ATDSR: Trusted On-Demand Routing Protocol based on Agents for Mobile Ad-hoc Networks

TL;DR

The paper introduces ATDSR, a trust-based routing protocol for MANETs that uses agent systems to improve route selection by evaluating node trustworthiness, resulting in better performance under malicious conditions.

Contribution

It presents a novel agent-based trust management protocol integrated with DSR, with a new trust measurement model considering packet size and number, reducing overhead.

Findings

ATDSR outperforms standard DSR in simulations.

Trust measurement model reflects selective forwarding behavior.

Protocol maintains efficiency with minimal message overhead.

Abstract

The routing performance in Mobile Ad-hoc Networks (MANETs) relies on the co-operation of the individual nodes that constitute the network. The existence of misbehaving nodes may paralyze the routing operation in MANETs. To overcome this behavior, the trustworthiness of the network nodes should be considered in the route selection process combined with the hop count. The trustworthiness is achieved by measuring the trust value for each node in the network. In this paper, a new protocol based on self monitoring (agent-based) and following the dynamic source routing (DSR) algorithm is presented. This protocol is called Agent-Based Trusted Dynamic Source Routing (ATDSR) Protocol for MANETs. The objective of this protocol is to manage trust information locally with minimal overhead in terms of extra messages and time delay. This objective is achieved through installing in each participated node in the network a multi-agent system (MAS). MAS consists of two types of agents: monitoring agent (MOA) and routing agent (ROA). A new mathematical and more realistic objective model for measuring the trust value is introduced. This model is weighted by both number and size of routed packets to reflect the selective forwarding behavior of a node. The performance evaluation via simulation shows that our protocol is better than standard and trusted DSR. The simulation is done over a variety of environmental conditions such as number of malicious nodes, host density and movement rates.