Detecting and adaptive responding mechanism for mobile WSN

Mobile wireless sensor network(WSN)composed by mobile terminals has a dynamic topology and can be widely used in various fields.However,the lack of centralized control,dynamic topology and limited energy supply make the network layer of mobile WSN be vulnerable to multiple attacks,such as black hole(BH),gray hole(GH),flooding attacks(FA)and rushing attacks(RU).Existing researches on intrusion attacks against mobile WSN,currently,tend to focus on targeted detection of certain types of attacks.The defense methods also have clear directionality and is unable to deal with indeterminate intrusion attacks.Therefore,this work will design an indeterminate intrusion attack oriented detecting and adaptive responding mechanism for mobile WSN.The proposed mechanism first uses a test sliding window(TSW)to improve the detecting accuracy,then constructs parameter models of confidence on attack(COA),network performance degradation(NPD)and adaptive responding behaviors list,finally adaptively responds according to the decision table,so as to improve the universality and flexibility of the detecting and adaptive responding mechanism.The simulation results show that the proposed mechanism can achieve multiple types of intrusion detecting in multiple attack scenarios,and can achieve effective response under low network consumption.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

A New Protocol for the Detection of Node Replication Attacks in Mobile Wireless Sensor Networks

Wireless sensor networks （WSNs） are often deployed in harsh environments. Thus adversaries can capture some nodes, replicate them and deploy those replicas back into the strategic positions in the network to launch a variety of attacks. These are referred to as node replication attacks. Some methods of defending against node replication attacks have been proposed, yet they are not very suitable for the mobile wireless sensor networks. In this paper, we propose a new protocol to detect the replicas in mobile WSNs. In this protocol, polynomial-based pair-wise key pre-distribution scheme and Counting Bloom Filters are used to guarantee that the replicas can never lie about their real identifiers and collect the number of pair-wise keys established by each sensor node. Replicas are detected by looking at whether the number of pair-wise keys established by them exceeds the threshold. We also derive accurate closed form expression for the expected number of pair-wise keys established by each node, under commonly used random waypoint model. Analyses and simulations verify that the protocol accurately detects the replicas in the mobile WSNs and supports their removal.