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.

This special issue titled ‘Sensing, Communication, and Localization in WSN, IoT & VANET’ appears in the CAAI Transactions on Intelligence Technology. We encourage contributions addressing localisation accuracy, network coverage, upper and lower bounding, lane and vehicle detection, and related topics.

In the first paper, (Hamil et al.) explore how smartphone sensors and IoT devices aid in rescuing individuals during emergencies like fires in tall buildings. It introduces a pioneering Sensor Management and Data Fusion-Wireless Data Exchange fusion scheme, leveraging an evolutionary algorithm within complex multi-storey buildings. This scheme aims to diversify particle sets effectively, capturing the user's real-time state using wearable device sensors. The authors further explore how smartphones sensors utilise data for object movement alongside Bluetooth Low Energy beacon based localisation with the help of Sensor Management security and Data Fusion-Wireless Data Exchange scheme. The effectiveness of this scheme and its impact on a smartphone user's real-time state within indoor settings were assessed through various experiments in controlled environments.

In the second paper, (Khan J et al.) proposed a novel method to fine-tune alpha-beta filter parameters using a feed-forward backpropagation neural network. This model, comprising the alpha-beta filter as the core predictor and a feedforward artificial neural network as the learning element, uses temperature and humidity sensor data for precise predictions from noisy readings. By integrating the feed-forward backpropagation neural network significantly boosts prediction accuracy, slashing both roots mean square error (RMSE) and mean absolute error (MAE). In experiments against traditional methods like alpha-beta and Kalman filters, the proposed model outperformed, showcasing a 35.1% improvement in MAE and 38.2% in RMSE.

In the third paper, (Imtiaz et al.) proposed a localisation scheme for industrial IoT in the presence of flipping ambiguities. The author proposed a novel greedy anchor selection strategy known as GSAP to reduce the localisation error estimation in IIoT networks. The author presents the whole idea using multidimensional scalling for initial position estimation that can reduce the convergence time of the algorithm. The expression of the Cramer Rao lower bound is derived for the proposed algorithm to test its optimality and compare the results with the state of the art.

In the fourth paper (Ismail et al.) derived the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co-channel interference, which impacts NOMA's performance enhancement. In the uplink EH relay NOMA Narrow Band IoT network, authors aim to optimise Narrow Band IoT device data rates while meeting their minimum requirements. Considering equipment energy, EH relay energy, and data cache constraints, the proposed model creates a complex indoor localisation framework involving power, data, and time slot scheduling. This model poses a non-convex optimisation challenge without a straightforward analytical solution. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%.

We thank all authors for their submissions and reviewers for their valuable feedback. We hope this Special Issue sparks new outcomes in recurrent dynamic neural networks for the research community.

Open Research

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Biographies

Dr. Ki-Il Kim received the M.S. and Ph.D. degrees in computer science from the Chungnam National University, Daejeon, Korea, in 2002 and 2005, respectively. He is with the Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea. Prior to joining, he has been with the Department of Informatics at Gyeongsang National University since 2006. His research interests include machine learning for networks, wireless/mobile networks, fog computing, MANET, QoS in wireless networks, and multicast and sensor networks.
Prof. Dr. Aswani Kumar Cherukuri is the Professor of the School of Information Technology and Engineering at Vellore Institute of Technology, Vellore, India. He has over 21+ years of experience in academics, research, and administration. He was awarded the Young Scientist Fellowship from the Tamilnadu State Council for Science and Technology and the Inspiring Teacher Award from The Indian Express. He led and worked on several major research projects funded by the DST, DAE, and MHRD of the Govt. of India and has published over 150 refereed research articles. He serves on editorial boards for a few international journals including JIKM and IEEE IT Professional. He is a Senior Member and distinguished speaker of the ACM, and Vice-Chair of the IEEE Taskforce on Educational Data Mining.
Dr. Xue Jun Li received his B.Eng. (with First Class Honours) and Ph.D. degrees from Nanyang Technological University, Singapore, in 2004 and 2008, respectively. From November 2007 to August 2008, he worked as a Research Engineer and as a Research Fellow successively at Network Technology Research Centre. From August 2008 to September 2008, he worked in Temasek Laboratories @ Nanyang Technological University as a Research Scientist. From September 2008 to May 2011, he was an academic staff with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. From June 2011 to January 2013, he was a Research Scientist with Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore. In January 2013, he joined the Department of Electrical and Electronic Engineering, School of Engineering, Computer and Mathematical Sciences, Faculty of Design and Creative Technologies, Auckland University of Technology, New Zealand, where he is currently an Associate Professor.
Dr. Tanveer Ahmad (Member, IEEE) received the Ph.D., M.S. and B.S. (Computer Science) degree from Auckland University of Technology, New Zealand, Lanzhou Jiaotong University, China, and COMSATS University Pakistan, in 2019, 2014 and 2006 respectively. Between November 2006 and July 2010, he worked as a Research Engineer and then as Research Fellow in Oracle University, Pearson VUE. Between February 2014 and May 2015, he worked as a Research Associate in the Shenzhen institute of Advanced technology, Chinese University of Hongkong. He also worked as a Senior lecturer in Abacus Institute of Studies here in New Zealand. After pursing a Doctoral degree, he is currently working as a postdoctoral researcher in Chungnam National University, South Korea. His research interests include wireless sensor network localisation, VANET, RFID, IoT based healthcare system, networking protocols, modelling/design of radio frequency integrate circuits, and system optimisations.
Dr. Muhammad Rafiq (Member, IEEE) received Ph.D. in Data Sciences from Yeungnam University, South Korea in 2022 and MSEE in electronics engineering from International Islamic University, Pakistan in 2008. He has more than 20 years of industry experience. From 1996 to 2008, he was serving as a technical officer in the research and development department at a Research Organization of Pakistan Air Force. From 2008, he was serving as a manager for the management information systems department in L.T. Engineering, and developed various technical solutions such as embedded systems, research data analytics, and ERP solution. From 2012 to 2017, he was delivering various software solutions as a consultant to the U.S.A. and Canada. He provided turnkey solutions in the domain of business applications, embedded solutions, computer vision, and deep learning. His research interests focus on computer vision with the deep-learning domain to consume and enhance his multidisciplinary skills.
Dr. Shehzad Ashraf Chaudhry received the master’s and Ph.D. degrees (Hons.). Before this, he worked as an Associate Professor of computer science with the University of Sialkot, and International Islamic University, Islamabad, Pakistan. He is currently an Associate Professor with the Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, Turkey. He is working in the field of information and communication security, he has published extensively in prestigious venues, such as IEEE Communication Standards Magazine, the IEEE Transactions on Industrial Informatics, the IEEE Internet of Things Journal, the IEEE Transactions on Industry Applications, the IEEE Transactions on Reliability, ACM Transactions on Internet Technology, Sustainable Cities and Society (Elsevier), FGCS, IJEPES, and Computer Networks. His current research interests include lightweight cryptography, elliptic/hyper elliptic curve cryptography, multimedia security, e-payment systems, MANETs, SIP authentication, smart grid security, IP multimedia subsystems, and next generation networks. He occasionally writes on issues of higher education in Pakistan. He was awarded gold medal for achieving maximum distinction of 4/4 CGPA in his master’s degree.