With the proliferation of Internet of Things (IoT) applications, a massive amount of data has been produced, requiring an efficient platform to store and process this data. Cloud computing has the ability to tackle such enormous data, but cannot provide real-time response to latency sensitive IoT applications. Fog computing delivers the cloud service at network edge with rapid response to IoT applications, but the computation offloading decision, unpredictable demands, unbalanced workload among fog servers and heterogeneity become challenging issues. Hence, this study applied an effective approach named Spider Monkey Optimization (SMO) to address the mentioned challenges and employs the proposed framework to ensure the Quality of Service (QoS) parameters performance. This framework uses an adaptive approach to allocate the workload based upon the computation ability of the resource, and continuously monitoring the workload among the fog nodes avoids the possibility of overloading and underloading the fog nodes. Exploration and exploitation ability of the SMO algorithm reduces the chances of being trapped in the local optimum and decides the optimal offloading destination. The performance of the proposed approach is assessed in a simulation environment, showing that the proposed algorithm reduces parameters such as latency, communication overhead, cost and energy consumption compared to baseline approaches Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) using the same experimental environments.

There are billions of inter-connected devices by the help of Internet-of-Things (IoT) that have been used in a number of applications such as for wearable devices, e-healthcare, agriculture, transportation, etc. Interconnection of devices establishes a direct link and easily shares the information by utilizing the spectrum of cellular users to enhance the spectral efficiency with low power consumption in an underlaid Device-to-Device (D2D) communication. Due to reuse of the spectrum of cellular devices by D2D users causes severe interference between them which may impact on the network performance. Therefore, we proposed a Q-Learning based low power selection scheme with the help of multi-agent reinforcement learning to detract the interference that helps to increase the capacity of the D2D network. For the maximization of capacity, the updated reward function has been reformulated with the help of a stochastic policy environment. With the help of a stochastic approach, we figure out the proposed optimal low power consumption techniques which ensures the quality of service (QoS) standards of the cellular devices and D2D users for D2D communication in 5G Networks and increase the utilization of resources. Numerical results confirm that the proposed scheme improves the spectral efficiency and sum rate as compared to Q-Learning approach by 14% and 12.65%.