A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms

Positioning by multicell fingerprinting in urban NB-IoT networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments

Location estimation and collective inference in indoor spaces using smartphones

In the last decade, indoor localization-based smart, innovative services have become very popular in public spaces (retail spaces, malls, museums, and warehouses). We have state-of-art RSSI techniques to more accurate CSI techniques to infer indoor location. Since the past year, the pandemic has raised an important challenge of determining if a pair of individuals are ``social-distancing,'' separated by more than 6ft. Most solutions have used `presence'-if one device can hear another--- which is a poor proxy for distance since devices can be heard well beyond 6 ft social distancing radius and across aisles and walls. Here we ask the key question: what needs to be added to our current indoor localization solutions to deploy them towards scenarios like reliable contact tracing solutions easily. And we identified three main limitations---deployability, accuracy, and privacy. Location solutions need to deploy on ubiquitous devices like smartphones. They should be accurate under different environmental conditions. The solutions need to respect a person's privacy settings. Our main contributions are twofold -a new statistical feature for localization, Packet Reception Probability (PRP) which correlates with distance and is different from other physical measures of distance like CSI or RSSI. PRP can easily deploy on smartphones (unlike CSI) and is more accurate than RSSI. Second, we develop a crowd tool to audit the level of location surveillance in space which is the first step towards achieving privacy. Specifically, we first solve a location estimation problem with the help of infrastructure devices (mainly Bluetooth Low Energy or BLE devices). BLE has turned out to be a key contact tracing technology during the pandemic. We have identified three fundamental limitations with BLE RSSI---biased RSSI Estimates due to packet loss, mean RSSI de-correlated with distance due to high packet loss in BLE, and well-known multipath effects. We built the new localization feature, Packet Reception Probability (PRP), to solve the packet loss problem in RSSI. PRP measures the probability that a receiver successfully receives packets from the transmitter. We have shown through empirical experiments that PRP encodes distance. We also incorporated a new stack-based model of multipath in our framework. We have evaluated B-PRP in two real-world public places, an academic library setting and a real-world retail store. PRP gives significantly lower errors than RSSI. Fusion of PRP and RSSI further improves the overall localization accuracy over PRP. Next, we solved a peer-to-peer distance estimation problem that uses minimal infrastructure. Most apps like aarogya setu, bluetrace have solved peer-to-peer distances through the presence of Bluetooth Low-Energy (BLE) signals. Apps that rely on pairwise measurements like RSSI suffer from latent factors like device relative positioning on the human body, the orientation of the people carrying the devices, and the environmental multipath effect. We have proposed two solutions in this work---using known distances and collaboration to solve distances more robustly. First, if we have a few infrastructure devices installed at known locations in an environment, we can make more measurements with the devices. We can also use the known distances between the devices to constrain the unknown distances in a triangle inequality framework. Second, in an outdoor environment where we cannot install infrastructure devices, we can collaborate between people to jointly constrain many unknown distances. Finally, we solve a collaborative tracking estimation problem where people audit the properties of localization infrastructure. While people want services, they do not want to be surveilled. Further, people using an indoor location system do not know the current surveillance level. The granularity of the location information that the system collects about people depends on the nature of the infrastructure. Our system, the CrowdEstimator, provides a tool to people to harness their collective power and collect traces for inferring the level of surveillance. We further propose the insight that surveillance is not a single number, instead of a spatial map. We introduce active learning algorithms to infer all parts of the spatial map with uniform accuracy. Auditing the location infrastructure is the first step towards achieving the bigger goal of declarative privacy, where a person can specify their comfortable level of surveillance

An Experimental Study on the Effect of Pattern Recognition Parameters on the Accuracy of Wireless-Based Task Time Estimation

Task time estimation is a core industrial engineering discipline. However, the process to collect the required data is manually intensive and tedious, thus making it expensive to keep the data current. Radio frequency signals have been used to automate the required data collection in some applications. However, such radio frequency data is subject to systemic and random noise, leading to a reduction in the accuracy of the task time estimation. This research investigates the use of a pattern recognition method, the k-nearest-neighbor algorithm, to improve the accuracy of task time estimation in a simulated assembly work area. The results indicate that the parameters of the kNN algorithm can be experimentally tuned to improve the accuracy and to dramatically reduce the necessary computational time and the costs of performing real-time task time estimation.Keywords: work measurement, task time estimation, radio frequency, pattern recognition, wireless sensor networks, data mining, k-nearest-neighborKeywords: work measurement, task time estimation, radio frequency, pattern recognition, wireless sensor networks, data mining, k-nearest-neighbo

Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours

Context and communication profiling for IoT security and privacy: techniques and applications

During the last decade, two major technological changes have profoundly changed the way in which users consume and interact with on-line services and applications. The first of these has been the success of mobile computing, in particular that of smartphones, the primary end device used by many users for access to the Internet and various applications. The other change is the emergence of the so-called Internet-of-Things (IoT), denoting a technological transition in which everyday objects like household appliances that traditionally have been seen as stand-alone devices, are given network connectivity by introducing digital communication capabilities to those devices. The topic of this dissertation is related to a core challenge that the emergence of these technologies is introducing: how to effectively manage the security and privacy settings of users and devices in a user-friendly manner in an environment in which an ever-growing number of heterogeneous devices live and co-exist with each other? In particular we study approaches for utilising profiling of contextual parameters and device communications in order to make autonomous security decisions with the goal of striking a better balance between a system's security on one hand, and, its usability on the other. We introduce four distinct novel approaches utilising profiling for this end. First, we introduce ConXsense, a system demonstrating the use of user-specific longitudinal profiling of contextual information for modelling the usage context of mobile computing devices. Based on this ConXsense can probabilistically automate security policy decisions affecting security settings of the device. Further we develop an approach utilising the similarity of contextual parameters observed with on-board sensors of co-located devices to construct proofs of presence that are resilient to context-guessing attacks by adversaries that seek to fool a device into believing the adversary is co-located with it, even though it is in reality not. We then extend this approach to a context-based key evolution approach that allows IoT devices that are co-present in the same physical environment like the same room to use passively observed context measurements to iteratively authenticate their co-presence and thus gradually establish confidence in the other device being part of the same trust domain, e.g., the set of IoT devices in a user's home. We further analyse the relevant constraints that need to be taken into account to ensure security and usability of context-based authentication. In the final part of this dissertation we extend the profiling approach to network communications of IoT devices and utilise it to realise the design of the IoTSentinel system for autonomous security policy adaptation in IoT device networks. We show that by monitoring the inherent network traffic of IoT devices during their initial set-up, we can automatically identify the type of device newly added to the network. The device-type information is then used by IoTSentinel to adapt traffic filtering rules automatically to provide isolation of devices that are potentially vulnerable to known attacks, thereby protecting the device itself and the rest of the network from threats arising from possible compromise of vulnerable devices

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications