Recent Advances in Resource Management in Mobile, Wireless and Ad-Hoc Networks
Mobile, wireless, and ad-hoc networks are becoming increasingly significant parts of distributed task-based applications. These types of network systems play a vital role in military defence, rescue missions, and civilian search parties. Due to the inherent nature of such systems in handling various applications that share a common network, high network topology variation and quality of communication are at risk. Hence novel resource management techniques are being tried and tested for the betterment of such critical ad-hoc networks. The most widely used resource management systems enable admission to applications to the network and arbitration of critical resources.
However, the major limitation of such resource management systems for mobile, wireless and ad-hoc networks is the maintenance of Quality of Service (QoS). This is mainly due to the high variation in the bandwidth, availability of resources, quality of the link, and delay. Overcoming this limitation has become a key objective in most recent studies and research methodologies. For instance, one research concentrates on the development of a middleware that will help in QOS specification, adaptive resource utilization, end-to-end negotiation etc. This is achieved with the help of a pipeline network topology which consists of several periodic processing phases. The greedy admission protocol used in such an innovative network topology adequately replaces the mixed-integer programming optimization with a real-time resource allocation system. Another interesting advance includes addressing critical issues such as policy-based resource management for ad-hoc networks and access policing and control. In order to address these issues, a concept based on the degree of participation of nodes in the routing process is employed.
Based on the agreed degree, the routing load is distributed to the nodes involved in the process. One recent and innovative advance preserves the bandwidth for high-priority tasks thus averting transmission conflicts. This is seamlessly achieved through the use of a location-aware bandwidth pre-reservation system. In such a system, the priority tasks are identified based on the geographical location of the nodes. This technique also offers a reservation algorithm in addition to the calculation of the end-to-end bandwidth. There are still a lot of research gaps in the design issues. Future research can focus on addressing these issues effectively and economically.
Potential list of topics includes, but are not limited to:
- Novel multiple access protocols for mobile radio networks and ad-hoc networks.
- Development of a cluster-based routing protocol for resource allocation and management in a mobile, wireless, and ad-hoc network.
- Optimization of the resource allocation process in a discrete ad-hoc network setting.
- Tools and technologies for developing self-organising ad-hoc network management systems.
- Sustainable and energy-efficient routing protocols for green resource management in mobile ad-hoc networks.
- Route configurability of ad-hoc mobile networks in a power-constrained working environment
- Innovative QoS routing algorithms for mobile, wireless, and ad-hoc networks.
- Methods to improve the privacy and reliability of the mobile ad-hoc networks for defence applications.
- Automatic bandwidth reservation using the internet of things and smart ad-hoc networks.
- Design and development of real-time multiple access ad-hoc network adaptation systems.
Tentative schedule of Submissions, Reviewing, and Publication: Date of Submission Deadline – 22nd April, 2023Date First Review Round Completed – 6th June, 2023Date Revised Manuscripts Due – 13th August, 2023Date of Final Notification – 15th October, 2023Guest Editor Details for this Special Issue: Dr.Gwanggil JeonFull ProfessorDepartment of Embedded Systems Engineering,College of Information Technology,Incheon National University, Incheon 22012, KoreaEmail: firstname.lastname@example.org, email@example.comGoogle Scholar Link: https://scholar.google.co.kr/citations?hl=en&user=AbWB9I8AAAAJBiography: Dr. Gwanggil Jeon received the B.S., M.S., and Ph.D. (summa cum laude) degrees from the Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea, in 2003, 2005, and 2008, respectively. From 2009.09 to 2011.08, he was with the School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada, as a Post-Doctoral Fellow. From 2011.09 to 2012.02, he was with the Graduate School of Science and Technology, Niigata University, Niigata, Japan, as an Assistant Professor. From 2014.12 to 2015.02 and 2015.06 to 2015.07, he was a Visiting Scholar at Centre de Mathématiques et Leurs Applications (CMLA), École Normale Supérieure Paris-Saclay (ENS-Cachan), France. From 2019 to 2020, he was a Prestigious Visiting Professor at Dipartimento di Informatica, Università degli Studi di Milano Statale, Italy. He is currently a Full Professor at Incheon National University, Incheon, Korea. Dr. Jeon is an ACM Member, IEEE Senior Member, an Associate Editor of Sustainable Cities and Society, IEEE Access, Real-Time Image Processing, Journal of System Architecture, and MDPI Remote Sensing.Research Interest: Signal & Image Processing, IoT, Healthcare, Sustainable Cities and Society, Embedded SystemDr. Shiping WenProfessorA/DRsch Australian Artificial Intelligence Inst (AAII)University of Technology Sydney,University of Technology Sydney, Ultimo, AustraliaEmail: shiping.Wen@uts.edu.auGoogle Scholar Link: https://scholar.google.com/citations?hl=en&user=xm_RRHEAAAAJBiography: Dr. Shiping Wen is a Professor in the AAII of Faculty of Engineering and Information Technology at University of Technology Sydney (UTS). He is also a Fellow of IOP (Institute of Physics), and a Fellow of BCS (British Computer Society). His research is at the forefront of spearheading the next generation of artificial intelligence based on neuromorphic computing, which is expected to have a monumental impact on many aspects of industry and economy including neural chips, self-driving cars, and automated medical diagnosis. He has significantly contributed to the research and development of AI algorithms, systems, and applications both at national and international (Australia, Hong Kong, USA) level. He has supervised 16 Higher Degree Research students (as principal supervisor). His research results have expounded in more than 200 publications in prestigious journals (JCR 25%) with 8 hot papers and 35 highly cited papers in the Essential Science Indicators (ESI), and 6700+ Google Scholar Citations. In 2018 and 2020, he was listed as a Highly Cited Researcher in the Cross-Field by Clarivate Analytics, respectively. He received the 2017 Young Investigator Award of Asian Pacific Neural Network Association and 2015 Chinese Association of Artificial Intelligence Outstanding PhD Dissertation Award. He currently serves as Associate Editor for KBS, EAAI, NPL, Energies, et al., and Leading Guest Editor for IEEE TNSE, SCS, ERL, et al. He also serviced the General Chair for several international conferences.Research Interest: Neural Network, Deep Learning, Computer Vision, Medical Informatics.Dr. Abdellah ChehriAssociate Professor,Department of Mathematics and Computer Science,Royal Military College of Canada, Kingston, CanadaEmail: firstname.lastname@example.orgGoogle Scholar Link: https://scholar.google.ca/citations?hl=en&user=1VXVtIkAAAAJBiography: Dr. Abdellah Chehri is an Associate Professor at the Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC), Kingston, Ontario. Before joining the RMC, he served as an Associate Professor in Electrical and Computer Engineering at the University of Quebec (UQAC) from 2018-2022. In addition, he has an affiliate professor at the University of Quebec in Outaouais and an adjunct professor at the University of Ottawa. From 2009-2012, he worked as a research fellow and member of Wireless Heterogeneous Sensor Networks in the e-Society Project (WiSense) Research Group at the University of Ottawa. He completed his Ph.D. in Electrical Engineering, with applied research in Information Communication and Telecommunications, at the University Laval (Quebec City). He received his Master’s degrees in Digital Communications and Signal Processing from the University Nice-Sophia Antipolis-Eurecom (France).Research Interest: Wireless Communications, Machine Learning, Internet of Things, Industry 4.0, 5G, Big Data, RF Sensors, Signal ProcessingProf. Giancarlo FortinoFull ProfessorDIMES - Department of Informatics, Modeling, Electronics and SystemsUniversity of Calabria, ItalyEmail: email@example.comGoogle Scholar Link: https://scholar.google.it/citations?hl=it&user=ecImn1MAAAAJBiography: Giancarlo Fortino is currently Full Professor of Computer Engineering at the Department of Informatics, Modeling, Electronics and Systems (DIMES) of the University of Calabria (Unical), Rende (CS), Italy. In 2013 he took the Italian Scientific Habilitation for Full Professorship. He received a Laurea Degree (BSc+MSc) in Computer Engineering and a PhD in Computer Engineering from the University of Calabria, Italy, in 1995 and 2000, respectively. He has been a Research Scholar at the International Computer Science Institute (ICSI), Berkeley (CA), USA, in 1997 and 1999, and Visiting Professor at Queensland University of Technology (QUT), Brisbane, Australia, in 2009. He was also Assistant Professor (2001-2006) and Associate Professor (2006-2018) at University of Calabria. . His research interests include distributed computing and networks, agent systems, agent oriented software engineering, body area networks and wearable computing, wireless sensor networks, Internet of Things (including social and trust mechanism), multimedia streaming content distribution networks, GRID/Cloud computing. He is the founder and director of the SPEME (Smart, PErvasive and Mobile systems Engineering) lab that is engaged in cutting-edge research on novel programming languages, systems, protocols and architectures, and in their implementation in smart, pervasive and mobile systems and their emerging application domains.
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