Robust 3D indoor positioning system based on radio map using Bayesian network

Indoor positioning remains a serious issue due to the difficulty in attaining sufficient accuracy within an indoor environment using tracking technology of low complexity. Currently, most positioning systems do not embed the off-the-shelf (OTS) system which allows mobile devices to estimate location without using any additional hardware. In this paper, we propose a robust 3D indoor positioning system that is suitable for an indoor IoT application. This system based on Bayesian network that depends on Wi-Fi signals strength. It was experimentally tested in a building with pre-deployed access points (APs). The experimental results indicate that localization accuracy of the proposed system is high with the use of a small-sized radio map

Effects of different types of RSS data on the system accuracy of indoor localization system

Indoor localization system becomes a substantial issue in recent research, especially in terms of the accuracy. Location based services have been used in many mobile applications as well as wireless sensor networks. High accuracy and fast convergence are very important issues for a good localization system. However, the type of obtained received signal strength (RSS) data is very important in order to get high accuracy. In this paper, we introduce three types of RSS data, which are: measured RSS, simulated RSS and average combined RSS. Bayesian network based on fingerprinting technique is used to investigate the effect of the three different types of RSS. The results show the effect of the three different RSS data on the accuracy of estimated location. The measured RSS has achieved an average accuracy of 4.3 meters using 10 training points while the average combined RSS has achieved a good accuracy of 2.1 meters

Simulation and experimental analysis of indoor localization systems

Indoor localization systems, especially its accuracy, have become a major topic in recent research. Moreover, location-based services have been used in many mobile applications and wireless sensor networks. High accuracy and fast convergence are crucial requirements for a good localization system. However, the type of obtained received signal strength (RSS) data is vital to obtain high accuracy. In this paper, we perform a simulation and an experimental analysis for an indoor localization system. Moreover, 2D and 3D localization models are considered to investigate the localization error. The simulation is developed to provide an accurate RSS compared with an experimental testbed by considering the multiwall path loss model. Results demonstrate that the simulated RSS data are consistent with the experimental RSS data; that is, the simulated data almost have same distribution form as the experimental data. Therefore, the simulation can be used to develop high-accuracy localization systems

Off-the-shelf indoor localization system using radio frequency for wireless local area network

The indoor localization system becomes a substantial issue in recent research, especially in terms of the accuracy. Location based services have been used in many mobile applications as well as wireless sensor networks. High accuracy and fast convergence are very important issues for a good localization system. However, the type of obtained received signal strength (RSS) data is very important in order to get high accuracy. Recently, several of indoor localization techniques that are based on signals of wireless local area network (WLAN) become a substantial issue in recent research. In this research, a fingerprinting-based location algorithm is applied in indoor environments using WLAN. The location fingerprinting algorithm consists of two phases: offline phase and online phase. In the offline phase the reference points (RPs) are collected at certain places in the experimental testbed. The measurement campaign is conducted by using developed Wi-Fi scanner software. During the offline phase an extensive study is performed on the RSS properties for indoor environment such as duration effects, RSS stationary, RSS dependency and a user’s presence. In the online phase, the proposed model infers the unknown locations based on the RPs available in the radio map. The user location is inferred based on three dimensional (3-D) Bayesian graphical model using the OpenBUGS program. The inference of user location in the environment is investigated and compared to the actual location. Besides, the numbers of iterations are examined in order to show its effectiveness on the proposed model. It shows that the model is converged at a level of 100000 iterations. Thus, the best choice of number of iterations for the proposed model is 100000 since there is no improvement if the number of iterations increases. Finally, the proposed Bayesian graphical model based on fingerprinting location algorithm is compared with Madigan model. The proposed Bayesian graphical model and Madigan model achieved an average accuracy of 2.9 and 3.8 meters for 50 RPs, respectively. Besides, the proposed model is off-the-shelf which does not require any additional hardware to integrate to the proposed model. The proposed system is enhanced further by using offline clustering (OC) algorithm to reduce the data size of radio map and improve the system’s accuracy. In the first stage, the OC tries to reduce the number RPs in the radio map by grouping sets of RPs that are close to each other into one cluster. In the second stage, one or more cluster joins together based on the distance of signal space between adjacent clusters. The proposed OC algorithm slightly reduced the localization error to 2.4 meters, while it significantly reduced the data size of radio map by 68%

Indoor localization system using particle swarm optimization

Tracking the user location in indoor environment becomes substantial issue in recent research. Location based services have been used in many mobile applications as well as wireless sensor networks. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The suggested algorithm is called circular PSO (CPSO) which depends on the distribution of the particles at each access point. The simulation results show the effectiveness of the suggest algorithm on average location error

Handover Management for Next-Generation Wireless Networks: A Brief Overview

The importance of Heterogeneous Networks (HetNets) is increased after enabling the high band spectrum such as millimeter Wave (mmWave), in the development of the Fifth Generation (5G) wireless communication system. In a HetNet, small cells are suitable to operate by using mmWave due to its short-range, while macrocells are liable to use long-range radio waves. As a result, the performance of the next generations of wireless communication systems will enhance dramatically. However, the networks' architecture has become more complex and challenging to manage and optimize. Besides, the handover (HO) among small cells is another big challenge and needs to address it on a prior basis. Different techniques are proposed in the literature to improve the current network architectures. In this paper, we provide fundamental background concepts and notions used in the 5G wireless communication system. Besides, we study different HO techniques and management schemes that perform acceptably in the dynamic nature of next-generation wireless networks. Finally, the software-defined network and machine learning-based approaches are suggested as solutions for HO management in the 5G HetNet system

A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

With the rapid increase in the number of mobile users, wireless access technologies are evolving to provide mobile users with high data rates and support new applications that include both human and machine-type communications. Heterogeneous networks (HetNets), created by the joint installation of macro cells and a large number of densely deployed small cells, are considered an important solution to deal with the increasing network capacity demands and provide high coverage to wireless users in future fifth generation (5G) wireless networks. Due to the increasing complexity of network topology in 5G HetNets with the integration of many different base station types, in 5G architecture mobility management has many challenges. Intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent handover (HO), HO failure, HO delays, ping-pong HO and high energy consumption which will result in lower user experience and heavy signal loads. In this paper, we provide a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment (UE) to the network, the paging procedure that provides the location of the UE within the network, connected mode mobility management schemes, beam level mobility and beam management. Besides, this paper addresses the challenges and suggest possible solutions for the 5G mobility management

Dynamic Handover Control Parameters for LTE-A/5G Mobile Communications

Deploying ultra-dense small-cell base stations in the next-generation mobile networks is one of the most expected approaches to overcome the uncertainty of millimeter wave (mm- wave) communications. Mobility management is a critical issue that requires much concern to achieve seamless and highly reliable connection through user mobility. Handover in heterogeneous networks (HetNets) has gained attention, especially when 5G ultradense small cells coexist with the current 4G networks. Handover failure is one of the main issues in mobility, and it can be avoided by adjusting handover control parameters (HCPs): time-to-trigger (TTT) and handover margin (HOM). In this paper, we proposed dynamic HCPs in HetNets (LTE-A and mm-wave networks) with dense small cells. The proposed algorithm was compared with different settings of HCPs for different user mobile speed scenarios. The simulation results show that the proposed algorithm significantly reduces the probability of ping pong handovers and radio link failure, thus improving network performance

An Overview of Indoor Localization Technologies: Toward IoT Navigation Services

Localization system is a process of monitoring any area or object using any known information gathered from different sources such as wired or wireless networks. Internet of Things (IoT) technology plays a more significant role, which directly affects our lives. Navigation IoT based system is becoming a very appealing research topic nowadays since it is used in various context-aware and localizationaware applications that cover many fields such as tracking, healthcare, or security. Moreover, this field has taken so much attention lately since there is continuous progress in sensing technologies and computing capabilities. This paper provides a brief review of the indoor localization technologies used for IoT-based navigation systems. This review gives fundamental guidelines for designing an intelligent indoor localization system

Self-optimization of Handover Control Parameters for Mobility Management in 4G/5G Heterogeneous Networks

A large number of small cells in the next-generation mobile networks is expected to be deployed to satisfy 5G requirements. Mobility management is one of the important issues that require considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with the current 4G networks. An efficient handover (HO) mechanism is introduced to address this issue and improve mobility management by adjusting HO control parameters (HCPs), namely, time-to-trigger and HO margin. Dynamic HCPs (D-HCPs), which explores user experiences to adjust HCPs and make an HO decision in a self-optimizing manner, is proposed in this paper. D-HCPs classify HO failure (HOF) into three categories, namely, too late, too early and wrong cell HO, and simultaneously adjust HCPs according to the dominant HOF. The algorithm is evaluated using different performance metrics, such as HO ping-pong, radio link failure and interruption time, with different mobile speed scenarios. Simulation results show that the proposed D-HCPs algorithm adaptively optimizes the HCPs and outperforms other algorithms from the literature