Intelligent Sensing and Computing in Wireless Sensor Networks for Multiple Target Tracking

Abstract

With sixth generation (6G) communication technologies, target sensing can be finished in milliseconds. The mobile tracking-oriented Internet of Things (MTT-IoT) as a kind of emerging application network can detect sensor nodes and track targets within their sensing ranges cooperatively. Nevertheless, huge data processing and low latency demands put tremendous pressure on the conventional architecture where sensing data is executed in the remote cloud and the short transmission distance of 6G channels presents new challenges into the design of network topology. To cope with the above difficulties, this paper proposes a new resource allocation scheme to perform delicate node scheduling and accurate tracking in multitarget tracking mobile networks. The dynamic tracking problem is formulated as an infinite horizon Markov Decision Process (MDP), where the state space that considers energy consumption, system responding delay, and target important degree is extended. A model-free reinforcement learning is applied to obtain satisfied tracking actions by frequent iterations, in which smart agents interact with the complicated environment directly. The performance of each episode is evaluated by the action-value function in search of the optimal reward. Simulation results demonstrate that the proposed scheme shows excellent tracking performance in terms of energy cost and tracking delay.

Performance Art Video Action Management Oriented to 6G Wireless Transmission Technology

“…In the above efficiency optimization problem, for any one of the MTC terminals, different transmission destination nodes choose to determine different transmission powers [40][41][42]. e set expression of D toSN and E res is…”

An Adaptive Biomimetic Ant Colony Optimization With 6G Integration for IoT Network Communication

“…Advancements in radar-communication waveform designs, as illustrated by Du et al [19], and intelligent sensing methods [20] demonstrate the potential synergies with our multiobjective optimization scheme. The explorations into beamforming optimization by Liu et al [21] further contribute to this area, highlighting potential influences on our ant colony optimization algorithm.…”

“…Possible limitations in specific communication systems [20] Intelligent sensing for multiple target tracking Constraints in real-world tracking scenarios [21] Transceiver beamforming optimization; Dual-functional radarcommunication Restricted to DFRC systems; Implementation complexity [22] Machine learning for network performance optimization Dependence on quality and availability of training data [23] Evolutionary algorithms in network optimization Generalizability concerns across diverse network scenarios [24] 6G in-X subnetworks; Resource allocation under limited sensing Exploration limited to specific subnetwork types [26] explored computational efficiency within UAV-based Mobile Edge Computing (MEC) by integrating GPU-based Particle Swarm Optimization (PSO) and Voronoi Diagrams. This confluence of technologies culminated in a refined MEC model, showcasing creative optimization applications.…”

Performance Art Video Action Management Oriented to 6G Wireless Transmission Technology

“…In the above efficiency optimization problem, for any one of the MTC terminals, different transmission destination nodes choose to determine different transmission powers Mehbodniya et al, 2021;Zou et al, 2022). The set expression of D toSN and E res is:…”