RAODV Routing Protocol for Congestion Detection and Relief in Ad Hoc Wireless Networks

Ad hoc wireless networks depend on mutual collaboration among nodes. Congestion in ad hoc wireless networks thus presents more of a challenge than for other network types. This article proposes RAODV (relieving AODV), a modification of the AODV routing protocol, to handle congestion via third party neighbour nodes in dense and static ad hoc networks. RAODV nodes use a T-entropy threshold-based congestion detection algorithm to determine the congestion status of their neighbours. If RAODV determines that congestion is occurring, it then tries to relieve congestion via a local repair modification algorithm that replaces the congested node by a suitable monitoring/third party neighbour node. This article also shows evidence that RAODV results in better network performance than AODV in simulations with random network topologies.

Network Routing Issues in Global Geographic Information System

Research background: A geographic information system (GIS) is a computer-based information system that gathers, manages, and disseminates different geographic (related to earth) information to provide intelligent analytics necessary for prompt decisions and actions. A GIS can aggregate data from several different streams and turn it into a smart dashboard for different users to enhance productivity. The use of intelligent technologies, remote sensing, and ad-hoc wireless networks has significantly improved GIS. In GIS, utilization of sensed data from different sources depends upon the use of efficient sensing equipment, smart mobile nodes, and network gateways and relays that can efficiently route traffic towards the sink or base station. GIS achieve attention in emergency response scenarios for their capability to collect, analyze, and process spatial-referred. Purpose of the article: Article is focused on studying and identifying suitable routing issues of wireless mobile nodes, which are the main transponders for smooth and errorless data transmission. Methods: We performed a simulation-based comparative study of three different routing protocols used in ad-hoc wireless networks, namely is to identify suitable routing protocols that can aid the GIS to improve its overall performance. We calculated and compared our results with different routing protocols. We were able, with the Random mobility model, to estimate the performance of various network parameters. Findings & Value added: We proposed a study in this work that includes geo-information services overall of routing protocols to support a team in stressful situations. The study shows that the AODV routing protocol performed better than the other two routing protocols (OLSR & TORA) under the given topology.

Machine Learning in Wireless Communication

As the circumstances are changing, mankind has turned out to be more inclined to snappy and speedier correspondence and access to information. The correspondence happens in numerous structures (e.g., presently, this correspondence is all the more a virtual substance than a physical one). So as to keep up fast correspondence, the coming age will depend on exceptionally tried and true, canny and self-learning/self-modifying correspondence organizers. In this context, this chapter reviews the most important machine learning techniques with the direct applicability in wireless ad-hoc systems. A guide of machine learning methods and their relevance is also provided. Different applications of ad-hoc wireless networks are discussed in terms of energy-aware communications, optimal node deployment and localization, resource allocation, and scheduling.