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The phenomenal advances in electronics contributed to a widespread use of distributed sensors in wireless communications. A set of biosensors can be deployed or implanted in the human body to form a Wireless Body Area Network (WBAN), where various WBAN PHY layers are utilized. The WBAN allows the measurement of physiological data, which is forwarded by the gateway to the base station for analysis purposes. The main issue in conceiving a WBAN communication mechanism is to manage the residual energy of sensors. The mobile agent system has been widely applied for surveillance applications in Wireless Sensor Networks (WSNs). It consists in dispatching one or more mobile agents simultaneously to collect data, while following a predetermined optimum itinerary. The continuous use of the optimal itinerary leads to a rapid depletion of sensor nodes batteries, which minimizes the network lifetime. This paper presents a new algorithm to equalize the energy consumption among sensor motes. The algorithm exploits all the available paths towards the destination and classifies them with respect to the end-to-end delay and the overall energy consumption. The proposed algorithm performs better compared to the optimal routing path. It increases the network lifetime to the maximum by postponing routing of data via the most-recently used path, and it also maintains data delivery within the delay interval threshold.

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Most of traditional intrusion detection systems, Anomaly-Based detection and Signature-based detection, suffer from many drawbacks. This paper exposes the limits and drawback of traditional Intrusion detection systems.Consequently the main goal of this paper is to expose data mining techniques and approaches to improve the performance of the traditional intrusion detection system to identify known and unknown attack's patterns.

Abstract-Wireless sensor network (WSN) consists of sensor nodes.Deployed in the open area, and characterized by constrained resources, WSN suffers from several attacks, intrusion and security vulnerabilities. Intrusion detection system (IDS) is one of the essential security mechanism against attacks in WSN. In this paper we present a comparative evaluation of the most performant detection techniques in IDS for WSNs, the analyzes and comparisons of the approaches are represented technically, followed by a brief. Attacks in WSN also are presented and classified into several criteria. To implement and measure the performance of detection techniques we prepare our dataset, based on KDD'99, into five steps, after normalizing our dataset, we determined normal class and 4 types of attacks, and used the most relevant attributes for the classification process. We propose applying CfsSubsetEval with BestFirst approach as an attribute selection algorithm for removing the redundant attributes. The experimental results show that the random forest methods provide high detection rate and reduce false alarm rate. Finally, a set of principles is concluded, which have to be satisfied in future research for implementing IDS in WSNs. To help researchers in the selection of IDS for WSNs, several recommendations are provided with future directions for this research.