K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Indoor localization in multi-floor buildings is an important research problem. Finding the correct floor, in a fast and efficient manner, in a shopping mall or an unknown university building can save the users' search time and can enable a myriad of Location Based Services in the future. One of the most widely spread techniques for floor estimation in multi-floor buildings is the fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WLAN and BLE. The clear advantage of RSS-based floor estimation is its ease of implementation on a multitude of mobile devices at the Application Programming Interface (API) level, because RSS values are directly accessible through API interface. However, the downside of a fingerprinting approach, especially for large-scale floor estimation and positioning solutions, is their need to store and transmit a huge amount of fingerprinting data. The problem becomes more severe when the localization is intended to be done on mobile devices which have limited memory, power, and computational resources. An alternative floor estimation method, which has lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) method. The trade-off is however paid in terms of a lower accuracy than the one obtained with traditional fingerprinting with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based method for floor estimation via fingerprint clustering of WiFi and various other positioning sensor outputs is introduced. Our method achieves a floor estimation accuracy close to the one with NN fingerprinting, while significantly improves the complexity and the speed of the floor detection algorithm. The decrease in the database size is achieved through storing and transmitting only the cluster heads (CH's) and their corresponding floor labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and Tracking: Indoors, Outdoors and Emerging Network

Advanced Multipath Mitigation Techniques for Satellite-Based Positioning Applications

Multipath remains a dominant source of ranging errors in Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS) or the future European satellite navigation system Galileo. Multipath is generally considered undesirable in the context of GNSS, since the reception of multipath can make significant distortion to the shape of the correlation function used for time delay estimation. However, some wireless communications techniques exploit multipath in order to provide signal diversity though in GNSS, the major challenge is to effectively mitigate the multipath, since we are interested only in the satellite-receiver transit time offset of the Line-Of-Sight (LOS) signal for the receiver's position estimate. Therefore, the multipath problem has been approached from several directions in order to mitigate the impact of multipath on navigation receivers, including the development of novel signal processing techniques. In this paper, we propose a maximum likelihood-based technique, namely, the Reduced Search Space Maximum Likelihood (RSSML) delay estimator, which is capable of mitigating the multipath effects reasonably well at the expense of increased complexity. The proposed RSSML attempts to compensate the multipath error contribution by performing a nonlinear curve fit on the input correlation function, which finds a perfect match from a set of ideal reference correlation functions with certain amplitude(s), phase(s), and delay(s) of the multipath signal. It also incorporates a threshold-based peak detection method, which eventually reduces the code-delay search space significantly. However, the downfall of RSSML is the memory requirement which it uses to store the reference correlation functions. The multipath performance of other delay-tracking methods previously studied for Binary Phase Shift Keying-(BPSK-) and Sine Binary Offset Carrier- (SinBOC-) modulated signals is also analyzed in closed loop model with the new Composite BOC (CBOC) modulation chosen for Galileo E1 signal. The simulation results show that the RSSML achieves the best multipath mitigation performance in a uniformly distributed two-to-four paths Rayleigh fading channel model for all three modulated signals

Multipath Mitigation Techniques for Satellite-Based Positioning Applications

This chapter addressed the challenges encountered by a GNSS signal due to multipath propagation. A wide range of correlation-based multipath mitigation techniques were discussed and the performance of some of these techniques were evaluated in terms of running average error and root-mean-square error. Among the analyzed multipath mitigation techniques, RSSML, in general, achieved the best multipath mitigation performance in moderate-to-high C/N0 scenarios (for example, 30 dB-Hz and onwards). The other techniques, such as PT(Diff2) and HRC showed good multipath mitigation performance only in high C/N0 scenarios (for example, 40 dB-Hz and onwards). The other new technique SBME offered slightly better multipath mitigation performance to the well-known nEML DLL at the cost of an additional correlator. However, as the GNSS research area is fast evolving with many potential applications, it remains a challenging topic for future research to investigate the feasibility of these multipath mitigation techniques with the multitude of signal modulations, spreading codes, and spectrum placements that are (or are to be) proposed.publishedVersionPeer reviewe

Privacy-Constrained Location Accuracy in CooperativeWearable Networks in Multi-Floor Buildings

This paper proposes a geometric dilution-of-precision approach to quantize the privacy-aware location errors in a cooperative wearable network with opportunistic positioning. The main hypothesis is that, a wearable inside a multi-floor building could localize itself based on cooperative pseudoranges measurements from nearby wearables, as long as the nearby wearables are heard above the sensitivity limit and as long as nearby wearables choose to disclose their own positions. A certain percentage of wearables, denoted by γ, is assumed to not want to disclose their positions in order to preserve their privacy. Our paper investigates the accuracy limits under the privacy constraints with variable γ and according to various building maps and received signal strength measurements extracted from real buildings. The data (wearable positions and corresponding power maps) are synthetically generated using a floor-and-wall path-loss model with statistical parameters extracted from real-field measurements. It is found that the network is tolerant to about 30% of the wearables not disclosing their position (i.e., opting for a full location-privacy mode).Peer reviewe

Towards the Advanced Data Processing for Medical Applications Using Task Offloading Strategy

Broad adoption of resource-constrained devices for medical use has additional limitations in terms of execution of delay-sensitive medical applications. As one of the solutions, new ways of computational offloading could be developed and integrated. The recently emerged Mobile Edge Computing (MEC) and Mobile Cloud Computing (MCC) paradigms attempt to address this problem by offloading tasks to a the resource-rich server. In the context of the availability of eHealth services for all patients, independently of the location, the implementation of MEC and MCC could help ensure a high availability of medical services. Remote medical examination, robotic surgery, and cardiac telemetry require efficient computing solutions. This work discusses three alternative computing models: local computing, MEC, and MCC. We have designed a Matlab-based tool to calculate and compare the response time and energy efficiency. We show that local computing demands 48 times more power than MEC/MCC with increasing packet workload. On the other hand, the throughput of MEC/MCC highly depends on the parameters of the communication channel. Finding an optimal trade-off between the response time and energy consumption is an important research question that could not be solved without investigating the system’s bottlenecks.acceptedVersionPeer reviewe

Machine-learning-based diabetes prediction using multi-sensor data

Diabetes is one such chronic disease that, if undetected, can result in several adverse symptoms or diseases. It requires continuous and active monitoring, for example, by using various smartphone sensors, wearable/smart watches, etc. These devices are minimally invasive in nature and can also track various physiological signals, which are important for the prediction of diabetes. Machine-learning algorithms and artificial intelligence are some of the most important tools used for the prediction/detection of diabetes using different types of physiological signals. In this study, we have focused on using multiple sensors such as glucose, ECG, accelerometer, and breathing sensors for classifying patients with diabetes disease. We analyzed whether a single sensor or multiple sensors can predict diabetes well. We identified various time-domain and interval-based features that are used for predicting diabetes and also the optimal window size for the feature calculation. We found that a multi-sensor combination using glucose, ECG, and accelerometer sensors gives the highest prediction accuracy of 98.2% with the xgboost algorithm. Moreover, multi-sensor prediction shows nearly 4 - 5% increase in the diabetes prediction rates as compared to single sensors. We observed that breathing-sensor-related data have very little influence on the prediction of diabetes. We also used the score-fit-times curve as one of the metrics for the evaluation of models. From the performance curves, we observed that three sensor combinations using glucose, ECG, and accelerometer converge faster as compared to a four-sensor combination while achieving with same accuracy.Peer reviewe

Self-Learning Detection and Mitigation of Non-Line-of-Sight Measurements in Ultra-Wideband Localization

Non-line-of-sight (NLOS) propagation is one of the main error sources in indoor localization, so a large body of work has been dedicated to identifying and mitigating NLOS errors. The most accurate NLOS detection methods often rely on large training data sets that are time-consuming to obtain and depend on the environment and hardware. We propose a method for detecting NLOS distance measurements without manually collected training data and knowledge of channel statistics. Instead, the algorithm generates LOS/NLOS labels for sets of distance measurements between fixed sensors and the mobile target based on distance residuals. The residual-based detection has 70-80% accuracy but has high complexity and cannot be used with high confidence on all measurements. Therefore, we use the predicted labels and the channel impulse responses of the measurements to train a classifier that achieves over 90% accuracy and can be used on all measurements, with low complexity. After we train the classifier during an initial phase that captures specifics of the devices and of the environment, we can skip the residual-based detection and use only the trained model to classify all measurements. We also propose an NLOS mitigation method that reduces, on average, the mean and standard deviation of the localization error by 2.2 and 5.8 times, respectively.Peer reviewe

The impact of Galileo Open Service on the Location Based Services markets: a review on the cost structure and the potential revenue streams

Many Location Based Services (LBS), such as navigation and tracking services, are using Global Satellite-based Navigation Systems (GNSS). GNSS is the most widely used positioning solution for LBS outdoors, therefore any improvement in the quality of GNSS positioning services will directly improve the quality of LBS and therefore it will generate more revenue and attract more users. One of the upcoming satellite navigation systems is Galileo, which is being deployed by the European Union (EU). Beside all political motivations behind Galileo, the availability of more satellites in view and a more accurate, reliable and continuous positioning service are some of the technological motivations of having yet another of GNSS on sky. Such improvement in positioning service and, as a result, in LBS applications will develop the market and attract more users. However, due to long delays, current powerful competitors which are making the GNSS market increasingly crowded, and also the cost of Galileo being covered by EU taxpayers only, there is a question if another of GNSS is really required and it is able to return all its cost in near future. This chapter assesses the financial aspects of Galileo at the time of writing the book, including increasing costs and impact of losing some parts of market and also its potential revenue and the economic impact of positioning and timing service improvement by Galileo, and finally the impact of Galileo on future markets of LBS is estimated

Demystifying Usability of Open Source Computational Offloading Simulators : Performance Evaluation Campaign

Along with analysis and practical implementation, simulations play a key role in wireless networks and computational offloading research for several reasons. First, the simulations provide the ability to easily obtain the data for a complex system’s model evaluation. Secondly, simulated data provides a controlled environment for experimentation, allowing models and algorithms to be tested for robustness and identifying potential limitations before deploying them in real-world applications. Choosing the most appropriate tool for simulation might be challenging and depends on several factors, such as the main purpose, complexity of data, researcher skills, community support, and available budget. As of the time of the present analysis, several system-level open-source tools for modeling computational offloading also cover the systems’ communications side, such as CloudSim , CloudSim Plus , IoTSim-Edge , EdgeCloudSim , iFogSim2 , PureEdgeSim , and YAFS . This work presents an evaluation of those based on the unique features and performance results of intensive workload- and delay-tolerant scenarios: XR with an extremely high data rate and workload; remote monitoring with a low data rate with moderate delays and workload requirements; and data streaming as a general human traffic with a relatively high bit rate but moderate workload. The work concludes that CloudSim provides a reliable environment for virtualization on the host resources, while YAFS shows minimal hardware usage, while IoTSim-Edge , PureEdgeSim , and EdgeCloudSim have fewer implemented features.Peer reviewe

Challenges in platform-independent UWB ranging and localization systems

The Ultra-Wideband (UWB) technology has grown in popularity to the point in which there are numerous UWB transceivers on the market that use different center frequencies, bandwidths, or hardware architectures. At the same time, efforts are made to reduce the ranging and localization errors of UWB systems. Until now, not much attention has been dedicated to the cross-platform compatibility of these methods. In this paper, we discuss for the first time the challenges in obtaining platform-independent UWB ranging and localization systems. We derive our observations from a measurement campaign conducted with UWB devices from three different developers. We evaluate the differences in the ranging errors and channel impulse responses of the devices and show how they can affect ranging mitigation methods customized for one device only. Finally, we discuss possible solutions towards platform-independent UWB localization systems.publishedVersionPeer reviewe