*2.9.15. TIBFIT - Trust index based fault tolerance for arbitrary data faults in sensor networks*

In [76], authors propose a protocol called TIBFIT to diagnose and mask arbitrary node failures in an event-driven wireless sensor network. An event driven model of behaviour for sensing finds many applications in civilian, military as well as industrial scenarios. The goal of the proposed TIBFIT protocol involves event detection and location determination in the presence of faulty sensor nodes, coupled with diagnosis and isolation of faulty or malicious nodes. In this system model, sensor nodes are organized into clusters with rotating cluster heads. The nodes, including the cluster head, can fail in an arbitrary manner generating missed event reports, false reports, or wrong location reports. Correct nodes are also allowed to make occasional natural errors. The accuracy of the system is defined in terms of fraction of instances when an event occurrence is correctly detected, and its location determined within the given error bound. The approach followed by the protocol is to maintain state of the sensing nodes in terms of the fidelity of their previous sensing actions, and use this information in making decisions involving those sensing nodes. Sensor nodes report the occurrence and location of events to a data sink (cluster head), and remain silent otherwise. The data sink then decides on whether the event occurred and were based on the aggregated data. To determine the location of the event, the data sink must aggregate all reports from nodes within the detection radius. In this approach, a new parameter called *trust index* for this aggregation is introduced. Each node is assigned a trust index to indicate its track record in reporting past events correctly. The cluster head analyzes the event reports using the trust index and makes event decisions. The *Trust Index(TI)* of a node is a quantitative measure of the fidelity of previous event reports of that node as seen by the data sink. In a system comprised of sensing nodes, the data sink assigns and maintains a TI for each node in its domain, and does voting in a state-full manner. As the system runs over a longer time, more state is built up concerning the performance of the associated sensing nodes, and hence tolerance for faults also goes up accordingly. Authors claim that TIBFIT can tolerate faults in a network with more than 50% of its nodes compromised *after* it has built up adequate state of the nodes.

The main contributions of this paper are the following:


iv. The protocol is generic and can be applied to any data sensing and aggregation application in sensor networks.

## *2.9.16. PLUS - Parameterized and localized trust management scheme protocol*

In [77] authors have proposed Parameterized and Localized trUst management Scheme (PLUS) for WSNs. The authors adopt a localized distributed approach and trust is calculated based on either direct observations or indirect observations. Whenever a node needs recommendation about another node, it will broadcast a request packet to its neighbors. This packet contains the identity of the evaluating node. In response all the nodes (except the node whose is going to be evaluated) send back a response packet to the requester. Once all the response packets are received, the requester will calculate the final trust value. If the node finds any misbehavior about the evaluated node, then the node will broadcast a exchange information packet to its neighbors. This packet contains information about identity of the node and error code. Based on the trust policy, the neighboring nodes send out its opinion: exchange Acknowledgement packet in case if they agree with the sender, otherwise neighbors will reply with exchange Argue packet indicating disagreement.
