Advanced Resource Allocation Optimization Techniques inIoT: A Comprehensive Review

Abstract

While the Internet of Things is revolutionizing industries through real-time data exchange, exponential growth of interconnecting devices
severely challenges a resource allocation approach to efficiency. The ultimate goals of IoT are high performance, low latency, low energy
consumption, and security. This paper reviews some of the important techniques developed with the purpose of optimizing effective
resource management in IoT. More complex optimization is more important in the heterogeneous devices of highly varying capabilities
and the power requirements in IoT networks. Here, the main techniques are heuristic algorithms, machine learning-based models, game-
theoretic models, and hybrid approaches. Heuristic algorithms such as genetic algorithms, PSO, and ACO provide relatively very fast
near-optimal results and are even more important for the dynamic nature of conditions that occur in IoT. Other uses include simulated
annealing and tabu search for constrained local search optimization. Advanced resource allocation in the form of learning and adaptation
in form of machine learning allow further working models such as CNNs, LSTMs, deep Q-networks (DQN), etc. Reinforcement learning
enables IoT systems to learn the correct policies over time. Data analytics are supported with the tools like Apache Spark and Hadoop,
thereby making it efficient in real-time processing and hence management of resources. Besides designing strategies for fair and stable
resource distribution, game-theoretic models envision the interaction between devices and networks through frameworks like Nash
equilibrium. Hybrid approaches combining heuristic techniques with those of machine learning provide an efficient way to deal with
multi-objective optimization. Such research will be further developed in order to efficiently make the resource distribution in IoT
networks’ adaptive: those are federated learning, block chain, and edge computing.