Link Failure Time Research Articles

Improvising QoS through Cross-Layer Optimization in MANETs

In Mobile Adhoc Networks (MANETs), nodes are mobile and interact through wireless links. Mobility is a significant advantage of MANETs. However, due to the unpredictable nature of mobility, the link may fail frequently, degrading the Quality of Service (QoS) of MANETs applications. This paper outlines a novel Ad hoc On-Demand Distance Vector with Proactive Alternate Route Discovery (AODV-PARD) routing protocol that uses signal strength-based link failure time estimation. The node predicts the link failure time and warns the upstream node through a warning message about the failure. On the basis of this information, a mechanism for identifying alternate routes is started in order to reroute traffic to the alternate route prior to the link failure. It significantly reduces packet loss and improves all the QoS parameters. The suggested protocol is compared to the traditional Ad hoc On-Demand Distance Vector (AODV) routing protocol and shows that the outlined protocol results in an improvement in QoS.

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This work aims to predict link failure and improve routing efficiency in MANET. The Fuzzy Linear Regression Method (FLRM) measures the long lifespan link based on the link failure. The mobile node group is built by the Received Signal Strength (RSS). The Hill Climbing (HC) method selects the Group Leader (GL) based on node mobility, node degree and node energy. Additionally, it uses a Data Gathering node forward the information from GL to the sink node through multiple GL. The GL is identified by linking lifespan and energy using the Particle Swarm Optimization (PSO) algorithm. The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

Intelligent energy-aware multiple quality of service restraints based secured optimal routing protocol with dynamic mobility estimation for wireless sensor networks

This research work confronts a sender-based responsive and novel protocol named “Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)” protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of ‘n’ number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node’s trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols.

A mathematical framework for link failure time estimation in MANETs

Mobile ad-hoc network (MANET) is an infrastructure-less network of mobile nodes, connected by wireless links. Although mobility is the key characteristic of MANETs, yet the frequent movement of nodes may lead to link failure. This frequent topology change due to evasive mobility is one of the primary reason of Quality of service (QoS) degradation in conventional MANETs. If link failure time of already established root is estimated in advance, data packet loss can be avoided by exploring alternate path well within time. Thus, accurate link failure time estimation is one of the key areas that have drawn the attention of researchers of the ad-hoc networking community. This paper outlines a least-square quadratic polynomial regression-based technique for estimating link failure time by using the signal strength of received data packets. The distance between the transmitting and receiving nodes is calculated from the received signal strength of data packets. The authors have generated an optimal error quadratic model between this node distance and data packet arrival time. After quadratic model formulation, the link failure time is estimated by finding the moment at which the maximum communication range will be achieved for considered transmitter–receiver pair. A detailed analysis has been done for observing the behaviour of link failure time estimation accuracy of the proposed technique with varying network parameters. The repercussion of mobility variation on estimated link failure time has also been analysed. The proposed technique has also been compared with an existing interpolation-based technique. Network Simulator 2.35 has been selected as the primary simulation tool along with BonnMotion and MOVE tool for various mobility scenarios generation.

Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework

Large blackouts in power grids are often the consequence of uncontrolled failure cascades. The ability to predict the failure cascade process in an efficient and accurate manner is important for power system contingency analysis. In this paper, we propose to apply the influence model for the prediction and screening of failure cascades in large scale DC and AC power networks. Then, the trained influence model is applied to some large power grids with thousands of buses and transmission lines. The prediction performance is evaluated in four different aspects, from the global perspective of the overall failure size to the individual information regarding the link failure time. The results show that under limited training samples, the proposed framework is capable of predicting the failure cascade size with a 7% error rate, the final state of links with a 10% error rate, and the failure time within 1 time unit for both the DC and AC model. One major advantage of the proposed method is that it can reduce the computational time of the failure cascade prediction by a few orders of magnitude with limited compromise in accuracy, as compared to the power flow based contingency analysis. Another important feature of the proposed method is that the trained influence parameters can reveal the critical initial contingencies. This information is very helpful for identifying the worst contingency scenarios for system operators.

A regression-based technique for link failure time prediction in MANET

Mobile ad-hoc network (MANET) is a collection of mobile terminals forming an infrastructure-less and quickly deployable network, in which nodes can communicate to each other via multiple hops. Mobility attribute is a notable one in MANET, as this leads to frequent topology changes, so this is the primary cause of link failure. Link failure time estimation has always remained an active area of research among researchers of the networking community. This paper explores various link failure time prediction techniques and proposes a novel least-squares polynomial regression-based statistical technique to estimate the link failure time in MANET. Each node in the network periodically broadcasts hello packets to register its presence with neighbouring nodes. A neighbour node receives these packets and uses its signal strength to estimate the link failure time with help of quadratic least-squares regression. The outlined technique is simulated using Network Simulator 2.35. The performance of the estimation accuracy of the suggested technique and existing interpolation-based technique has been examined for numerous mobility and scalability scenarios.

A regression-based technique for link failure time prediction in MANET

Mobile ad-hoc network (MANET) is a collection of mobile terminals forming an infrastructure-less and quickly deployable network, in which nodes can communicate to each other via multiple hops. Mobility attribute is a notable one in MANET, as this leads to frequent topology changes, so this is the primary cause of link failure. Link failure time estimation has always remained an active area of research among researchers of the networking community. This paper explores various link failure time prediction techniques and proposes a novel least-squares polynomial regression-based statistical technique to estimate the link failure time in MANET. Each node in the network periodically broadcasts hello packets to register its presence with neighbouring nodes. A neighbour node receives these packets and uses its signal strength to estimate the link failure time with help of quadratic least-squares regression. The outlined technique is simulated using Network Simulator 2.35. The performance of the estimation accuracy of the suggested technique and existing interpolation-based technique has been examined for numerous mobility and scalability scenarios.

Property values associated with the failure of individual links in a system with multiple weak and strong links

Representations are developed and illustrated for the distribution of link property values at the time of link failure in the presence of aleatory uncertainty in link properties. The following topics are considered: (i) defining properties for weak links and strong links, (ii) cumulative distribution functions (CDFs) for link failure time, (iii) integral-based derivation of CDFs for link property at time of link failure, (iv) sampling-based approximation of CDFs for link property at time of link failure, (v) verification of integral-based and sampling-based determinations of CDFs for link property at time of link failure, (vi) distributions of link properties conditional on time of link failure, and (vii) equivalence of two different integral-based derivations of CDFs for link property at time of link failure.

Enhancing Routing Performance of AODV Protocol using 3rd Ordered Newton’s Difference Equation

Objective: The main objective of our work is to use the interpolation based approach for the prediction of the time for which the active link will be available for the ongoing transmission of the packets containing information from the initial hub to the final destination hub. By using this interpolation method the performance of the routing protocol has been increased. Method Analysis: In our research we are using 3rd ordered newton's divided difference mathematical model for the estimation of the link failure time, so that prior to failure of link the message is divided through alternate path or local route repair is initiated. Findings: By applying the proposed algorithm with AODV routing protocol various quality of service parameter are calculated and compared with the previous proposed algorithm. From the results it has been concluded that the proposed algorithm gives better result in terms of performance gain of the routing protocol. It has been seen that throughput of the network increases with the use of this algorithm and also the various other parameters. The estimated time for link failure is more precisely measured, as a result of which the route recovery can be done in advance. Improvement: In our work we have proposed an algorithm that will be an improved version of previous existing algorithm. By the use of this algorithm performance has been enhanced incredibly.

SIMULATION AND PERFORMANCE ANALYSIS EVALUATION FOR MULTIPATH EXTENSION OF AODV TO IMPROVE END TO END DELAY, ROUTE ERROR SENT, ROUTING LOAD AND PACKET DROP RATIO

This paper describes improvement in standard routing protocol AODV for mobile ad-hoc networks. Our mechanism setups multiple optimal paths based on bandwidth and delay. It allows to store multiple optimal paths based on Bandwidth and delay. At time of link failure, it will switch to next available path. To set up multiple paths, we have used the information that we get in the RREQ packet and also send RREP packet to more than one path. It reduces overhead of local route discovery at the time of link failure and because of this End to End Delay and Drop Ratio decreases. The main feature of our mechanism is that it is simple, efficient. We evaluate through simulations the performance of the AODV routing protocol including our scheme and we compare it with HLSMPRA (hot link split multi-path routing algorithm) Algorithm. Indeed, our scheme reduces routing load, End to End Delay, route error sent, and Packet drop ratio. The simulations have been performed using network simulator OPNET-14.0. The network simulator OPNET is discrete event simulation software for network simulations which means it simulates events such as sending, receiving, forwarding and dropping packets.

Optimal Route Based Advanced Algorithm using Hot Link Split Multi-Path Routing Algorithm

Abstract —Present research work describes advancement in standard routing protocol AODV for mobile ad-hoc networks. Our mechanism sets up multiple optimal paths with the criteria of bandwidth and delay to store multiple optimal paths in the network. At time of link failure, it will switch to next available path. We have used the information that we get in the RREQ packet and also send RREP packet to more than one path, to set up multiple paths, It reduces overhead of local route discovery at the time of link failure and because of this End to End Delay and Drop Ratio decreases. The main feature of our mechanism is its simplicity and improved efficiency. This evaluates through simulations the performance of the AODV routing protocol including our scheme and we compare it with HLSMPRA (Hot Link Split Multi-Path Routing Algorithm) Algorithm. Indeed, our scheme reduces routing load of network, end to end delay, packet drop ratio, and route error sent. The simulations have been performed using network simulator OPNET. The network simulator OPNET is discrete event simulation software for network simulations which means it simulates events not only sending and receiving packets but also forwarding and dropping packets. This modified algorithm has improved efficiency, with more reliability than Previous Algorithm.

OPTIMAL ROUTE SELECTION STRATEGY FOR QOS IMPROVEMENT

An ad-hoc mobile network is a collection of mobile nodes that are dynamically and arbitrarily located in such a manner that the interconnections between nodes are capable of changing on a continual basis. Ad hoc nodes operating in a single path, single channel model might experience packet loss and high latency due to the congestion caused. Nodes along the heavy traffic path could reach the maximum bandwidth limit and experience undesirable amounts of packet drop. In Ad hoc networks the nodes are mobile and contain no infrastructure. Certain emergency messages have to be delivered with very low latency and high reliability. An efficient routing protocol should ensure reliable packet delivery in a timely manner. Our Proposed solution is to setup multiple optimal paths based on bandwidth and delay. It allows storing multiple optimal paths based on Bandwidth and delay. At time of link failure, it will switch to next available path. To set up multiple paths, we have used the information that we get in the RREQ packet and also send RREP packet to more than one path It reduces overhead of local route discovery at the time of link failure. We investigated the performance metrics namely Retransmission Attempt, Media Access Delay, Network Load by through OPNET simulation.