Efficient Load Balancing and Resource Allocation in Networked Sensing SystemsAn Algorithmic Study
AI algorithms; energy efficiency; Load balancing; machine learning; performance optimization; resource allocation
In the current environment, data generation and data transmission are increasing exponentially in day-to-day life. These exponentially growing data might create heavy traffic when transmitted between systems. Also, this affects many functionalities like configuration of networked systems, system and routing configuration parameters, load managing factors of network devices, etc. A dynamic traffic control mechanism needs to be adopted with the help of load-balancing algorithms and efficient resource allocation mechanisms to deal with heavy data traffic. Load balancing algorithms in networked sensing systems aim to distribute the workload evenly among sensor nodes to optimize network performance and energy efficiency and prolong the network lifetime. Resource allocation mechanisms in a networked sensing system involve allocating and distributing network resources efficiently, such as energy, bandwidth, processing power, etc., to optimize performance and increase the networks lifetime. To achieve efficient resource allocation with a balanced load, notable works have been done in optimization and machine learning. The work gives a scientific analysis of traditional and Artificial Intelligence algorithms from a centralized and distributed perspective. Researchers can take this analysis forward when deciding on algorithms based on their application and infrastructural needs. 2025 Scrivener Publishing LLC.
Krishnasamy, Lalitha; Vetriveeran, Divya; Sambandam, Rakoth Kandan; Jenefa, J.
https://doi.org/10.1002/9781394310890.ch6 https://www.scopus.com/pages/publications/85219153943?origin=resultslist
Krishnasamy L., Department of Artificial Intelligence & Data Science, Nandha Engineering College, Tamil Nadu, Erode, India; Vetriveeran D., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India; Sambandam R.K., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India; Jenefa J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India