Abstract
Computational Intelligence (CI) has been addressed as a great challenge in recent years, particularly the aspects of routing, task scheduling, and other high-complexity issues. Especially for the Contact Plan Design (CPD) that schedules contacts in dynamic and resource-constrained networks, a suitable CI algorithm can be exchanged for a high-quality Contact Plan (CP) with the appropriate computational overhead. Previous works on CPD mainly focused on the optimization of satellite network connectivity, but most of them ignored network topology characteristics. In this paper, we study the CPD issue in the spatial node based Internet of Things (IoT), which enables the spatial nodes to deliver data cooperatively via intelligent networking. Specifically, we first introduce a Multi-Layer Space Communication Network (MLSCN) model consisting of satellites, High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground stations, on which a Time-Evolving Graph (TEG) is used to illustrate the CPD process. Then, according to the characteristics of each layer in the MLSCN, we design the corresponding CPs for the inter-layer contacts and intra-layer contacts. After that, a CI algorithm named as Multidirectional Particle Swarm Optimization (MPSO) is proposed for inter-layer CPD, which utilizes a grid-based initialization strategy to expand the diversity of individuals, in which a quaternary search method and quaternary optimization are introduced to improve efficiency of particle swarms in iterations and to ensure the continuous search ability, respectively. Furthermore, an optimized scheme is implemented for the intra-layer CPD to reduce congestion and improve transmission efficiency. Simulation results show that the proposed CPD scheme can realize massive data transmission with high efficiency in the multi-layer spatial node-based IoT.
Highlights
As an important part of current and generation networks, Internet of Things (IoT) is composed of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and seamless connectivity, which enables these things to connect and exchange data [1,2,3,4,5,6,7,8,9]
A Computational Intelligence (CI) algorithm named as Multidirectional Particle Swarm Optimization (MPSO)
In order to cope with intermittent connectivity caused by the orbital motion of spatial nodes, the spatial node-based IoT networks are often modeled through scheduling contacts as a temporal topology within a certain duration [23]
Summary
As an important part of current and generation networks, Internet of Things (IoT) is composed of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and seamless connectivity, which enables these things to connect and exchange data [1,2,3,4,5,6,7,8,9]. Sensors 2018, 18, 2852 application areas for the IoT technologies. Bringing wide-area connectivity to the IoT using spatial nodes and satellite technology is becoming an attractive solution to complementing terrestrial networks [11,12,13,14]. For data transmission in the spatial node-based IoT with limited resource, it is likely to affect the system performance seriously if the classical CI algorithm is applied bluntly [21,22]. In order to cope with intermittent connectivity caused by the orbital motion of spatial nodes, the spatial node-based IoT networks are often modeled through scheduling contacts as a temporal topology within a certain duration [23]
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