An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the Internet of Things (IoT) Environment

Abstract

Major problems and issues in Internet of Things (IoT) systems include load balancing, lowering operational expenses, and power usage. IoT devices typically run on batteries because they lack direct access to a power source. Geographical conditions that make it difficult to access the electrical network are a common cause. Finding ways to ensure that IoT devices consume the least amount of energy possible is essential. When the network is experiencing high traffic, locating and interacting with the next hop is critical. Finding the best route to load balance by switching to a less crowded channel is hence crucial in network congestion. Due to the restrictions indicated above, this study analyzes three significant issues—load balancing, energy utilization, and computation cost—and offers a solution. To address these resource allocation issues in the IoT, we suggest a reliable method in this study termed Dynamic Energy-Efficient Load Balancing (DEELB). We conducted several experiments, such as bandwidth analysis, in which the DEELB method used 990.65 kbps of bandwidth for 50 operations, while other existing techniques, such as EEFO (Energy-Efficient Opportunistic), DEERA (Dynamic Energy-Efficient Resource Allocation), ELBS (Efficient Load-Balancing Security), and DEBTS (Delay Energy Balanced Task Scheduling), used 1700.91 kbps, 1500.82 kbps, 1300.65 kbps, and 1200.15 kbps of bandwidth, respectively. The experiment’s numerical analysis showed that our method was superior to other ways in terms of effectiveness and efficiency.