CMIP: Clone Mobile-agent Itinerary Planning Approach for Enhancing Event-to-Sink Throughput in Wireless Sensor Networks

TL;DR

This paper introduces CMIP, a novel itinerary planning approach for mobile agents in wireless sensor networks, significantly reducing task duration and greatly increasing Event-to-sink throughput in real-time data collection scenarios.

Contribution

The paper proposes a Clone Mobile-agent Itinerary Planning (CMIP) method that enhances throughput and reduces task duration, addressing limitations of existing MIP approaches.

Findings

CMIP reduces task duration by about 56% compared to CL-MIP.

CMIP improves Event-to-sink throughput by approximately 93%.

CMIP outperforms existing MIP methods in real-time WSN applications.

Abstract

In order to mitigate the problem of data congestion, increased latency, and high energy consumption in Wireless Sensor Networks (WSNs), Mobile Agent (MA) has been proven to be a viable alternative to the traditional client-server data gathering model. MA has the ability to migrate among network nodes based on an assigned itinerary, which can be formed via Single Itinerary Planning (SIP) or Multiple Itinerary Planning (MIP). MIP-based data gathering approach solves problems associated with SIP in terms of task duration, energy consumption, and reliability. However, the majority of existing MIP approaches focus only on reducing energy consumption and task duration, while the Event-to-sink throughput has not been considered. In this paper, a Clone Mobile-agent Itinerary Planning approach (CMIP) is proposed to reduce task duration while improving the Event-to-sink throughput in real-time applications, especially when the MA is assigned to visit a large number of source nodes. Simulation results show that the CMIP approach outperforms both Central Location-based MIP (CL-MIP) and Greatest Information in Greatest Memory-based MIP (GIGM-MIP) in terms of reducing task duration by about 56% and 16%, respectively. Furthermore, CMIP improves the Event-to-sink throughput by about 93% and 22% as compared to both CL-MIP and GIGM-MIP approaches, respectively.