Exploiting Neuro-Fuzzy System for Mobility Prediction in Wireless Ad-Hoc Networks

Abstract

Abstract. Ad-hoc mobile wireless network is characterized by a very dynamic environment. However, the major obstacle to be resolved is to sustain the links of continuity and improved routing performance. In this paper, we propose a predictor based Neuro-fuzzy for the prediction of mobility. It predicts the trajectory of an ad-hoc mobile node in order to improve routing performance by reducing overhead and the number of broken connections. It allows estimating the stability of paths in Ad-Hoc mobile wireless networks. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the trajectory of an ad-hoc mobile, we demonstrate the effectiveness of the proposed predictor by testing it on a time series prediction problems.

Survey on artificial intelligence based techniques for emerging robotic communication

“…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]…”

“…The main advantages of ANN are the ability to be learned and trained from the failures as well as it can detect the relationship between nonlinear variables. Furthermore, ANNs output strongly provides accurate prediction and estimation from the input [21]. The statistical performance techniques and ANN for different applications are discussed [22].…”

“…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.…”

“…Comparative analysis of different works involving approaches for intelligent robots communicationANNIoRT and desired QoS They proposed intelligent technique to allow IoRT robots for working in the desired coverage area for provisioning desired QoS, and reducing energy consumption. Elleuch et a.l, 2015[21] ANFIS Mobility prediction They proposed an effective technique for prediction the trajectory of an ad-hoc mobile. It was also demonstrated by testing on a time series prediction problems Li et al, 2009.…”

Fuzzy Control Based Mobility Framework for Evaluating Mobility Models in MANET of Smart Devices.

“…In the last years, smartphones, tablets and other mobile communication devices have become popular [32]. When a smart device or smart user moves from one location to a new location it has to establish a new connection with the target access point or a base station or neighborhood smart device [18]. Every smart device user is free to connect any other smart device, also they are free to move randomly [43].…”