over Large-Scale 802.11 Wireless Networks,” Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, pp. 70-84. Hatami, A., and Pahlavan, K., 2004, “In-Building Intruder Detection for Wlan Access,” IEEE PLANS, Position Location and Navigation Symposium, pp. 592-597. He, T., Huang, C., Blum, B. M., Stankovic, J. A., and Abdelzaher, T. F., 2003, “Range-Free Localization Schemes in Large Scale Sensor Networks,” Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, pp. 81-95. Hightower, J., Vakili, C., Boriello, G., and Want, R., 2001, “Design and Calibration of the Spoton Adhoc Location Sensing System,” Technical Report UW CSE 01-08-??, University of Washington, Seattle, WA, USA. Hightower, J., Boriello, G., and Want, R., 2000, “Spoton: An Indoor 3d Location Sensing Technology Based on Rf Signal Strength,” Technical


Report UW CSE 2000-02-02, University of Washington, Seattle, WA, USA. Hightower, J., and Borriello, G., 2001, “Location Systems for Ubiquitous Computing,” IEEE Computer, Vol. 34, No. 8, pp. 57-66. Hromin, D., Chladil, M., Vanatta, N., Naumann, D., Wetzel, W., Anjum, F., and Jain, R., 2003, “CodeBLUE: A Bluetooth Interactive Dance club System,” IEEE Global Telecommunications Conference, Vol. 5, pp. 2814 - 2818. IST-2000-25382-CELLO, “Cellular Network Optimisation Based on Mobile Location: Final Report,” Technical Report, Available: CELLOWP1-VTT-D36-010-Apr.http://www.vtt.fi/tte/ tte35/pdfs/IST-CELLOFinal-Report.pdf, 2004. IST-2000-25382-CELLO, 2001, “Cellular Location Technology,” No. CELLOWP2-VTT-D03-007Int. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L., and Rubenstein, D., 2002, “Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with Zebranet,” International Conference on Architectural Support for Programming Languages and Operating Systems -ASPLOS, pp. 96-107. Kee, C., Jun, H., Yun, D., Kim, B., Kim, Y., Parkinson, B., Langenstein, T., Pullen, S., and Lee, J., 2000, “Development of Indoor Navigation System Using Asynchronous Pseudolites,” Proceedings of ION GPS, pp. 1038-1045. Ko, Y.-B., and Vaidya, N. H., 2000, “Location-Aided Routing (lar) in Mobile Ad Hoc Networks,” Wireless Network, Vol. 6, No. 4, pp. 307-321. Krishnakumar, A. S., and Krishnan, P., 2005, “On the Accuracy of Signal Strength-Based Location Estimation Techniques,” Proceedings of IEEE INFOCOM, Vol. 1, pp. 642-650. Krishnan, P., Krishnakumar, A. S., Ju, W.-H., Mallows, C., and Ganu, S., 2004, “A system for LEASE : System for Location Estimation Assisted by Stationary Emitters for Indoor RF Wireless Networks,” Proceedings of IEEE INFOCOM, Vol. 2, pp. 1001-1011. Kuhn, M. G., 2004, “An Asymmetric Security Mechanism for Navigation Signals,” Proceedings of the Information Hiding Workshop. Ladd, A. M., Bekris, K.E., Rudys, A. P., Wallach, D. S., Kavraki, L. E. , 2004, “On the Feasibility of Using Wireless Ethernet for Indoor Localization,” IEEE Transactions on Robotics and Automation, Vol. 20, No. 3, pp. 555-559. Laitinen, H., Lhteenmki, J., and Nordstrm, T., 2001, “Database Correlation Method for GSM Location,” IEEE Vehicular Technology Conference, Vol. 4, pp. 2504-2508. Larder, S., 2001, “EOTDbased Location Service

1147

Implementation,” Mobile Location Workshop (MLW 2001). Lazos, L., Capkun, S., and Poovendran, R., 2005, “ROPE: Robust Position Estimation in Wireless Sensor Network,” Proceedings of The Fourth International Conference on Information Processing in Sensor Networks (IPSN ’05). Lazos, L., and Poovendran, R., 2004, “SeRLoc: Secure Range-Independent Localization for Wireless Sensor Networks,” Proceedings of the 2004 ACM Workshop on Wireless Security, WiSe, pp. 21-30. Li, Z., Trappe, W., Zhang, Y., and Nath, B., 2005, “Robust Statistical Methods for Securing Wireless Localization in Sensor Networks,” Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN). Liu, D., Ning, P., and Du, W., 2005, “Attack-Resistant Location Estimation in Sensor Networks,” In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN), pp. 99-106. Madigan, D., Elnahrawy, E., Martin, R. P., Ju, W. -H., Krishnan, P., and Krishnakumar, A. S., 2005, “Bayesian Indoor Positioning Systems,” Proceedings of IEEE INFOCOM, Vol. 2, pp. 1217-1227. Moeglein, M., and Krasner, N., 1998, “An Introduction to Snaptrack Server-Aided GPS Technology,” Avaliable: http://www.snaptrack.com/pdf/ion.pdf. Nadeem, T., Ji, L., Agrawala, A., and Agre, J., 2005, “Location Enhancement to IEEE 802.11 DCF,” Proceedings of IEEE INFOCOM, Vol. 1, pp. 651663. Nagpal, R., Shrobe, H., and Bachrach, J., 2003, “Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network,” Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks (IPSN ’03). Nagpal, R., 1999, “Organizing a Global Coordinate system from Local Information on an Amorphous Computer,” Technical Report 1666, MIT Artificial Intelligence Laboratory, MA, USA. Najar, M., Huerta, J. M., Vidal, J., and Castro, A., 2004, “Mobile Location with Bias Tracking in Non-Line-of-Sight,” Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 3, pp. 956-959. Nicolescu, D., and Nath, B., 2001, “Ad-Hoc Positioning Systems (APS),” IEEE Global Telecommunications Conference, Vol. 5, pp. 2926-2931. Nicolescu, D., and Nath, B., 2003, “DV Based Positioning in Ad Hoc Networks,” In Journal of Telecommunication Systems, pp. 267-280. Pandey, S., Kim, B., Anjum, F., and Agrawal, P., 2005, “Client Assisted Location Data Acquisition Scheme

1148


for Secure Enterprise Wireless Network,” IEEE Wireless Communications and Networking Conference, WCNC, Vol. 2, pp. 1174-1179. Pandey, S., Prasad, P., Sinha, P., and Agrawal, P., 2005, “Localization of Sensor Networks Considering Energy Accuracy Tradeoffs,” To appear in IEEE CollaborateCom 2005. Pandey, S., Anjum, F., Kim, B., and Agrawal, P., 2005, “A low-Cost Secure Localization Scheme for Wlan Network,” Technical Report, Available: http://www.eng.auburn.edu/ pandesg/pub/ implementation.pdfr. Priyantha, N. B., Chakraborty, A., and Balakrishnan, H, 2000, “The Cricket Location-Support System,” Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, pp. 32-43. Priyantha, N. B., Balakrishnan, H., Demaine, E., and Teller, S, 2003, “Anchorfree Distributed Localization in Sensor Networks,” Technical Report 892.Zeimpekis, V., Giaglis, G. M., and Lekakos, G., 2003, “A Taxonomy of Indoor and Outdoor Positioning Techniques for Mobile Location Services,” ACM SIGecom Exchanges, Vol. 3, No. 4, pp. 19-27. Priyantha, N. B., Balakrishnan, H., Demaine, E., and Teller, S., 2003, “Anchor Free Distributed Localization in Sensor Networks,” Technical Report 892, MIT Laboratory for Computer Science, MA, USA. Rappaport, T. S., 2001, Wireless Communications, Principles and Practice, 2nd ed. Prentice Hall, Upper Saddle River, NJ, USA. Sakagami, S., Aoyama, S., Kuboi, K., Shirota, S., and Akeyama, A., 1992, “Vehicle Position Estimates by Multibeam Antennas in Multipath Environments,” IEEE Transactions on Vehicular Technology, Vol. 41, No. 1, pp. 63-68. Sastry, N., Shankar, U., and Wagner, D., 2003, “Secure Verification of Location Claims,” Proceedings of the Workshop on Wireless Security, pp. 1-10. Savarese, C., Rabaey, J. M., and Beutel, J., 2001, “Locationing in Distributed Ad-Hoc Wireless Sensor Networks,” Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 4, pp. 2037-2040. Savvides, A., Han, C., and Srivastava, M. B., 2001, “Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors,” Proceedings of the Annual International Conference on Mobile Computing

and Networking, MOBICOM, pp. 166-179. Shang, Y., Ruml, W., Zhang, Y., and Fromherz, M., 2003, “Localization from Mere Connectivity,” Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 201-212. Sichitiu, M., and Ramadurai, V., 2004, “Localization of Wireless Sensor Networks with a Mobile Beacon,” IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 174183. Smart Dust, 2005, Autonomous Sensing and Communication in a Cubic Millimeter, Available:http:// robotics.eecs.berkeley.edu/ pister/SmartDust/. Swales, S., Maloney, J., and Stevenson, J., 1999, “Locating Mobile Phones and the Us Wireless e911 Mandate,” IEE Colloquium (Digest), No. 46, pp. 7-12. Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., and Estrin, D., 2004, “Habitat Monitoring with Sensor Networks,” Communications of ACM, Vol. 47, pp. 34-404. Tao, P., Rudys, A., Ladd, A. M., and Wallach, D. S., 2003, “Wireless LAN Location-Sensing for Security Aapplications,” Proceedings of the Workshop on Wireless Security, pp. 11-20. Venkatraman, S., and Caffery, J., Jr., 2004, “Hybrid toa/Aoa Techniques for Mobile Location in Nonline-of-Sight Environments,” IEEE Wireless Communications, and Networking Conference, WCNC, Vol. 1, pp. 274-278. Want, R., Hopper, A., Falco, V., and Gibbons, J., 1992, “The Active Badge Location System,” ACM Transactions on Information Systems, Vol. 10, No. 1, pp. 91-102. Ward, A., Jones, A., and Hopper, A., 1997, “A New Location Technique for the Active Office,” IEEE Personal Communications Magazine, Vol. 4, No. 5, pp. 42-47. Whitehouse, K., 2002, “The Design of Calamari: An AdHoc Localization System for Sensor Networks,” Master’s thesis, University of California at Berkeley, CA, USA. Youssef, M., and Agrawala, A., 2005, “The Horus WLAN Location Determination System,” Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, pp. 205-218. Manuscript Received: Feb. 21, 2006 and Accepted: Apr. 11, 2006

a survey on localization techniques for wireless networks

a survey on localization techniques for wireless networks

Suggest Documents

A Survey on Localization for Mobile Wireless Sensor Networks

A survey on mobile anchor assisted localization techniques in wireless ...

A survey on energy efficient techniques in wireless sensor networks

a survey of routing mac techniques for wireless sensor networks ...

A Survey of Modeling Techniques for Wireless Sensor Networks

Localization in Wireless Sensor Networks: A Survey - Semantic Scholar

Survey on Signaling Techniques for Cognitive Networks

Wireless sensor networks: A survey on monitoring

Routing Techniques in Wireless Sensor Networks: A Survey

Techniques to Enhance Lifetime of Wireless Sensor Networks: A Survey

A Survey on Beamforming Techniques for Wireless MIMO Relay ...

A Survey on Clustering Techniques for Wireless Sensor ... - CiteSeerX

Localization Techniques in Wireless Sensor Networks - SAGE Journals

A Survey on Security in Wireless Sensor Networks: Networks: Attacks ...

Localization Techniques in Wireless Sensor Networks - SAGE Journals

A survey on service-oriented middleware for wireless sensor networks

A survey on clustering algorithms for wireless sensor networks

A Survey on Middleware for Wireless Sensor Networks - Scientific ...

A survey on cross-layer solutions for wireless sensor networks

A Survey on Wireless Body Area Networks for eHealthcare ... - MDPI

A Survey on Middleware for Wireless Sensor Networks - Scientific ...

Localization Systems for Wireless Sensor Networks

wireless sensor networks localization algorithms: a

Indoor Localization for Wireless Sensor Networks - Embedded ...