*2.9.12. RFSN - Reputation based framework for high integrity sensor networks*

This work[2] proposes a reputation-based framework for sensor networks (RFSN) where nodes maintain reputation for other nodes and use it to evaluate their trustworthiness. The authors tried to focus on an abstract view that provides a scalable, diverse and a generalized approach hoping to tackle all types of misbehaviors resulting from malicious and faulty nodes. They also designed a system within this framework and employed a Bayesian formulation, using a beta distribution model for reputation representation. RFSN integrates tools from statistics and decision theory into a distributed and scalable framework. Bayesian formulation, specifically a beta reputation system is employed for the algorithm steps of reputation representation, updates, integration and trust evolution. This output metric of trust can be used by a node in several ways. For example, a data reading reported by a node can be weighted by the trust of the node when aggregating data from several nodes, thus reducing the impact of the faulty readings. Additionally, the evolution of trust over time provides an on-line tool to the end-user to detect compromised or faulty nodes. This can help the end-user to take appropriate countermeasures such as replacing the corrupted node or sensor.

The system starts the operation by monitoring. Monitoring mechanism follows the classic watchdog methodology in which a node is assumed to be in a promiscuous mode to overhear neighbors' packets. Monitoring behavioral events can result in either cooperative event, α, in which a node is behaving well or non cooperative behavior, β, in which a node misbehaves. The count of each type is injected into the beta distribution formula as the distribution parameters to calculate the node reputation R. This formula calculates node's reputation based on first hand information. The reputation is updated based on the new monitoring events, second hand information received and according to the age of the current reputation value. Any response action is based on selecting the most trusted node. The trust value of a node that is used for decision making is calculated as the statistical expectation of the reputation value.

## *2.9.13. DRBTS - Distributed reputation-based beacon trust system*

In [74] authors propose a reputation based scheme called Distributed Reputation-based Beacon Trust System (DRBTS) for excluding malicious Beacon Nodes(BNs) that provide false location information. It is a distributed security protocol aimed at providing a method by which BNs can monitor each other and provide information so that the Sensor Nodes(SNs) can choose who to trust, based on a quorum voting approach. In order to trust a BN's information, a sensor must get votes for its trustworthiness from at least half of their common neighbor(s). In this approach, every BN monitors its 1-hop neighborhood for misbehaving BNs and accordingly updates the reputation of the corresponding BN in the Neighbor-Reputation-Table (NRT). The BNs then publish their NRT in their 1-hop neighborhood. BNs use this second-hand information published in NRT for updating the reputation of their neighbors after it qualifies a deviation test. On the other hand, the SNs use the NRT information to determine whether or not to use a given beacon's location information, based on a simple majority voting scheme.

252 Wireless Sensor Networks – Technology and Protocols

concept of re-evaluation and reputation fading.

corrupted node or sensor.

expectation of the reputation value.

*2.9.13. DRBTS - Distributed reputation-based beacon trust system* 

*2.9.11. Robust reputation system for P2P and mobile ad-hoc networks* 

*2.9.12. RFSN - Reputation based framework for high integrity sensor networks* 

The main contribution in this work [68] is its proposal for a distributed reputation system that can handle false disseminated information. Every node maintains a reputation rating and a trust rating about every node that is of interest. The authors use a modified Bayesian approach so that they will accept only a second hand information set that is compatible with the current reputation rating. Also, Trust ratings are updated based on the compatibility of second-hand reputation information with prior reputation ratings. The work avoids exploitation of good behavior that can be incorrectly built over time by introducing a

This work[2] proposes a reputation-based framework for sensor networks (RFSN) where nodes maintain reputation for other nodes and use it to evaluate their trustworthiness. The authors tried to focus on an abstract view that provides a scalable, diverse and a generalized approach hoping to tackle all types of misbehaviors resulting from malicious and faulty nodes. They also designed a system within this framework and employed a Bayesian formulation, using a beta distribution model for reputation representation. RFSN integrates tools from statistics and decision theory into a distributed and scalable framework. Bayesian formulation, specifically a beta reputation system is employed for the algorithm steps of reputation representation, updates, integration and trust evolution. This output metric of trust can be used by a node in several ways. For example, a data reading reported by a node can be weighted by the trust of the node when aggregating data from several nodes, thus reducing the impact of the faulty readings. Additionally, the evolution of trust over time provides an on-line tool to the end-user to detect compromised or faulty nodes. This can help the end-user to take appropriate countermeasures such as replacing the

The system starts the operation by monitoring. Monitoring mechanism follows the classic watchdog methodology in which a node is assumed to be in a promiscuous mode to overhear neighbors' packets. Monitoring behavioral events can result in either cooperative event, α, in which a node is behaving well or non cooperative behavior, β, in which a node misbehaves. The count of each type is injected into the beta distribution formula as the distribution parameters to calculate the node reputation R. This formula calculates node's reputation based on first hand information. The reputation is updated based on the new monitoring events, second hand information received and according to the age of the current reputation value. Any response action is based on selecting the most trusted node. The trust value of a node that is used for decision making is calculated as the statistical

In [74] authors propose a reputation based scheme called Distributed Reputation-based Beacon Trust System (DRBTS) for excluding malicious Beacon Nodes(BNs) that provide Each BN is responsible for monitoring its neighborhood. When a sensor within its range asks for location information, it responds with its location, as do all other beacon nodes within the range of the requesting node. Due to the promiscuity of broadcast transmissions, a BN can overhear the responses of other BNs in its area. It can then determine its location using this claimed location of each BN and comparing them against its true location. If the difference is within a certain margin of error, then the corresponding BN is considered benign, and its reputation increases. If the difference is greater than the margin of error, then that BN is considered malicious and its reputation is decreased. This distributed model not only alleviates the burden on the base station to a great extent, but also minimizes the damage caused by the malicious nodes by enabling sensor nodes to make a decision on which beacon neighbors to trust, on the fly, when computing their location.
