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With the rapid implementation and expansion of the Internet of things (IoT) technologies in smart cities, network congestion control and energy-efficient routing in wireless sensor and actuator networks (WSANs) for virtualising IoT have emerged as a crucial area of research in recent years. This study implements the particle swarm optimisation (PSO) algorithm to realise network congestion control and energy-efficient routing in the transport layer of WSANs. This algorithm is based on the flocking behaviour of birds. The simulation results show that the proposed PSO-based approach provides better performance in terms of network lifetime and packet drop ratio compared with the ant colony optimisation and the artificial bee colony algorithm.

By taking facts such as oil depletion, increasing number of population and energy demand into account, alternative electric generation scheme called renewable energy has entered into a new phase. These new energy sources are environmentally clean, exhaustible and friendly with affordable cost, and high reliability. Nowadays, energy generators such as photovoltaic (PV), wind turbine (WT), and geothermal energies are among the commonly used renewable sources. In this article, grey wolf optimization (GWO) methodology is proposed for minimizing the total annual cost of hybrid of wind and solar renewable energy system. Here, determining the optimal number of solar panels, WTs, and batteries which can satisfy the desired load is the main objective of this research. The obtained result shows that the proposed methodology finds optimal solution of sizing of the hybrid system with relatively lower total annual cost and fast convergence rate. To check whether the obtained result was feasible, GWO results are compared with the results of PSO, iteration method and by the work of other scholars in literature. Here the superior capabilities of GWO algorithm have been seen. It is hoped that this research would be beneficial and can be benchmark for researchers of the field.

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