Neighbor Co-ordination based Fault Node Detection Algorithm for Distributed Wireless Sensor Networks

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1 Neighbor Co-ordination based Node Detection Algorithm for Distributed Wireless Sensor Networks Prashant Shukla*, Rama Ranjan Panda Department of Computer Science and Engineering RSR Rungta College of Engineering & Technology Kohka-Kurud Road, Bhilai, Chhattisgarh Abstract In this paper a neighbor coordination approach based fault node detection technique for distributed wireless sensor networks (WSNs) is presented. Because of large deviation in incorrect data transmitted by various faulty sensor nodes(sns), the statistical based approach of fault detection using mean, mode, median and other hypothetical test based schemes becomes inappropriate for large scale distributed WSNs. This research concentrates on fault node identification in distributed WSNs where the neighboring nodes attempt to identify the faulty SN through comparing their sensed data with all of their neighbor s data. The main aim of this study is to design a less complex fault node detection algorithm, which can easily detect faulty sensor nodes present in distributed WSNs by using a neighbor coordination approach based fault node detection technique. The simulation results show the proposed algorithm is effective in terms of performance measures like detection accuracy, false alarm rate and false positive rate which are far better in than conventional methods used to detect the faulty SNs in WSNs. Keywords Wireless Sensor Networks; Node Detection; Neighbor Coordination Approach. I. INTRODUCTION The problem of fault node detection in distributed wireless sensor networks (WSNs) have acquired universal scientific interest not only because of their small dimension, power efficient hardware platforms which incorporates sensing and processing of data along with wireless communication capability, but also due to their wide range of applications and ease of deployment. In the recent years, WSNs have influenced our daily life by providing various services like remote environmental monitoring, infrastructure management, target tracking, target localization, home and office security, public safety, event detection, event boundary identification, medicine, transportation and many more [1, 2]. SNs have large number of hardware and software restrictions in terms of processing capability, memory power, power efficient, and communication capability due to their small dimensions. However SNs are likely to have faults because of many reasons like mechanical or electrical problems in their internal circuits, environmental degradation, power-supply depletion, or even hostile tampering. Sometimes SNs may also be expected to operate autonomously, if they are installed in unattended or harsh environments. Because of this reason faults are likely to occur frequently in WSNs. Moreover, if deployment of small, cheap SNs is done on a large scale then it will lead to node failure due to fabrication defects and due to which occurrence of faults in SNs will be quite common. Thus, it is quite essential to identify and trace out the faulty SNs in the network, and exclude them before routine operation as they can be used as typical sensing nodes [3, 4]. The sensor faults can be broadly divided into two major categories as crash faults and soft faults. In crash fault, a SN is unable to communicate with rest of the nodes and becomes dormant in the network, while in soft fault, the SN starts behaving randomly in the network. Therefore it becomes very important to identify the set of faulty SNs within a network. The process of determining whether the SN is good or faulty is known as fault detection in WSNs [13, 14]. Several fault node detection and fault diagnosis techniques of distributed WSNs have been proposed in [5-10]. Krishnamachari et al. [5] have proposed a Bayesian fault identification approach to resolve the fault event disambiguation problem in WSNs. Koushanfar et al. [6] have presented a cross validation based approach for online detection of faulty SNs in WSNs. In this approach, statistical methods are used to detect the sensors which are having the highest of faults. Ruiz et al. [7] have proposed an external manager based fault node identification approach for event driven based WSNs. Even though the external manager is capable of performing more complex tasks than the typical SNs, still there exist a problem of communication between the SNs and the external manager. Ding et al. [8] have presented a neighbor co-ordination based localized fault node detection approach, in which each SN compares its own sensed data with the median of all of its neighboring node s data to evaluate its fault status. Luo et al. [9] have presented an energy-efficient fault-tolerant event detection approach for distributed WSNs, in which minimum numbers of neighbors are selected for fault node detection in order to minimize the communication overhead. Chen et al. [10] have presented a distributed fault node detection approach for WSNs. In this approach, local comparisons are done using a modified majority voting technique. In this approach, each SN compares its own sensed data with its neighboring node s data and based on which, a decision has to be taken by taking all neighboring nodes in confidence. However, the approach becomes little complicated because the exchange of information between two neighboring nodes is done twice in order to reach a local decision of fault status which is based on a threshold value. 261

2 In this paper, we have presented an efficient algorithm for identifying faulty SNs in a distributed WSNs based on a neighbor coordination approach based fault node detection technique. Examples and simulation results have validated its efficiency. II. MODEL AND PROBLEM FORMULATION In recent past, fault node detection in WSNs has become prime area of study. node detection approach can be categorized into two basic types: centralized approach and distributed approach. In this section, model for the distributed sensor network is implemented by using the open source software NS2 and the problem of fault node detection is investigated using distributed approach. Distributed approach is based upon ultra-reliable SNs having high computation capacity and large storage. The SNs are connected with each other within a specified transmission range T. Here the connection of one node with other neighbor nodes is based upon disc that formed by transmission range T. All the nodes within the disc are connected with each other and share their sensed data among themselves. Each node compares its own sensed data with the data of its neighbors, which are inside a disk and hence identifies its own fault status within a WSN. Distributed WSNs consist of a large number of SNs which are presented using network simulator NS2. The parameters selected for this simulation are listed in Table I. TABLE I. PARAMETERS USED IN NS2 SIMULATION Number of Sensor Nodes(N) 50 Simulation Area Simulation Time Routing Protocol Transmission Range Traffic Type Packet size 20 x 20 meters 0.02 sec per node UDP 1 meter CBR(Constant Bit Rate) 25 Bytes We have implemented a distributed topology which consist of 50 nodes (N=50) and simulated in network simulator NS2 as shown in Fig. 1. Each SNs are treated as the vertices in a graph G = G (V,E) where V is the number of vertices (nodes) and E is the number of edges between two vertices in the graph G. Each node-n present inside a disc and its neighbor nodes are linked with each other. Each node communicates its sensed data with all other SN present inside the disc and thus identifies its fault status using data of all the neighboring SNs in WSN. In this paper, the statistical distributed fault node detection algorithm to detect the soft faulty nodes in WSN is described. The overall algorithm can be divided into three distinct major stages:- 1. Data Collection stage 2. Analysis stage 3. Decision stage In various stages, every SN Ni does various tasks. In the initial stage, every SN Ni requests all their neighbors by transmitting their own sensed data X(i) at an instance of time. At the same instance of time, every node Ni collects sensed data X(j) from all their neighbor nodes Nj ϵ Neb(i) which are present in its transmission range T. Now in analysis phase, every SN Ni estimates its probable sensed value with the help of all of their neighbor s data. In decision stage, after estimation of probable sensed data, every SN Ni equates its sensed data with the probable sensed data to evaluate its fault status. Based on this fault status, Ni will actively participate in the network operation. The perceptive descriptions of all events that occur during the three various stages of the suggested distributed fault detection algorithm are as follows:- 1) Data Collection Stage: In this stage, each SN Ni, transmits its own sensed data X(i) to all neighbor nodes within its transmission range T. Based on the received data, every SN Ni identifies its neighbor nodes Nj ϵ Neb(i). It is supposed that at the time of installation, every SN Ni deployed in network is good and free from faults. Hence initial fault status of SN IFS(i)=0. 2) Analysis stage: Every SN Ni computes their estimated sensed data PX(i) from the data which is received from their neighbor nodes Nj which are present in its transmission range T. A Z-score test is a statistical approach, in which the dissemination of the test statistics under the null hypothesis can be estimated by a normal distribution. Since it is assumed that the data received by various SNs are independent of each other. Hence if the degree of the each node is high, the z-score test is preferable. The value of mean M(i) of the data received by neighbor node N(j) at SN Ni is calculated by using the formula. M(i) = 1 Neb(i) X(j) Degree(Ni) j=1 (1) Now, to evaluate the value of standard deviation S(i) of the data received by node N(i), the following formulas is used. S(i) = Neb(i) j=1 (X(j) Degree(Ni) 1 µ) 2 (2) Finally, by using z-score test and with the help of values of M(i) and S(i), we can calculate the value of PX(i) as follows. Fig.1. Unit disk model based network topology SE(i) = S Degree(Ni) (3) 262

3 PX(i) = X(i) M SE(i) 3) Decision Stage: In this stage every SN Ni compares its own estimated data PX(i) with the actual sensed data X(i). (4) D(i) = X(i)-PX(i) (5) If difference between the sensed data X(i) and estimated data PX(i) is within the range of 2 to 3 then SN Ni declares itself as a fault free node otherwise it is decided as a faulty SN. FFS(i)={ 0 if 2 D(i) 3 (good) 1 if D(i) < 2 or D(i) > 3 (faulty) Since the algorithm is based on distributed WSN, so all the stages in the given algorithm A are to be executed by the every individual SN Ni of a WSN. The notations used for developing this algorithm are summarized in Table II. Once again the detection accuracy (), fault alarm rate () and positive rate () calculated for different probabilities of faulty nodes. But as the presence of outliers affect the actual mean (M) and standard deviation (S), estimated mean (M) and standard deviation (S) this leads to further investigation. We have analyzed the presence of faulty SN in WSNs by calculating the Z-score. The neighbor coordination algorithms for detecting the faulty node in distributed WSNs can be described as follows: A. Algorithm for detection of y Node Input: SN Position (xi, yi), sensed data X[i]. Output: detection accuracy (), fault alarm rate () and fault positive rate (). 1. FOR each node i = 1 to N DO 2. Generate sensed data X[i] 3. Find all neighbors and keep them in set Neb[i] 4. Set IFS[i] = 0; 5. END FOR 6. Calculate F = N * P 7. FOR j = 1 to F DO 8. random[j] = generate(n) 9. set UIFS[random[j]]=1 10. END FOR 11. Sum[i] = 0; 12. FOR j = 1 to Neb[i] and N[j] = Neb[i] DO 13. Sum[i]=Sum[i] + X[i]; 14. END FOR 15. [i] = Sum[i] / Degree(Ni); 16. CSD[i] = 0; 17. FOR j = 1 to Neb[i] and N[j]=Neb[i] DO 18. CSD[i] = CSD[i] + (X[j] - [i]) 2 ; 19. END FOR 20. SD[i] = sqrt (CSD / (Degree(Ni) - 1)); 21. SE[i] = sqrt (SD[i] / Degree(Ni)); 22. PX[i] = X[i] CRDN[i] / SE[i]; 23. If ( X[i]-PX[i] >= 2) and ( X[i]-PX[i] <= 3) then 24. Sensor node Ni is detected as -Free 25. FFS[i] = 0; 26. Else 27. Sensor node Ni is detected as y 28. FFS[i] = 1; 29. FOR i = 1 to N DO (6) 30. IF UIFS[i] == 1 && FFS[i] == 1 THEN 31. initialize count_ = count_ END IF 33. IF UIFS[i] = = 0 && FFS[i] = = 1 THEN 34. initialize count_ = count_ END IF 36. IF UIFS[i] = = 1 && FFS[i] = = 0 THEN 37. Initialize count_ = count_ END IF 39. END FOR 40. Evaluate = count_ / F 41. Evaluate = count_ / N-F 42. Evaluate = count_ / F First of all, we have used neighbor coordination based distributed algorithm to evaluate the value of, and using mean. Then we have used neighbor coordination based distributed algorithm to evaluate the value of, and using z-score. The list of parameters which are used to develop the neighbor coordination based distributed algorithm for detection of faulty SN in WSNs are given in Table II. TABLE II. PARAMETER USED TODEVELOP THE ALGORITHM Parameter N Ni T X[i] Neb[i] Degree[i] Sum[i] [i] CSD[i] SD[i] PX[i] P F Generate(N) random[i] IFS[i] FFS[i] Count_ Count_ Count_ Description of Parameter Number of sensor nodes Sensor node at location (xi, yi) Transmission range of sensor node Ni Sensed data of sensor node Ni Set of neighbor nodes of sensor node Ni Degree of sensor node Ni Sum of sensed data of all neighbors nodes at sensor node Ni Estimated mean value of all neighbors nodes at sensor node Ni Cumulative standard deviation of sensed data of all neighbors at sensor node Ni Standard deviation of sensed data of all neighbor nodes at sensor node Ni Z score test value of sensor node Ni Probability of fault node Total number of fault node Pseudo random number generation for fault node Array to store Pseudo random number for fault node i Initial fault status of the sensor node Ni Final fault status of the sensor node Ni Counter for data accuracy() Counter for fault alarm rate() Counter for fault positive rate () Detection Accuracy Alarm Rate Positive Rate 263

4 III. EXPERIMENTAL RESULT The performance measures of the proposed neighbor coordination based fault node detection algorithms are evaluated by using network simulation. We have build a distributed sensor network in network simulator tool (NS2) and network is simulated using the proposed algorithm described in the section II to evaluate the detection accuracy (), fault alarm rate () and fault positive rate () over different of fault nodes (p). Where can be defined as the ratio of the number of faulty SNs detected to the total number of faulty SNs, can be defined as the ratio of the number of fault-free nodes detected as faulty to the total number of fault-free node and can be defined as the ratio of the number of faulty SNs diagnosed as fault free to the total number of faulty SNs present in the network. Initially we have analyzed the faulty SNs in a WSN with algorithm-a to evaluate the, and for 1024 nodes with the help of actual sensed data X(i) and standard deviation S(i). Then we have used Z-score with algorithm-a to evaluate the, and with estimated PX(i) and standard deviation S(i). Here the performance is improved as compared to the actual sensed data X(i) and standard deviation S(i). TABLE III. DETECTION ACCURACY () WITH MEAN AND Z-SCORE Table III shows the comparison of using and using, over various values of fault p. With the increase in value of fault p, the value of decreases. When the value of p=0.05, 99.8% of the faulty nodes are accurately detected using where as 99.9% of the faulty nodes are accurately detected using Z-score. Fig.2 shows the graph between with respect to fault p using and. In case of decreases from 99.8% to 87.8% against the increasing values of fault p from 0.05 to 0.5, where as in case Z-score the performance gets improved, as the decreases from 99.9% to 93.4% against the increasing values of fault p from 0.05 to 0.5. The presence of outliers affect the actual sensed data X(i) and standard deviation S(i) from estimated data PX(i) and standard deviation S(i) which leads to further investigation. From the result we can observe that z-score gives better result as compared to mean. Detection Accuracy Probability of fault Fig. 2. Detection accuracy () versus Probability of by and Z- score. Table IV shows a comparison analysis of over various values of fault p using and. With the increase in value of fault p, the increases. In case of the values of increases from 2.8% to 16.7% against the increasing values of p form 0.05 to 0.5. TABLE IV. FAULT ALARM RATE () WITH MEAN AND Z-SCORE. using using Similarly for Z-score test the value of increases from 2% to 13.5% against the increasing values of p form 0.05 to 0.5. The Fig.3 shows the with respect to fault p using and Z-score test. 264

5 Alarm Ratio using using Z-score IV. CONCLUSION In this paper, we have proposed a neighbor coordination based solution for identifying faulty SNs in WSNs. The simulation results show that the proposed fault node detection algorithm is effective in terms of performance measures like, and. Moreover, the proposed fault node detection algorithm does not use any complex operations, which makes it energy efficient. However in this study, the number of faulty SNs is kept fixed during the whole simulation process. Hence, this work will be improved for dealing with varying number of faulty SNs in a network. Also, we will extend and modify the proposed fault node detection algorithm to tolerate transient faults as our further work Probability of Fig. 3. Alarm Rate () versus Probability of using and Z- score. Table V shows another comparison analysis of over various values of p using and. With the increase in value of fault p, the increases. In case of the increases from 0.4% to 9.4% against the increasing values of p form 0.05 to 0.5. TABLE V. FAULT POSITIVE RATE () WITH MEAN AND Z-SCORE Similarly for Z-score test the value increases from 0.1% to 8.5% against the increasing values of p from 0.05 to 0.5. In case of mean is larger as compared to that of in Z-score. The Fig.5 shows the with respect to fault p, using and Z-score test. Positive Ratio using using 0 Probability of Fig. 5. positive rate () versus Probability of using and Z-score. REFERENCES [1] B. R. Badrinath, M. Srivastava, K. Mills, J. Scholtz,and K. Sollins. Special issue on smart spaces and environments. IEEE Personal Communications, Oct [2] S. Lindsey, C. Raghavendra, and K. Sivalingam. Data gathering in sensor networks using the energy delay metric. In International Workshop on Parallel and Distributed Computing: Issues in Wireless Networks and Mobile Computing, San Francisco, USA, Apr [3] Aboelaze M, Aloul F. Current and future trends in sensor networks: A survey. Wireless and Optical Communications Networks, 2005: [4] Koushanfar F, Potkonjak M, Sangiovanni-Vincentell A. tolerance techniques for wireless ad hoc sensor networks. In: Proceedings of IEEE on Sensors : [5] B. Krishnamachari, S. Iyengar, Distributed Bayesian algorithms for fault tolerant event region detection in wireless sensor networks, IEEE Transactions on Computers 53 (3) (2004). [6] F. Koushanfar, M. Potkonjak, A. Sangiovanni-Vincentelli, On-line fault detection of sensor measurements, IEEE Sensors 2 (2003) [7] L.B. Ruiz, I.G. Siqueir a, L.B. Oliveira, H.C. W ong, J.M.S. Nogueira, A.A.F. Liureiro, management in event-driven wireless sensor networks, in: MSWIM 04, [8] M. Ding, D. Chen, K. Xing, X. Cheng, Localized fault-tolerant event boundary detection in sensor networ ks, IEEE Info com (2005) [9] X. Luo, M. Dong, Y. Hu ang, On distributed fault-tolerant detection in wireless sensor networks, IEEE Tr ansactions on Computers 55 (1) (2006) [10] J. Chen, S. Kher, A. Somani, Distributed fault detection of wireless sensor networks, in: Proceedings of 2006 Workshop DIWANS, pp [11] S. Chessa, P. Santi, Comparion-based system-level fault diagnosis in ad hoc networks,20 th Symp. Reliable Dist. Syst., pp ,2011. [12] T. Panigrahi,M. Panda and G.Panda, Tolerant Distributed Estimation in Wireless Sensor Networks,Journal of Network and Computer Applications, vol. 69, 2016, pp [13] M. Panda, P.M. Khilar Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test, Ad Hoc Networks, vol. 25, 2015, [14] A Mahapatro,PM Khilar, Diagnosis in Wireless Sensor Networks: A Survey,IEEE communication surveys & tutorials,vol.15,no.4,fourth Quarter 2013, pp [15] R. R. Panda, B.S.Gaudo and T. Panigrahi, Efficient Node Detection Algorithm for Wireless Sensor Networks, in the IEEE International Conference on High Performance,Computing and Application (ICHPCA-2014), Dec [16] T. Panigrahi,B.Mulgrew and B.Majhi, Robust Distributed Linear Parameter Estimation in Wireless Sensor Network, in the IEEE International Conference on Energy, Automation and Signal (ICEAS- 2011), Dec