Optimization of an RFID location identification scheme based on the neural network

An indoor localization technology is increasingly critical as location‐aware applications evolve. Researchers have proposed several indoor localization technologies. Because most of the proposed indoor localization technologies simply involve using the received signal strength indicator value of radio‐frequency identification (RFID) for indoor localization, radio‐frequency interference, and environmental factors often limit the accuracy of localization results. Therefore, this study proposes an accurate RFID localization based on the neural network (ARL‐N²), a passive RFID indoor localization scheme for identifying tag positions in a room, combining a location identification based on dynamic active RFID calibration algorithm with a backpropagation neural network (BPN). The proposed scheme composed of two phases: in the training phase, an appropriate BPN architecture is constructed using the training data derived from the coordinates of reference tags and the coordinates obtained using the localization algorithm. By contrast, the online phase involves calculating the tracking tag coordinates and using these values as BPN inputs, thereby enhancing the estimated location. A performance evaluation of the ARL‐N² schemes confirms its high localization accuracy. The proposed method can be used to locate critical objects in difficult‐to‐find areas by creating minimal errors and applying and economical technique. Copyright © 2013 John Wiley & Sons, Ltd.     

LANDMARC: Indoor Location Sensing Using Active RFID

Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of context-aware applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.     

基于射频识别的室内定位技术综述

用于室内定位的技术主要包括红外传播技术、802.11 WLAN技术、无线传感器网络(WSNs)、超声波定位。而射频识别(RFID)技术依赖其合理的系统造价、良好的定位精确度、较强的抗干扰能力、无接触通信以及RFID射频标签的部署、携带便捷等诸多优点,正逐渐应用于室内追踪定位中。本文从用于室内定位的RFID标签的角度出发,将当前主要的RFID室内定位技术分为无源、有源和半无源RFID定位3类,并对每一类中涉及到的主要定位算法的当前发展状况及其优缺点做了详细分析。     

Context‐Aware Active Services in Ubiquitous Computing Environments

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context‐aware active services based on context‐aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context‐aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context‐aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.     

基于牛顿插值的RFID室内定位算法

随着近年无线网络的发展,出现了多种与室内定位相关的技术与应用。文中提出了一种基于牛顿插值的室内定位算法。该算法主要是基于射频识别技术(RFID)并结合Larndmarc系统利用插值求解虚拟的标签,从而对待测标签得到更精确地定位。实验证明,与k邻域算法相比,该算法在定位精度上得到了显著提高。     

Quality of service‐aware approaches in fog computing

In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.     

Multi‐system‐multi‐operator localization in PLMN using neural networks

Providing the localization algorithm for context‐aware services is the focus of many studies. This paper explores the properties of positioning models based on received signal strength (RSS) in PLMN (Public Land Mobile Network) networks. The effects of using the RSS at a mobile terminal from various systems, such as GSM and UMTS, as well as from multiple operators, have been investigated and discussed. Twenty‐two models, based on artificial neural networks, have been developed and verified using the data from an immense measurement campaign. The obtained results show that augmenting the model with additional RSS data, even from systems with poor radio‐visibility, may improve the positioning accuracy to as much as a 35thinspacem median distance error in a light urban environment. The degradation of accuracy in indoor environments and the complexity and latency of the models were also scrutinized. Copyright © 2011 John Wiley & Sons, Ltd.     

Integrating RFID and WLAN for indoor positioning and IP movement detection

Location awareness in an indoor environment and wireless access to Internet applications are major research areas towards the overwhelming success of wireless and mobile communications. However, the unpredictable indoor radio propagation and handover latency due to node mobility are the main challenging issues that need to be addressed. For tackling efficiently both problems of indoor localization and handover management, we propose combining key benefits of two outstanding wireless technologies, i.e. radio frequency identification (RFID) and a wireless local area network (WLAN) infrastructure. WLANs, such as IEEE 802.11 (WiFi), are now very common in many indoor environments for providing wireless communication among WiFi-enabled devices by accessing an Access Point (infrastructure mode) or through peer to peer connections (ad hoc mode). However, the small cell size of the Access Points (APs) in a WiFi-based network drives the need for frequent handovers leading to increased latency. RFID is an emerging technology consisting of two basic components, a tag and a reader, and its main purpose is the automatic identification of tagged objects by a reader. However, in the presence of multiple readers, RFID suffers from the so-called reader collision problem, mainly due to the inability for direct communication among them. In this paper, we propose a hybrid RFID and WLAN system; the RFID technology is employed for collecting information that is used for both localization and handover management within the WLAN, whereas the WLAN itself is utilized for controlling and coordinating the RFID reading process. In our system architecture, tag IDs of a RFID tag deployment are correlated with both location and topology information in order to determine the position and predict the next subnetwork of a Mobile Node (MN) with a reader attached to its mobile device. The role of the WLAN is to coordinate the readers when accessing the RFID channel for retrieving tags?? IDs, hence compensating the persisting RFID collision problem among multiple readers. Numerical results based on extensive simulations validate the efficiency of the proposed hybrid system in providing accurate and time efficient localization and reducing the IP handover latency.     

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement.     

Accurate indoor localization based on crowd sensing

Indoor localization is an important primitive that can enable many ubiquitous computing applications. This paper improves the scheme of landmark and inertial navigation through crowd sensing. The location of landmark can calibrate the position of users, and accurate localization can optimize the landmark's location in turn. In this work, we define landmarks as certain characteristic structures with iBeacons. To tackle the challenge of low efficiency of a landmark, we combine Bluetooth signals and sensor readings to reflect the distinctive signature of a landmark and then design a method for a reliable detection of a landmark. Moreover, we investigate the calibration bias of a landmark to ensure the calibration accuracy. For improving the accuracy of inertial navigation, we personalize the step length model and correct the user's heading with the aid of crowd sensing. We have built an indoor localization system integrating the above modules and an indoor floor map, which can be further improved with more users using our system. We demonstrate for the first time a meter‐level indoor localization system that is self‐improving, user adaptive, and easy to deploy. Extensive experiments on users with mobile devices, with over 37 subjects walking over an aggregate distance of over 30 km, were carried out. Evaluation results show that our system can achieve a mean accuracy of 2 m initially and 1 m with the calibration of landmarks in a 39 m  ×  21 m testing area. Copyright © 2016 John Wiley & Sons, Ltd.     

基于RFID标签的定位原理和技术

识别和定位是用于室内服务的关键信息,常见方法是估计RFID标签的位置。而在室内环境下,信号遭受严重的损耗,标签的性能也会受到一定的限制。针对这些问题,文中对现存的RFID定位技术和定位原理加以总结,在对RFID定位技术进行阐释和分类分析的基础上,讨论了在此领域的未来发展趋势。     

Multilayer context cloud framework for mobile Web 2.0: a proposed infrastructure

Existing context‐aware systems focus only on characterizing the situation of an entity to exhibit the advantage of contextual information association, but they have no mechanism to facilitate the interoperation and reuse of contextual information. Cloud computing offers an adaptable and flexible solution for existing context‐aware applications, integrating Mobile Web 2.0 technologies. This work presents a multilayer context cloud framework (MCCF) that integrates Web 2.0 technologies into a mobile context‐aware system for use in a cloud computing environment. The proposed MCCF includes a context sensor layer, a context information layer, a context service layer, a context representation layer, a cloud computing layer, and a mobile Web 2.0 context‐aware Software as a Service layer. To demonstrate the feasibility of this approach, a Mobile Web 2.0‐based context‐aware Software as a Service platform, which is a cloud computing application based on MCCF, is implemented to provide continuous and context‐aware monitoring of a specific application. Copyright © 2011 John Wiley & Sons, Ltd.     

Pseudo‐3D RSSI‐based WSN localization algorithm using linear regression

Receiver Strength Signal Indication based Wireless Sensor Networks offer a cheap solution for location‐aware applications. For a final breakthrough these systems need fast deployment and easy auto‐configuration. In this study, we use the real‐life iMinds test bed to expand a two‐dimensional localization algorithm to the pseudo third dimension with very low additional computational time. Our experiments show that this fast three‐dimensional algorithm has no outliers and avoids manual calibration. Our algorithm has lower position errors than a maximum likelihood algorithm with a mean square error cost function. Furthermore, with non‐parametric statistical tests, we show that our previously designed two‐dimensional preprocessing performs equally well in pseudo‐three dimensions: the preprocessing reduces the position error in a statistically significant way.Copyright © 2013 John Wiley & Sons, Ltd.     

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.     

基于CC2431的无线定位系统

在固定环境下对移动目标定位可使用的技术主要有超声波定位技术、射频识别技术以及基于接收信号强度（RSSI）的定位技术。经过比较,基于接收信号强度（RSSI）的定位技术更适合于复杂的环境。文中介绍了基于RSSI定位机制的zigbee芯片／CC2431片内集成定位引擎在定位系统中的应用,并在室内环境下进行了实测,其定位效果良好。     

基于信号分布混合假设检验的Wi-Fi室内定位方法

Wi-Fi室内定位技术是目前移动计算领域的研究热点之一,而传统位置指纹定位方法没有考虑复杂室内环境下Wi-Fi信号分布的多样性问题,从而导致Wi-Fi室内定位系统的鲁棒性较差。为了解决这一问题,该文提出一种基于信号分布混合假设检验的Wi-Fi室内定位方法。首先根据Jarque-Bera(JB)检验结果对各个参考点处的Wi-Fi信号分布进行正态性评价;然后针对不同Wi-Fi信号分布特性,利用混合Mann-Whitney U检验/T检验方法构造匹配参考点集合,以实现对目标的区域定位;最后通过计算定位区域中匹配参考点的K近邻(K-Nearest Neighbor, KNN),完成对目标的位置坐标估计。实验结果表明,所提方法相比于传统Wi-Fi室内定位方法具有更高的定位精度和更强的系统鲁棒性。     

Localization in terrestrial and underwater sensor‐based m2m communication networks: architecture,classification and challenges

Localizing machine‐type communication (MTC) devices or sensors is becoming important because of the increasing popularity of machine‐to‐machine (M2M) communication networks for location‐based applications. These include such as health monitoring, rescue operations, vehicle tracking, and wildfire monitoring. Moreover, efficient localization approaches for sensor‐based MTC devices reduce the localization error and energy consumption of MTC devices. Because sensors are used as an integral part of M2M communication networks and have achieved popularity in underwater applications, research is being conducted on sensor localization in both underwater and terrestrial M2M networks. Major challenges in designing underwater localization techniques are the lack of good radio signal propagation in underwater, sensor mobility management, and ensuring network coverage in 3D underwater M2M networks. Similarly, predicting the mobility pattern of MTC devices, trading‐off energy consumption and location accuracy pose great design challenges for terrestrial localization techniques. This article presents a comprehensive survey on the current state‐of‐the‐art research on both terrestrial and underwater localization approaches for sensor‐based MTC devices. It also classifies localization approaches based on several factors, identifies their limitations with potential solutions, and compares them. Copyright © 2015 John Wiley & Sons, Ltd.     

Performance Analysis of Tags Collection Time on A New Architecture of 2.4 GHz Embedded Active RFID System for Indoor and Outdoor Tracking and Monitoring Applications Based on IEEE 802.15.4 Standard

This paper focuses on the analysis of tags collection time of 2.4 GHz embedded active Radio Frequency Identification (RFID) system for indoor and outdoor real-time tracking and monitoring applications based on IEEE 802.15.4 standard. The main novelty of the system is the implementation of the communication method in order to provide Machine to Machine (M2M) communication and automated switching mechanism between indoor and outdoor location by utilizing active RFID, Wireless Sensor Network (WSN), Global Positioning System (GPS) and mobile communication on a single platform. In this work, GPS receiver covers outdoor location tracking, while active RFID provides identification and Receive Signal Strength Indicator (RSSI) reading for each tag holder to cover indoor location tracking especially near or inside building where location information is not detected by GPS. Several experiments were conducted on three different RFID tags which were active RFID tag embedded with GPS and GSM (ERFIDG2), active RFID tag embedded with GPS (ERFIDG) and standalone RFID tag communicating with the same active RFID reader. The experiment was done to evaluate the communication performance of the active RFID in terms of tags collection time using Transparent (AT) and Application Programming Interface (API) mode. The experiment was extended to measure tags collection time in single hop and multi hops communication for Tag Talk First (TTF) and Reader Talk First (RTF) protocols. The results show that the proposed active RFID system (ERFIDG2) is better than the standalone and ERFIDG systems. The in-depth research done in this work is to study the experience and identify the challenges that will be faced in the development and implementation of a wireless RFID-based system for tracking and monitoring applications.     

Performance of a MANET directional MAC protocol with angle‐of‐arrival estimation

The use of directional antennas in mobile ad hoc networks (MANETs) has shown to offer large throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium‐access‐control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS). Although GPS systems are designed to be as nearly accurate as possible, there are still estimation errors that can cause a relatively large deviation from the actual GPS receiver position. In this paper, we investigate the effect of inaccurate node position estimation on the throughput of these protocols. Our results clearly indicate that the advantages of DMAC protocols diminish if the available position information is not accurate enough. As an alternative, we propose an efficient DMAC protocol that utilizes signal parameter estimation via the rotational invariance technique (ESPRIT) for direction‐of‐arrival (DOA) estimation; alleviating the need for GPS and, hence, avoiding the degrading associated with typical GPS position estimation errors. Moreover, unlike GPS‐based protocols, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11. Copyright © 2007 John Wiley & Sons, Ltd.     

Task scheduling mechanisms in cloud computing: A systematic review

Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.