An Intelligent Computing Method for Contact Plan Design in the Multi-Layer Spatial Node-Based Internet of Things

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.

Hesitant Fuzzy Soft Combined Compromise Solution Method for IoE Companies’ Evaluation

“…Its outstanding feature is that all things will have the function of context perception, with stronger computing power and perception ability [5]. By integrating people and information into the Internet, the network will have billions or even trillions of connection nodes [6]. The IoE is based on the physical network, which increases the network intelligence and realizes the functions of integration, collaboration [7] and visualization between ''everything'' of the Internet.…”

A Routing Model Based on Multiple-User Requirements and the Optimal Solution

“…The experiment showed that the algorithm has better congestion handling ability under high-traffic conditions than for other conditions. With the continuous improvements in the routing strategies of ground-based wireless networks, intelligent optimization algorithms such as the bee colony algorithm, ant colony algorithm and particle swarm algorithm have been extended to topological optimization and routing decision making for satellite networks in recent years [8][9][10]. Reference [30] verified the application value of the ant colony algorithm in single-layer NGEO.…”

“…However, if the head-wolf satellite odor is the highest in a local area, the routing algorithm will not be adjusted. Moreover, if calculating the concentration, it is necessary to eliminate the poor-wolf satellites according to formula (10). The purpose of the above operation is to eliminate the satellite nodes that do not meet the QoS guarantee requirements under the specific link queue length; this process is similar to pheromone release along the worst path in the improved ant colony algorithm.…”

“…In reference [8], a routing strategy based on an artificial fish swarm algorithm was designed for global searches while iteratively determining the optimal path. Some common solutions are the ant colony algorithm [9], particle swarm algorithm [10], etc. Although these routing algorithms have some shortcomings related to the long calculation time, these issues can be avoided by establishing an ideal routing cost function or adopting a method for satellite queue length prediction.…”