Bayesian Fusion Algorithm for Inferring Trust in Wireless Sensor Networks

Abstract

Abstract-This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust) to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary) and the data trust component (continuous) and proves that either component by itself, can mislead the network and eventually cause a total breakdown of the network. As a result of this, new algorithms are needed to combine more than one trust component to infer the overall trust. The proposed algorithm is simple and generic as it allows trust components to be added and deleted easily. Simulation results demonstrate that a node is highly trustworthy provided that both trust components simultaneously confirm its trustworthiness and conversely, a node is highly untrustworthy if its untrustworthiness is asserted by both components.

Wi-Trust: Computational Trust and Reputation Management for Stronger Hotspot 2.0 Security

“…Momani et al [24] introduce a new algorithm of trust formation in wireless sensor networks based on the QoS to be fulfilled by the network's nodes. They use three main sources to compute trust, namely direct observations (past experiences), recommendations from the surrounding nodes and fixed dispositional trust in nodes.…”

“…First, we modeled and implemented an adaptive dispositional trust metric [27] where we don't use the dispositional trust level as a constant value as in Momani et al [24] mentioned above, but as a value that can change over the time depending on the surrounding environment. Then, we have integrated trust management and cooperation incentives with our "trust transfer" trust metric [18], which has been proven to protect against Sybil attacks [17].…”

Wi-trust: Improving Wi-Fi hotspots trustworthiness with computational trust management

“…In their model, the behavior of each WISP is characterized by a reputation record, which is generated and signed by a trusted Central Authority (CA). Momani et al [17] introduce a new algorithm of trust formation in wireless sensor networks based on the QoS to be fulfilled by the network's nodes. They use three main sources to compute trust, namely direct observations (past experiences), recommendations from the surrounding nodes and fixed dispositional trust in nodes.…”

“…We have also advanced computational trust management for hotspots in the other FP7 ULOOP project. First, we modeled and implemented an adaptive dispositional trust metric [19] where we don't use the dispositional trust level as a constant value as in Momani et al [17] mentioned above, but as a value that can change over the time depending on the surrounding environment. Then, we have integrated trust management and cooperation incentives with our "trust transfer" trust metric [11], which has been proven to protect against Sybil attacks [10].…”

A Credible Bayesian-Based Trust Management Scheme for Wireless Sensor Networks

“…Bayesian methodology, in which trust calculation is based on nodes' history behavior records, enriches the comprehension of trust in a probabilistic view [24]. Entropy is used to evaluate the randomness in a signal or event [11].…”