scholarly journals Topology Control Using Distributed Power Management Algorithm for Mobile Ad Hoc Networks

2009 ◽  
Vol 5 (4) ◽  
pp. 128
Author(s):  
Nuraj Pradhan ◽  
Tarek Saadawi

In order to be strongly connected in the network, a node may increase its power indiscriminately causing interference. Since interference is one of the major problems in wireless network, the proposed algorithm will co-operatively reduce inter-node interference in the network. Further, uni-directional links are a major source of interference as most of the routing protocol only utilizes bi-directional links. The algorithm will attempt to prevent such links or if required convert them into bi-directional links. We will show that the proposed algorithm provides strongly connected and more reliable network over dynamic physical channel modeled by log-distance path loss model, log-normal shadowing model and rayleigh fading model. It stabilizes node connectivity over the dynamic network and environment and even, to a certain extent, prevent node from being completely disconnected from the network. For the selected simulation environment, we will show that the proposed algorithm provides a shorter packet delay, improves the network throughput by as much as 37%, decreases the routing overhead and reduces interference.

Author(s):  
Rakesh Kumar Singh

Mobile Ad Hoc Network (MANET) is a collection of communication devices or nodes that wish to communicate without any fixed infrastructure. The nodes in MANET themselves are responsible for dynamically discovering other nodes to communicate. A number of challenges like open peer-to-peer network architecture, stringent resource constraints, shared wireless medium, dynamic network topology etc. are posed in MANET. In this research, we identify the existent security threats an ad hoc network faces, the security services required to be achieved and the countermeasures for attacks in each layer. To accomplish our goal, we have done literature survey in gathering information related to various types of attacks and solutions, as well as we have made comparative study to address the threats in different layers. Finally, we have identified the challenges and proposed solutions to overcome them. There is no general algorithm that suits well against the most commonly known attacks such as wormhole, rushing attack, etc.


2020 ◽  
Vol 9 (2) ◽  
pp. 23 ◽  
Author(s):  
Rajorshi Biswas ◽  
Jie Wu

Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model.


Author(s):  
Manas Ranjan Mishra ◽  
Mohit Ranjan Panda ◽  
Sukant Kishoro Bisoyi

Wireless mobile ad-hoc networks (MANET) are characterized as infrastructure less networks. Topologies are formed with movement of regular nodes which has multi radio links and these regular nodes under demand behaves as backbone node (router) to forward packets across the network. These networks suffer frequent topology changes due to the dynamic stochastic process behavior of incoming nodes. Mobile ad-hoc networks lack load balancing that causes unnecessary packet loss and route break up in real-time data transmission. Area of operation, interference, and communication link range and path loss are the factors to affect the throughput of MANET. In this paper we evaluated the performance of AODV and DSR routing protocols which are enhanced by an Automation Topography, In our proposed Topographical Automation the location of incoming nodes are completely random and those will be confined themselves within a certain communication range such that the throughput is enhanced to meet better QoS level. As location of the nodes are system defined and quite automatic, nodes before being forwarded with the full assurance of successful session flows. It is often advantageous to position stable and capable relay nodes, including unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs), and unmanned under sea vehicles (UUVs) used by Defense to save cost as well as life.


Author(s):  
Seyedakbar Mostafavi ◽  
Vesal Hakami ◽  
Fahimeh Paydar

In Mobile Ad Hoc Networks (MANETs), lack of a fixed infrastructure, dynamic network topology, device mobility and data communication over wireless channels make the multi-hop routing a very challenging task. Due to mission-critical applications of MANET, dealing with these challenges through the design of a Quality of Service (QoS)-assured protocol is a substantial problem. Mobility in MANETs is commonly considered as a negative factor on quality, although we suggest that the right approach to mobility awareness using wisely selected metrics can lead to a robust and QoS-assured protocol. In this paper, we propose QMAR-AODV, a QoS-assured Mobility-Aware Routing protocol which is an optimized version of AODV protocol. We utilize a combination of stability and quality metrics including Mobility Ratio (MR(C,E)) between nodes in a route, Energy Efficiency and congestion load to choose the most stable and QoS-assured routes. Our simulation results show that QMAR-AODV protocol outperforms E2E-LREEMR and reduces route instability, end-to-end delay, data retransmissions and packet loss by 8.3% 10.9% 10.6% and 5.4 respectively, while increases data reception and network throughput by 5.1% and 4.8% respectively, compared to E2E-LREEMR routing protocol.


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