A Centralized Routing Protocol With a Scheduled Mobile Sink-Based AI for Large Scale I-IoT

Abstract

Extensive efforts have been undertaken to enhance the centralised 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 the 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 optimisation (PSO) and genetic algorithm (GA), 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-centre units by using a centralised SDN controller-based AI to calculate: a load-balanced table of clusters (CT), an optimal SDG, and an optimal path for the MS (M S Opath ). Moreover, the proposed new routing technique will prevent significant energy dissipation by the cluster head (CH) 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 SDG and 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 M S Opath by 61% in comparison to other approaches.

An efficient data collection path planning scheme for wireless sensor networks with mobile sinks

“…The main goal of this scheme is to reduce the data transmission distances of the sensor nodes by employing a dynamic sorting algorithm to achieve tree-cluster routing using multihop concepts. In [9], the authors proposed an energy-efficient routing protocol based on particle swarm optimization and a genetic algorithm to prevent energy dissipation between sensor nodes; by calculating the load-balanced table of clusters, the optimal set of data gathering points and an optimal path for the mobile sink can be obtained. In [10], an energy-efficient intracluster routing algorithm was proposed to balance the energy consumption among member nodes by optimizing the sojourn locations of the mobile sink in each cluster.…”

“…Because a sink can randomly move within the sensing area to gather the detection data, the data transmission distances of the sensor nodes can be reduced. The mobility of the sink enables convenient data collection and reduces the energy consumption by the sensor nodes [6][7][8][9][10]. WSNs with mobile sinks are expected to be used to collect sensing data in highly dangerous regions, including battlefields, disaster areas and environmental pollution areas.…”

An Energy Efficient Hybrid MAC Protocol With Dynamic Sleep-Based Scheduling for High Density IoT Networks

“…The authors in [29] focused on reducing the collisions caused by the hidden node problem in IEEE 802.15.4 networks using the GTS slots. Recently, Al-Janabi et al [30] introduced a solution that expands network scalability and prolongs the lifetime of the IoT network protocol by developing a scheduling sleep/wake-up model based on artificial intelligence (AI). In addition, Ayadi et al [31] provided two methods that aim to better manage the energy exhausted by the WSNs.…”

SeC‐SDWSN: Secure cluster‐based SDWSN environment for QoS guaranteed routing in three‐tier architecture

“…But the convergence time FJAPSO algorithm is large because it is amended on traditional PSO algorithm. For a large‐scale network, a centralized routing protocol was proposed with a mobile sink node in SDWSN . Here, cluster formation and route selection were performed in a centralized manner.…”