An Efficient Fuzzy Localization Approach in Wireless Sensor Networks Pritee Parwekar
Ramana Reddy
Jaypee Institute of Information Technology Nodia-62 Email:
[email protected]
Indian Institute of Science Bangalore-12 Email:
[email protected]
Abstract—A successful node localization is of great importance to design efficient localization algorithms in Wireless Sensor Networks (WSNs). A proper node deployment can assist in routing, data fusion and communication in large scale ad hoc networks. Furthermore, it can extend the lifetime of WSNs by minimizing energy consumption. In this paper, we propose rangefree localization using Fuzzy logic to achieve a more optimum solution. We model edge weights using Fuzzy logic system, which utilize the current value of Received Signal Strength Indicator (RSSI) and the Link Quality Indicator (LQI) from the anchor nodes for deciding location of sensor nodes. We have implemented our approach as a TinyOS module, and evaluated it through extensive TOSSIM simulations. Simulation results have shown promising results and demonstrate the effectiveness of the proposed approach. Keywords—Wireless Sensor Networks, Fuzzy Logic, Received Signal Strength Indicator, Link Quality Indicator
I.
I NTRODUCTION
WSNs is one of the potential emerging computing technologies, edging gradually from the concept stage to practical reality. Present day applications of WSN have been identified to monitor inhospitable environs of dynamic nature. Wireless sensor network can be deployed in various application rights from military, biomedical, and environmental utilization. The typical issues in wireless sensor networks like their limited energy and computational capability are being progressively addressed by newer research. Although there has been an extensive research work towards energy, it still remains a major obstruction towards development of the effective routing algorithm in wireless sensor networks. Wireless Sensor platforms are now carrying sensors ranging from acoustic, temperature, pressure, humidity and also video amongst other sensors. With the increasing complexity of data to be captured and transmitted, the networks have to evolve further with ability to retain energy and thereby life, transmit more complex and voluminous data and intelligently decide the most optimum direction for this data exchange. In each case, determining the location of wireless nodes is a very sensitive operation and is highly important in order to make measured data significant. Existing truth table based logic, as being advocated, would not suffice to accomplish this relatively complex network requirement. We propose to introduce Fuzzy logic in this scheme of things to achieve a more optimum solution in sensor nodes position estimation. There are two types of nodes in WSNs: anchor nodes and sensor nodes. An anchor node possess sufficient energy and
accurate information about their position, while sensor nodes do not. The nodes location can be directly available by satellite communication using low-power GPS receptors. Global Positioning System (GPS) is a popular commercial localization system. However this may not be a practical solution for outdoor sensor networks without other assisting technologies like beacons. Moreover, it is prohibitively expensive to install GPS on each sensor in terms of cost, form factors, power consumption and antenna requirements. Further, GPS requires direct Light-Of-Sight (LoS) communication, which renders it unfeasible for many multi-terrain outdoor application environments. Existence of beacons which are globally fixed based on GPS or manually fed data are also considered for sensor localization. A typical sensor first measures the distances or angles from it to several beacons, and then obtains position estimation through techniques such as triangulation, trilateration, multilateration. This technique has been adopted from erstwhile coastal navigation system for ships. The localization method that relies on the availability of point-to-point distance or angle information is called as Range-based localization. The distance/angle can be obtained by measuring Time of Arrival (ToA), Time-Difference-ofArrival (TDOA), Received-Signal-Strength-Indicator (RSSI), and Angle-of-Arrival (AOA), etc. A fine grained resolution can be produced by this technique of range-based localization. But this technique has strict requirements on signal measurements and time synchronization. In ToA, distances are calculated based on signal arrival times and the transmission times and speeds which is loaded on the signal data. The AoA techniques require special directional antenna and the performance may be clouded due to omnidirectional multipath reflections. This problem becomes more pronounced in an environment where the sensor is surrounded by scattering objects. Optimising energy issues in WSN would involve understanding of the WSN events and triggering micro level activities in communication. Properties of Fuzzy logic can be used for describing such events [1]. The noteworthy properties amongst these are as follows: •
Fuzzy logic has tolerance towards unreliable and imprecise sensor readings.
•
Fuzzy logic is much closer to human thinking than the erstwhile truth table based crisp logic. For example, humans consider fire as an event described by high temperature and smoke rather than an event
characterized by temperature above 60o C and smoke obscuration level above 15%. •
Fuzzy logic has a wider scope due to its inherent intuitive nature and thus finds better application than other classification algorithms based on probability theory.
Fuzzy logic has a disadvantage in terms of storage of the rule-base, which might require a considerable amount of memory and cannot be expected from a resource constrained wireless sensor. Further constant traversing of a large rule-base might considerably slow down the event detection. Here the usage of a powerful sink would be necessary. The number of rules have an exponential effect when the number of variables increase i.e. with n variables taking m values, the number of rules is mn . Optimization and reduction of the size of the rule-base is necessary without decreasing the event detection capability of the system. Our contribution can be summarized as follows: 1)
2)
We present a range-free localization approach based on Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI). The edge weights of anchor nodes are modelled using fuzzy logic system and the estimated location of sensor nodes are calculated based on weighted average formula [2].
table is maintained which takes the values of buffer state and incoming to outgoing packets ratio as input and gives us an output in the form of fuzzy variable as to whether a decision needs to be taken or not. Fuzzy logic system can be easily used in locationdependent applications for WSNs, without any hardware implementation. In addition, it can perform fast data processing that can help in reducing the delay in the localization estimation process. The WSN localization method used to estimate the unknown positions of each sensor node with the available prior knowledge of position sensors (anchor nodes) in the network which are used as reference for the others. Range free methods do not depend on the distance. Therefore, in this method, hardware design is simplified since only the reference nodes have information about their locations. Many range-free localization algorithms were proposed in the literature. In [9], the authors propose a range-free localization algorithm that uses the broadcost anchor node position (Xi , Yi ) by anchor beacons. In their algorithm, each sensor node computes its position as a centroid of the positions of all connected anchor nodes to itself by: Pn (Xest , Yest ) =
i=1
N
Xi
Pn ,
i=1
Yi
N
(1)
We have implemented our approach as a TinyOS [3] module , and evaluated it through TOSSIM [4] simulations. We compare its performance to the existing RSSI approach in the literature.
Where (Xest , Yest ) represents the estimated position of the sensor node and N is the number of the connected anchor nodes to the sensor node. The scheme is simple and economic. However, it shows large localization errors.
The rest of the paper is organized as follows. Section 2 describes the related works and previous studies. Section 3 delineates the implementation details of the proposed approach. Section 4 provides simulation results and discusses the efficiency of proposed approach. Finally, Section 5 gives concluding remarks.
An improved version of [9] was proposed by the authors of [2]. In the improved version, the location of a sensor node is calculated by using the edge weights of anchor nodes, which is connected to that sensor node. Therefore, each sensor node computes its position by:
II.
R ELATED WORK
A fuzzy logic system consists of a fuzzifier, a rule based decision making processor and a defuzzifier [1]. Fuzzy sets and logic were introduced by L. Zadeh in 1965. Numerous elds have taken advantage of their properties since then. In WSNs, fuzzy logic has been used to improve decision-making, reduce resource consumption, and increase performance. Some of the areas it has been applied to are: In cluster-head election [5], cluster-heads were elected by the base station in each round by calculating the chance each node has to become the cluster-head using three fuzzy descriptors. Data aggregation [6] where a fuzzy logic algorithm is another area is to reduce the number of received and sent messages without affecting the quality of the aggregate estimation. In [7] a routing algorithm for routing analysis in wireless sensor networks utilizing a fuzzy logic system at each node to determine its capability to transfer data based on its relative energy levels, distance and traffic load to maximize the lifetime of the sensor networks has been proposed QoS [8] a fuzzy logic approach for efficiently estimating the congestion and then reacting to mitigate the congestion. Here, a fuzzy
Pn Pn Xi wi Yi wi i=1 i=1 Pn (Xest , Yest ) = , Pn i=1 wi i=1 wi
(2)
where wi is the edge weight of ith anchor node connected to the sensor node. The performance of this approach highly depends on the design of the edge weights. In [10], [11], [12], the authors used the weighted average formula that is used in ( [2], [9]) to calculate the estimated position for each sensor node based on Received Signal Strength Indicator (RSSI). Conventinal wisdom in the sensornet community says that RSSI is not a good indicator. This belief is based on experimental work with early platforms [13], which showed that while detecting good links was possible with RSSI, with imperfect links it is difficult to make good estimates. Many current platforms (micaZ, Telos, Shimmer Mote, Tmote Sky, Zolertia Z1 mote, and Intel Mote2) use the same radio chip, the CC2420 [14]. In addition to RSSI, CC2420 provides an additional hardware indicator, LQI, which is effectively a measure of chip error rate. RSSI is the estimate of the signal power and is calculated over 8 symbol periods and
stored in the RSSI VAL register. The RF230 chip (Iris mote) calculates the LQI directly in hardware (values from 0 to 255) but in the CC2420 radio chip, the LQI is calculated over 8 bits following the start frame delimiter (SFD). It only provides an average correlation value, and the values are usually between 110 and 50, and corresponding to maximum and minimum quality frames respectively. LQI is calculated in software using RSSI value. In [15], the authors presented preliminary evaluation results based on CC2420 and suggested that if the RSSI is above sensitivity threshold (about -87 dBm), the packet reception rate (PRR) is atleast 85%. Around this sensitivity threshold PRR is not correlated. However, the average LQI was a better indicator over many packets and has a better correlation with PRR.
Fig. 1.
Membership function of RSSI
Fig. 2.
Membership function of LQI
In our proposed approach, we use the weighted average formula that is used by [10], [11], [12], but we use both Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) for calculating edge weights. III.
PROPOSED APPROACH
In this paper, we propose fuzzy-based range-free localization algorithm for WSNs. It relies on the RSSI and LQI as the primary parameters in order to estimate the unknown locations of the sensor nodes. LQI is a metric of the current quality of the received signal, RSSI is a signal strength indication. It does not care about the ”quality” or ”correctness” of the signal. LQI does not care about the actual signal strength, but the signal quality often is linked to signal strength. This is because a strong signal is likely to be less affected by noise and thus will be seen as ”cleaner” or more ”correct” by the receiver. LQI is best used as a relative measurement of the link quality (a high value indicates a better link than what a low value does) We consider the following five ”extreme cases” to illustrate how RSSI and LQI work: 1) 2) 3) 4) 5)
A weak signal in the presence of noise may give low RSSI and low LQI. A weak signal in ”total” absence of noise may give low RSSI and high LQI. Strong noise (usually coming from an interferer) may give high RSSI and low LQI. A strong signal without much noise may give high RSSI and high LQI. A very strong signal that causes the receiver to saturate may give high RSSI and low LQI.
Though we mentioned five cases above, to increase the accuracy of the estimation, we decompose both the input space of RSSI and LQI into three triangular-shaped membership functions: Low, Medium, and High respectively as shown in Fig. 1 and 2. Using Fuzzy rules the weights are calculated based on the Table I. In order to obtain better simulations for WSNs, the characteristics of both the radio, and the environment (channel) where they are placed should be provided. We use one of the most common radio propagation model which is the log-normal path loss model [16]. This model can be used for large and small coverage systems [17].
Furthermore, empirical studies have shown that the log-normal model provides more accurate multipath channel models than the well-known Nakagami and Rayleigh models for indoor environments [18]. Measurements have shown that at any value d, the path loss P L(d) at a particular location is random and distributed lognormally ( normal in dB) about the mean distance-dependent value. That is, P L(d) = P L(d0 ) + 10n log10
d + Xσ d0
(3)
where d0 is the distance of reference, n is the path loss exponent that captures the rate at which the signal decays with respect to distance, Xσ is a zero-mean Gaussian distributed random variable (in dB) with standard deviation σ (also in dB). Here we use d0 = 8 and n =3.3. We implemented our approach as a TinyOS module and evaluated through TOSSIM simulations. It is easy to go back and forth between a set of real motes and a TOSSIM simulation because the same nesC application code runs on real motes and in the TOSSIM environment. Currently TOSSIM does not have any implementation which provides RSSI and LQI values. In order to get RSSI value, the TOSSIM code is modified, and LQI is calculated using RSSI value. To calculate LQI from RSSI, we used linear polynomial model with coefficients (95% confidence bounds). Similar approach was used in the blip application in the openWSN project at the university of berkely [19]. The polynomial is, P oly(x) = p1 x4 + p2 x3 + p3 x2 + p4 x + p5
(4)
where x is the RSSI value received from anchor node. A curve fitting technique is employed and a 4th polynomial curve is best fitted with the data, RSSI to LQI, in a least squares sense. Based on our experiment, the coefficients computed for
Rule 1 2 3 4 5 6 7 8 9
TABLE I.
RSSI Low Low Low Medium Medium Medium High High High
LQI Low Medium High Low Medium High Low Medium High
Weight Low Low Medium Low Medium High Medium High High
F UZZY RULE BASE TABLE
the approximating polynomial of degree 4 are -0.6475, 3.761, -5.997, 3.418 and 105.9 for p1 -p5 respectively. In our approach, we assumed that the anchor nodes know their positions through GPS or by other means such as manual configuration. Initially, the anchor nodes broadcast beacons with their IDs and positions to all sensor nodes. When each sensor node receives the transmitted beacons from each anchor node, it measures the RSSI values based on them. Then it calculates LQI value from RSSI measured value. After that, each node uses fuzzy system to calculate its estimated position. Once the estimated position is ready, the node sends a location message to one of the anchor nodes whose weight is High or the sink node for the whole network. IV.
SIMULATION AND RESULTS
The performance of our proposed approach implemented as a TinyOS module and evaluated through extensive TOSSIM simulations. First we conducted an experiment to evaluate how RSSI and LQI varies over the distance with two TelosB motes. The setup used for collecting RSSI and LQI values is based on the TinyOS RSSI Demo tutorial [20]. The tutorial sofware is capable of collecting RSSI values as it is. We modified the same code to collect LQI values as well. There are two motes in this setup, one Base station mote and one Sender mote. The Base station mote is used to collect data from the Sender mote. We used java as a logging application and through serial connection it will receive data forwarded by the base station. In our test, we used the default transmission power for the CC2420, CC2420 DEF RF POWER=31, which corresponds to maximum power (0dB). In our tests the Base station is fixed, and the Sending mote is the moving node. The Sending mote is placed in different distances varying from one meter to fifty meters from the Base station and the RSSI and LQI values were recorded. This experiment was carried out in the hostel in the first floor corridor with a clear line of sight (LOS) between the base station and remote node. The exact scenario for the experiment can be seen in Figure 3. Figures 4 and 5 show the RSSI and LQI according to the distance between the two motes. We have only limited telosb motes. For this reason, we used TOSSIM to evaluate our approach. As mentioned in the previous section, the same nesC code runs on real motes and in the TOSSIM environment. TOSSIM requires topology and noise file as inputs and the topology file is genarated using log-distance path loss model. The noise file is taken from TOSSIM source code. We evaluated our approach with 25, 50, 75, and 100 nodes. First four nodes are acting as anchor
Fig. 3.
Base station connected to Laptop
Fig. 4.
RSSI Vs. Distance
nodes, and the remaining nodes are acting as sensor nodes and they are randomly distributed with unknown positions. And the topology for 25 nodes network as shown in Fig. 6. It consists of 100m X 100m square area, 4 anchor nodes located at the center of each edge of the squared area. Initially, we evaluated by placing the 4 anchor nodes at each corner of the squared area. After proper evaluation, we observed that the anchor nodes are not reachable themselves and the sensor nodes are also not reachable from some of the 4 anchor nodes. The reason for this is, if two nodes are separated more than 100m and above, then those nodes are not reachable and the observerd link gain is -94.70dB or less. With this setup, the anchor nodes are separated with 100m, and the possible distance between any random position sensor node and anchor node is also more than 100m (the diagonal distance of the given square is 141m). The first limitation prevents to form a network among anchor nodes, and the second limilation limits the number of anchor nodes reachable to a given sensor node. The first limitation can be elimated and possibly it improves the reachabilty from a given sensor node to all anchor nodes. In this setup, even the anchor nodes 2 and 4 are not reachable from 1 and 3, but they can reach via other anchor nodes. There is another possiblilty that some randomly positioned sensor node may not be reachable from a particular anchor node (maximum possible distance is 111m). This problem can be elimated by placing the sensor nodes in such a way that they are reachable from all 4 anchor nodes. For this, we regenerated the random position if the distance between the anchor node and the particular sensor node is more than 100 meters. This process takes only few iterations. For evaluating our proposed approach, we use the following
Fig. 5.
LQI Vs. Distance
Fig. 6.
Simulation Environment
Fig. 8.
Performance of our approach Vs. RSSI
Fig. 9.
Performance of our approach Vs. RSSI
two performance indices can be used [12]: Location error: The distance between the estimated position and the actual position of sensor node, Location error =
p
(Xest − Xa )2 + (Yest − Ya )2
(5)
Average location error: The average distance between the estimated position and the actual position of all sensor nodes, Pp (Xest − Xa )2 + (Yest − Ya )2 Avg. location error = number of sensor nodes (6) The results that are presented in Figs. 7, 8 and 9. The plots shown in each of the Figs. 7, 8 and 9 correspond to the number of sensor nodes with localization error less than 10m,15m and 20m respectively. We observe that our approach shows the better results compare to RSSI approach. We see that our approach consistently gives better results and the RSSI approach drops very fast with an increase in the number of sensor nodes.
V.
In this paper, we proposed a fuzzy-based range free localization algorithm for wireless sensor networks. The RSSI and LQI information between sensor nodes and its anchor nodes is used to estimate the positions without any hardware. Fuzzy logic system is the main component of the proposed scheme. The edge weight of each anchor node are found out using Mamdani fuzzy system. Our approach is implemented as a TinyOS module and evaluated using TOSSIM simulator. Our simulation results highlight the improvement in perfomance obtained by our approach over the RSSI approach in the literature. Some of the possible future directions are, plan to study the effectiveness of the proposed approach in a full sensor mobility environment. R EFERENCES [1]
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[5] Fig. 7.
Performance of our approach Vs. RSSI [6]
Figure 10 shows the average location error of our approach with RSSI. It can be seen that the average error of our approach is better than the RSSI approach.
C ONCLUSION
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