Network Life Time

The network life time of the DSFDNC, DFD and IDFD algorithms with respect to varying Na and fp is shown in Figure 3.11 and Figure 3.10 respectively. From the figures, it is found that the network life time for DSFDNC algorithm is 33% and 66% less as compared to that of IDFD and DFD algorithms respectively. This is due to the DSFDNC needs less energy compared to the IDFD and DFD algorithms.

The network life time decreases with the increase in average degrees of sensor nodes in WSNs.

Improvement in the results of the DSFDNC over DFD and IDFD algorithms is tabulated in Table 3.6 for Na= 16 and fault probability Pf = 0.3.

Table 3.6: Improvement of DSFDNC algorithm over DFD and IDFD algorithms when Na= 16 and Pf = 0.3 Performance

parame-ter

DSFDNC algorithm

DFD Al-gorithm

IDFD Algorithm Improvements over DFD Algorithm

Improvements over IDFD Algorithm

Diagnosis accuracy 0.943976 0.911297 0.92766 3% 2%

False alarm rate 0.0523 0.3086 0.2807 3% 2%

False positive rate 0.0561 0.0888 0.0724 3% 2%

Message exchange 1024 2560 1536 60% 33%

Network life time 2890 1156 1926 60% 33%

Eenergy consumption 0.0995251 0.223813 0.134288 55% 28%

Diagnosis latency 5.76698 17.1334 8.6442 54% 33%

3.6 Conclusion

The distributed self fault diagnosis algorithm using neighbor coordination (DSFDNC) is proposed in this chapter based on a realistic fault model such as stuck at zero, stuck at one, random and hard fault. The accuracy and complete-ness of the DSFDNC algorithm are evaluated by using the neighbor coordination method. The result shows that the diagnosis accuracy and false positive rate of the new algorithm is improved by 3%, and 1% as compared to that of DFD and IDFD algorithms when the average degree of the network is 15. The algorithm outperforms over the DFD and IDFD algorithms by providing higher network life time and lower diagnosis latency due to less consumption of energy and message overhead on WSNs.

In order to improve the performance of the DSFDNC algorithm, in the forthcoming chapter, we use the hypothesis testing based approach to diagnose the soft faulty

sensor node instead of comparing the observation with the mean of their neighbors data.

Algorithm in WSNs using

Hypothesis Testing

Distributed Self Fault Diagnosis Algorithm for WSNs

Using Hypothesis Testing

The existing fault diagnosis algorithms in wireless sensor networks based on compari-son of neighbor node’s data require more computation and communication overheads and yields poor performance when the degree of the network is less. This chapter presents a novel distributed fault diagnosis algorithm to diagnose soft faulty sensor nodes by gathering information from the neighbors. The developed scheme is based on the Newman-Pearson test to predict the fault status of each sensor node. The performance is evaluated in terms of diagnosis accuracy and false alarm rate. The simulation results show that the performance of the proposed algorithm is much better when the average degree of sensor nodes is less. The time and message com-plexity, diagnosis latency, network life time and energy consumption of the algorithm are also analyzed.

4.1 Introduction

Distributed self fault diagnosis in wireless sensor networks (WSNs) have been the main focus of research in recent years [5, 16, 33, 35]. This is due to the fact that, the sensor nodes are deployed in human inaccessible and hostile environments, where the sensor nodes are subjected to hard and soft faults. In fact, soft faults are more frequent than the hard faults [16]. The occurrence of these faults in sensor nodes prevents the normal operation of the WSNs in various ways. In WSNs, the

accuracy of the observed data is sent by a sensor node is important for the overall network’s performance. Therefore, diagnosis of soft faulty sensor node (the sensor nodes which accumulates erroneous readings) is an essential issue of the reliability of WSNs [33, 35].

In Chapter 3, a distributed self fault diagnosis algorithm based on neighbor coordination is developed where the sensor nodes are comparing the data with the mean of neighbors data. Since the mean approaches to its true value if the number of samples is more (central limit theorem), it needs more number of neighboring nodes. In this chapter, a distributed self fault diagnosis algorithm is developed which can provide better diagnosis accuracy for lower average degree network. Instead of comparing own data with the mean of neighbor’s data here the statistical hypothesis testing is chosen to diagnose the faulty sensor node. Further, in order to minimize computation and communication in the fault diagnosis process, each sensor node first tests the presence of faulty sensor node in its neighbor and then predicts the probable fault status of each of them. For this, the Neyman-Pearson (NP) detection method is used. Then, each sensor node shared the probable fault status among the neighbors. A fusion scheme is used at each of the sensor nodes to take the final decision on its fault status as discussed in Chapter 3.

The major contribution of this chapter are (i) the design and evaluation of an ef-ficient distributed self fault diagnosis algorithm using hypothesis testing (DSFDHT) for diagnosing soft faulty sensor nodes in WSNs, (ii) the Neyman-Pearson (NP) de-tection method is used to diagnose the faulty sensor node (iii) the performance is compared with the existing distributed algorithms such as DFD [6] and IDFD [40], (iv) the algorithms are implemented in NS3 [38]. (v) The performance of the algo-rithms is evaluated using generic parameters as discussed in Chapter 3.

The remaining part of the chapter is organized as follows. The system models assumed for the proposed algorithm DSFDHT are provided in Section 4.2. The proposed distributed self fault diagnosis algorithm is described in Section 4.3. The analysis of the DSFDHT algorithm and its correctness is given in Section 4.3.2. The simulation results are provided in Section 4.4. Finally, Section 4.5, concludes the

chapter with a discussion.

4.2 System Model

The system model for this work is similar to that of Chapter 3 except the fault model, where only soft fault is considered in this chapter. It is because the soft faults are more frequent in WSNs and diagnosing those soft faults are more challenging than hard faults.