On-demand Multipath Routing Protocols for Mobile Ad-Hoc Networks: A Comparative Survey

A Mobile Ad Hoc Network (MANET) is an infrastructure-less, self-organized and multi-hop network with a rapidly changing topology causing the wireless links to be broken at any time. Routing in such a network is challenging due to the mobility of its nodes and the challenge becomes more difficult when the network size increases. Due to the limited capacity of a multi-hop path and the high dynamics of wireless links, the single-path routing approach is unable to provide efficient high data rate transmission in MANETs. The multipath routing is the routing technique of using multiple alternative paths through a network. Furthermore, whenever a link failure is detected on a primary route, the source node can select the optimal route among multiple available routes. Therefore, the multipath routing approach is broadly utilized as one of the possible solutions to overcome the single-path limitation. Most of the multipath routing protocols are based on Ad Hoc On-Demand Distance Vector (AODV). The objective of this paper is to provide a survey and compare sets of multipath routing protocols for mobile ad-hoc networks. This survey will motivate the design of new multipath routing protocols, which overcome the weaknesses identified in this paper

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

A Coverage Prediction Technique for Indoor Wireless Campus Network

The placement of an Access Point (AP) is an important key to determine the spread of the signal. To get the optimal spread of signals, a network designer is required to understand how much coverage an AP can generate. A prediction is given to describe the coverage area produced based on AP placement for the wireless campus network, using a coordinate map modeling based on the real size for the indoor environment. The theoretical approach is used to determine the coverage area of an AP device by testing the function of the distance between the AP and the user. The results show that the signal generated by an AP will cover the entire area that is still on the LOS propagation path. The coverage area generated through AP placement in this case study reached 77.5%. The maximum distance between the AP and the user so that it is within the coverage area is 13.851m. There are still areas that are not covered by the AP, especially for the NLOS propagation path because of the obstruction around the AP.The placement of an Access Point (AP) is an important key to determine the spread of the signal. To get the optimal spread of signals, a network designer is required to understand how much coverage an AP can generate. A prediction is given to describe the coverage area produced based on AP placement for the wireless campus network, using a coordinate map modeling based on the real size for the indoor environment. The theoretical approach is used to determine the coverage area of an AP device by testing the function of the distance between the AP and the user. The results show that the signal generated by an AP will cover the entire area that is still on the LOS propagation path. The coverage area generated through AP placement in this case study reached 77.5%. The maximum distance between the AP and the user so that it is within the coverage area is 13.851m. There are still areas that are not covered by the AP, especially for the NLOS propagation path because of the obstruction around the AP

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs