[Paper Review] Dynamic Cluster Head Selection Using Fuzzy Logic on Cloud in Wireless Sensor Networks

Payal Pahwa, Deepali Virmani+4 more|arXiv (Cornell University)|Nov 3, 2015

Energy Efficient Wireless Sensor Networks4 references4 citations

TL;DR

This paper proposes a fuzzy logic-based dynamic cluster head selection algorithm for wireless sensor networks, leveraging cloud-assisted processing to optimize energy efficiency. By calculating node potential using fuzzy logic and enforcing spatial distribution and trust checks, the method reduces cluster overlap and blocks malicious nodes, significantly improving network lifetime and reliability in dynamic environments.

ABSTRACT

One of the most vital activities to reduce energy consumption in wireless sensor networks is clustering. In clustering, one node from a group of nodes is selected to be a cluster head, which handles majority of the computation and processing for the nodes in the cluster. This paper proposes an algorithm for fuzzy based dynamic cluster head selection on cloud in wireless sensor networks. The proposed algorithm calculates a Potential value for each node and selects cluster heads with high potential. The proposed algorithm minimizes cluster overlapping by spatial distribution of cluster heads and discards malicious nodes i.e. never allows malicious nodes to be cluster heads.

Motivation & Objective

• To address energy inefficiency in wireless sensor networks (WSNs) through intelligent cluster head selection.
• To minimize cluster overlapping and improve spatial distribution of cluster heads.
• To enhance network security by preventing malicious nodes from becoming cluster heads.
• To leverage cloud computing for centralized, real-time decision-making in cluster formation.
• To improve overall network lifetime and energy efficiency through dynamic, adaptive cluster head selection.

Proposed method

• A fuzzy logic system evaluates node potential based on residual energy, distance to base station, and node centrality.
• The system computes a potential value for each node using fuzzy inference rules to balance energy efficiency and network coverage.
• Cluster heads are selected based on the highest potential values, ensuring optimal distribution and load balancing.
• The cloud server coordinates cluster formation, enforces spatial separation between cluster heads, and validates node trustworthiness.
• Malicious nodes are detected and excluded from cluster head selection through trust evaluation mechanisms.
• The algorithm dynamically reselects cluster heads in response to changes in energy levels and network topology.

Experimental results

Research questions

• RQ1 How can fuzzy logic be effectively applied to dynamically select cluster heads in WSNs for improved energy efficiency?
• RQ2 What criteria should be used to evaluate node potential in a way that minimizes cluster overlap and maximizes network lifetime?
• RQ3 How can cloud-based coordination enhance the scalability and reliability of cluster head selection in WSNs?
• RQ4 To what extent can fuzzy logic and trust-based filtering reduce the risk of malicious nodes becoming cluster heads?
• RQ5 How does the proposed method compare to static or non-fuzzy cluster head selection in terms of energy consumption and network longevity?

Key findings

• The proposed algorithm reduces cluster overlapping through spatial distribution of cluster heads, improving network coverage and reducing interference.
• Malicious nodes are effectively prevented from becoming cluster heads, enhancing network security and trust.
• The use of fuzzy logic for potential calculation enables adaptive, energy-aware cluster head selection that responds to dynamic network conditions.
• Cloud-based coordination allows for centralized, real-time decision-making, improving scalability and consistency in cluster formation.
• The method contributes to extended network lifetime by balancing energy consumption across nodes and deferring cluster head exhaustion.
• The integration of fuzzy logic and cloud infrastructure results in a more robust and efficient clustering mechanism compared to conventional approaches.