[Paper Review] Data Load Balancing In Mobile Ad Hoc Network Using Fuzzy Logic (DBMF)

Abu Sufian, Farhana Sultana+1 more|arXiv (Cornell University)|May 28, 2019

Mobile Ad Hoc Networks23 references4 citations

TL;DR

This paper proposes DBMF, a fuzzy logic-based data load balancing scheme for Mobile Ad Hoc Networks (MANETs) that dynamically selects optimal multipath routes by evaluating node mobility, residual energy, and packet drop rate. By fusing these three metrics via fuzzy inference, DBMF achieves superior load balancing, reducing end-to-end delay and improving throughput compared to traditional multipath protocols in NS-2 simulations.

ABSTRACT

Volume and movement of data rapidly increasing in every type of data communications and networking, and ad hoc networks are not spared from these challenges. Traditional Multipath routing protocols in Mobile Ad-hoc Networks (MANETs) did not focus on data load distribution and balancing as much as required. In this scheme, we have proposed data load distribution and balancing through multiple paths simultaneously. We have considered three important parameters of ad hoc network those are: mobility of node, the energy of node and packet drop rate at a node. This scheme combines these three metrics using fuzzy logic to get the decisive parameter. We have shown improvement of this scheme over similar kind of protocols in NS-2 network simulator.

Motivation & Objective

• To address the imbalance in data load distribution across multiple paths in MANETs, a critical issue in dynamic, decentralized networks.
• To enhance routing efficiency by integrating node mobility, residual energy, and packet drop rate as decision metrics.
• To reduce network congestion and improve end-to-end delay and throughput through intelligent path selection.
• To overcome the limitations of traditional multipath protocols that do not prioritize load balancing.
• To validate the effectiveness of the proposed scheme using NS-2 simulation under realistic MANET conditions.

Proposed method

• The DBMF scheme employs a fuzzy logic controller to evaluate three key network parameters: node mobility, residual energy, and packet drop rate at each node.
• Each input parameter is mapped to linguistic variables (e.g., low, medium, high) using membership functions defined over normalized ranges.
• A set of 27 fuzzy rules is defined to map the combination of input parameters to a single output: path selection score.
• The output score is defuzzified using the center of gravity method to determine the optimal path for data transmission.
• The scheme dynamically updates path selection based on real-time network conditions, ensuring load balancing across multiple paths.
• The algorithm is implemented and evaluated in the NS-2 network simulator using standard MANET mobility and traffic models.

Experimental results

Research questions

• RQ1 How can data load be effectively balanced across multiple paths in a mobile ad hoc network?
• RQ2 To what extent can fuzzy logic improve path selection by integrating mobility, energy, and packet loss metrics?
• RQ3 How does the proposed DBMF scheme compare to traditional multipath protocols in terms of end-to-end delay and throughput?
• RQ4 What is the impact of dynamic network conditions on load distribution and path reliability in MANETs?
• RQ5 Can a hybrid metric based on fuzzy inference reduce congestion and improve QoS in mobile networks?

Key findings

• DBMF significantly reduces end-to-end delay compared to conventional multipath protocols by optimizing path selection based on real-time network states.
• The scheme improves network throughput by effectively distributing traffic across multiple paths, minimizing congestion on any single route.
• The integration of fuzzy logic enables adaptive and robust path selection, even under high mobility and varying energy levels.
• Simulation results show a measurable reduction in packet drop rate due to balanced load distribution across the network.
• The proposed method outperforms existing protocols in terms of stability and fairness of data transmission under dynamic MANET conditions.
• The fuzzy inference system successfully translates complex, imprecise network metrics into actionable routing decisions with minimal computational overhead.