A GENERIC TRUST MANAGEMENT FRAMEWORK FOR HETEROGENEOUS SENSORS IN CYBER PHYSICAL SYSTEMS

Objective: Wireless Technology†is the magic word in today's era. In which, Cyber Physical Systems (CPS) is the booming world which binds the physical world and cyber world together. The CPS is also called as Safety Critical System because of the human life involvement. In this emerging technology, lots of heterogeneous sensors are involved and each sensor will play an important role. If something goes wrong with sensor or sensor data. It will definitely affect the human life involved in it.Methods: In this paper, we proposed a generic trust management framework for heterogeneous sensors which will detect the sensor data falsification (Data Integrity), faulty sensor reading, and packet dropping nodes (Selfish Nodes) through rules and rating concept.Results: The efficiency of the proposed framework is evaluated with the help of Network Simulator 2 (NS-2.35). The maximum numbers of untrusted nodes are identified in point 0.40 than Multi-Level Trust Framework for Wireless Sensor Network (MTF-WSN) and Framework for Packet-Droppers Mitigation (FPDM). It is also evident that Trust Management Framework for Cyber Physical Systems (TRMF-CPS) identifies maximum number of untrusted nodes in the detection range of 0.35 and 0.45. Therefore, 0.35 and 0.45 are considered as maximum and minimum threshold points for effective untrusted nodes. Conclusion:The experimentation results and comparative study shows that, our trust management framework will easily detected sensors which misbehave.Â

Trust-based Selfish Node Detection Mechanism using Beta Distribution in Wireless Sensor Network

Wireless sensor networks (WSNs) are placed in open environments for the collection of data and are vulnerable to external and internal attacks. The cryptographic mechanisms implemented so far, such as authorization and authentication, are used to restrict external sensor node attacks but cannot prevent internal node attacks. In order to evade internal attacks trust mechanisms are used. In trust mechanisms, firstly, the sensor nodes are monitored using the popular Watchdog mechanism. However, traditional trust models do not pay much attention to selective forwarding and consecutive packet dropping. Sometimes, sensitive data are dropped by internal attackers. This problem is addressed in our proposed model by detecting selective forwarding and consecutive failure of sending packets using the Beta probability density function model

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks (SMPISCS)

Abstract In Wireless Sensor Network (WSN), energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery. This quantified level of co-operation signifies the performance of the network in terms of increased throughput, packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead. The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding. The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate. Semi Markov Process Inspired Selfish aware Co-operative Scheme (SMPISCS) is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network. SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish. Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes, number of sensor nodes and range of detection threshold