Mobility prediction in mobile ad hoc networks using neural learning machines

Ghouti, Lahouari

doi:10.1016/j.simpat.2016.03.001

Cited by 30 publications

(22 citation statements)

References 23 publications

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“…The performance of Wrap‐5 has been compared with reinforcement learning algorithm and shows better result in shortest path routing. Ghouti has developed a neural learning‐based solution to a problem that occurs due to the advancement in the wireless network and mobility nature of a MANET but in the proposed solution future mobility of a MANET node that may cause change in topology can be efficiently predicted. The proposed predictor uses the traces of real‐world mobility and achieves higher accuracy than the existing prediction algorithm as it captures the mobility patterns and interaction between the nodes more accurately.…”

Section: Literature Surveymentioning

confidence: 99%

ORuML: Optimized Routing in wireless networks using Machine Learning

Chaudhary

Johari

2020

Int J Communication

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Summary Routing is a process of selecting a path in a network for delivering a packet from source node to destination node. Successful delivery of a message is a challenge, and therefore, this paper proposes an algorithm for a wireless network called Optimized Routing in wireless networks using Machine Learning (ORuML), which uses machine learning algorithm namely, K‐nearest neighbor (KNN), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR), to predict the network type of the source and destination nodes. The ML model is trained by using characteristic features of a node collected in real time such as battery power utilization, available internal storage, IP address, and range of a node. Intuitively, the MLR should outperform KNN and SVM in terms of accuracy and Area under ROC Curve (AUC). The proposed algorithm determines whether the source and destination nodes are co‐located and also, determines the best neighboring hop for efficient routing.

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Section: Literature Surveymentioning

confidence: 99%

ORuML: Optimized Routing in wireless networks using Machine Learning

Chaudhary

Johari

2020

Int J Communication

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“…Zaouche et al [59] Zhong et al [41] Skobelev et al [50] Rampinelli et al [48] Shibata [42] Elleuch et al [21] Schleich et al [30] Ghouti et al [38,39]…”

Section: Alsamhi Et Al [54]mentioning

confidence: 99%

“…Elleuch et al [21] predicted optimal paths for node mobility of wireless ad hoc networks by using ANFIS. Furthermore, Ghouti et al [38,39] predicted the same by using machine learning techniques. Ad hoc network technology was used for wireless communication between fly ad-hoc network (FANET) and ground networks [40].…”

Section: Fig6 Overview Of Current Studiesmentioning

confidence: 99%

“…The proposed optimization algorithm to provide efficient cognitive relaying between two ad hoc robots. Ghouti et al, 2013Ghouti et al, ,2016 Machines learning Mobility prediction They proposed machines learning approach for mobility prediction. It was led to a significant improvement over conventional methods based on multilayer perceptions.…”

Section: Applications Findingsmentioning

confidence: 99%

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Survey on artificial intelligence based techniques for emerging robotic communication

Alsamhi

Ansari

2019

Telecommun Syst

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This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The study of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently.

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“…Mobility prediction has been studied in cellular networks and wireless ad-hoc networks and represents a precious instrument for estimating the future location, mobility speed, and the direction of the mobile users [3]. Several analytical formulations have been proposed for modelling the mobility prediction; for example, the most common methods and approaches are based on the Markov model [4] and Hidden Markov Models (HMM) [5]. However they are not generic enough in order to face all types of mobility.…”

Section: Introductionmentioning

confidence: 99%