Ad-hoc networks are a set of mobile nodes that are connected via a wireless channel. Some of the nodes in this network behave selfishly and do not send data to other nodes so that in order to increase network performance these nodes must be identified. A credit-based algorithm is proposed to detect the selfish nodes. Three watchdog nodes are selected to monitor suspicious nodes in each cluster. The cluster head nodes detect the existence of selfish nodes by controlling general features of network, such as delay, the total number of sent packets, the total number of received packets, throughput, and network traffic. The watchdog nodes send their comment on selfishness or cooperation of the node to the cluster head. Cluster head makes decisions with a majority vote on a suspicious node. The simulation results show that the rate of detection accuracy and the life time of network are considerably high and the false alarm rate and energy consumption are low comparing to that of similar methods.

Wireless sensor networks (WSNs) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or Sink. Since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, they are prone to failure. Thus, maintaining a network with its proper functions even when undesired events occur is necessary and is called fault tolerance. Hence, fault management is essential in these networks. In this paper, a new method has been proposed with particular attention to fault tolerance and fault detection in WSN. The performance of the proposed method was simulated in MATLAB. The proposed method was based on majority vote, which can permanently detect faulty sensor nodes accurately. High accuracy and low false alarm rate helped exclude them from the network. To investigate the efficiency of the new method, the researchers compared it with Chen, Lee, and hybrid algorithms. Simulation results indicated that the proposed method has better performance in parameters such as detection accuracy (DA) and a false alarm rate (FAR) even with a large set of faulty sensor nodes.