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"Ad hoc networks"
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VANET : challenges and opportunities
\"VANET (vehicular ad hoc network) is a subgroup of MANET (mobile ad hoc network). It enables communication among vehicles on the road and between related infrastructures. This book addresses the basic elements of VANET along with components involved in the communication with their functionalities and configurations. It contains numerous examples, case studies, technical descriptions, scenarios, procedures, algorithms, and protocols, and addresses the different services provided by VANET with the help of a scenario showing a network tackling an emergency.\"-- Provided by publisher.
Book
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.
Journal Article
Interconnecting smart objects with IP : the next Internet
Interconnecting Smart Objects with IP: The Next Internet explains why the Internet Protocol (IP) has become the protocol of choice for smart object networks. IP has successfully demonstrated the ability to interconnect billions of digital systems on the global Internet and in private IP networks. Once smart objects can be easily interconnected, a whole new class of smart object systems can begin to evolve. The book discusses how IP-based smart object networks are being designed and deployed. The book is organized into three parts. Part 1 demonstrates why the IP architecture is well suited to smart object networks, in contrast to non-IP based sensor network or other proprietary systems that interconnect to IP networks (e.g. the public Internet of private IP networks) via hard-to-manage and expensive multi-protocol translation gateways that scale poorly. Part 2 examines protocols and algorithms, including smart objects and the low power link layers technologies used in these networks. Part 3 describes the following smart object network applications: smart grid, industrial automation, smart cities and urban networks, home automation, building automation, structural health monitoring, and container tracking. Shows in detail how connecting smart objects impacts our lives with practical implementation examples and case studies Provides an in depth understanding of the technological and architectural aspects underlying smart objects technology Offers an in-depth examination of relevant IP protocols to build large scale smart object networks in support of a myriad of new services
eBook
Cloud computing enabled big-data analytics in wireless ad-hoc networks
\"This reference text covers intelligent computing through Internet of Things (IoT) and Big Data in Vehicular Environment in a single volume. The text covers important topics including topology-based routing protocols, heterogeneous wireless networks, security risks, software-defined vehicular Ad-hoc network, vehicular delay tolerant networks, and energy harvesting for WSNs using rectenna\"-- Provided by publisher.
Book
Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for example, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks.
Journal Article
Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
In recent years, flying ad hoc networks have attracted the attention of many researchers in industry and universities due to easy deployment, proper operational costs, and diverse applications. Designing an efficient routing protocol is challenging due to unique characteristics of these networks such as very fast motion of nodes, frequent changes of topology, and low density. Routing protocols determine how to provide communications between drones in a wireless ad hoc network. Today, reinforcement learning (RL) provides powerful solutions to solve the existing problems in the routing protocols, and designs autonomous, adaptive, and self-learning routing protocols. The main purpose of these routing protocols is to ensure a stable routing solution with low delay and minimum energy consumption. In this paper, the reinforcement learning-based routing methods in FANET are surveyed and studied. Initially, reinforcement learning, the Markov decision process (MDP), and reinforcement learning algorithms are briefly described. Then, flying ad hoc networks, various types of drones, and their applications, are introduced. Furthermore, the routing process and its challenges are briefly explained in FANET. Then, a classification of reinforcement learning-based routing protocols is suggested for the flying ad hoc networks. This classification categorizes routing protocols based on the learning algorithm, the routing algorithm, and the data dissemination process. Finally, we present the existing opportunities and challenges in this field to provide a detailed and accurate view for researchers to be aware of the future research directions in order to improve the existing reinforcement learning-based routing algorithms.
Journal Article
Routing Protocols and Security Issues in Vehicular Ad hoc Networks: A Review
Vehicular Ad hoc Networks, also known as VANETs, are temporary wireless networks consisting of different types of automobiles as network nodes and the connections among these vehicles as links. VANET is a booming technology that has become quite popular in research, academia, and industry domains. A VANET can be considered as a moving network in which the nodes are automobiles. As VANETs are basically ad hoc networks, they have all the fundamental characteristics of ad hoc networks including computational and storage constraints. A network of VANETs can also be formed to enhance the safety and other services for vehicle drivers. VANETs are proven to be efficient in many real-life scenarios including traffic control and driver assistance. Information communicated through VANETs can definitely help to improve the user driving experience and avoid collisions. This paper discusses various deployment scenarios for VANETs along with the communication requirements and security concerns.
Journal Article