Journal of Emerging Technologies in Web Intelligence Contents

90 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 2, NO. 2, MAY 2010route based on link lifetime has been proposed. In thismodel the edge effect has been explored to find morestable route to the destination. The lifetime and thestability of the route is calculated by eliminating the edgeeffect to reduce route maintenance and route overheads.The disadvantage of this model however is that eachmethod proposed under the model requires either pilotsignal generation and monitoring of the pilot signal of theother nodes or monitoring of signal strength of the othernodes. With these weaknesses, the stable route still getsestablished and performance of the network also getsincreased. Lifetime Prediction Routing (LPR) proposed in[7] also was suggested to find the stability of the route. Inthis the service life of the MANET is predicted andcorrespondingly the route is established. The servicelifetime is predicted based on the past activity of thebattery lifetime of the node. A simple moving averagepredictor is used to keep track of last N values of residualenergies and the corresponding time instances for the lastN packets received/ relayed by each mobile node. Thisway LPR not only captures the remaining batterycapacity but also accounts for rate of energy discharge.This way the route stability and lifetime is estimatedbased on the battery life which is also an important factor.This method has some serious concerns like, when thenode mobility increases it becomes difficult to predict thelifetime of the node and also the use of LPR involvescertain overheads. One more model to predict the linkstability was proposed in [8]. This model was named asthe signal stability based routing protocol (SSA + ) model.SSA + is the enhanced version of SSA for finding theroute stability in MANETs. In this method a route ismaintained with the help of active neighbouring nodes.The neighbours are considered active if it relays ororiginates at least one packet within the most recentactive timeout period. The SSA + solution was mainlyoffered to remove the problems of high node density andhigh mobility and the low node density and low mobility.In high node density and high mobility scenario themobility is high, the probability of link failures remainalso high. To cater to the problem the signal strength ofthe link is estimated and classified into the categories ofweak, normal and strong signals. The nodes exchangeinformation regarding the signal strength by sending thehello packets and based upon the signal strength valuestored in the link state table, the life time of the route isdetermined. In low node density and low mobility thesignals of the nodes remain weaker and the stability andthe route lifetime is maintained based on two importantconditions.1) The distance between any two nodes getsshorter for the past few clicks.2) Secondly the distance between any two nodesgets longer but the distance changes largerslowly for the past few clicks (the condition oflow mobility).The method above mentioned above, even thoughshowed better results compared to SSA in terms of lownode density and low mobility and high node density andhigh mobility has the drawback of predicting the lifetimeof the route in a complicated way as the node has tomaintain the Link stability table which adds lots ofoverheads in terms of route control and maintenance andwhich are difficult to maintain. In [9] [10] and [11],algorithms are proposed as DV-Hop, Hop-TERRAIN,and link stability with dynamic delay prediction, todetermine the location of nodes based on hop counts andthe appropriate route to the destination. The hop countsprovided an estimate for the overall distance between thenodes and the dynamic delay ensured stability. Thesehowever, were mainly focusing on the Quality of service(QoS) based route establishment and hence were silent onthe route lifetime parameters like mobility and batterypower. A Novel route metric based on the fragility of theroute was also proposed in [12]. In this approach thedynamic nature of the route is captured by studying thedistance variation of the next hop neighbor in terms ofexpansion and contraction. A distributed algorithm isexecuted in every node to calculate the relative speedestimate of the neighbours. The destination node gets theinformation of every route and selects the best routebased on the route fragility coefficient (RFC) which inturn depends on the cumulative expansion metrics (CEM)and cumulative contraction metrics (CCM). Thisapproach finds the stability of the route and is good interms of not requiring time bound measurements, like theglobal positioning system. This method however, fails topredict the node mobility and includes the destinationlatency. The estimation of link quality and residual timein vehicular Adhoc networks proposed in [13] is also themethod to determine stability of the route in MANETs bypredicting the active time of the link. In this method thesignal processing along with empirical decompositionand robust regression is used to predict the link qualityand the residual time. This method unlike the methodproposed in this paper uses a three stage approach whichinvolves lots of complexity in terms of practicaldeployment. This also involves additional burden ofcalculating the various essential parameters mentionedunder signal processing, empirical decomposition androbust regression methods. The above mentionedliterature even though provides excellent way ofestimating the distance and the location of neighboringnodes through different techniques, however, are mostlysilent on the mobility issue. This paper provides the routestability estimation based upon the neighboring nodedistance ,mobility and signal strength.III. DESCRIPTIONMobile Ad hoc Networks (MANETs) being dynamic innature create challenges in terms of Quality of Service(QoS) at each level from application to physical. Inphysical layer level the mechanism to predict the lifetimeof the neighboring node increases reliability from sourceto destination delivery. To achieve this we have identifiedthe following three parameters for next hop path selection1) Node Distance and position2) Mobility3) Signal quality© 2010 ACADEMY PUBLISHER