The convergence of Internet of Things(IoT),vehicularad hoc network(VANET),and mobile ad hoc network relies on sensor networks to gather data from nodes or objects.These networks involve nodes,gateways,and anchors,oper...The convergence of Internet of Things(IoT),vehicularad hoc network(VANET),and mobile ad hoc network relies on sensor networks to gather data from nodes or objects.These networks involve nodes,gateways,and anchors,operating on limited battery power,mainly used in broadcasting.IoT applications,like healthcare,smart cities,and transportation,often need position data and face challenges in delay sensitivity.Localisation is important in ITS and VANETs,influencing autonomous vehicles,collision warning systems,and road information dissemination.A robust localisation system,often combining GPS with techniques like Dead Reckoning and Image/Video Localisation,is essential for accuracy and security.Artificial intelligence(AI)integration,particularly in machine learning,enhances indoor wireless localisation effectiveness.Advancements in wireless communication(WSN,IoT,and massive MIMO)transform dense environments into programmable entities,but pose challenges in aligning self‐learning AI with sensor tech for accuracy and budget considerations.We seek original research on sensor localisation,fusion,protocols,and positioning algorithms,inviting contributions from industry and academia to address these evolving challenges.展开更多
文摘The convergence of Internet of Things(IoT),vehicularad hoc network(VANET),and mobile ad hoc network relies on sensor networks to gather data from nodes or objects.These networks involve nodes,gateways,and anchors,operating on limited battery power,mainly used in broadcasting.IoT applications,like healthcare,smart cities,and transportation,often need position data and face challenges in delay sensitivity.Localisation is important in ITS and VANETs,influencing autonomous vehicles,collision warning systems,and road information dissemination.A robust localisation system,often combining GPS with techniques like Dead Reckoning and Image/Video Localisation,is essential for accuracy and security.Artificial intelligence(AI)integration,particularly in machine learning,enhances indoor wireless localisation effectiveness.Advancements in wireless communication(WSN,IoT,and massive MIMO)transform dense environments into programmable entities,but pose challenges in aligning self‐learning AI with sensor tech for accuracy and budget considerations.We seek original research on sensor localisation,fusion,protocols,and positioning algorithms,inviting contributions from industry and academia to address these evolving challenges.
