Design of energy efficient protocols-based optimisation algorithms for IoT networks

Abstract

The increased globalisation of information and communication technologies has transformed the world into the internet of things (IoT), which is accomplished within the resources of wireless sensor networks (WSNs). Therefore, the future IoT networks will consist of high density of connected nodes that suffer from resource limitation, especially the energy one, and distribute randomly in a harsh and large-scale areas. Accordingly, the contributions in this thesis are focused on the development of energy efficient design protocols based on optimisation algorithms, with consideration of the resource limitations, adaptability, scalability, node density and random distribution of node density in the geographical area. One MAC protocol and two routing protocols, with both a static and mobile sink, are proposed. The first proposed protocol is an energy efficient hybrid MAC protocol with dynamic sleep/wake-up extension to the IEEE 802.15.4 MAC, namely, HSW-802.15.4. The model automates the network by enabling it to work exibly in low and high-density networks with a lower number of collisions. A frame structure that offers an enhanced exploitation for the TDMA time slots (TDMAslots) is provided. To implement these enhanced slots exploitation, this hybrid protocol rst schedules the TDMAsslots, and then allocates each slot to a group of devices. A three-dimensional Markov chain is developed to display the proposed model in a theoretical manner. Simulation results show an enhancement in the energy conservation by 40% - 60% in comparison to the IEEE 802.15.4 MAC protocol. Secondly, an efficient centralised clustering-based whale optimisation algorithm (CC- WOA) is suggested, which employs the concept of software de ned network (SDN) in its mechanism. The cluster formulation process in this algorithm considers the random di- versi cation of node density in the geographical area and involves both sensor resource restrictions and the node density in the tness function. The results offer an efficient con- servation of energy in comparison to other protocols. Another clustering algorithm, called centralised load balancing clustering algorithm (C-LBCA), is also developed that uses par- ticle swarm optimisation (PSO) and presents robust load-balancing for data gathering in IoT. However, in large scale networks, the nodes, especially the cluster heads (CHs), suffer from a higher energy exhaustion. Hence, in this thesis, a centralised load balanced and scheduling protocol is proposed utilising optimisation algorithms for large scale IoT net- works, named, optimised mobile sink based load balancing (OMS-LB). This model connects the impact of the Optimal Path for the MS (MSOpath) determination and the adjustable set of data aggregation points (SDG) with the cluster formulation process to de ne an op- timised routing protocol suitable for large scale networks. Simulation results display an improvement in the network lifespan of up to 54% over the other approaches.