Mobile Enabled Large Scale Wireless Sensor Networks - CiteSeerX

Mobile Enabled Large Scale Wireless Sensor Networks Canfeng Chen, Jian Ma Nokia Research Center, House 1, No.11 He Ping Li Dong Jie, Beijing, China 100013 Email: [email protected], [email protected]

Abstract— The combined and separated research advancement in the fields of sensing, communication, and computing have driven the research efforts in wireless sensor networks (WSN) in recent years. According to the traditional definition of WSNs, a dense, static, and prohibitively expensive sensor deployment is implicitly required, which has limited the feasibility of WSNs in nonmilitary approaches. Aside from stationary sensor and sink deployment, the introduction of mobility of mobile terminals into WSNs can be leveraged to alleviate the burden of certain static sinks in order for efficient information repository, dissemination and sharing. In fact, the rapid development of diverse short distance wireless communication methods, such as Bluetooth and Zigbee, have not only popularized multi-radio functioned mobile devices, but also found their potential applications via WSN networking. To realize the goal of large scale wireless sensor network deployment, in this paper, we introduce an architecture of multi-radio enabled mobile wireless sensor network (MEMOSEN), which has taken the heterogeneity and mobility of sensors and sinks into account. The capacity gain and topology dynamics due to mobility in MEMOSEN have been addressed. The design challenges and potential applications of MEMOSEN have been analyzed as well. Index Terms—wireless sensor network, mobility, heterogeneity

I. I NTRODUCTION Recent technological advancement in low power wireless communications and low cost multi-functional sensors has attracted research efforts in the field of microsensor networking technologies, which means to coordinate by means of wireless transmissions among large number of sensors for remote object monitoring and tracking. As illustrated in Fig.1, a wireless sensor network (WSN) is usually composed of densely deployed sensor nodes that have been spatially scattered in a sensor field. These unattended sensors will collect the information in interest and deliver it back to the sink node in a multihop and energy efficient manner. Generally, the interest or task description is initiated by a task manager node, which can communicate with the sink node through infrastructured networks such as satellite networks or Internet. Despite the self-organization mechanism that is shared by both WSNs and MANETs (Mobile Ad hoc Networks), the primary difference that distinguishes WSNs from general ad hoc networks is that, WSNs will mainly face challenges like data aggregation and energy conservation due to the resource constraint in power, computational capacity, and memory of sensor nodes. Many sensor nodes and one sink node altogether form the peculiar “many-to-one” communication style in WSNs. In addition, the unique event-driven and data-centric processing mechanisms in WSNs have made collaborative in-network processing capability a prerequisite.

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Another uniqueness about WSNs lies in that the topology dynamics are assumed to arise mainly from sensor node failures rather than the nodal mobility (sensor or sink). In other words, the topologies of WSNs are generally assumed to be invariant. Subsequently, there will be two principal problems in static WSNs: Firstly, in case of sensor failure or malfunctioning (e.g. due to obstacles or energy depletion), the network connectivity and coverage may not be guaranteed, which will lead to a disconnection between sensor nodes and the sink node. Secondly, there exists a significant non-uniformity of energy consumption among the sensor nodes. In fact, the nearer a sensor node lies with relative to the sink node, the faster it will deplete its energy. Intuitively, solutions to the above two problems will turn to enabling topology alteration of WSNs as follows. On the one hand, if some sensor nodes withdraw from the network due to energy exhausting such that the network loses necessary connectivity and sensing coverage, there must be other supplementary sensor nodes deployed. On the other hand, the sensor nodes should be capable of finding and reaching the sink node in possibly different positions, whether there be multiple sink nodes or the sink node be able to change its location.

Sensor field

Internet

Task Manager

Cluster Head Sensor node Sink node

Fig. 1. An example of cluster-based wireless sensor network: Sensor nodes will respond to the query from task manager, following a multihop trace of sensor node→cluster head→sink node→task manager.

In this paper we will introduce a novel mobile WSN architecture: multi-radio and mobile enabled wireless sensor network (MEMOSEN), which has been proposed as a new paradigm for wireless pervasive networking and mobile application by combining cellular terminals with wireless sensor networks. By incorporating heterogeneity and mobility, MEMOSEN will be a type of hybrid wireless sensor network, in which the mobile terminals with multiple radio transceivers can act as either a sensor

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or a sink node. Besides, the topology change of MEMOSEN can be attributed mainly to the mobility of sensor nodes and/or sink nodes, rather than node failure or nodal energy depletion. The paper is organized as follows. In Section II, recent research efforts in the field of mobile wireless sensor network have been reviewed. The architecture and application scenarios of MEMOSEN are introduced in Section III. The analysis about capacity gain and topology dynamics will be presented in Section IV, and Section V concludes the paper.

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II. R ELATED W ORK There has been recently a trend of exploiting the heterogeneity in WSNs and the mobility of either the sensor nodes or the sink nodes to facilitate data dissemination in WSNs. Different types of heterogeneity in WSNs, namely computational, link, and energy heterogeneity, have been discussed in [1], and the authors have mainly discussed task partitioning by optimal resource deployment and hierarchical networking. Another complementary and more theoretical work about exploiting heterogeneity in hybrid WSNs is presented in [2]. Results from [1] and [2] prove that by exploiting long range short-cuts in WSNs, the average hop count , energy dissipation and energy dissipation non-uniformity can be reduced, and the average delivery rate and network lifetime can be increased correspondingly. Similar results can also be found in [3], where a joint mobility and routing algorithm has been proposed for mobile relays in order to reach the same performance as mobile sinks. Sensor mobility and sink mobility have been discussed in [4], [5] and [6], [7], respectively. On the one hand, mobile sensors can improve network coverage and target detection performance [5]. Besides, by identifying redundant sensors first and then relocating the sensors via cascaded movement, time and energy efficiency can be achieved [4]. On the other hand, network lifetime can be maximized by changing the sink location and balancing the energy expenditure among the sensor nodes [6]. In addition, a more energy-efficient data dissemination protocol for WSNs with multiple mobile sinks has been proposed in [7]. Security mechanisms that can tolerate compromised mobile sinks have been discussed in [8]. Furthermore, the authors of [9] discussed the problem of robots forming a geometric pattern as a potential military application in mobile wireless sensor networks. The most related work is the nomadic user based mobile wireless sensor network proposed in [10]. The author has cited applications based on RFID (Radio Frequency Identification) for example and discussed the new networking approach against traditional direct diffusion by peer-to-peer networking, NEMO (Network Mobility), and Mobile IP. However, there is only one hop between the sensors and gateway and multi-radio heterogeneity has not been considered in [10], and we furthermore focus on studying performance of mobile WSN such as self-organized group formation and message delivery latency. The main contributions of this paper that distinguish MEMOSEN from other related work in mobile WSNs are: • First, we have proposed an architecture that has combined the functions of classical sensor nodes and sink nodes into the mobile phones. With the belief that sensors will be

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widely scattered all over our neighboring environment, such a sensor/sink-integrated mobile phone will facilitate mobile subscriber’s interaction with the smart neighborhood and help to create a ubiquitous context-aware communication and computing environment. Second, the introduction of cellular radio into WSN makes it natural and feasible to apply hierarchical architecture for MEMOSEN to enhance the connectivity and scalability for large scale sensor networks, which means separated sensor nodes with different functions will be linked via cellular radios. Besides, mobility issue has been considered as well. Third, the topology dynamics and capacity gain after the introduction of mobile phones as mobile sinks/sensors have been analyzed. We can infer from these preliminary results that mobility will degrade the performance in MEMOSEN. Instead, when the number of mobile phones reaches a certain threshold, network capacity will increase linearly.

III. MEMOSEN A RCHITECTURES AND S CENARIOS The popularity of multi-radio mobile phones and recent research advance in wireless sensor networking have motivated our exploring the feasibility of combining both sensor and sink functions into the mobile phones. Besides, human mobility and some social behavior have been taken into considerations of the MEMOSEN design. We will first describe the network architecture of MEMOSEN, then discuss some possible application scenarios.

A. Assumptions for MEMOSEN For ease of describing MEMOSEN architecture and understanding, we make some assumptions as follows: • The mobile phones are equipped with two kinds of simultaneously operating radio transceivers with different communication ranges, of which we name the one with long transmission range as LR, and the other with short range as SR. 1 With the aid of global timing mechanism in LR, time synchronization for SR in MEMOSEN can be achieved. • The global position information of individual mobile phones in MEMOSEN can be obtained by predefined technologies with conceivable errors, such as GPS (Global Positioning System) or GPS-aided positioning methods. Besides, relative position information among mobile phones is also obtainable by self-organized local positioning mechanisms. • The mobile users in MEMOSEN are assumed to be cooperative such that security and privacy issues have not been considered in current phase of our work. Besides, messages originating from several source users will usually follow some multihop routes before arriving at the desired sink user. 1 In MEMOSEN, LR may refer to various wide-area radio types such as GSM or WCDMA, and SR may denote Bluetooth, ZigBee, WiMedia, or WiFi.

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B. Design Rationale for MEMOSEN Multi-radio or multi-mode terminals are not new things, and they are becoming more and more popular for local area and wide area wireless communication integrations. For instances, nearly all mobile phones have been equipped with Bluetooth to eliminate the cables when performing data communications with neighboring apparatus or personal computers. Another example will be the newly emerged WiFi-enabled mobile phones, wherein WiFi will provide high speed data communications when the user enters WiFi hotspots. Importing multi-radio heterogeneity into MEMOSEN has been identified as a beneficial method to enhance the efficiency of WSNs in terms of energy consumption, group organization, and network coverage. In fact, the multi-radio heterogeneity will naturally lead to a multitiered/hierarchical WSN architecture, which is of benefit to network resilience and performance. MEMOSEN is defined as a type of hybrid wireless pervasive networking that has combined features of mobile cellular networks with those of wireless sensor networks. Multi-radio function equipped mobile phones in MEMOSEN can act as either sensor nodes or sink nodes depending on specific scenarios. The general interaction between SR and LR is explained below. On the one hand, mobile users can leverage SR to enjoy new features provided by WSN, e.g. context information collection and dissemination in the smart environment created by networking large amount of sensor nodes. When mobile phones act as sinks, they can be the information deposit point before conveying the information in interest to its retriever who has initiated queries via LR. When mobile phones behave as sensors, however, the information to be collected will be extracted from the mobile users themselves. On the other hand, LR can largely reduce the scalability problem that is one common problem in WSNs, since mobile user groups in separate locations can be linked together and small world networks can be subsequently created. Besides, by limiting the message hop count traversed via SR in MEMOSEN (i.e. a messages must be relayed by LR if its SR hop count exceeds a certain threshold), we can further enhance the efficiency of message delivery in terms of delivery delay and energy consumption. Mobility enabled MEMOSEN will allow sensor/sink relocatability and coverage performance will get enhanced with no requirement for additional node deployment. Allowing the mobility of sensors might be more energy consuming than their tasks of communication and computation, and that mobile phones may contrastively have more energy supply, we turn to study the feasibility of leveraging the parasitic or controlled mobility for mobile sensor and sink nodes. By parasitic mobility we mean the sensor or sink node will be coupled with certain mobile hosts or terminals, and by controlled mobility we mean mobile sensor and sink nodes will follow certain mobility pattern or route to collect the desired information. Apparently, the host mobility in mobile networks such as cellular networks can be leveraged to realize such sensor/sink mobility requirement. C. Three-layered Architecture for MEMOSEN It has been recognized that a multi-tiered network structure will guarantee the network scalability for large scale deploy-

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ment. Based on the above analysis and as been illustrated in Fig.2, a mobile and multiradio enabled hierarchical architecture has been designed for MEMOSEN, which consists of three layers: • The lowest layer is “sensor layer”, denoting various types of sensor devices that are capable of collect information (including RFID tags). • The highest layer is “sink layer”, referring to the information fusion point before accessing Internet or other wireless transportation network. • The middle layer is “mobile agent layer”, which is composed of mobile phones, PDAs, laptops, and other mobile and portable devices.

Layer 3

Layer 2

Layer 1

Fig. 2. The 3-layered architecture for MEMOSEN, where layer-1 and layer-3 consist of static sensors and static sinks, respectively. Layer-2 cam be viewed as a hybrid layer which has realized the functions of mobile sink and mobile sensor.

As pointed out above, when designing the architecture for MEMOSEN, mobility and heterogeneity have been taken into considerations. Heterogeneity is referring to differences among the sensor nodes’ in computing capability, storage capacity, radio transmission range, power consumption, sensing content, etc. By mobility, we mean the ability of mobile user to roam around different sensor fields to gather their desired information. Further embodiment of mobility in MEMOSEN is twofold: • From the sensor layer’s point of view, mobile phones will act as mobile sinks or mobile gateways. After entering an intelligent zone with sensors deployed, the mobile phones will collect information in interest for the user and generate some notification signals, waiting for the mobile user to take further actions. (In this scenario, task manager generally denotes end user who is accessing MEMOSEN via Internet or mobile network.) • While from the sink layer’s point of view, mobile phones are in fact mobile sensors. When in strange sites or isolated regions where the desired information cannot be obtained, the mobile user can try to discover the information published by other peers and then initiate the queries to certain users. (In this scenario, sink nodes may locate at two positions: if the task manager send queries via shortdistance radio, e.g. Bluetooth, then the nearest relaying mobile phone acts as sink node; if the task manager is operating via long-distance radio, e.g. GSM or WCDMA, then the base station will be the sink node.)

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The heterogeneity and mobility issues in MEMOSEN have been illustrated in Fig.3 as well. When the mobile phone acts as a mobile sink, the average path length and energy consumption can be reduced (illustrated in sensor field 1). However, if the mobile phone can only communicate with a specific type of sensor, then it must gather the information in interest with the aid of other mobile phones (for example, through Bluetooth as the case in sensor field 2). In other words, if the mobile phone has the communication functions for all types of sensors, then it can gather various type of information when moving across different types of sensor fields. Otherwise, it has to turn to the other mobile users or the mobile network.

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mobile sink + static sensor (Example 1) Consider an intelligent transportation system, where the road crossing and other traffic information in the vicinity can be read from roadside sensors by the mobile sink in the moving vehicle. In order to know whether there is traffic jam in the scheduled route, the driver can send out a retrieval message via cellular networks. (Example 2) Consider the remote health care service, where the patients’ physiological or psychological situation can be efficiently monitored and recorded by nurses scanning the sensors around the patients. Similarly, parents caring for their children in home or kindergartens can also query via cellular networks.

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static sink + mobile sensor (Example 3) When children go outing with the family, sensor ring can be put on around their wrists in order to monitor their status. If one child go beyond secure range from the view of his supervisors, an alert signal in the form of sound or light can be generated. Similar scenarios (e.g. to locate lost children) may take position information into consideration as well. (Example 4) Imagine after entering one smart building with sinks mounted, the sensors scattered around you can provide necessary information of your physical/psychological state without intervening your privacy. For instance, the lights will spontaneously turn on and the air conditioner will automatically moisturize and cool off the cabin after detecting you are feeling hot and nervous.

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mobile sink + mobile sensor (Example 5) Still in the intelligent transportation system, in cases of accidents happening, the subsequent same-lane or downstream vehicles can be notified in advance to carefully bypass the spots. In cases that one lane is required to be reserved by a vehicle that is to handle emergent affairs, such information can be conveyed to upstream vehicles similarly. (Example 6) Since the cumulative time that the velocity is below a certain threshold can be a indication of the jamming status of a road, the drivers can collect vicinal traffic information and inquire about that of the ahead region along the scheduled route. When the driver wants to catch a train or deliver an emergent mail, he must search for a most light-traffic (usually not the shortest) route.

Sensor field 2 Sensor field 1

Internet

Cluster Head Task Manager

Sensor node Sink node

Fig. 3. Mobile phones can act not only as task managers, but also mobile sinks/sensors in MEMOSEN. (left) mobile phones play the roles of mobile sinks, with the advantage of reducing route length and bridging separated sensors; (right) mobile phones as mobile sensors. Information sharing will become easy with the aid of personal mobile server, such as mApache or moblog.

By importing multi-radio heterogeneity and node mobility, we can summarize the salient features of MEMOSEN over traditional WSN as follows. • Multi-radio can be leveraged to make large-area sensor networks more resilient with perspective to enhancing the scalability of sensor networks. Neighboring sensor nodes will self-organize into sensor groups, and cellular radio can connect separated sensor groups in different locations and create small world networks. • The introduction of sensor mobility can compensate for sensor failures and improve the network coverage, while the introduction of sink mobility can reduce average path length, sensor energy consumption, and energy dissipation non-uniformity. • From the tiered architecture point of view, sink mobility will be of benefit to improve network transport capacity as well. Apparently, adding an overlay “mobile agent” layer between sensor layer and sink layer will reduce the sink deployment cost, at the cost of increasing message delivery latency. D. Application Scenarios for MEMOSEN The possible scenarios of MEMOSEN are classified according to the embodiment of mobile sinks and mobile sensors. To be more specific, whether the mobile phones behave as mobile gateway to collect desired information or as mobile sink to relay relevant information for other peers through mobile networks. We have listed some possible scenarios below, with the goals of achieving efficiency, remote control and security.

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Worthy to note that RFID reader/scanner has been attached to mobile phones to enable some recently emerging mobile commerce services. Price and other information relating to the commodity can be obtained by reading the RFID tags pasted on advertisements, posts, or goods. In addition, if the customer wishes to compare the price of the same commodity in different shops, he can send out a retrieval message via cellular networks. Strictly speaking, such type of service can not be considered as one MEMOSEN application, because the separation between sensor and sink is only one hop and there is no communication and coordination among the sensors/tags.

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IV. A NALYSIS OF C APACITY G AIN AND T OPOLOGY DYNAMICS The following analysis will be based upon the same network model as that described in [11]. For clarity of presentation, assume there are n static sensor nodes in layer-1, m mobile phones in layer-2, and only 1 base station in layer-3 that covers all mobile phones and static sensors. Without mobile phones as mobile sinks, there is in fact only 1 sink node for all the n static sensor nodes, though different sensor nodes may alternate playing the role as sink node. From the previous theoretical study we can conclude that, due to the bottleneck effect around the sink node, the asymptotic throughput capacity for each sensor node will be O(1/n). The proof is as follows. N1

X1

Y1

relay1

N

Y

X

Y2

relay2

Fig. 4. A simple two parallel relay channel model. X may refer to an event, causing correlations betweem X1 amd X2 , which get relayed and fused to output Y .

According to the result from capacity analysis in Gaussian parallel relay channel (illustrated in Fig.4), we know an upper bound of channel capacity in the presence of relays will be p R ≤ log 1 + S1 + S2 where S1 = PNX1 , S2 = PNX2 , which denote the signal to noise ratio parameters before reaching the two parallel channels, respectively. If we derive from the physically degraded Gaussian relay channel, then a more tighter upper bound will be R

≤

Mobility can increase the capacity of wireless networks. As pointed out by [12], an aggregate throughput capacity of O(n) will be achieved for mobile ad hoc networks, at the cost of unbounded delay and buffer requirement. Other studies show that if considering mobility and allowing only one end-to-end session, the network capacity will scale as O(log n). Contrastively pointed out by earlier [11], the wireless network q capacity under fixed/static scenarios will be limited within O( logn n ). So, comparing mobility case with staticq case, there will be at least 3

a capacity gain in the magnitude of (lognn) . As to the capacity gain in MEMOSEN, the relationship between the number of mobile phones and that of static sensor nodes has to be taken into considerations. The MEMOSEN scenario in fact resembles that in[13], where the scaling behavior of the throughput capacity of a hybrid network is expressed √ as follows: When m grows asymptotically slower than n, the maximum throughput capacity for such a hybrid network q √ is O( logn n ). However, when m increases faster than n, m2

N2

X2

A. Network Capacity Gain due to Mobility

p max min(log (1 + S1 )(1 + (1 − α)S4 ), α∈[0,1] q p log 1 + S3 + S4 + 2 αS3 S4 )

where S3 = PN1 , S4 = PN2 , which denote the signal to noise ratio parameter after leaving the two parallel channels, respectively. When α approaches 1, or the power of noise N is relatively small, the network capacity will become p R = log 1 + 2S1

the aggregate throughput capacity will increase linearly with the number of m, i.e. the network capacity will scale as O(m). Combining the above analysis, we can briefly conclude the capacity advantage of MEMOSEN as: • When the number of mobile phones scales larger than the square root of number of the static deployed sensor nodes, the aggregate capacity of MEMOSEN will scale as O(m), √ in other words,√at least O( n). The capacity gain is in the magnitude of n. • When the number of mobile phones does not exceed the √ n threshold, the capacity gain will mainly root from the fact that sink mobility can reduce the bottleneck effect, thus the aggregate network capacity will scale as O(log n). The capacity gain is in the magnitude of log n. B. Network Topology Dynamics due to Mobility As been put forward above, the topology variation in MEMOSEN will mainly stem from the mobility of mobile phones. Assume simple Random Markov Mobility model for the m mobile phones, and a cluster-based network partition for the mapping between mobile phones and sensor nodes. Let p0 denote the probability that a mobile phone will stay in the same cluster during an unit time step. In other words, the mobile phone will enter other cluster or re-organize new cluster with probability (1 − p0 ) at the end of current unit time span. Assume any mobile phone will moves to any of the (m − 1) 0 neighboring clusters with equal probability 1−p m−1 . Thus, the holding time of a mobile phone in the same cluster will be (approximately) geometric distributed, with the mean holding time

Further deduction from the above analysis will indicate the asymptotic capacity for n-to-1 relay channel as 1 log(1 + nS1 ) n which denotes a O(1) network capacity bound when n approaches infinity. C=

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t=

p0 1 − p0

As to the topology variation, considering the independence between individual mobile phone’s mobility, thus the topology will remain invariant only if all the mobile phones remain in their clusters. Similarly, let p1 denote the probability that the

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topology remain unchanged, i.e. p1 = pm 0 . Hence, the average holding time for a certain topology is T =

∞ X

i(1 − p1 )pi1 =

i=1

p1 1 − p1

Based on the expectation of topology holding time, we can calculate the subsequent cost for other issues such as cluster re-formation delay. V. C ONCLUSION AND F UTURE W ORK Multiradio and heterogeneity inspired architecture design scheme for mobile enabled wireless sensor network has been presented in this paper. Theoretical analysis has pointed out the main features of applying multi-tiered structure for networking is the scalability and resilience for large scale deployment. In addition, the mobile agent layer inserted between the sink layer and sensor layer will be of benefit to increase network capacity. Designing mobile wireless sensor networks will face a lot of tradeoffs, mainly between the efficiency and complexity of the system. For examples, in order to achieve quasi-realtime services for mobile user, the sensors should be always powered on. However, such a system design will lead to a non-eligible increase demand for the power consumption, operation and maintenance cost. Similar considerations include the contradictions between user-defined service with special purposes and general service under central administrations, and the compromise between protecting user privacy and autonomous functions. The first challenge for MEMOSEN may be the infrastructure cost for large scale sensor deployment. Such questions have to be answered as “can we realize maximal reuse of existing infrastructure, platform, design tool, run-time service, etc?”, and “is it indispensable to re-design for every new purpose?”. Another challenge for MEMOSEN implementation lies in the desired service granularity that may exhibit great difference among different users. Appropriate mobility management schemes and fault tolerance mechanisms should be taken into considerations. Traditionally, energy conservation has been deemed as the principal design rule for WSN. However, in MEMOSEN where mobile phones can get recharged at times, we believe the message delivery related performance evaluation should be more highlighted in our future work.

[6] Wang, Z., Basagni, S., Melachrinoudis, E., Petrioli, C.“ Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime,” Proceedings of the 38th Hawaii International Conference on System Sciences(HICSS-38), January 3-6, 2005. [7] Kim, H., Abdelzaher, T., Kwon, W.“ Minimum-Energy Asynchronous Dissemination to Mobile Sinks in Wireless Sensor Networks,” Proceedings of ACM SenSys 2003, Los Angeles, US, November 5-7, 2003. [8] Zhang, W., Song, H., Zhu, S., Cao, G.“ Least Privilige and Privilige Deprivation: Towards Tolerating Mobile Sink Compromises in Wireless Sensor Networks,” Proceedings of ACM MobiHoc 2005, UrbanaChampaign, US, May 25-28, 2005. [9] Lee, J., Venkatesh, S., Kumar, M.“ Formation of a geometric pattern with a mobile wireless sensor network,” Journal of Robotic System, 21(10), 517–530, October 2004. [10] Sarikaya, B.“ Nomadic User Approach to Building Mobile Wireless Sensor Networks,” Proceedings of International Workshop on Network Security and Wireless Communications, Sendai, Japan, January 27, 2005. [11] Gupta, P., Kumar, P.R.“The Capacity of Wireless Networks,” IEEE Transactions on Information Theory, 46(2), March 2000. [12] Grossglauser, M., Tse, David. N.C.“Mobility Increases the Capacity of Ad Hoc Wireless Networks,” IEEE/ACM Transactions on Networking, 10(4), August 2002. [13] Liu, B., Liu, Z., Towsley, D.“On the Capacity of Hybrid Wireless Networks,” Proceedings of IEEE INFOCOM 2003, San Francisco, US, March 30 - April 3, 2003.

R EFERENCES [1] Yarvis, M., Kushalnagar, N., Singh, H., Rangarajan, A., Liu, Y., Singh, S. “Exploiting Heterogeneity in Sensor Networks,” Proceedings of IEEE INFOCOM 2005, Miami, US, March 13-17, 2005. [2] Sharma, G., Mazumdar, R.“ Hybrid Sensor Networks: A Small World,” Proceedings of ACM MobiHoc 2005, Urbana-Champaign, US, May 25-28, 2005. [3] Wei, W., Srinivasan, V., Chiang, C.“ Using Mobile Relays to Prolong the Lifetime of Wireless Sensor Networks,” Proceedings of ACM MobiCom 2005, Cologne, Germany, August 28 - September 2, 2005. [4] Wang, G., Cao, G., Porta, T., Zhang, W.“ Sensor Relocation in Mobile Sensor Networks,” Proceedings of IEEE INFOCOM 2005, Miami, US, March 13-17, 2005. [5] Liu, B., Brass, P., Dousse, O., Nain, P., Towsley, D.“ Mobility Improve Coverage of Sensor Networks,” Proceedings of ACM MobiHoc 2005, Urbana-Champaign, US, May 25-28, 2005.

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