Optimal resource allocation for route selection in ad-hoc networks

Nowadays, the selection of the optimum path in mobile ad hoc networks (MANETS) is being an important issue that should be solved smartly. In this paper, an optimal path selection method is proposed for MANET using the Lagrange multiplier approach. The optimization problem considers the objective function of maximizing bit rate, under the constraints of minimizing the packet loss, and latency. The obtained simulation results show that the proposed Lagrange optimization of rate, delay, and packet loss algorithm (LORDP) improves the selection of optimal path in comparison to ad-hoc on-demand distance vector protocol (AODV). We increased the performance of the system by 10.6 Mbps for bit rate and 0.133 ms for latency

A Review of Various Swarm Intelligence Based Routing Protocols for Iot

The paper provides insight into various swarm intelligence based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of Internet of Things. These include the reliability, link failures, routing, heterogeneity etc. The MANETs and WSNs routing issues impose almost same requirements for IoT routing mechanism. The recent work of the worldwide researchers is focused on this area. protocols are based on the principles of swarm intelligence. The swarm intelligence is applied to achieve the optimality and the efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ACO technique tries to provide answers to many routing issues. Using the swarm intelligence and ant colony optimization principles, it has been seen that, the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT. As the various standard protocols available for MANETs and WSNs are not reliable enough, the paper finds the need of some efficient routing algorithms for IoT

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods

Systems Methodology and Framework for Problem Definition in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks are communication networks built up of a collection of mobile devices which can communicate through wireless connections. Mobile Ad Hoc Networks have many challenges such as routing, which is the task of directing data packets from a source node to a given destination. This task is particularly hard in Mobile Ad Hoc Networks: due to the mobility of the network elements and the lack of central control, robustness and adaptability in routing algorithms and work in a decentralized and self organizing way. Through the principles of systems architecting and Engineering; the problem statement in Mobile Ad Hoc Networks could be defined more specifically and accurately. The uncertainties and techniques for mitigating and even taking positive advantages of them can be achieved through a framework of uncertainties as in [1]. The systems methodology framework called Total Systems Intervention (TSI) described by Flood and Jackson [2] select a systems methodology for Mobile Ad Hoc Networks. The purpose of this paper is to show how TSI when integrated with a framework created to understand the risks and opportunities can help develop strategies to minimize the risks and to take advantage of the opportunities for facing challenges in Mobile Ad Hoc Networks

Efficient route selection in ad hoc on-demand distance vector routing

The protocol diversities of mobile ad hoc have already got hold of the field to a peak of a matured and developed area. Still, the restraint of delay and bandwidth of mobile ad hoc network have kept a little room to draft a routing protocol for the pursuit of providing quality of service. In the paper, we proposed protocol namely Efficient Route Selection in Ad Hoc On-Demand Distance Vector Routing. We select the best path among multiple paths from source to destination using covariance and delay. We consider the delay, link stability and energy to devise a covariance-based metric to discover the most balanced path. We also propose a metric for the selection of a node that acts as a local backup node for the most vulnerable nodes on the selected path. We accomplish our implementation in NS3and it shows the more reliable path and less end to end delay than other counterpart protocols

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

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms