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ZigBee-based intelligent indoor positioning system soft computing

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Abstract

Nowadays positioning system is no longer only for military purpose, while it has been widely applied to various livelihood purposes such as biological information, emergency rescue, public facilities and individual safety. While the most frequently used to identify the coordinates of users is global positioning system (GPS), however, it tends to be interfered by indoor buildings such that it cannot be effectively used in indoor environment. Recently, wireless sensor network has become a trendy research topic, the positioning service of indoor positioning system can be achieved by the measurements of received signal strength (RSS) or link quality indicator (LQI). In this paper, the average RSS is first adopted for reducing the noise interference of LQI, and then the object to be detected will be trained by radial basis function network (RBFN) with the capability of identifying the environment of location. ZigBee module will then be integrated to realize a set of convenient wireless indoor positioning system with low cost. In addition, multiple similar artificial neural networks within the same region will be adopted to further improve the positioning accuracy. Experiments shown that this study is capable of effective enhancement of existing IPS accuracy with the average error of indoor positioning at 2.8 meters 100 % comparing with other approaches.

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... The authors of [42] have proposed the use of ANN to identify or calculate a storm's intensity. Also, ANN, in integration with IoT technology, can be used to detect the user's location [43]. In this way, the location of an infected user can be identified, and social distancing measures can be followed in a better way. ...
... ANN is combined with a robot-based model to increase location accuracy in [43]. With this integration, the model becomes more scalable, cost-effective, and user-friendly, and it showed an error of only 2.7m. ...
... Also, we expect that many existing models that use different technologies can be combined with ML algorithms to increase their accuracy. Some examples of such models are [43], [23]. ...
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... Zigbeeenabled devices can communicate with each other at the range of up to 65 feet ( 20 meters) with an unlimited number of hops. Compared with Wi-Fi and Bluetooth technologies, Zigbee is designed to be cheaper and simpler, making it possible for low-cost and low-power communications for smart devices [84], [85]. Moreover, Zigbee can operate at several frequencies, such as 2.4 GHz, 868 MHz, and 915 MHz. ...
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... Various methods for indoor localization have been proposed for different applications. 9 These methods use different kinds of physical signals, such as radio-frequency waves, [10][11][12] light, [13][14][15] magnetic fields, 16,17 and acoustic waves. [18][19][20][21][22][23][24][25][26] Among these methods, acoustic waves have advantages because they are less affected by fading than radio-frequency waves or light, and the operating range can be larger than that for low-frequency magnetic fields. ...
... This is attributed to the fact that the estimation accuracy for n x and n y depends on the accuracy of the dipole approximations in Eqs. (8)- (11). In this simulation, the approximation accuracy is higher for P G y ð r; x 2 Þ than for P G x ð r; x 1 Þ because k 1 d > k 2 d. ...
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... Commercially available UWAC position estimation techniques are Long Baseline (LBL) and Short Baseline (SBL) [25][26][27][28][29][30]. Recently, position estimation techniques based on machine learning were proposed in [31][32][33][34][35][36][37][38]. Table 1 summarizes the position estimation techniques. ...
... Range based techniques [19][20][21][22] Range free based techniques [23,24] Commercial position estimation techniques [25][26][27][28][29][30] Machine learning based techniques [31][32][33][34][35][36][37][38] Similarly as in terrestrial cellular systems, an initial access procedure is required for an underwater equipment or sensor node (UE/SN) in UWAC systems to establish a communication link with an underwater base station (UWBS). To establish a communication link, the UE should perform downlink synchronisation and cell search by receiving synchronisation and broadcasting signals. ...
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... In response to that, digital contact tracing, a more autonomous method, was deployed during the early stages of the COVID-19 pandemic [11]. Many countries, such as Singapore [12], Australia [13], China [14], and India [15], adopted a range of new contact tracing technologies [10], such as Wi-Fi [16], Bluetooth [17], cell tower triangulation [18], Global Positioning System (GPS) [19], QR codes [20], Zigbee [21], RFID [9], along with IoT [22]. have pointed out for the impracticability for hospital and offices staff to continuously carry their mobile devices and, more importantly, these devices could be potential carriers for infection within indoor environments [43,44]. ...
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... ZigBee is the technology based on the Institute of Electrical and Electronics Engineers (IEEE) Standards Association's 802.15 specifications and is known for low power consumption and secure networking because the information travelling through the system is secured with encryption. It was designed to remedy the shortcomings of Bluetooth and Wi-Fi [27][28][29]. ...
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... ZigBee implements this protocol and has gained interest because of its low-power, low-range, and low-data transmission features. In (Luoh, 2013), a ZigBee-based indoor localization system was proposed with the radial basis function network (RBFN) to determine the location with the fingerprinting method. In (Urad, 2017), the nearest neighbor and Bayesian were adopted, which promised less than or equal to the 0.81m accuracy. ...
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Nowadays, indoor localization systems using IEEE 802.11 have been actively explored for location-based services, since GPS cannot identify floors or rooms in buildings. However, the user-side device is usually large and consumes high energy. In this paper, the authors propose a fingerprint-based indoor localization system using IEEE 802.15.4 that allows the use of a small device with a long-life battery, named FILS15.4. A user carries a small transmitter whose signal is received by multiple receivers simultaneously. The received signal strengths are compared with the fingerprints to find the current location. To address signal fluctuations caused by the low-power narrow-band signal, FILS15.4 limits one room as the localization unit, prepares plural fingerprints for each room, and allocates a sufficient number of receivers in the field. For evaluations, extensive experiments were conducted at #2 Engineering Building in Okayama University and confirmed high detection accuracy with sufficient numbers of receivers and fingerprints.
... In [18], Luoh et al. proposed a ZigBee-based indoor localization system using the radial basis function network (RBFN) with the fingerprinting method. They conducted measurements in real environments where human effects were not evaluated in experiments. ...
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... This device consists of a hub internally, which can be used to identify the user's location. As a result, this technology can be used to maintain effective social distance guidelines in crowdpulling areas [44]. ...
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... ANN is utilized to detect or compute the storm intensity [40] and it is involved in detecting the user's position when combined with IoT technique [41]. This allows for the improved identification of an infected user's area and the implementation of social distancing techniques. ...
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... The output of one layer becomes the input for the next layer. ANN along with IoT was used to detect the location of the user (Luoh, 2013). Here the data was limited, yet there was a high accuracy. ...
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... Furthermore, other positioning systems are realized using standard existing hardware, such as ZigBee [36]- [39] or Bluetooth [40], [41] devices. They require low power consumption and show low complexity and costs. ...
Preprint
div> This paper has the main purpose to give an introductory survey on the various systems that exploit magnetic fields for positioning. Such systems find applications in those scenarios, both indoors and outdoors, where Global Navigation Satellite Systems (GNSS) are not available or fail to provide information with the needed accuracy. While the main idea of using electromagnetic fields to provide position information dates back to the past century, new application–led research on this topic has emerged in recent years. Results have expanded the application range of Magnetic Positioning (MP) technologies and form now a domain of knowledge that enables realization of positioning systems applicable to indoor and outdoor environments. The paper provides the main characteristics of different positioning systems with focus on those solutions that are based on low–frequency magnetic fields. Some background theory is presented and positioning results from the literature are analyzed and compared. </div
... Zigbee is also a potential technology that can be used in maintaining social distancing. Zigbee is a standard-based wireless communication technology used for low-cost and low-power wireless networks [21] . Zigbee-based devices can communicate with each other in the range of about 65 feet (20 m) and can take unlimited hops. ...
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The coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems in many countries. The spread of COVID-19 pandemic has negatively impacted the human mobility patterns such as daily transportation-related behavior of the public. There is a requirement to understand the disease spread patterns and its routes among neighboring individuals for the timely implementation of corrective measures at the required placement. To increase the effectiveness of contact tracing, countries across the globe are leveraging advancements in mobile technology and Internet of Things (IoT) to aid traditional manual contact tracing to track individuals who have come in close contact with identified COVID-19 patients. Even as the first administration of vaccines begins in 2021, the COVID-19 management strategy will continue to be multi-pronged for the foreseeable future with digital contact tracing being a vital component of the response along with the use of preventive measures such as social distancing and the use of face masks. After some months of deployment of digital contact tracing technology, deeper insights into the merits of various approaches and the usability, privacy, and ethical trade-offs involved are emerging. In this paper, we provide a comprehensive analysis of digital contact tracing solutions in terms of their methodologies and technologies in the light of the new data emerging about international experiences of deployments of digital contact tracing technology. We also provide a discussion on open challenges such as scalability, privacy, adaptability and highlight promising directions for future work. https://doi.org/10.1016/j.treng.2021.100072
... Zigbee is also a potential technology that can be used in maintaining social distancing. Zigbee is a standard-based wireless communication technology used for low-cost and low-power wireless networks [21]. Zigbee-based devices can communicate with each other in the range of about 65 feet (20 meters) and can take unlimited hops. ...
Preprint
Full-text available
The coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems in many countries. The spread of COVID-19 pandemic has negatively impacted the human mobility patterns such as daily transportation-related behavior of the public. There is a requirement to understand the disease spread patterns and its routes among neighboring individuals for the timely implementation of corrective measures at the required placement. To increase the effectiveness of contact tracing, countries across the globe are leveraging advancements in mobile technology and Internet of Things (IoT) to aid traditional manual contact tracing to track individuals who have come in close contact with identified COVID-19 patients. Even as the first administration of vaccines begins in 2021, the COVID-19 management strategy will continue to be multi-pronged for the foreseeable future with digital contact tracing being a vital component of the response along with the use of preventive measures such as social distancing and the use of face masks. After some months of deployment of digital contact tracing technology, deeper insights into the merits of various approaches and the usability, privacy, and ethical trade-offs involved are emerging. In this paper, we provide a comprehensive analysis of digital contact tracing solutions in terms of their methodologies and technologies in the light of the new data emerging about international experiences of deployments of digital contact tracing technology. We also provide a discussion on open challenges such as scalability, privacy, adaptability and highlight promising directions for future work.
... Here, the authors combine Bluetooth beacons with proposed algorithms to improve the accuracy but they are not able to reach a precision of less than a few meters. Several approaches are built based on ZigBee [11] [12] [13] or WLAN [14] [15]. These approaches offer relatively cheap solutions with a good range as well as a one to ten meters accuracy. ...
Preprint
Although indoor localization has been a wide researched topic, obtained results may not fit the requirements that some domains need. Most approaches are not able to precisely localize a fast moving object even with a complex installation, which makes their implementation in the automated driving domain complicated. In this publication, common technologies were analyzed and a commercial product, called Marvelmind Indoor GPS, was chosen for our use case in which both ultrasound and radio frequency communications are used. The evaluation is given in a first moment on small indoor scenarios with static and moving objects. Further tests were done on wider areas, where the system is integrated within our Robotics Operating System (ROS)-based self-developed 'Smart PhysIcal Demonstration and evaluation Robot (SPIDER)' and the results of these outdoor tests are compared with the obtained localization by the installed GPS on the robot. Finally, the next steps to improve the results in further developments are discussed.
... The ANN are becoming more and more important and are used to predict the location of the unknown sensor nodes because they have faster convergence speed and demand lower computational cost [22]. Luoh [23] trained the feedforward neural network according to the measurement results of the link quality indicators from three ZigBee sensor nodes, and then positioned the indoor robot. Gogolak et al. [24] predicted the position of mobile nodes in the indoor environment by using the feedforward neural network model of the LM training algorithm. ...
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Positioning accuracy is one of the main challenges in wireless sensor networks. Both the existing localization algorithms and the ranging accuracy need to be improved. This paper demonstrates that the PSO-BP method can effectively estimate the coordinates of mobile nodes in the indoor three-dimensional space. Convergence speed and estimation accuracy of the BP neural network model optimized for the PSO are also discussed. A hybrid GA-PSO-BP algorithm is then proposed. It improves the BP neural network’s prediction performance by optimizing parameters such as the number of neurons for each hidden layer, the learning rate and the error target of the BP network. The value of the coordinates of the specified beacon node and the RSSI value are the inputs of the BP neural network. The method for estimating the floor which the mobile node is on is used to further improve the node coordinate prediction accuracy. Compared with the PSO-BP, the simulation results show that the convergence speed of the hybrid GA-PSO-BP model is faster. It can effectively improve the node coordinate estimation accuracy of the mobile sensor networks. The average coordinate estimation error of the hybrid GA-PSO-BP algorithm proposed in this paper is about 0.215 m, which is 49.2.
... Localization is one of the core technologies for indoor surveying and mapping services [1,2]. In order to achieve accurate indoor navigation and positioning, many navigation and positioning technologies have been rapidly developed based on ultra wide band (UWB) [3,4], radio frequency (RF) [5,6], Wi-Fi [7,8], Bluetooth [9,10], vision [11,12], ZigBee [13,14], and multi-sensor combinations [15,16]. Vision sensors are a streaming media technology that can achieve rich texture information and transmit information in real time, which can be used to localize and collect environmental information, and have been used in the Changʹe III inspections of China and the spirit and opportunity rovers in America [17,18]). ...
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With the extensive development and utilization of urban underground space, coal mines, and other indoor areas, the indoor positioning technology of these areas has become a hot research topic. This paper proposes a robust localization method for indoor mobile platforms. Firstly, a series of coding graphics were designed for localizing the platform, and the spatial coordinates of these coding graphics were calculated by using a new method proposed in this paper. Secondly, two spatial resection models were constructed based on unit weight and Tukey weight to localize the platform in indoor environments. Lastly, the experimental results show that both models can calculate the position of the platform with good accuracy. The space resection model based on Tukey weight correctly identified the residuals of the observations for calculating the weights to obtain robust positioning results and has a high positioning accuracy. The navigation and positioning method proposed in this study has a high localization accuracy and can be potentially used in localizing practical indoor space mobile platforms.
... Among these technologies, GPS has been widely used for outdoor environments; however, the lack of receiving powerful GPS signal makes it difficult to estimate the position of a mobile user in indoor environments [2]. On the other hand, Wi-Fi network is available nearly in every building or public areas and together with ultra wideband (UWB) and ZigBee, it is among three main systems that are commonly used for indoor positioning [3][4][5]. Compared with UWB, Wi-Fi network is available in almost everywhere and compared to Zigbee, Wi-Fi transmission is less susceptible to blockage. Hence, recently a number of indoor location fingerprinting systems that use Wi-Fi networks have been proposed [6]. ...
Article
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In this paper, we propose a proximity-based indoor positioning system which is capable of monitoring mobile device user’s indoor locations where the commonly used GPS signal is unavailable or weak. The designed system is aimed to be integrated into a context-aware communications system to prevent transmission of irrelevant content to all users but easing delivery of the location-based information. Similar to the beacon technology that assigns a code to each targeted position in an indoor location, our system labels the locations with audio watermark codes where user’s mobile device monitors and receives the watermarked audio. The proposed encoder performs code-division multiplexing that allows insertion of several location indexes into the same audio file. Watermark embedded through spread spectrum improves robustness to noise and guarantees a satisfactory performance even though the mobile device has a low band microphone. The designed decoder installs synchronization between the mobile device and the watermarked audio emitter in real time, and extracts the embedded watermark code words assigned to specific indoor locations. This invokes the context-aware content delivery module and the delivery is initiated. Position displacements of the mobile users are estimated by the time-of-flight technique and the users moving within the coverage range of the emitters are continuously monitored. Decoding is achieved in real time that enables the mobile users to reach to content delivered from different emitters within their coverage range. Performance tests demonstrate that the developed system enables to estimate the user position within the 7-m distance from the emitter while keeping inaudibility. We reached 2-m spatial resolution in discrimination of different emitters. The proposed framework can be considered as a promising alternative to latest technologies, i.e., Wi-Fi-based fingerprinting systems or beacons.
... With the development and popularity of wireless sensor network technology [1], location-based services are widely used in military [2], intelligent transportation [3], environmental monitoring [1], agricultural production [4], emergency communications [5], Medical and health [6] and other fields. Indoor positioning technology mainly includes Wi-Fi [7], Bluetooth [8], ZigBee [9], RFID [10], ultrasonic [11], infrared [12], ultra wideband [13] and so on. Commonly used localization methods are TDOA [14], approximate triangulation (APIT) [15], DV-Hop [16], centroid method [17], etc. ...
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In view of the influence of the time-variation of RSSI on the positioning accuracy in Wi-Fi indoor positioning, this paper proposes to use the probability distribution of RSSI value as a fingerprint feature over a period of time, and combines the dimension reduction algorithm and the weighted K nearest neighbor algorithm to achieve positioning. The method firstly calculates the probability distribution of the received RSSI value, uses the dimensionality reduction algorithm to reduce the dimension of the statistical probability distribution.The K-reference points with the smallest Euclidean distance were combined with the weighted nearest neighbor algorithm to obtain the positioning results. Through simulation experiments, it is shown that the positioning accuracy is higher than the traditional method, and the positioning time is significantly reduced.
... So different indoor positioning technology and methods have been produced. Positioning technologies commonly used include infrared, Bluetooth, radio frequency identification, Zigbee, Wi-Fi [2][3][4][5][6]. According to different measurement methods, there are positioning methods based on AOA (Angle of Arrival), TOA (Time of Arrival) and TDOA (Time Difference of Arrival) [7]. ...
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This paper presents a kind of using Wi-Fi location fingerprint to locate the interior regions of the weighted Bayesian algorithm based on the strongest AP method. The algorithm locates the regions with the strongest AP signal by voting according to the distribution rule of the online data, and then uses the weighted Bayesian algorithm to locate. The experimental scene is a typical "aisle" interior region, through the data measured and algorithm matching. The results show that compared with the Bayesian algorithm, the weighted Bayesian algorithm based on the strongest AP method has higher positioning precision and accuracy.
... Several attempts have been made to integrate soft computing techniques into WSN localisation problems. Artificial neural network for robot indoor positioning with low power consumption, high scalability, and construction simplicity is proposed [19]. The accuracy is enhanced by adjusting the number of hidden layers within a certain range. ...
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Node localisation plays a significant role in wireless sensor networks, as most of the applications require exact location of sensor nodes. To obtain the exact location of sensor nodes, a range‐based localisation method based on Jaya optimisation using received signal strength indicator (RSSI) is proposed. The distance between the target node and the reference nodes is obtained from the measured RSSIs using regression‐based log normal shadowing model. Further, improvement in the localisation accuracy is accomplished by formulating the location of target node as an optimisation problem. Jaya optimisation algorithm is adopted, as it is parameter‐free and efficient. The Jaya algorithm is used for estimating the distance as well as the coordinates of the target node. The proposal is compared with particle swarm optimisation and validated through simulation and hardware experiments. The maximum localisation error using Jaya and particle swarm optimisation through simulation is observed to be 0.08 and 0.37 m, respectively. Real‐time experiments using Jaya algorithm exhibited the maximum localisation error of about 0.14 m for indoor environment and 0.3 m for outdoor environment. The results of the proposed methodology show significant improvement in terms of accuracy.
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Chapter
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The number of older adults with Alzheimer’s disease is increasing every year. The associated memory problems cause many difficulties for Alzheimer’s patients and their caretakers; patients may even become lost in familiar surroundings. In this paper, a proposed localization system based on a wireless sensor network (WSN) and backpropagation based artificial neural network (BP-ANN) was practically implemented to detect and determine the position of an Alzheimer’s patient in an indoor environment. The proposed system consisted of four ZigBee-based XBee S2C anchor nodes and one mobile node carried by the Alzheimer’s patient. The received signal strength indicator (RSSI) of the anchor nodes was collected by the mobile node using a laptop supported by X-CTU software. The obtained RSSI values were used as input for training, testing, and validation processes of the BP-ANN, while two-dimension (2D) locations (x and y) were used as the output of the ANN. The results showed that the obtained mean localization errors were 0.964 and 0.921m for validation and testing phases, respectively, after applying the ANN. Based on a comparison with state-of-the-art technology, we deduced that the proposed ANN method outperformed other techniques in previous studies in terms of mean localization error.
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For generating hotel recommendations, clustering travelers has been demonstrated to be a viable method to elevate traveler satisfaction with the recommendation results. However, most of the existing methods that adopt this approach cluster travelers according to a variety of traveler or hotel attributes, which may not necessarily be appropriate for use in an online application such as ubiquitous hotel recommendation. To overcome this problem, a fuzzy ubiquitous traveler clustering and hotel recommendation (FUTCHR) system was developed in this study. The FUTCHR system clustered travelers according to their decision-making mechanisms that are fitted by comparing travelers’ choices with the recommendation results in the historical data. To generate recommendations, a fuzzy mixed binary-nonlinear programming model was constructed and solved. The novelty of the proposed methodology is to cluster travelers without knowing their characteristics but according to the differences in their decision-making mechanisms. The FUTCHR system was employed in a regional study, and the successful recommendation rate was superior to three existing methods in this field.
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Wireless sensor networks (WSNs) and their applications have received considerable interest in the last few years. In WSNs, accurate path loss models should be considered to achieve a successful distribution of several nodes. In this work, two path loss models are proposed to evaluate the distance between two ZigBee WSNs. First, a path loss model based on conventional Log-Normal Shadowing Model (LNSM) is derived using the collected received signal strength indicator (RSSI) of the ZigBee in real time. Second, a new path loss model based on Particle Swarm Optimization (PSO) algorithm hybridized with Polynomial Equation (PE) is proposed. The PSO algorithm is used to select the optimum coefficients of PE. These coefficients can be utilized to optimize the distance estimation error based on the curve fitting. Therefore, the new path loss model called hybrid PE-PSO is innovated in this work. The hybrid PE-PSO model considerably improves the distance estimation accuracy compared with the LNSM. Results show that the hybrid PE-PSO achieves 85% improvement in distance error compared with the traditional LNSM. The mean absolute error of 0.77 m is obtained for distance estimation, which outperforms that by state of the arts.
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A novel approach for indoor localisation is presented and it is composed of two mathematical models. One is a stochastic differential equation proposed to modify time of arrival of a mobile terminal (MT) to a base station (BS). Another is a multivariate optimisation model implemented to estimate the location of the MT by a random search algorithm. Through simulations using only the time of arrivals and measured coordinates of the BSS, the performance of the proposed scheme is evaluated by the measured coordinates of the 25 MTs. Simulation results demonstrate that the mean localisation error is only 0.5138 m and this method is very effective and applicable.
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The Internet of Robotic Things is an emerging vision that brings together pervasive sensors and objects with robotic and autonomous systems. This survey examines how the merger of robotic and Internet of Things technologies will advance the abilities of both the current Internet of Things and the current robotic systems, thus enabling the creation of new, potentially disruptive services. We discuss some of the new technological challenges created by this merger and conclude that a truly holistic view is needed but currently lacking.
Thesis
In this PhD dissertation, the main research activities of the PhD studies are described. In particular, the indoor positioning scenario is analyzed and a summary of the conducted research together with the obtained results is presented. A contribution on the improvement of the state of the art is given with respect to the following features:  Low cost and system complexity: the developed systems are mostly realized using off-the shelf components and the system operation is not based on fingerprinting. Thus, no efforts to build and update a database are required;  Low power consumption: using time varying magnetic fields and high Q resonant current-driven coils, the power consumption requirements of the developed Magnetic Positioning Systems (MPSs) are significantly relaxed;  High multipath resistance and harsh NLOS operation: the low frequency magnetic fields used by the MPS are characterized by good penetration properties and are not affected by multipath phenomena. The first Chapter contains a summary of the main terms, concepts and metrics regarding Positioning Systems (PSs) while the second Chapter describes the state of the art in this field. Specifically, the characteristics of different technologies are described and compared and then a particular attention is given to PSs based on magnetic fields since they are the fundamentals of the developed positioning system prototypes. In Chapter 3 a developed 2D PS prototype is considered. In particular, first the theoretical principle of operation is described and then preliminary validation results are reported. Moreover, an analysis of the environment influence was considered and an improved system prototype was developed. Following, the architecture of the improved prototype is described and the experimental results in different environments are reported and analyzed. Results show a mean positioning error of approximately 0.3 m over a large area of 15 m × 12 m (180 m2) in harsh NLOS conditions or an area of 30 m × 14 m (420 m2) in LOS conditions. Additionally, the system was deployed in the same time and in the same environment with a commercial state of the art solution based on UWB. Results show a comparable performance together with some complementary characteristics as discussed in Chapter 3.6. Moreover, in order to achieve low cost and low complexity, an embedded solution of the developed prototype was realized using of-the-shelf components. A 3D short range positioning prototype was developed and is described in Chapter 4. Experimental results show a 3D positioning error of the order of 3-4 cm over operating ranges of the order of 2 m. As a future development a performance improvement it is expected to be achieved since the system shows a resolution of the order of 1 mm. In addition to the described activities, the integration of the developed 2D positioning system with a foot mounted Dead Reckoning Inertial Navigation System (DR-INS) was considered and is described in Chapter 5. The system integration was advantageous, improving the various performance metrics with respect to the standalone MPS and DR-INS. In particular, the positioning error of the DR-INS was bounded in the order of 1-2 m for significantly long operating times of the order of 45 min. The coverage area of the MPS was significantly increased, at least by a factor of 4. Moreover, positioning information was also available in those scenarios where less than three magnetic beacons were within the operating range of the magnetic receiver. The second research line, which is described in Chapter 6, is related with the analysis of multipath mitigation algorithms in order to develop low cost positioning systems based on narrowband RF transmissions operating at 2.4 GHz Industrial Scientific Medical (ISM) band. Different antenna orientations and the related performance were considered and analyzed by simulations. The effectiveness of space diversity multipath mitigation techniques and an iterative procedure in order to improve the estimation accuracy was investigated. Future developments include improvement of the simulation model in order to consider noise in the measurement procedure and experimental verifications. After Conclusions and References, a list of the Author’s publications is reported.
Thesis
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Reliable transport systems, defense, public safety and quality assurance in the Industry 4.0 are essential in a modern society. In a mission-critical scenario, a mission failure would jeopardize human lives and put at risk some other assets whose impairment or loss would significantly harm society or business results. Even small degradations of the communications supporting the mission could have large and possibly dire consequences. On the one hand, mission-critical organizations wish to utilize the most modern, disruptive and innovative communication systems and technologies, and yet, on the other hand, need to comply with strict requirements, which are very different to those of non critical scenarios. The aim of this thesis is to assess the feasibility of applying emerging technologies like Internet of Things (IoT), Cyber-Physical Systems (CPS) and 4G broadband communications in mission-critical scenarios along three key critical infrastructure sectors: transportation, defense and public safety, and shipbuilding. Regarding the transport sector, this thesis provides an understanding of the progress of communications technologies used for railways since the implantation of Global System for Mobile communications-Railways (GSM-R). The aim of this work is to envision the potential contribution of Long Term Evolution (LTE) to provide additional features that GSM-R would never support. Furthermore, the ability of Industrial IoT for revolutionizing the railway industry and confront today's challenges is presented. Moreover, a detailed review of the most common flaws found in Radio Frequency IDentification (RFID) based IoT systems is presented, including the latest attacks described in the literature. As a result, a novel methodology for auditing security and reverse engineering RFID communications in transport applications is introduced. The second sector selected is driven by new operational needs and the challenges that arise from modern military deployments. The strategic advantages of 4G broadband technologies massively deployed in civil scenarios are examined. Furthermore, this thesis analyzes the great potential for applying IoT technologies to revolutionize modern warfare and provide benefits similar to those in industry. It identifies scenarios where defense and public safety could leverage better commercial IoT capabilities to deliver greater survivability to the warfighter or first responders, while reducing costs and increasing operation efficiency and effectiveness. The last part is devoted to the shipbuilding industry. After defining the novel concept of Shipyard 4.0, how a shipyard pipe workshop works and what are the requirements for building a smart pipe system are described in detail. Furthermore, the foundations for enabling an affordable CPS for Shipyards 4.0 are presented. The CPS proposed consists of a network of beacons that continuously collect information about the location of the pipes. Its design allows shipyards to obtain more information on the pipes and to make better use of it. Moreover, it is indicated how to build a positioning system from scratch in an environment as harsh in terms of communications as a shipyard, showing an example of its architecture and implementation.
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In this paper, a system capable of obtaining the 3-D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robot's rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3-D geometrical model of the robot's structure. Such a model allows direct pose estimation from image measurements, and it can easily be enriched at each iteration with new points as the robot motion evolves. The process is divided into an initialization step, where the structure of the robot is obtained, and an online step, which is solved using sequential Bayesian inference. The approach allows the proper modeling of uncertainty in measurements and estimations, and at the same time, it serves as a regularization step in pose estimation. The proposed system is verified using simulated and real data.
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Nonline-of-sight (NLOS) propagation is one of the challenges in radio positioning. Significant attention has been drawn to the mitigation of the NLOS effect in recent years. This paper focuses on the identification of NLOS conditions by employing the statistical decision theory. A Neyman-Pearson (NP) test method is first derived for scenarios where either 1-D or 2-D angular measurements are provided. A time-of-arrival (TOA) based method is then developed under idealized conditions to provide a performance reference. In the presence of both TOA and received signal strength (RSS) measurements, a joint identification method is derived to efficiently exploit the TOA and RSS measurements. Analytical expressions of the probability of detection (POD) and the probability of false alarm (PFA) are derived for all the scenarios considered. Two theorems and one corollary regarding the line-of-sight (LOS) conditions based on the angle of arrival (AOA) are also presented, and the proofs are provided. Simulation results demonstrate that the proposed methods perform well, and the joint TOA- and RSS-based method considerably outperforms the TOA-based methods. The proposed methods are robust to the model errors, as demonstrated through simulations. It is also shown that the analytical results agree well with the simulated ones.
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This paper describes more efficient home energy management system to reduce power consumption in home area. We consider the room easily controllable with an IR remote control of a home device. The room has automatic standby power cut-off outlets, a light, and a ZigBee hub. The ZigBee hub has an IR code learning function and educates the IR remote control signal of a home device connected to the power outlet. Then the power outlets and the light in the room can be controlled with an IR remote control. A typical automatic standby power cut-off outlet has a waiting time before cutting off the electric power. It consumes standby power during that time. To eliminate the waiting time, we turn off the home device and the power outlet simultaneously with an IR remote control through the ZigBee hub. This method actively reduces the standby power. The proposed HEMS provides easy way to add, delete, and move home devices to other power outlets. When a home device is moved to the different outlet, the energy information of the home device is kept consistently and seamlessly regardless of location change. The proposed architecture gives more efficient energy-saving HEMS.
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A self-contained direction sensing radio frequency identification (RFID) reader is developed employing a dual-directional antenna for automated target acquisition and docking of a mobile robot in indoor environments. The dual-directional antenna estimates the direction of arrival (DOA) of signals from a transponder by using the ratio of the received signal strengths between two adjacent antennas. This enables the robot to continuously monitor the changes in transponder directions and ensures reliable docking guidance to the target transponder. One of the technical challenges associated with this RFID direction finding is to sustain the accuracy of the estimated DOA that varies according to environmental conditions. It is often the case that the robot loses its way to the target in a cluttered environment. To cope with this problem, the direction correction algorithm is proposed to triangulate the location of the transponder with the most recent three DOA estimates. Theoretical simulation results verify the reliability of the proposed algorithm that quantifies the potential error in the DOA estimation. Using the algorithm, we validate mobile robot docking to an RFID transponder in an office environment occupied by obstacles.
Conference Paper
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We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.
Conference Paper
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Location aware services are becoming attractive with the deployment of next generation wireless networks and broadband multimedia wireless networks especially in indoor and campus areas. To provide location aware services, obtaining the position of a user accurately is important. While it is possible to deploy additional infrastructure for this purpose, using existing communications infrastructure is preferred for cost reasons. Because of technical restrictions, location fingerprinting schemes are the most promising. In this paper we present a systematic study of the performance tradeoff and deployment issues. In this paper we present some experimental results towards such a systematic study and discuss some issues related to the indoor positioning problem.
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In this paper, we present an in-depth study of two collaborative-localization methods, called the multidimensional scaling (MDS) and maximum-likelihood estimator (MLE), for wireless sensor networks. From theoretical analysis, it is shown that MLE is more appropriate than MDS, given the underlying assumption of statistical signal models of the received-signal-strength-based localization problem. We also show that MDS can approximately achieve asymptotic efficiency with appropriate weighting schemes in some scenarios. From extensive simulation results, it is noted that the nonlinear least square algorithms that are commonly used to determine MLE are not as efficient as the iterative MDS algorithms. Thus, we propose a new integrated method MDS-MLE to effectively benefit from the strength of both methods. In the new method, MDS is used as an initialization method for MLE. With the solution of MDS as an initial value, MLE converges much faster and achieves significantly better performance than with random initial values. Superior performance of the new method is clearly demonstrated through simulation results. The effects of the deployment density of sensor nodes and reference nodes (RNs), as well as the deployment structure of RNs, are also studied through various simulations.
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As one of the fundamental issues in wireless sensor networks (WSNs), the sensor localization problem has recently received extensive attention. In this work, we investigate this problem from a novel perspective by treating it as a functional dual of target tracking. In traditional tracking problems, static location-aware sensors track and predict the position and/or velocity of a moving target. As a dual, we utilize a moving location assistant (LA) (with a global positioning system (GPS) or a predefined moving path) to help location-unaware sensors to accurately discover their positions. We call our proposed system Landscape. In Landscape, an LA (an aircraft, for example) periodically broadcasts its current location (we call it a beacon) while it moves around or through a sensor field. Each sensor collects the location beacons, measures the distance between itself and the LA based on the received signal strength (RSS), and individually calculates their locations via an Unscented Kalman Filter (UKF)-based algorithm. Landscape has several features that are favorable to WSNs, such as high scalability, no intersensor communication overhead, moderate computation cost, robustness to range errors and network connectivity, etc. Extensive simulations demonstrate that Landscape is an efficient sensor positioning scheme for outdoor sensor networks.
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RFID and ZigBee technologies are used to establish a remote medical care system with a network server developed to collect and analyze IDs, the physiological sensed signals from the persons in care. The collected data transmitted to a medical-care center monitoring system through ZigBee wireless devices. The proposed system serves as a remote medical care and security monitoring system. Multiple physiological sensor chips are embedded using a wireless network with RFID and ZigBee technologies. FPGA- based portable embedded physiological sensor bears flexibility because of the prevailing technology. Moreover, a short-range wireless standard protocol, ZigBee provides reliable low cost, low power consumption, and bidirectional transmission capabilities. Over past years, integrated chip design based RFID and digital wireless ZigBee protocol, ZigBee has gained great market attention for monitoring, control and automation applications.
Article
Service-oriented architecture (SOA) is one of the IT structures attempting to alleviate the problem, of interoperability among complex dissimilar systems. Specific research addressing this issue in multi-network environments is scant. Accordingly, this paper extends our earlier home-based EmQRG application to the more complex demands of a multi-network SOA environment. The presented practical SOA-based embedded QoS-aware residential gateway (SOA-QRG) is capable of integrating diverse components and systems into easily set up and easily upgraded networks which can themselves be embedded into larger networks of similar design. The proposed SOA-QRG system is verified by real experiment. A diverse variety of services are integrated, including multimedia streaming and a wireless location-based service (LBS) network with emergency medical monitor/alarm. One of the primary services of the SOA-QRG is to classify forwarded traffic for optimal use under limited network bandwidth resources. Experimental results confirm that SOA-QRG maintains high standards of multimedia QoS even during network congestion on an experimentally bottlenecked network, at all times maintaining excellent general position monitoring and critical emergency warning behavior. The presented system uses only conventional components and software. It is capable of embedding and being embedded in a broad scale of system, thereby usefully extending contemporary SOA research.
Article
The economic situation in the air transportation industry claims for new business models supported by accurate management processes, which need constant feedback of the real status of the environment. The objective of this work is to achieve an updated/real decision support systems (DSS) to allocate resources in an airport even when disturbances occur by combining artificial intelligent techniques with visibility technologies. This work proposes the combined use of Multi-agent systems (MAS) along with Wireless Sensor Networks (WSN) to provide the required information on the status of the resources and the environment. The MAS is based on a double layer of decision-taking levels and on a Markov reward function whereas the WSN is based on a Zigbee network of Radio Frequency Identification (RFID) readers with active tags as end nodes, which are carried by the physical resources. The proposed distributed DSS has been tested at Ciudad Real Central Airport in Spain.
Article
with the improvement of people's living standard and the development of the embed technology, the expectation of art lighting is becoming more dazzling and artificial intelligent .Generally speaking, the approaches of art lighting's control methods are limited to two methods (1)(2): one is pre-programed control method, and the other is one controller to one light respectively. This paper brings the WSN (wireless sensor network) technology to art lighting system, and the art lighting become more artificial intelligent consequently. On the one hand, the lights in the art lighting system are working as a whole cluster. So the light-nodes in the system can communicate with each other to transmit commands and data (3). On the other hand, the system can monitor the changes of the environment, and can do many reaction effects on the environment changes by using a PWM (Pulse Width Modulation) algorithm, e.g. visitor going across, and then diffuse it to all the light-nodes according to the genetic simulation algorithm, which will be descripted in this paper. What's more, nothing will be done when you increase and decrease the quantity of the light-nodes in the system, because of the wsn can easily extend.
Article
The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF) based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
Article
The two-step indoor location estimation method based on received signal strength in wireless sensor network is proposed. Measuring the received signal strength (RSS) of radio signals transmitted by multiple training points in wireless sensor network, least-squares approach has been applied to determine parameters of signal propagation model. Consider the estimated parameters of the signal propagation model obtained in the first step, minimum mean squares error (MMSE) method is applied to estimate the position of target node. Experiment results show that the proposed method has good performance.
Article
A low-complexity and accurate receive-signal strength (RSS)-based algorithm is proposed to estimate a target location. To achieve the aim of low complexity, a local linearization technique based on the local linearity in the surface of power decay profile (PDP), established by training logarithmic RSS measurements and collected from each individual access point (AP), was devised. To achieve a near-optimum solution, the stochastic properties of measurement errors and the reliability of the measurement data are introduced into the factor graph framework. Numerical experiments show that the proposed algorithm not only achieves a near maximum likelihood (ML) solution based on training RSS measurements, but also enjoys low complexity.
Article
This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted k -nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.
Article
Wireless personal area network and wireless sensor networks are rapidly gaining popularity, and the IEEE 802.15 Wireless Personal Area Working Group has defined no less than different standards so as to cater to the requirements of different applications. The ubiquitous home network has gained widespread attentions due to its seamless integration into everyday life. This innovative system transparently unifies various home appliances, smart sensors and energy technologies. The smart energy market requires two types of ZigBee networks for device control and energy management. Today, organizations use IEEE 802.15.4 and ZigBee to effectively deliver solutions for a variety of areas including consumer electronic device control, energy management and efficiency, home and commercial building automation as well as industrial plant management. We present the design of a multi-sensing, heating and airconditioning system and actuation application - the home users: a sensor network-based smart light control system for smart home and energy control production. This paper designs smart home device descriptions and standard practices for demand response and load management "Smart Energy" applications needed in a smart energy based residential or light commercial environment. The control application domains included in this initial version are sensing device control, pricing and demand response and load control applications. This paper introduces smart home interfaces and device definitions to allow interoperability among ZigBee devices produced by various manufacturers of electrical equipment, meters, and smart energy enabling products. We introduced the proposed home energy control systems design that provides intelligent services for users and we demonstrate its implementation using a real testbad.
Conference Paper
It is well known that the TDOA estimation technique has been widely used in many kinds of location systems, such as radar, sonar and GPS etc. But, because of its serious multipath propagation the cellular location system has much more difficulty in using TDOA than those traditional location-oriented systems mentioned. And so, looking for a new TDOA estimation model and algorithm that is robust and accurate in all kinds of propagation environments is an especially important task for cellular systems. This article combines multipath equalization and identification techniques and proposes a new model and algorithm for TDOA estimation in a cellular positioning system. Theory and computer simulation both prove that a simple estimation algorithm can obtain good estimation results in this model
Article
In this paper, techniques and algorithms developed in the framework of Statistical Learning Theory are applied to the problem of determining the location of a wireless device by measuring the signal strength values from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi–Fi, so that no special-purpose hardware is required.The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques.
Conference Paper
We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building's unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95% of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures.
Article
With the increasing automation of factories, factory floors are being covered with machinery. Spaces crowded with machinery are more difficult and dangerous for personnel to operate in. This system attempts to uses the ZigBee embedded system to improve industrial safety quality. In addition to performing existing typical monitoring functions, this system utilizes ZigBee wireless transmission technology for remote monitoring. The measurement items of the industrial applications of this system platform include length filtering, ground vibration sensing, weight grading, electricity sensing, energy monitoring, temperature monitoring, and carbon dioxide concentration. Our application of ZigBee combined with an embedded system to industrial real-time measurements represents an innovative technology. In addition to discussing the system platform in this study, we also discuss statistics and analysis of measurement data. Performing wired and wireless synchronized measurement and monitoring using this system can achieve correct and efficient industrial monitoring operations.
Article
Thesis (Ph. D.)--School of Electrical and Computer Engineering, Georgia Institute of Technology, 1999. Directed by Gordon L. Stüber. Vita. Includes bibliographical references (leaves 152-164).
Article
Thesis (M.S.)--Worcester Polytechnic Institute. Includes bibliographical references (p. 109-110). Mode of access: World wide web via internet. Keywords: indoor radio propagation; time of arrival estimation; indoor geolocation; super-resolution algorithms.
Conference Paper
The paper presents a new two-step cellular location determination (CLD) method based on signal strength and wave scattering models. The received signal level (RSL) method is first used in combination with maximum likelihood estimation (MLE) and triangulation to obtain an estimate of the location of the mobile. Due to non line of sight (NLOS) conditions and multipath propagation, this estimate lacks acceptable accuracy and consistency for demanding services, as numerical simulations reveal. Thus, the wave scattering 3D multipath channel model of Aulin is employed together with extended Kalman filtering (EKF) to obtain improved location estimates with high accuracy. The EKF is initialized at the MLE obtained from the RSL method, which is proved to be highly appropriate. Numerical simulations under urban, suburban and rural environments were utilized to evaluate the accuracy and consistency of the proposed two-step enhanced RSL method; the results of the worst-case rural environment are presented.
Conference Paper
Using TOA to determine the distance between the transmitter and the receiver is the most popular technique for accurate indoor positioning. Accuracy of measuring the distance using TOA is sensitive to the bandwidth of the system and the multipath condition between the wireless terminal and the access point. The behavior of the distance measurement error using TOA techniques in LOS and OLOS indoor environments are substantially different. In general, as the bandwidth increases the distance measurement error decreases. However, for the so called undetected direct path (UDP) conditions the system exhibits substantially high distance measurement errors that can not be eliminated with the increase in the bandwidth of the system. In this paper we provide an analysis of the behavior of super-resolution and traditional TOA estimation algorithms in LOS, OLOS and UDP conditions in indoor areas. The analysis is based on the frequency domain measurements of the indoor radio channel propagations in several indoor areas with special attention to the UDP conditions.
Conference Paper
The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy
Article
This paper proposes a new localization strategy for indoor service robots. A mobile robot localization problem is difficult to solve by a single continuous algorithm. Major difficulties include dynamic changes of the real world, various uncertainties, limitation of sensor information, and so forth. To develop a practical localization solution, this paper proposes an integrated localization strategy based on the discrete status of the mobile robot. Uncertainties of navigation are specified and classified into discrete status, and then modeled as a Petri net-based discrete localization system. The proposed algorithm integrates developed computational schemes and robot behaviors with respect to the defined status. Major criteria of status discretization include geometric properties of the environment, existence of dynamic obstacles, and reliability level of the estimated position. An efficient map-matching scheme and a map-building strategy are developed toward practical implementations. This paper focuses on providing a synthesized practical localization method, which can deal with various uncertainties by explicit discretization of robot status. The feasibility of the proposed method is experimentally verified with prototype public service robots in dynamic real environments
Article
Vehicle-to-vehicle communications via dedicated-short-range-communication (DSRC) devices will enable safety applications such as cooperative collision warning. These devices use the IEEE 802.11p standard to support low-latency vehicle-to-vehicle and vehicle-to-infrastructure communications. However, a major challenge for the cooperative collision warning is to accurately determine the location of vehicles. In this paper, we present a novel cooperative-vehicle-position-estimation algorithm which can achieve a higher accuracy and more reliability than the existing global-positioning-system-based positioning solutions by making use of intervehicle-distance measurements taken by a radio-ranging technique. Our algorithm uses signal-strength-based intervehicle-distance measurements, vehicle kinematics, and road maps to estimate the relative positions of vehicles in a cluster. We have analyzed our algorithm by examining its performance-bound, computational-complexity, and communication-overhead requirements. In addition, we have shown that the accuracy of our algorithm is superior to previously proposed localization algorithms.
Article
Computing as a discipline can be traced to the 1960s, when universities such as Stanford and CMU established computer science as a stand-alone department. Since its inception, computing has been constantly evolving as a vibrant discipline. Here, I make 10 observations on its recent development, which can serve as references for computing stakeholders - including academic leaders, faculty members, administrators, staff, students and their parents, alumni, and potential employers - to rationally embrace the changes and opportunities ahead.
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Realization of U campus service with room-based indoor positioning by using ZigBee
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