A novel load-balancing scheme for cellular-WLAN heterogeneous systems with cell-breathing technique

This paper proposes a novel load-balancing scheme for an operator-deployed cellular-wireless local area network (WLAN) heterogeneous network (HetNet), where the user association is controlled by employing a cell-breathing technique for the WLAN network. This scheme eliminates the complex coordination and additional signaling overheads between the users and the network by allowing the users to simply associate with the available WLAN networks similar to the traditional WLAN-first association, without making complex association decisions. Thus, this scheme can be easily implemented in an existing operator-deployed cellular-WLAN HetNet. The performance of the proposed scheme is evaluated in terms of load distribution between cellular and WLAN networks, user fairness, and system throughput, which demonstrates the superiority of the proposed scheme in load distribution and user fairness, while optimizing the system throughput. In addition, a cellular-WLAN interworking architecture and signaling procedures are proposed for implementing the proposed load-balancing schemes in an operator-deployed cellular-WLAN HetNet

Dual-sensor fusion for indoor user localisation

In this paper we address the automatic identification of in- door locations using a combination of WLAN and image sensing. Our motivation is the increasing prevalence of wear- able cameras, some of which can also capture WLAN data. We propose to use image-based and WLAN-based localisa- tion individually and then fuse the results to obtain better performance overall. We demonstrate the effectiveness of our fusion algorithm for localisation to within a 8.9m2 room on very challenging data both for WLAN and image-based algorithms. We envisage the potential usefulness of our ap- proach in a range of ambient assisted living applications

User tracking using a wearable camera

Abstract—This paper addresses automatic indoor user tracking based on fusion of WLAN and image sensing. Our motivation is the increasing prevalence of wearable cameras, some of which can also capture WLAN data. We propose a novel tracking method that can be employed when using image-based, WLAN-based and fusion-based approach only. The effectiveness of combining the strengths of these two complementary modalities is demonstrated for a very challenging data

Adaptive stochastic radio access selection scheme for cellular-WLAN heterogeneous communication systems

This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular-wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN-first and load balancing

Procedure for assessment of general public exposure from Wlan in offices and in wireless sensor network testbed

A fast and accurate measurement procedure to determine experimentally wireless local area network (WLAN) radiofrequency (RF) exposure and to test compliance with international guidelines for the general public is proposed. This is the first paper where all optimal settings for the measurement equipment (sweep time, resolution bandwidth, etc.) are investigated, selected, and validated. The exposure to WLAN access points is determined for 222 locations with 7 WLAN networks present in office environments. The WLAN exposure is also characterized for the first time in a wireless sensor lab environment (WiLab) at IBBT-Ghent University in Belgium. Average background exposure to WLAN (WiLab off) is 0.12 V m(-1), with a 95(th) percentile of 0.90 V m(-1). With the WiLab in operation, average exposure increases to 1.9 V m(-1), with a 95(th) percentile of 4.7 V m(-1). All values are well below the International Commission on Non Ionizing Radiation Protection guidelines of 61 V m(-1) in the 2.4 GHz band (at least 9.1 times for distances of more than 1 m from the access points) but a significant increase of exposure is possible in WiLabs due to high duty cycles. By applying the proposed measurement method a relevant reduction in measurement time is obtained. Health Phys. 98(4):628-638; 201

AROMA: Automatic Generation of Radio Maps for Localization Systems

WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure