425 research outputs found

    A review of RFID based solutions for indoor localization and location-based classification of tags

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    Wireless communication systems are very used for indoor localization of items. In particular, two main application field can be identified. The former relates to detection or localization of static items. The latter relates to real-time tracking of moving objects, whose movements can be reconstructed over identified timespans. Among the adopted technologies, Radio-Frequency IDentification (RFID), especially if based on cheap passive RFID tags, stands out for its affordability and reasonable efficiency. This aspect makes RFID suitable for both the above-mentioned applications, especially when a large number of objects need to be tagged. The reason lies in a suitable trade-off between low cost for implementing the position sensing system, and its precision and accuracy. However, RFID-based solutions suffer for limited reading range and lower accuracy. Solutions have been proposed by academia and industry. However, a structured analysis of developed solutions, useful for further implementations, is missing. The purpose of this paper is to highlight and review the recently proposed solutions for indoor localization making use of RFID passive tags. The paper focuses on both precise and qualitative location of objects. The form relates to (i) the correct position of tags, namely mapping their right position in a 2D or 3D environment. The latter relates to the classification of tags, namely the identification of the area where the tag is regardless its specific position

    Robotic Wireless Sensor Networks

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    In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

    Communication Subsystems for Emerging Wireless Technologies

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    The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

    Real-time performance-focused on localisation techniques for autonomous vehicle: a review

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    Array signal processing for source localization and enhancement

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    “A common approach to the wide-band microphone array problem is to assume a certain array geometry and then design optimal weights (often in subbands) to meet a set of desired criteria. In addition to weights, we consider the geometry of the microphone arrangement to be part of the optimization problem. Our approach is to use particle swarm optimization (PSO) to search for the optimal geometry while using an optimal weight design to design the weights for each particle’s geometry. The resulting directivity indices (DI’s) and white noise SNR gains (WNG’s) form the basis of the PSO’s fitness function. Another important consideration in the optimal weight design are several regularization parameters. By including those parameters in the particles, we optimize their values as well in the operation of the PSO. The proposed method allows the user great flexibility in specifying desired DI’s and WNG’s over frequency by virtue of the PSO fitness function. Although the above method discusses beam and nulls steering for fixed locations, in real time scenarios, it requires us to estimate the source positions to steer the beam position adaptively. We also investigate source localization of sound and RF sources using machine learning techniques. As for the RF source localization, we consider radio frequency identification (RFID) antenna tags. Using a planar RFID antenna array with beam steering capability and using received signal strength indicator (RSSI) value captured for each beam position, the position of each RFID antenna tag is estimated. The proposed approach is also shown to perform well under various challenging scenarios”--Abstract, page iv

    A Tutorial on Clique Problems in Communications and Signal Processing

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    Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and kk-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

    Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion

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    Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm

    Cluster Control of a Multi-Robot Tracking Network and Tracking Geometry Optimization

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    The position of a moving object can be tracked in numerous ways, the simplest of which is to use a single static sensor. However, the information from a single sensor cannot be verified and may not be reliable without performing multiple measurements of the same object. When multiple static sensors are used, each sensor need only take a single measurement which can be combined with other sensor measurements to produce a more accurate position estimate. Work has been done to develop sensors that move with the tracked object, such as relative positioning, but this research takes this concept one step further; this dissertation presents a novel, highly capable strategy for utilizing a multi-robot network to track a moving target. The method optimizes the configuration of mobile tracking stations in order to produce the position estimate for a target object that yields the smallest estimation error, even when the sensor performance varies. The simulations and experiments presented here verify that the optimization process works in the real world, even under changing conditions and noisy sensor data. This demonstrates a simple, robust system that can accurately follow a moving object, as illustrated by results from both simulations and physical experiments. Further, the optimization led to a 6% improvement in the target location estimate over the non-optimized worst-case scenario tested with identical sensors at the nominal fixed radius distance of 2.83 m and even more significant improvements of over 90% at larger radial distances. This method can be applied to a wider variety of conditions than current methods since it does not require a Kalman filter and is able to find an optimal solution for the fixed radius case. To make this optimization method even more useful, it is proposed to extend the mathematical framework to n robots and extend the mathematical framework to three dimensions. It is also proposed to combine the effect of position uncertainty in the tracking system with position uncertainty of the tracking stations themselves in the analysis in order to better account for real-world conditions. Additionally, testing should be extended to different platforms with different sensors to further explore the applicability of this optimization method. Finally, it is proposed to modify the optimization method to compensate for the dynamics of the system so that sensor systems could move into an intercept course that would result in the optimal configuration about the tracked object at the desired time step. These proposals would result in a more applicable and robust system than is currently available

    AN INDOOR BLUETOOTH-CENTRIC PROXIMITY BASED POSITIONING SYSTEM

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    In recent years, positioning and navigation become an important topic in research. The most popular positioning system is an outdoor positioning called Global Positioning System (GPS). However, due to the influence of weak signal strength, weather conditions, diverse geographical and living environments, GPS sometimes cannot support indoor positioning and, if it can, the 5-10 meters error range does not meet the indoor positioning requirement. In order to provide a better solution with higher accuracy for indoor localization, we can benefit from the proliferation of indoor communication devices. Different technologies such as WiFi, Radio Frequency Identification (RFID) and Ultra-wideband (UWB) have been commonly used in indoor positioning systems. However, WiFi has a high energy consumption for indoor localization, as it consumes 3 to 10 watts per hour in the case of using 3 routers to do the job. In addition, due to its dependency on reference tags, the overall cost of the RFID-based approaches may usually cost more than $300 which is economically prohibitive. In terms of UWB, its low area coverage brings great challenges to popularizing its acceptance as a device for indoor positioning. The Bluetooth Low Energy (BLE) based iBeacon solution primarily focuses on the proximity based detection, and its low power consumption and low price bring great potential for its popularity. In this report, assuming that the resident owns a smartphone which is powered on, we present an iBeacon based indoor positioning system and provide some strategies and algorithms to overcome the indoor noise of possibly weak indoor Bluetooth signals. In our system, the Received Signal Strength Index (RSSI) is pre-processed to eliminate noise. Then, the distance between a mobile device and a BLE signal source can be calculated by combination use of pre-processed RSSI, Kalman Filter, and machine learning. In the end, the current mobile device position can be determined by using a triangulation algorithm. Our experimental results, acquired through running experiments in a real-world scenario, show that the localization error can be as low as 0.985m in the 2D environment. We also compared our results against other works with the same research objectives

    Models for Efficient Automated Site Data Acquisition

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    Accurate and timely data acquisition for tracking and progress reporting is essential for efficient management and successful project delivery. Considerable research work has been conducted to develop methods utilizing automated site data acquisition for tracking and progress reporting. However, these developments are challenged by: the dynamic and noisy nature of construction jobsites; the indoor localization accuracy; and the data processing and extraction of actionable information. Limited research work attempted to study and develop customized design of wireless sensor networks to meet the above challenges and overcome limitations of utilizing off-the-shelf technologies. The objective of this research is to study, design, configure and develop fully customized automated site data acquisition models, with a special focus on near real-time automated tracking and control of construction operations embracing cutting edge innovations in wireless and remote sensing technologies. In this context, wireless and remote sensing technologies are integrated in two customized prototypes to monitor and collect data from construction jobsites. This data is then processed and mined to generate meaningful and actionable information. The developed prototypes are expected to have wider scope of applications in construction management, such as improving construction safety, monitoring the condition of civil infrastructure and reducing energy consumption in buildings. Two families of prototypes were developed in this research; Sensor Aided GPS (SA-GPS) prototype, which is designed and developed for tracking outdoor construction operations such as earthmoving; and Self-Calibrated Wireless Sensor Network (SC-WSN), which is designed for indoor localization and tracking of construction resources (labor, materials and equipment). These prototypes along with their hardware and software are encapsulated in a computational framework. The framework houses a set of algorithms coded in C# to enable efficient data processing and fusion that support tracking and progress reporting. Both the hardware prototypes and software algorithms were progressively tested, evaluated and re-designed using Rapid Prototyping approach. The validation process of the developed prototypes encompasses three steps; (1) simulation to validate the prototypes’ design virtually using MATLAB, (2) laboratory experiments to evaluate prototypes’ functionality in real time, and (3) testing on scaled case studies after fine-tuning the prototype design based on the results obtained from the first two steps. The SA-GPS prototype consists of a microcontroller equipped with GPS module as well as a number of sensors such as accelerometer, barometric pressure sensor, Bluetooth proximity and strain gauges. The results of testing the developed SA-GPS prototype on scaled construction jobsite indicated that it was capable of estimating project progress within 3% mean absolute percentage error and 1% standard deviation on 16 trials, in comparison to the standalone GPS which had approximately 12% mean absolute percentage error and 2% standard deviation. The SC-WSN prototype incorporates two main features. The first is the use of the Kalman filtering and smoothing for the RSSI signal to provide more stable and predictable signal for estimating the distance between a reader and a tag. The second is the use of a developed dynamic path-loss model which continually optimizes its parameters to cope with the dynamically changing construction environment using Particle Swarm Optimization (PSO) algorithm. The laboratory testing indicated the improvement in location estimation, where the produced location estimates using SC_WSN had an average error of 0.66m in comparison to 1.67m using the raw RSSI signal. Also the results indicated 60% accuracy improvement in estimating locations using the developed dynamic model. The developed prototypes are not only expected to reduce the risk of project cost and duration overruns by timely and early detection of deviations from project plan, but also enables project managers to observe and oversee their project’s status in near real-time. It is expected that the accuracy of the developed hardware, can be achieved on large-scale real construction projects. This is attributed to the fact that the developed prototype does not require any scalable improvements on its hardware technology, nor does it require any additional computational changes to its developed algorithms and software
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