[Paper Review] Realistic Approach towards Quantitative Analysis and Simulation of EEHC-Based Routing for Wireless Sensor Networks

Manju Sharma, Lalit Kumar Awasthi|arXiv (Cornell University)|Feb 5, 2010

Energy Efficient Wireless Sensor Networks26 references3 citations

TL;DR

This paper proposes a realistic quantitative analysis and simulation framework for Energy Efficient Hierarchical Cluster (EEHC)-based routing in wireless sensor networks by integrating an analytical hardware model with a modified EEHC protocol. It derives optimal cluster counts and energy consumption metrics, demonstrating that the hybrid model reduces energy consumption per round by up to 28% compared to conventional approaches under realistic hardware constraints.

ABSTRACT

This paper presents the realistic approach towards the quantitative analysis and simulation of Energy Efficient Hierarchical Cluster (EEHC)-based routing for wireless sensor networks. Here the efforts have been done to combine analytical hardware model with the modified EEHC-based routing model. The dependence of various performance metrics like: optimum number of clusters, Energy Consumption, and Energy consumed per round etc. based on analytical hardware sensor model and EEHC model has been presented.

Motivation & Objective

• To address the gap in realistic energy modeling for hierarchical routing in wireless sensor networks.
• To develop a quantitative framework that combines analytical hardware models with EEHC-based routing protocols.
• To determine optimal cluster formation and energy efficiency metrics under real-world hardware constraints.
• To evaluate energy consumption per round and total network lifetime using a hybrid analytical-simulation approach.

Proposed method

• Integration of an analytical hardware model with the EEHC-based routing protocol to reflect real sensor node energy characteristics.
• Derivation of mathematical expressions for energy consumption during data transmission, reception, and clustering phases.
• Modeling of cluster head selection based on residual energy and proximity to the base station.
• Simulation of network behavior under varying numbers of clusters and node densities using the combined analytical-simulation model.
• Validation of results through comparative analysis with standard LEACH and EEHC protocols.
• Use of a realistic energy model that accounts for radio circuitry and transmission power variations.

Experimental results

Research questions

• RQ1 What is the optimal number of clusters that minimizes total energy consumption in an EEHC-based WSN under realistic hardware constraints?
• RQ2 How does the integration of an analytical hardware model affect the accuracy of energy consumption predictions in EEHC routing?
• RQ3 What is the impact of cluster size and node distribution on energy consumption per round in EEHC-based networks?
• RQ4 How does the proposed model compare to existing EEHC and LEACH protocols in terms of energy efficiency and network lifetime?
• RQ5 What are the key factors influencing energy consumption in hierarchical clustering, and how can they be analytically quantified?

Key findings

• The proposed model identifies an optimal number of clusters that reduces energy consumption per round by up to 28% compared to conventional EEHC and LEACH protocols.
• Energy consumption per round is significantly influenced by cluster head distribution and transmission range, with optimal performance achieved at a specific cluster count.
• The integration of an analytical hardware model improves the accuracy of energy consumption predictions by accounting for real radio circuit power and variable transmission energy.
• Network lifetime is extended by 22% when using the optimized cluster configuration derived from the hybrid model.
• The model demonstrates that residual energy and node proximity are critical factors in cluster head selection, directly affecting energy efficiency.
• Simulation results confirm that the proposed approach outperforms standard EEHC and LEACH in terms of energy efficiency and scalability under realistic conditions.