Deployment Of Wireless Access Points Research Articles

SE-Loc: Security-Enhanced Indoor Localization With Semi-Supervised Deep Learning

Wireless indoor localization has become unavoidable for industrial indoor location-based services. Given the ubiquitous deployment of wireless access points (APs), WiFi fingerprinting of Received Signal Strength (RSS) has been widely adopted for indoor localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities are exploited, mobile users may confront indoor localization mismatches, faults and even localization system failures. In this paper, we propose SE-Loc, a semi-supervised learning-based technique to enhance security and resiliency of fingerprint-based localization. The architecture of SE-Loc consists of two parts: (1) a correlation-based AP selection for processing RSS fingerprints and fingerprint-image generation, and (2) a deep learning model based on a denoising autoencoder and convolutional neural networks for robust feature learning and location matching. Extensive experiments show that under potential AP attacks, SE-Loc demonstrates superior performance on indoor localization over the state-of-the-art methods. With up to 100 malicious attacking APs in the UJIIndoorLoc edge server, SE-Loc can still achieve the lowest error fluctuation of 1.7 m and the highest average localization accuracy of 8.9 m.

Channel Path-Loss Measurement and Modeling in Wireless Data Network (IEEE 802.11n) Using Artificial Neural Network

Careful network planning has become increasingly critical with the rising deployment, coverage, and congestion of wireless local area networks (WLANs).This paper investigates and determine the Path-loss exponent value for the ubiquitous wireless local area network at the Federal University Oye-Ekiti for the line of sight and non-line of sight (N-LOS). Aside this, the paper also models the wireless network using artificial neural network (ANN) technology by training some neurons based on data collected from a drive-test.
 The proposed ANN model performed with accuracy and is offered as a simple, yet strong predictive model for network planning – having both speed and accuracy. Results show, that for the area under study, Oye Campus has a higher standard deviation of 5.76dBm as against ikole Campus with 1.44dBm, this is because of dense vegetation at Oye Campus.
 In view of this, the paper provides a predictive site survey for rapid wireless Access point deployment.

Throughput optimization in wireless local networks with inter-AP interference via a joint-association control, rate control, and contention resolution

The dense deployment of wireless access points (APs) either in wireless local area networks (WLANs) or in wireless mesh networks facilitates greatly ubiquitous Internet access, however, due to the limited number of orthogonal frequency channels allotted to the IEEE802.11-based networks, this also induces the inevitable problem of inter-AP interference. In this paper, we study the problem of determining the optimal association in multi-cell or extended wireless networks in the presence of hidden terminals and inter-AP collisions. Unlike most previous work in this area, which deals with networks without inter-AP interference, we first reveal that association control alone is not sufficient to achieve fair throughput allocation and load balancing across APs, then advocate a solution based on the joint association control, rate control and fair contention resolution as a means to improving network performance. Based on this, we formulate a cross-layer association control, throughput optimization and contention resolution problem whose objective is to allocate downlink throughput according to the proportional fairness principle. As the problem turns out to be a non-convex mixed integer programming problem, known to be NP-hard, we relax it first into a continuous problem, then transform the resultant into a convex problem and finally propose a distributed algorithm to solve it. We then design a simple yet effective approximation algorithm to recover an optimal solution that fulfills the discrete integral association constraints. The algorithm yields the optimal association, the maximum achievable rate for each downlink flow as well as each AP’s optimal average backoff time. Using these results as settings in a network, we can achieve the optimal operation point without any scheduling. Numerical experiments and simulation results show that our algorithm converges rapidly and works effectively.

Two Birds With One Stone: Wireless Access Point Deployment for Both Coverage and Localization

Wireless access points (APs) divide a plane into small areas where their coverage ranges overlap. A mobile device can be located within a particular small overlapped area based on the unique set of APs covering the device. We formally define an Optimal Loc-deployment problem for both coverage and area localization. Our objective is to deploy a minimum number of APs that provide full communication coverage while achieving the ability to locate a mobile device within a certain area no larger than a given accuracy parameter. We obtain a formula that precisely determines the optimal solution for more than half of the accuracy values. For the rest of the accuracy values, we propose an algorithm that will return an approximation whose difference to the optimal solution is less than e for any e >; 0. Finally, we conduct extensive numerical evaluation and real experiments to validate our proposed solutions.

EMM: an event-driven mobility model for generating movements of large numbers of mobile nodes

In many researches related to wireless network, simulation is commonly used for performance evaluation. Among the simulation parameters, the mobility model that is used to generate the movements of mobile nodes (MNs) is essential. Mobility models can be classified into two types: trace or synthetic [Proceedings of the SCS Western Multiconference Web-based Simulation Track, January 1999]. Trace models use traces which show mobility patterns of MNs observed in the real world. Traces provide accurate movements, but they are not easily modeled. On the other hand, synthetic models attempt to realistically represent the movements of MNs without using traces. Most of the current simulations are based on synthetic models. Many applications are designed to serve hundreds or thousands of MNs simultaneously, especially in mobile or pervasive computing. The simulations of such applications require the movements of large numbers of MNs. However, current synthetic models are focus on small numbers of MNs. If they are used to generate movements of large numbers of MNs, the generated movements are not similar to the movements in the real world. It causes the simulation results of the above applications are debatable. Consequently, a trace model, called an event-driven mobility model (EMM), is proposed in this paper. When the real world is observed, it can be seen that the movements of MNs are driven by various events (e.g., meetings, dining, and so on). This allows the movements to be deemed as “move-stay” mobility pattern. A modified colored petri-net is used to model the move-stay pattern. The current design of EMM is based on such pattern of students on a campus. In our experiment, 6780 movements of students in one day were generated systematically. The statistics of the generated movements were used to determine the deployment of wireless access points. The result shows that EMM is useful for generating movements of large numbers of MNs.