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Wireless sensor networks represent a fascinating domain, with a wide area of applications in various environments. For this reason, in the world of wireless networks professionals, there is a major interest concerning the security issues in network communication and data flow between a sensor network and other types of intelligent devices. This paper proposes a new intrusion detection system approach for assuring the security of a wireless sensor network against malicious attacks. Our method relies on the huge potential provided by the mixture between software modules developed in low-level or high-level software languages with modules developed in powerful numerical-oriented software (Matlab/Simulink) to increase the accuracy of data processing. The main result of this paper is the implementation know-how for an intelligent mixed system that use both hardware and software resources for proofing a wireless sensor network.

In the last decade, wireless sensor network (WSN) domain had benefit from a huge development effort and a major technological boom. These wireless networks equipped with tiny sensors can be used in a wide area of applications and various environments. This is one of the reasons that conducted to a continuous concern for improving the communication and the data flow between a WSN and other types of intelligent devices. In this paper we propose a new concept of the complex software development, including Matlab/Simulink modules, for intelligent distributed systems developed around WSN and a base computer. The system as a whole deals with network real-time data by using complex numerical mechanisms and reacts based on specific rules.

With the continuous development of the wireless technology, securing wireless sensor networks became more and more a crucial but also a demanding task. In this paper we propose a combined strategy that is meant to discover malicious nodes within a wireless sensor network and to expel them from the network using a self-destruction node technique. Basically, we will compare every sensor reading with its estimated value provided by an autoregressive predictor. In case the difference between the two values is bigger then a chosen threshold, the sensor node becomes suspicious and a decision block is activated. If this block decides that the node is malicious, a self-destruction procedure will be started against that specific node.

In this paper we propose a strategy based on past/present values provided by each sensor of a network for detecting their malicious activity. Basically, we will compare at each moment the sensor's output with its estimated value computed by an autoregressive predictor. In case the difference between the two values is higher then a chosen threshold, the sensor node becomes suspicious and a decision block is activated.

A new model of vehicles and pedestrians behavior for complex crossroads road traffic control is developed here. The crossroad is equipped with a wireless sensor network as traffic detector reporting real time traffic events used in the modeling phase. The crossroad model is designed and implemented regarding the three traffic lights colors for each lane entering the junction and permits usage of complex test cases independent of the number of lanes.