The support of multipath routing in IPv6-based internet of things

The development of IPv6-based network architectures for Internet of Things (IoT) systems is a feasible approach to widen the horizon for more effective applications, but remains a challenge. Network routing needs to be effectively addressed in such environments of scarce computational and energy resources. The Internet Engineering Task Force (IETF) specified the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to provide a basic IPv6-based routing framework for IoT networks. However, the RPL design has the potential of extending its functionality to a further limit and incorporating the support of advanced routing mechanisms. These include multipath routing which has opened the doors for great improvements towards efficient energy balancing, load distribution, and even more. This paper fulfilled a need for an effective review of recent advancements in Internet of Things (IoT) networking. In particular, it presented an effective review and provided a taxonomy of the different multipath routing solutions enhancing the RPL protocol. The aim was to discover its current state and outline the importance of integrating such a mechanism into RPL to revive its potentiality to a wider range of IoT applications. This paper also discussed the latest research findings and provided some insights into plausible follow-up researches

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Evaluation of the performance of underwater wireless sensor networks routing protocols under high-density network

Nowadays, research and development of Underwater Wireless Sensor Networks (UWSNs) widely supporting various available application such as oil/gas monitoring system, tsunami monitoring, disaster prevention, and environmental monitoring has become increasingly popular among academicians and industries. However, to develop efficient communication in UWSNs is a difficult duty due to the irregular nature of the underwater environment. In our previous review [14], we did an elaborate theoretical survey on UWSNs routing protocols. In this work, we are going to evaluate the performance of some of the UWSNs routing protocols under high-density network condition. To simulate a high-density UWSNs, we are placing hundreds of underwater nodes in a small three-dimensional topographical area and study the behavior of the routing protocol and the network. We have chosen to evaluate some of the frequently addressed underwater routing protocols such as Underwater Flooding (UWFlooding), Vector-Based Forwarding (VBF), and Hop by Hop Vector-Based Forwarding (HH-VBF) under this high-density network scenarios. The result of our study shows that VBF and HH-VBF perform better in term of the number of packets received, dropped packets and PDR, while UWFlooding performs better in term of cumulative delay

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio