Faults In Wireless Sensor Networks Research Articles

An Adaptive Approach to Discriminate the Persistence of Faults in Wireless Sensor Networks

This paper presents a parametric fault detection algorithm which can discriminate the persistence (permanent, intermittent, and transient) of faults in wireless sensor networks. The main characteristics of these faults are the amount the fault appears. We adopt this state-holding time to discriminate transient from intermittent faults. Neighbor-coordination-based approach is adopted, where faulty sensor nodes are detected based on comparisons between neighboring nodes and dissemination of the decision made at each node. Simulation results demonstrate the robustness of the work at varying transient fault rate.

Application of fuzzy inference systems to detection of faults in wireless sensor networks

In this paper we present a fault detection strategy for wireless sensor networks. The strategy is based on modeling a sensor node by Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS), where a sensor measurement of a node is approximated by a function of the sensor measurements of the neighboring nodes. We also model a node by recurrent TSK-FIS (RFIS), where the sensor measurement of the node is approximated as function of real measurements of the neighboring nodes and the previously approximated value of the node itself. Temporary errors in sensor measurements and/or communication are overcome by redundancy of data gathering. A node which has developed a faulty sensor is not completely discarded because it is useful for relaying the information among the other nodes. Each node has its own fuzzy model that is trained with input of neighboring sensors' measurements and an output of its actual measurement. A sensor is declared faulty if the difference between the outcome of the fuzzy model and the actual sensor measurement is greater than the prescribed amount depending on the physical quantity being measured. Simulations are performed using the fuzzy logic toolbox of Matlab. We also give a comparison of obtained results to those from a feed-forward artificial neural network, recurrent neural network and the median [1] of measured values of the neighboring nodes.

Fault Localization Using Passive End-to-End Measurements and Sequential Testing for Wireless Sensor Networks

Faulty components in a network need to be localized and repaired to sustain the health of the network. In this paper, we propose a novel approach that carefully combines active and passive measurements to localize faults in wireless sensor networks. More specifically, we formulate a problem of optimal sequential testing guided by end-to-end data. This problem determines an optimal testing sequence of network components based on end-to-end data in sensor networks to minimize expected testing cost. We prove that this problem is NP-hard, and propose a recursive approach to solve it. This approach leads to a polynomial-time optimal algorithm for line topologies while requiring exponential running time for general topologies. We further develop two polynomial-time heuristic schemes that are applicable to general topologies. Extensive simulation shows that our heuristic schemes only require testing a very small set of network components to localize and repair all faults in the network. Our approach is superior to using active and passive measurements in isolation. It also outperforms the state-of-the-art approaches that localize and repair all faults in a network.

A cluster based WSN fault tolerant protocol

A Wireless Sensor Network (WSNs) fault-tolerant protocol is proposed in this paper. By setting up a robust cluster topology, the fault-tolerant algorithm can search any faulty node in the path and revise the path furthermore. Once the cluster head fails, it will be substituted by other alternative cluster heads with the lowest cost, and the path will be re-established. Experiments show that this algorithm can not only locate the faulty nodes in the path accurately, shield the influence of the error node in clusters, but also reduce the retransmission times of relevant data caused by error node. Therefore, the proposed algorithm is effective and sufficient enough to resist malicious attack and node losses. In energy consumption aspect, compared with others, the proposed algorithm has a more advantage in energy conservation.

A Power-Saving Data Aggregation Algorithm for Byzantine Faults in Wireless Sensor Networks

The wireless sensor network is a resource-constrained self-organizing system that consists of a large number of tiny sensor nodes. Due to the low-cost and low-power nature of sensor nodes, sensor nodes are failure-prone when sensing and processing data. Most presented fault-tolerant research for wireless sensor networks focused on crash faults or power faults and less on Byzantine faults. Hence, in this paper, we propose a power-saving data aggregation algorithm for Byzantine faults to provide power savings and high success rates even in the environment with high fault rates. The algorithm utilizes the concept of Byzantine masking quorum systems to mask the erroneous values and to finally determine the correct value. Our simulation results demonstrate that when the fault rate of sensor nodes is up to 50%, our algorithm still has 48% success rate to obtain the correct value. Under the same condition, other fault-tolerant algorithms are almost failed.

Adaptive correctness monitoring for wireless sensor networks using hierarchical distributed run-time invariant checking

This article presents a hierarchical approach for detecting faults in wireless sensor networks (WSNs) after they have been deployed. The developers of WSNs can specify “invariants” that must be satisfied by the WSNs. We present a framework, Hierarchical SEnsor Network Debugging (H-SEND), for lightweight checking of invariants. H-SEND is able to detect a large class of faults in data-gathering WSNs, and leverages the existing message flow in the network by buffering and piggybacking messages. H-SEND checks as closely to the source of a fault as possible, pinpointing the fault quickly and efficiently in terms of additional network traffic. Therefore, H-SEND is suited to bandwidth or communication energy constrained networks. A specification expression is provided for specifying invariants so that a protocol developer can write behavioral level invariants. We hypothesize that data from sensor nodes does not change dramatically, but rather changes gradually over time. We extend our framework for the invariants that includes values determined at run-time in order to detect data trends. The value range can be based on information local to a single node or the surrounding nodes' values. Using our system, developers can write invariants to detect data trends without prior knowledge of correct values. Automatic value detection can be used to detect anomalies that cannot be detected in existing WSNs. To demonstrate the benefits of run-time range detection and fault checking, we construct a prototype WSN using CO 2 and temperature sensors coupled to Mica2 motes. We show that our method can detect sudden changes of the environments with little overhead in communication, computation, and storage.

Weighted-Median Based Distributed Fault Detection for Wireless Sensor Networks

The existence of faulty sensor measurements in wireless sensor networks (WSNs) will cause not only a degradation of the network quality of service but also a huge burden of the limited energy. This paper investigates using the spatial correlation of sensor measurements to detect the faults in WSNs. Specially, (1) a novel approach of weighting the neighbors' measurements is presented, (2) a method to characterize the difference between sensor measurements is introduced, (3) a weighted median fault detection scheme (WMFDS) is proposed and evaluated for both binary decisions and real number measurements. Theoretical analysis and simulation results show that the proposed WMFDS can attractively obtain the high detection accuracy and considerably reduce the false alarm probability even in the existence of large fault sets. It is demonstrated that the proposed WMFDS is of excellent performance in fault detection for WSNs.