The success of the mission assigned to a Wireless Sensor Network (WSN) depends heavily on the cooperation between the nodes of this network. Indeed, given the vulnerability of wireless sensor networks to attack, some entities may engage in malicious behavior aimed at undermining the proper functioning of the network. As a result, the selection of reliable nodes for task execution becomes a necessity for the network. To improve the cooperation and security of wireless sensor networks, the use of Trust Management Systems (TMS) is increasingly recommended due to their low resource consumption. The various existing trust management systems differ in their methods of estimating trust value. The existing ones are very rigid and not very accurate. In this paper, we propose a robust and accurate method (RATES) to compute direct and indirect trust between the network nodes. In RATES model, to compute the direct trust, we improve the Bayesian formula by applying the chaining of trust values, a local reward, a local penalty and a flexible global penalty based on the variation of successful interactions, failures and misbehaviors frequency. RATES thus manages to obtain a direct trust value that is accurate and representative of the node behavior in the network. In addition, we introduce the establishment of a simple confidence interval to filter out biased recommendations sent by malicious nodes to disrupt the estimation of a node's indirect trust. Mathematical theoretical analysis and evaluation of the simulation results show the best performance of our approach for detecting on-off attacks, bad-mouthing attacks and persistent attacks compared to the other existing approaches.

This paper introduces PREFAP, an approach to solve the multi-sensor patrolling problem in unknown environment. The multi-sensor patrolling problem consists in moving a set of sensors on a pre-set territory such that each part of this territory is visited by the sensor agents as often as possible. Eachsensor has a communicational radius and a sensory radius. indeed, optimal patrol can only be achieved if the duration between two visits of the same area of the environment is as minimal as possible. This time between two visits is called idleness. Thus, an effective patrol technique must make it possible to minimize idleness in the environment.That is why after a deep analysis of the existing resolution’s approaches, we propose a hybrid approach of resolution with three components: perception-reaction, field of strength and learning. In absence of obstacles, the perception-reaction component gives to the sensors a purely reactive behavior, as a function of their local perceptions, which permit them to move easily in their environment. The strength module enables the sensors to avoid the obstacles in the environment. As regards to the learning module, it allows the sensors to get out of blocking situations encountered during obstacle avoidance. This approach, called PREFAP, must be able to minimize idleness in different areas of the environment. The simulation results obtained show that the approach developed effectively minimizes idleness in the environment. This allows on the one hand, to have a regular patrol in the environment; on the other hand, thanks to the minimization of idleness of the areas of the environment, PREFAP will allow the sensors to quickly detect the various possible events which can occur in different areas of the environment.

Background subtraction plays an important role in intelligent video surveillance since it is one of the most used tools in motion detection. If scientific progress has enabled to develop sophisticated equipment for this task, algorithms used should be improved as well. For the past decade a background subtraction technique called ViBE is gaining the field. However, the original algorithm has two main drawbacks. The first one is ghost phenomenon which appears if the initial frame contains a moving object or in the case of a sudden change in the background situations. Secondly it fails to perform well in complicated background. This paper presents an efficient background subtraction approach based on ViBE to solve these two problems. It is based on an adaptive radius to deal with complex background, on cumulative mean and pixel counting mechanism to quickly eliminate the ghost phenomenon and to adapt to sudden change in the background model.