Agent based trusted neighbor identification in Wireless Sensor Networks

Abstract

Abstract

Wireless Sensor Networks (WSNs) have been extensively used in various applications such as environmental monitoring, industrial monitoring, agriculture, green house monitoring, structural monitoring, passive localization, tracking and battlefield surveillance. Sensor nodes in these applications are required to sense and process the physical conditions like temperature, pressure, humidity, rainfall, fog, etc. and route the data to a predefined base station or a sink node. In most of these applications, sensor nodes are deployed in public domain and they are prone to be attacked by many types of attacks where in the data confidentiality, integrity and authentication are compromised. Some times, it is difficult to correctly locate the compromised data unless we use autonomous third party that uses intelligent software techniques to safeguard our data and correctly means route it to destined party.

In this paper, we propose a Trust based Neighbor Identification in Wireless Sensor Networks (TNIWSN) using agents to identify trustworthy nodes in a network. The trusted neighbor identification is necessary for routing the data through trustworthy neighbors and avoid untrusted neighbors that are compromised by various threats. The proposed scheme operates in following phases. (1) Defining safeguard agency that consists of one static agent known as Safeguard Manager Agent (SMA) and one mobile agent known as Trusted Neighbor Agent (TNA) and a knowledge base. (2) Safeguard agency identifies trustworthy neighbor nodes using static and mobile agents by means of trust model that comprise of the probability model and Message Authentication Code (MAC) model. The probability model identifies trusted neighbors based upon the probabilities of trustworthiness of wireless channel and the trustworthiness of sensor node. MAC model encrypts the message using the two keys $\mathit{k}1$ and $\mathit{k}2$ are generated with k-ERF (Error Resilient Function) key generation process to ensure the trustworthiness of neighbors identified by the probability model. (3) MACs are dynamically computed by agents (either on sender node or on neighbor node) by generating keys with the help of k-ERF. (4) Agents effectively identify possible security threats on wireless channel and node. Simulation analysis shows that TNIWSN outperforms Neighbor based Malicious Node Detection (NMND) in Wireless Sensor Networks in terms of average success ratio and memory overhead.