Survey of clustering algorithms for MANET (0912.2303v1)

Abstract: Many clustering schemes have been proposed for ad hoc networks. A systematic classification of these clustering schemes enables one to better understand and make improvements. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance. Protocols try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control protocol. The clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. A large variety of approaches for ad hoc clustering have been developed by researchers which focus on different performance metrics. This paper presents a survey of different clustering schemes.

• The paper reviews diverse clustering algorithms, including identifier, connectivity, mobility, power-aware, and combined-weight approaches for MANETs.
• It analyzes the trade-offs among network stability, overhead reduction, and energy efficiency in dynamic mobile environments.
• The survey highlights the need for adaptive hybrid models and low-maintenance protocols to enhance real-world MANET deployments.

An Analysis of Clustering Algorithms in Mobile Ad Hoc Networks (MANETs)

The paper "Survey of Clustering Algorithms for MANET" presents a comprehensive review of various clustering algorithms developed for Mobile Ad Hoc Networks (MANETs). MANETs are characterized by dynamic topologies and the absence of any fixed infrastructure, such as base stations, necessitating efficient routing protocols to sustain communication across mobile nodes. The paper explores the challenge of efficiently organizing these nodes into clusters and reviews a multitude of schemes aimed at optimizing this process.

Clustering Strategies

The review encompasses diverse clustering algorithms, systematically categorizing them based on their underlying mechanism and performance metrics:

1. Identifier-based Clustering:
   • The Lowest ID Cluster Algorithm (LIC) elects clusterheads based solely on the node identifier without regard to other node capabilities. This approach, while straightforward, can lead to power imbalances, with nodes possessing lower IDs often overburdened as clusterheads.
   • The Max-Min d-cluster formation seeks to minimize the number of clusters, thus maintaining dominance sets of minimal size.
2. Connectivity-based Clustering:
   • The Highest Connectivity Clustering Algorithm (HCC) selects the node with the maximum degree as the clusterhead. Although effective, this method does not limit the number of members within a cluster, potentially causing resource bottlenecks.
   • The K-hop Connectivity ID Clustering (K-CONID) integrates node connectivity and node ID to form clusters, balancing between excessive and insufficient clustering.
3. Mobility-aware Clustering:
   • Algorithms in this category, such as the Mobility-based d-hop clustering, leverage node mobility metrics to form clusters. Such algorithms dynamically adapt cluster diameters based on mobility patterns, improving resilience to topology changes.
4. Low Maintenance Cost Clustering:
   • The Least Cluster Change Algorithm (LCC) reduces the frequency of reclustering by adopting an event-driven approach, significantly enhancing cluster stability.
   • Passive Clustering eliminates dedicated control messages, thereby minimizing control overhead.
5. Power-aware Clustering:
   • Load Balancing Clustering (LBC) and Power-aware Connected Dominant Sets prioritize energy efficiency, ensuring prolonged network operation by judicious energy consumption across clusterheads and ordinary nodes.
6. Combined-weight Based Clustering:
   • Algorithms like the Weighted Clustering Algorithm (WCA) and Vote-based Clustering utilize composite metrics—integrating factors such as node mobility, power, and connectivity—to determine optimal clusterhead positions.

Implications and Future Directions

The paper critically highlights the implications of each clustering algorithm, underscoring the trade-offs between maintaining network stability, minimizing overhead, and optimizing energy consumption. Connectivity-based algorithms, while effective for high-density networks, might incur higher maintenance costs, whereas mobility-aware strategies provide robust solutions for highly dynamic topologies.

The survey also sheds light on the growing importance of adaptive schemes that offer flexibility in response to real-time network conditions. The practicality of implementing these algorithms in real-world deployments is contingent upon advancements in computational efficiency and the integration of machine learning techniques to predict nodal behavior accurately.

Conclusion

This in-depth survey of clustering algorithms reveals that there is no one-size-fits-all solution for MANET infrastructure. Future research could focus on hybrid models leveraging the strengths of multiple clustering strategies, as well as further exploration into low-overhead and energy-efficient protocols suitable for heterogeneous network environments. The ongoing evolution in wireless technology promises new avenues for enhancing the scalability and performance of MANETs, adapting seamlessly to an ever-expanding ecosystem of mobile applications.