Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such an issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a seven-fold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed.

Location information is a valuable source of context that can be utilized by end-user applications and wireless networks to optimize their performance and usability. When used, location information should ideally be considered jointly with the estimate of its accuracy. Most of the current approaches for estimating the accuracy rely on performing a static performance benchmark of a localization solution in a deployment environment, which fails to capture the dynamic nature of the environment. We address this problem for fingerprinting-based localization by grounding the estimation of localization errors on the low-level features, i.e. the RSS values from APs used in fingerprinting. We use these low-level features measured at different locations in an environment, as well as their respective localization errors, to train different regression models, allowing us to predict the localization errors at new locations, given new observed values of the low-level features at these locations. Our evaluation results show substantially better performance of the proposed regression-based estimation of localization errors compared to static performance benchmarks.

Aggregation of location estimates from multiple services for provisioning of location information enhances the accuracy and robustness of the final location information. Recent Internet of Things (IoT)-based localization service architectures therefore envision a "manager" for selecting and invoking provisioning services and, in the later step, aggregating the received information and providing it to location-based applications. The selection of provisioning services should take into account the accuracy and latency requirements from the applications and accuracy, latency, and power consumption characteristics of provisioning services. However, it is yet unclear how such selection should be made. We propose two algorithms for the selection of provisioning services aiming at meeting latency and subsequently accuracy requirements from the applications, one subject to minimizing per-request power consumption, while the other subject to a per-time bucket power minimization. In the considered examples, we show that the per-time bucket optimization achieves around 25% better performance in terms of power consumption, while trading-off accuracy satisfaction.

Location information of mobile devices is a foundational input to location-based services and a valuable source of context information in wireless networks. To maximize the value, we need location information that is accurate, robust, and promptly and seamlessly available. Unfortunately, individual localization services seldom satisfy all these requirements. For achieving that vision, a set of challenges has to be addressed, pertaining to handover, fusion, and integration of different sources of location information. Current approaches for integration of individual localization services are either not specific enough or are limited in scope and lack flexibility. In the following, we provide a detailed design and a prototypical implementation of the Standardized Localization Service (SLSR), a middleware architecture for achieving those goals. We instantiate the service in an office environment and perform exhaustive performance benchmarking in a testbed specifically designed for supporting such experimentation. Our results characterize the effects of different functional components envisioned in the SLSR on its performance. Our results also quantify the accuracy benefits of fusion of representative sources of location information.

Mobile app testing and evaluation requires exposing the app to a wide array of real world context conditions viz. location, sensor values, network conditions etc. Such comprehensive context conditions are difficult to create in a development environment on a real device, therefore, simulating them in a mobile emulator is a promising alternative. We present ContextMonkey, a framework for context generation in a mobile emulator. ContextMonkey can generate realistic context by leveraging traces in correlated and interdependent way from heterogenous sources: remote trace databases, models, and trace files. It eases the burden on developers to collect correlated traces, convert them to a common format, and feed them to the emulator in an orchestrated manner. We present examples demonstrating the simplicity and potential of ContextMonkey in app testing scenarios.

Individual localization services seldom satisfy the requirements for accurate, robust, punctual, and seamless location information. To enhance the provisioning, a set of challenges has to be addressed, pertaining to handover, fusion, and integration of individual localization services. In the following, we advertise the Standardized Localization Service (SLSR), a middleware architecture for achieving those goals. We instantiate the SLSR in a office-like indoor environment and evaluate its performance in a specifically designed testbed for evaluation of indoor localization approaches. Our results typify the contributions of different functional components envisioned in the SLSR on its overall performance.

Device-to-Device (D2D) communication has a high potential in reducing the amount of network traffic and improving the latency and energy efficiency of communication. Currently, D2D link establishment decisions are based on active probing between devices that wish to establish a D2D link. The main drawback of such approaches is a large overhead as during active probing no data communication can take place. We leverage physical locations of the devices that wish to establish a D2D link in order to estimate the probability of success in establishing the link before making an attempt to communicate. The probability of success is given as a closed form equation that takes into account the imperfections of location information of the devices and intrinsic randomness of wireless environments. We experimentally evaluate the proposed location-based decision- making mechanism for D2D link establishment in a complex office-like indoor environment. We show that setting the Signal-to-Noise Ratio (SNR) threshold of the proposed mechanism to a value that is 5 dB higher than the nominal SNR required for communication results in reliable link establishment with false positive rate of less than 2%. Furthermore, we show a relatively small loss of link establishment potential due to an increase in the inaccuracies of location information.

The interest for RF-based indoor localization, and in particular for WiFi RSSI-based fingerprinting, is growing at a rapid pace. This is despite the existence of a trade-off between the accuracy of location estimation and the density of a laborious and time consuming survey for collecting training fingerprints. A generally accepted concept of increasing the density of a training dataset, without an increase in the amount of physical labor and time needed for surveying an environment for additional fingerprints, is to leverage a propagation model for the generation of virtual training fingerprints. This process, however, burdens the user with an overhead in terms of implementing a propagation model, defining locations of virtual training fingerprints, generating virtual fingerprints, and storing the generated fingerprints in a training database. To address this issue, we propose the Enriched Training Database (ETD), a web-service that enables storage and management of training fingerprints, with an additional “enriching” functionality. The user can leverage the enriching functionality to automatically generate virtual training fingerprints based on propagation modeling in the virtual training points. We further propose a novel method for defining locations of virtual training fingerprints based on modified Voronoi diagrams, which removes the burden of defining virtual training points manually and which automatically “covers” the regions without sufficient density of training fingerprints. The evaluation in our testbed shows that the use of automated generation of virtual training fingerprints in ETD results in more than 25% increase in point accuracy and 15% in room-level accuracy of fingerprinting.

Localization services available on today's mobile devices are proprietary and leverage a limited set of sources of location information. Integration of new location estimation methods is therefore cumbersome, requiring adaptation to the specific interfaces of the proprietary location service. In addition, location-based applications are tightly interwoven with the location service that is typically provided by the operating system, hence these applications require significant restructuring to be able to run with another location service. To address these problems, we propose a modular localization service architecture that consists of location-based applications, an integrated location service enabling a fusion of so-called elementary location services, and resources for generating location information. A unified style of interaction among these components is enabled by a set of well-defined Application Programming Interfaces (APIs). The practicability and advantages of the proposed design is demonstrated by outlining how the APIs can be realized using modern types of component interactions.

This demonstration presents a user-friendly web-based platform that supports intuitive remote benchmarking of different indoor localization solutions. It reduces the barriers for experimental evaluation and fair comparison of their performance across a set of testing environments. The platform is aimed at addressing the limitations in the current praxis of publishing indoor localization research evaluated only in local, potentially biased environments. To this end, it provides a holistic support for the benchmarking process offering: (i) multiple pre-collected raw data-traces from different RF technologies, (ii) high-level interface to control remote wireless testbed facilities, and (iii) a set of tools for creating, storing, comparing and visualizing the performance results of multiple indoor localization solutions.

mmWave (millimeter-Wave) is a very promising technology for the future wireless communication. To mitigate its high attenuation characteristics, mmWave communication frequently employs directional beamforming for both transmission and reception. Localization commonly takes advantage of directionality in RF frequencies in urban and indoor environments. In this paper, we use lessons learned from classical RF-based localization for discussing a set of feasible localization approaches in the context of mmWave bands. We further map the requirements of each discussed localization approach to design requirements for future mmWave devices and assess the expected accuracy of such approaches for a set of realistic scenarios. Our results show that mmWave-based localization is promising in both its availability and accuracy, even in the presence of a limited number of localization anchor nodes.

This paper describes a major effort to collect smartphone data useful for indoor localization. Data has been collected with four Android phones according to numerous scenarios in each of four large buildings. The data includes time-stamped traces of various environmental, position, and motion sensors available on a smartphone, Wi-Fi and cellular signal strengths, and GPS fixes, whenever available. Quantitative evidence is presented to validate the collected data and attest to its quality. This unique, extensive data repository is made available to the R&D community through the PerfLoc web portal to facilitate development of smartphone indoor localization apps and to enable performance evaluation of such apps according to the ISO/IEC 18305 international standard in the near future.

Industrial control systems have traditionally been built around dedicated wired solutions. The requirements of flexibility, mobility, and cost have created a strong push toward wireless solutions, preferably solutions requiring low power. Simultaneously, the increased need for interoperability and integration with the wider Internet made a transition to IP-based communication unavoidable. Following these trends, we survey 6TiSCH, the emerging family of standards for IP-based industrial communication over low-power and lossy networks. We describe the state of the standardization work, the major issues being discussed, and open questions recently identified. Based on extensive first-hand experience, we discuss challenges in implementation of this new wave of standards. Lessons learned are highlighted from four popular open-source implementations of these standards: OpenWSN, Contiki, RIOT, and TinyOS. We outline major requirements, present insights from early interoperability testing and performance evaluations, and provide guidelines for chip manufacturers and implementers.

We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study's results, discuss the current state-of-the-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.

Accurate performance testing of mobile apps require comprehensive simulation of real world context in a mobile emulator viz. location, sensor values, network conditions etc. Existing mobile emulators support such simulation, however they lack the capability for automated generation of realistic, correlated, and dependent context traces. As a result, the burden of their generation and injection is left to the developers who have to create their own traces. This is not straightforward: there are heterogenous remote databases of traces, mathematical models and trace files can be used as well, but the trace values should be correlated, and traces have to be converted to a common format. We are developing ContextMonkey - a framework that addresses these concerns by leveraging context traces from databases such as FourSquare [1], OpenSignal [2], Google Street View [3]. It acts as a harness to mobile emulators, and aims at improving the efficiency of mobile app performance tests.

Many indoor positioning applications focus on determining the location of a device in a particular area, like a room of a building, rather than its metric coordinates. It is common that most buildings are partitioned in more or less a regular way and discovering the relative positions of the nodes can give us an accurate estimate of the space, e.g. a room, they are located in. In this study, we have developed a probabilistic methodology to detect the sequence of wireless sensor nodes placed in an indoor environment. We assume no preliminary training, preconfiguration or infrastructure other than the identity of one reference node at the beginning of the sequence. We trigger the nodes under consideration to transmit a series of packets, and use received signal strength information as observed by the neighbors to infer about the sequence of the nodes. In this paper, a centralized processing of the information is used. The proposed methodology is verified both by simulation and by experiments in a building with regular rooms, high shadowing and multipath distortions. Rarely observed classification errors in the experiments have motivated some extension to the basic methodology.

Despite their popularity, the current praxis of comparative experimental evaluation of fingerprinting-based localization algorithms is lacking rigor, with studies typically following an ad-hoc evaluation process and focusing on black-box comparison of complete algorithms. In this paper we present a systematic benchmarking methodology that is focused on gaining finegrained insight about the relative contributions of the individual phases of the fingerprinting-based localization algorithms to their overall performance. To this end, we decompose the localization algorithms in common phases (collection of raw measurements, creation of fingerprints, pattern matching and post-processing) and systematically asses the performance of different procedures that can be applied in each of these phases. We illustrate the application of the proposed methodology using a comprehensive experimental case-study of 3 WiFi fingerprinting algorithms with 4 raw RSSI collection procedures, 3 fingerprint creation and pattern matching procedures, 4 different post-processing procedures in 3 testbeds and 4 evaluation scenarios, resulting in 36 individual experiments. The results demonstrate that in the evaluated scenarios, a lower number of WiFi APs and rather simple fingerprint creation and pattern matching can achieve better performance in terms of location accuracy than more sophisticated alternatives. The results also show that postprocessing steps like k-Nearest Neighbours (kNN) procedure are indeed effective in reducing the localization error variability and extremes, thus increasing the stability of the location estimation.

In many application areas for wireless sensor networks, having some location knowledge about the sensor nodes is important, but this information is often not readily available. In the past, plethora of different solutions have been proposed that focused on recovering the locations of the sensor nodes, often at the cost of high complexity or limited accuracy. We claim that, in many scenarios the precise location in the x, y, z coordinates is not really needed. Frequently, due to the scenario of usage, the set of potential locations of the nodes is limited, and assigning the nodes to proper potential position is sufficient. In this study, we consider a single dimensional case and we introduce a novel methodology to detect the geographic sequence of wireless sensor nodes indoors, using only a limited number of message exchanges among the involved nodes and statistical reasoning applied to the set of received signal strength measurements. The proposed approach is verified experimentally in heavy non-line-of-sight settings with high shadowing and multipath distortions.

The handshake gesture is an important part of the social etiquette in many cultures. It lies at the core of many human interactions, either in formal or informal settings: exchanging greetings, offering congratulations, and finalizing a deal are all activities that typically either start or finish with a handshake. The automated detection of a handshake can enable wide range of pervasive computing scanarios; in particular, different types of information can be exchanged and processed among the handshaking persons, depending on the physical/logical contexts where they are located and on their mutual acquaintance. This paper proposes a novel handshake detection system based on body sensor networks consisting of a resource-constrained wrist-wearable sensor node and a more capable base station. The system uses an effective collaboration technique among body sensor networks of the handshaking persons which minimizes errors associated with the application of classification algorithms and improves the overall accuracy in terms of the number of false positives and false negatives.

Energy management is a critical concern in wireless sensornets. Despite its importance, sensor network operating systems today provide minimal energy management support, requiring applications to explicitly manage system power states. To address this problem, we present ICEM, a device driver architecture that enables simple, energy efficient wireless sensornet applications. The key insight behind ICEM is that the most valuable information an application can give the OS for energy management is its concurrency. Using ICEM, a low-rate sensing application requires only a single line of energy management code and has an efficiency within 1.6 % of a hand-tuned implementation. ICEM’s effectiveness questions the assumption that sensornet applications must be responsible for all power management and sensornets cannot have a standardized OS with a simple API.

Summary form only given. Controlled experiments, with larger sensor networks configurations (100 + nodes), as well as discovering inefficiencies in their operation are rather complex. In this paper we present a concept of a testbed for WSNs supporting easy change of configuration, multi tier operation and precise observation of network behaviour using a wired backbone connectivity. The design considerations are accompanied by early usage experience

No abstract available.