Wireless sensor networks (WSNs) attract considerable amount of research efforts from both industry and academia. With limited power and computational capability available on a sensor node, robustness and efficiency ar...Wireless sensor networks (WSNs) attract considerable amount of research efforts from both industry and academia. With limited power and computational capability available on a sensor node, robustness and efficiency are the main concerns when designing a routing protocol for WSNs with low complexity. There are various existing design approaches, such as data-centric approach, hierarchical approach and location-based approach, which were designed for a particular application with specific requirements. In this paper, we study the design and implementation of a routing protocol for data acquisition in WSNs. The designed routing protocol is named Centralized Sensor Protocol for Information via Negotiation (CSPIN), which essentially combines the advertise-request-transfer process and a routing distribution mechanism. Implementation is realized and demonstrated with the Crossbow MicaZ hardware using nesC/TinyOS. It was our intention to provide a hand-on study of implementation of centralized routing protocol for WSNs.展开更多
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Wireless sensor networks (WSNs) attract considerable amount of research efforts from both industry and academia. With limited power and computational capability available on a sensor node, robustness and efficiency are the main concerns when designing a routing protocol for WSNs with low complexity. There are various existing design approaches, such as data-centric approach, hierarchical approach and location-based approach, which were designed for a particular application with specific requirements. In this paper, we study the design and implementation of a routing protocol for data acquisition in WSNs. The designed routing protocol is named Centralized Sensor Protocol for Information via Negotiation (CSPIN), which essentially combines the advertise-request-transfer process and a routing distribution mechanism. Implementation is realized and demonstrated with the Crossbow MicaZ hardware using nesC/TinyOS. It was our intention to provide a hand-on study of implementation of centralized routing protocol for WSNs.
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.
