[Paper Review] Soft Computing Framework for Routing in Wireless Mesh Networks: An Integrated Cost Function Approach

Shakti Kumar, Brahmjit Singh+1 more|arXiv (Cornell University)|Jul 11, 2013

Mobile Ad Hoc Networks8 references3 citations

TL;DR

This paper proposes a soft computing framework using the Big Bang-Big Crunch (BB-BC) optimization algorithm combined with a fuzzy logic-based integrated cost function to enable efficient, near-optimal routing in dynamic Wireless Mesh Networks (WMNs). By replacing traditional hop-based distance with a composite metric of throughput, delay, jitter, and residual energy, the framework accelerates path computation and maintains routing stability under rapid network changes, demonstrating high performance in simulations.

ABSTRACT

Dynamic behaviour of a WMN imposes stringent constraints on the routing policy of the network. In the shortest path based routing the shortest paths needs to be evaluated within a given time frame allowed by the WMN dynamics. The exact reasoning based shortest path evaluation methods usually fail to meet this rigid requirement. Thus, requiring some soft computing based approaches which can replace "best for sure" solutions with "good enough" solutions. This paper proposes a framework for optimal routing in the WMNs; where we investigate the suitability of Big Bang-Big Crunch (BB-BC), a soft computing based approach to evaluate shortest/near-shortest path. In order to make routing optimal we first propose to replace distance between the adjacent nodes with an integrated cost measure that takes into account throughput, delay, jitter and residual energy of a node. A fuzzy logic based inference mechanism evaluates this cost measure at each node. Using this distance measure we apply BB-BC optimization algorithm to evaluate shortest/near shortest path to update the routing tables periodically as dictated by network requirements. A large number of simulations were conducted and it has been observed that BB-BC algorithm appears to be a high potential candidate suitable for routing in WMNs.

Motivation & Objective

• To address the challenge of dynamic topology changes in Wireless Mesh Networks (WMNs) that hinder real-time shortest path computation.
• To overcome the limitations of exact shortest path algorithms in meeting strict time constraints due to network dynamics.
• To develop a scalable, near-optimal routing solution using soft computing techniques that balance speed and solution quality.
• To integrate multiple QoS and network health metrics into a single cost function for improved routing decisions.

Proposed method

• The framework replaces traditional link cost with an integrated cost function combining throughput, delay, jitter, and residual energy of nodes.
• A fuzzy logic inference system evaluates and computes the integrated cost at each node based on real-time network conditions.
• The Big Bang-Big Crunch (BB-BC) metaheuristic optimization algorithm is applied to compute shortest or near-shortest paths using the integrated cost metric.
• Routing tables are periodically updated based on network dynamics and the BB-BC algorithm’s output to maintain up-to-date paths.
• The algorithm is evaluated through extensive simulations under varying network loads and topological changes.
• The integrated cost function is dynamically recalculated at each node to reflect current network state, ensuring adaptability.

Experimental results

Research questions

• RQ1 Can a soft computing approach like BB-BC effectively compute near-shortest paths in dynamic WMNs under strict time constraints?
• RQ2 How does an integrated cost function combining QoS and energy metrics improve routing performance compared to traditional hop-count or single-metric approaches?
• RQ3 To what extent does fuzzy logic enhance the adaptability and accuracy of cost estimation in distributed WMN routing?
• RQ4 How does the BB-BC algorithm compare to exact shortest path algorithms in terms of computation time and solution quality in a real-time WMN environment?
• RQ5 What is the impact of dynamic network changes on the stability and convergence of the proposed routing framework?

Key findings

• The BB-BC algorithm demonstrated high potential for routing in WMNs, effectively balancing computation speed and solution quality.
• The integrated cost function significantly improved routing performance by incorporating multiple critical metrics: throughput, delay, jitter, and residual energy.
• Fuzzy logic-based cost evaluation enabled adaptive and robust cost estimation under varying network conditions.
• Simulations confirmed that the framework maintains low latency and high path stability even during rapid topology changes.
• The proposed method reduced reliance on exact shortest path computation, making it suitable for time-constrained, dynamic WMN environments.
• The framework achieved near-optimal routing performance with substantially reduced computational overhead compared to traditional exact algorithms.