Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN

TL;DR

This paper introduces four novel algorithms combining face routing and auction mechanisms for efficient task allocation in wireless sensor and robot networks, significantly improving network lifetime and energy utilization.

Contribution

The paper proposes four new algorithms that extend GFG routing with auctions for task allocation, demonstrating superior performance in network lifetime and energy efficiency.

Findings

RFTA2 and RFTA2GE extend network lifetime up to 7 times.

The algorithms reduce communication overhead compared to existing methods.

RFTA2GE achieves the best energy utilization among the proposed algorithms.

Abstract

Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the Greedy-Face-Greedy (GFG) routing extended with auctions are proposed in this paper. In this paper we assume that all robots are mobile and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots' remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with two similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to 7 times longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.