A Centralized Routing Protocol With a Scheduled Mobile Sink-Based AI for Large Scale I-IoT
Thair Al-Janabi, Hamed Saffa Al-Raweshidy · IEEE Sensors Journal · 2018
Extensive efforts have been undertaken to enhance the centralized monitoring-based software-defined network (SDN) concept of the large-scale intelligent-Internet of Things (I-IoT). Furthermore, the number of IoT devices in vast environments is increasing and a scalable routing protocol has therefore become essential. However, due to associated resource restrictions, only very small functions can be configured using IoT nodes, principally those related to the power supply. One solution for increasing network scalability and prolonging the life of the network is to use the mobile sink (MS). However, determining the optimal set of data gathering points (SDG), optimal path, scheduling the entire network with MS in an energy efficient manner and prolonging the life of the network present huge challenges, particularly in large-scale networks. This paper therefore proposes an energy efficient routing protocol based on artificial intelligence (AI), i.e., particle swarm optimization and genetic algorithm, for large scale I-IoT networks under the SDN and cloud architecture. The basic premise is to exploit cloud resources, such as storage and data-center units by using a centralized SDN controller-based AI to calculate: a load-balanced table of clusters, an optimal SDG, and an optimal path for the MS (MSopath). Moreover, the proposed new routing technique will prevent significant energy dissipation by the cluster head and by all nodes in general by scheduling the whole network. Consequently, the SDN controller essentially balances energy consumption by the network during the routing construction process as it considers both the SDGand the movement of the MS. Simulation results demonstrate the effectiveness of the suggested model by improving the network lifespan up to 54%, volume of data aggregated by the MS up to 93% and reducing the delay of the MSopath by 61% in comparison with other approaches.