PDA-misbehaving node detection &amp;amp; prevention for MANETs

Prasannavenkatesan, T.; Raja, R.Vinston; Ganeshkumar, P.

doi:10.1109/iccsp.2014.6950037

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…The frequent link breakage among nodes is possible when the network considers the high‐speed nodes for routing. Indeed, the high‐speed node may move beyond the source node's coverage area while routing is approaching . Therefore, the routing path and routing discovery process should exclude the high‐speed nodes.…”

Section: Proposed Speed Prediction Methodologymentioning

confidence: 99%

“…Such movements are generated using mobility scenario generation and analysis tool such as BonnMotion . When the node reaches its destination, then the node pauses for some interval of time and then repeats the same process until the end of the simulation . Based on the simulation environment summarized in Table , the nodes are simulated (mobility generated) for 2 hours (7200 seconds).…”

Section: Simulation In Realistic Environmentsmentioning

confidence: 99%

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Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

Theerthagiri

Thangavelu

2019

Int J Communication

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In this paper, we propose a speed prediction model using auto-regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time-series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto-correlation function (ACF), and partial auto-correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.

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Section: Proposed Speed Prediction Methodologymentioning

confidence: 99%

Section: Simulation In Realistic Environmentsmentioning

confidence: 99%

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

Theerthagiri

Thangavelu

2019

Int J Communication

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“…The repeated link-breakage in the connectivity between nodes is conceivable when the high-speed node considered for routing. The higher mobility (speed) node could move outside the node transmission zone while routing in progress [19]. Thus, the necessity to omit the higher speed node in the route discovery process emerges.…”

Section: Futuristic Mobility Prediction Model (Fmpm)mentioning

confidence: 99%

“…It creates movements to the nodes; when the simulation started, each node randomly moves towards the destination with varying speed from zero to 100, based on the random waypoint model. Once the node reaches its destination, then it pauses for some time and then repeats the same process until the end of the simulation [19,22]. The similar simulation environment simulates the nodes for 100 seconds.…”

Section: Speed Prediction Using Random Waypoint Model In Ns2mentioning

confidence: 99%

FMPM: Futuristic Mobility Prediction Model for Mobile Adhoc Networks Using Auto-Regressive Integrated Moving Average

Theerthagiri

Thangavelu

2018

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The mobility of the node plays a crucial role in the route discovery process for Mobile Ad hoc networks (MANET). The high-speed node affects the routing process by packet relaying, packet delivery. This paper proposes a mobility prediction model FMPM using Auto-Regressive Integrated Moving Average (ARIMA) method to estimate the futuristic speed values of the nodes in MANET. It uses the autocorrelations of previous data by time series forecasting approach. The ARIMA model applied to mobile nodes for predicting its future speeds in the network. This mobility prediction supports the route discovery process to select the moderate mobility nodes for the reliable routing in the network. The nodes are trained by ARIMA model using neural network time-series tool of Matlab. Roughly, it trains each node with ten iterations. Simulation results show that the forecasted values almost match with the simulated node speed values. Performance analysis of the predicted mobility values exhibits the improved results compared to existing works on performance metrics such as mean square error, covariance, and computation overhead. The proposed FMPM produces more perfection in the speed prediction of a mobile node with a maximum of 80-100 % throughout the time series and has the lowest error values as 0.1085. But, the neural based prediction has the lowest MSE as 0.852. Therefore, the ARIMA prediction has approximately 0.75 reduced error values.

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“…1 shows the typical Aircraft ad hoc network. In AANET, the direct aircraft to aircraft as well as aircraft to ground and aircraft to satellite communication are possible [2,13].…”

Section: Introductionmentioning

confidence: 99%

Routing Protocols for the Aircraft AD HOC Networks

T*,

2019

IJRTE

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The communication between the aircraft-to-aircraft and aircraft-to-ground can be established with the support of Aircraft Ad hoc Networks (AANET). Routing in the aircraft ad hoc networks is a challenging task due to its unique attributes such as very high mobility of the aircraft nodes and dynamic topology. Few research works had developed routing environment and protocols for the dynamic topology based AANET. This paper analyses the developments of the routing protocols for the aircraft ad hoc networks. This paper extensively discusses the routing protocols and comparative analyses of the performance metrics namely throughput, packet delivery ratio, end to end delay, routing overhead, and number of handoffs. Further, this work deliberates the aircraft ad hoc networks simulation environment, aircraft’s velocity, different radio propagation models of aircraft simulation model. Various challenges and issues of routing protocols are extensively analyzed and compared with existing methodologies in this paper.

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PDA-misbehaving node detection & prevention for MANETs

Cited by 9 publications

References 5 publications

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

FMPM: Futuristic Mobility Prediction Model for Mobile Adhoc Networks Using Auto-Regressive Integrated Moving Average

Routing Protocols for the Aircraft AD HOC Networks

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