Energy Aware Routing Protocol for Energy Constrained Mobile Ad-hoc Networks

Dynamic topology change and decentralized makes routing a challenging task in mobile ad hoc network. Energy efficient routing is the most challenging task in MANET due to limited energy of mobile nodes. Limited power of batteries typically use in MANET, and this is not easy to change or replace while running communication. Network disorder can occur for many factors but in middle of these factors deficiency of energy is the most significant one for causing broken links and early partition of the network. Evenly distribution of power between nodes could enhance the lifetime of the network, which leads to improving overall network transmission and minimizes the connection request. To discourse this issue, we propose an Energy Aware Routing Protocol (EARP) which considers node energy in route searching process and chooses nodes with higher energy levels. The EARP aim is to establish the shortest route from source to destination that contains energy efficient nodes. The performance of EARP is evaluated in terms of packet delivery ratio, network lifetime, end-to-end delay and throughput. Results of simulation done by using NS2 network simulator shows that EARP can achieve both high throughput and delivery ratio, whereas increase network lifetime and decreases end-to-end delay

Active Path Updation for Layered Routing (APULAR) in Wireless Mesh Networks

One of the major research issues in the Wireless Mesh Network (WMN) is routing. The routing protocols of ad-hoc networks can be applied for WMN, but have limited success because, ad-hoc networks are mainly structure less networks with highly dynamic topology and harmonized nodes, where WMN are relatively static network with two types of nodes, one fixed mesh routers and mobile clients. In layered routing protocol, source node initiates a path establishing process whenever path breaks. It will cause huge control packets and increase packet loss. This is not an ideal method in WMN where every nodes rather than source and destination in the path are motionless. One way of overcoming this is by initiating the local route repair by destination node.. In this paper, we propose an active path updating procedure (APULAR) for quickly updating the broken path to recover from packet loss. Moreover, to improve throughput and to reduce the co-channel interference, we use multiple interface with multi channels. We are considering 4-hop as an interference range and will use fixed channel assignment within the mesh routers to reduce the inter flow interference. Our procedure is simulated in NS2 and compared with AODV and Infrastructure Wireless Mesh Routing Architecture (IWMRA). Simulation results show that our protocol performs better than IWMRA and AODV in key performance metrics like packet delivery ratio, control overhead, average throughput and end-to-end delay. Keywords: Active path repair, channel assignment, multi channel routing, and wireless mesh network routing

Routing and mobility strategies for mobile ad hoc networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Variable power transmission in highly Mobile Ad-Hoc Networks

Mobile Ad Hoc Networks pose challenges in terms of power control, due to their fixed transmission power, the mobility of nodes and a constantly changing topology. High levels of power are needed in wireless networks, particularly for routing. As a result of the increase in the number of communication devices being used, there is the challenge of increased density within these networks, and a need to extend the battery life of communication devices. In order to address this challenge, this thesis presents the development of a new protocol (Dynamic Power AODV), which is an enhancement of the Ad Hoc On Demand Distance Vector (AODV) protocol. The new protocol dynamically adjusts the transmission power based on the range, which depends on node density. This thesis provides a systematic evaluation of the performance of DP-AODV, in a high speed and high density environment, in comparison with three other routing protocols. The experiments demonstrated that DP-AODV performed better than two of the protocols in all scenarios. As compared to the third protocol (AOMDV), DP-AODV gave better performance results for throughput and Power Consumption, but AOMDV performed better in terms of Packet Delivery Fraction rate and End-to-End Delay in some cases

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

A framework for traffic flow survivability in wireless networks prone to multiple failures and attacks

Transmitting packets over a wireless network has always been challenging due to failures that have always occurred as a result of many types of wireless connectivity issues. These failures have caused significant outages, and the delayed discovery and diagnostic testing of these failures have exacerbated their impact on servicing, economic damage, and social elements such as technological trust. There has been research on wireless network failures, but little on multiple failures such as node-node, node-link, and link–link failures. The problem of capacity efficiency and fast recovery from multiple failures has also not received attention. This research develops a capacity efficient evolutionary swarm survivability framework, which encompasses enhanced genetic algorithm (EGA) and ant colony system (ACS) survivability models to swiftly resolve node-node, node-link, and link-link failures for improved service quality. The capacity efficient models were tested on such failures at different locations on both small and large wireless networks. The proposed models were able to generate optimal alternative paths, the bandwidth required for fast rerouting, minimized transmission delay, and ensured the rerouting path fitness and good transmission time for rerouting voice, video and multimedia messages. Increasing multiple link failures reveal that as failure increases, the bandwidth used for rerouting and transmission time also increases. This implies that, failure increases bandwidth usage which leads to transmission delay, which in turn slows down message rerouting. The suggested framework performs better than the popular Dijkstra algorithm, proactive, adaptive and reactive models, in terms of throughput, packet delivery ratio (PDR), speed of transmission, transmission delay and running time. According to the simulation results, the capacity efficient ACS has a PDR of 0.89, the Dijkstra model has a PDR of 0.86, the reactive model has a PDR of 0.83, the proactive model has a PDR of 0.83, and the adaptive model has a PDR of 0.81. Another performance evaluation was performed to compare the proposed model's running time to that of other evaluated routing models. The capacity efficient ACS model has a running time of 169.89ms on average, while the adaptive model has a running time of 1837ms and Dijkstra has a running time of 280.62ms. With these results, capacity efficient ACS outperforms other evaluated routing algorithms in terms of PDR and running time. According to the mean throughput determined to evaluate the performance of the following routing algorithms: capacity efficient EGA has a mean throughput of 621.6, Dijkstra has a mean throughput of 619.3, proactive (DSDV) has a mean throughput of 555.9, and reactive (AODV) has a mean throughput of 501.0. Since Dijkstra is more similar to proposed models in terms of performance, capacity efficient EGA was compared to Dijkstra as follows: Dijkstra has a running time of 3.8908ms and EGA has a running time of 3.6968ms. In terms of running time and mean throughput, the capacity efficient EGA also outperforms the other evaluated routing algorithms. The generated alternative paths from these investigations demonstrate that the proposed framework works well in preventing the problem of data loss in transit and ameliorating congestion issue resulting from multiple failures and server overload which manifests when the process hangs. The optimal solution paths will in turn improve business activities through quality data communications for wireless service providers.School of ComputingPh. D. (Computer Science