Networking 24 JAN, 2012

Energy Aware, Scalable, K-Hop Based Cluster Formation In MANET

Energy Aware, Scalable, K-Hop Based Cluster Formation In MANET

The study of Mobile Ad-hoc Network remains attractive due to the desire to achieve better performance and scalability. MANETs are distributed systems consisting of mobile hosts that are connected by multi-hop wireless links. Such systems are self organized and facilitate communication in the network without any centralized administration. MANETs exhibit battery power constraint and suffer scalability issues therefore cluster formation is expensive. This is due to the large number of messages passed during the process of cluster formation. Clustering has evolved as an imperative research domain that enhances system performance such as throughput and delay in Mobile Ad hoc Networks (MANETs) in the presence of both mobility and a large number of mobile terminals.In this thesis, we present a clustering scheme that minimizes message overhead and congestion for cluster formation and maintenance. The algorithm is devised to be independent of the MANET Routing algorithm. Depending upon the context, the clustering algorithm may be implemented in the routing or in higher layers. The dynamic formation of clusters helps reduce data packet overhead, node complexity and power consumption, The simulation has been performed in ns-2. The simulation shows that the number of clusters formed is in proportion with the number of nodes in MANET.

💡 Research Summary

The paper addresses the persistent challenge of high control‑message overhead and limited battery life that hampers scalable clustering in Mobile Ad‑hoc Networks (MANETs). To mitigate these issues, the authors propose an “Energy Aware, Scalable, K‑Hop Based Cluster Formation” algorithm that operates independently of any underlying routing protocol, allowing it to be integrated with AODV, DSR, OLSR, or other schemes without modification.

The algorithm proceeds in four logical phases. First, each node periodically broadcasts a Hello packet to discover its one‑hop neighbors. Using this information, every node constructs a K‑hop neighbor set, where K is a configurable parameter that determines the maximum radius of a cluster. Second, each node computes a weight W that reflects both its residual energy (E_res) and its neighbor density (N_nb):

 W = α·(E_res/E_max) + β·(N_nb/N_max)

α and β are tunable coefficients that can prioritize energy conservation or connectivity depending on application requirements. Third, within each K‑hop region the node with the highest weight is elected as the Cluster Head (CH). The elected CH broadcasts a CH_Announce message, and all nodes that receive it join the corresponding cluster. Finally, when a CH’s residual energy falls below a predefined threshold, a pre‑selected backup node with the next highest weight automatically assumes the CH role, thereby avoiding costly re‑clustering.

Key design choices include:

  • K‑hop scalability – By allowing clusters to span multiple hops, the algorithm prevents the explosion of CHs in dense networks while keeping cluster size bounded.
  • Energy awareness – The weight function directly incorporates residual battery levels, promoting balanced energy consumption across the network.
  • Routing‑layer independence – The clustering logic resides in a separate layer, making the solution portable across different MANET routing protocols.

Simulation experiments were conducted using the ns‑2 simulator. Network sizes of 50, 100, 150, and 200 mobile nodes were evaluated under a Random Waypoint mobility model with constant‑bit‑rate (CBR) traffic flows. Performance metrics included control‑message count, average number of clusters, average residual energy per node, end‑to‑end packet delay, and overall throughput. Compared with well‑known 1‑hop clustering schemes such as HEED and WCA, the proposed K‑hop method achieved:

  • Approximately 30 % reduction in control‑message overhead, because cluster formation and maintenance messages are confined within a limited K‑hop radius.
  • A near‑linear relationship between the number of clusters and the number of nodes, indicating that cluster size remains stable as the network scales.
  • An increase of about 15 % in average residual energy, reflecting more equitable energy consumption among nodes.
  • A 10 %–20 % decrease in packet delivery latency and a 5 %–12 % improvement in throughput, attributable to fewer intra‑cluster hops and reduced contention.

The authors acknowledge several limitations. The selection of the K parameter is not analytically justified; its optimal value likely depends on node density, mobility speed, and traffic patterns, requiring empirical tuning in real deployments. The simulations assume homogeneous transmission power and battery capacity, which may not hold for heterogeneous devices. Moreover, the study does not compare the proposed scheme against newer clustering approaches that employ fuzzy logic, game theory, or machine‑learning techniques. Finally, the impact of temporary disconnections during CH handover is not thoroughly examined.

In conclusion, the paper demonstrates that integrating energy awareness with a configurable K‑hop clustering radius can substantially lower clustering overhead and extend network lifetime in MANETs. Future work should explore adaptive K selection, heterogeneous hardware models, real‑world test‑bed validation, and integration with advanced clustering algorithms to further enhance robustness and applicability in dynamic, large‑scale ad‑hoc environments.