The ability to detect and distinguish interactions in the workplace can shed light over productivity, team work and on employees' use of space. Questionnaires and direct observations have often been used as mechanisms to identify office based interactions, however, these are either very time consuming, yield coarse grained information or do not scale to large numbers of people. Technology has been recently employed to cut costs and improve output, however precise interaction dynamics gathering often requires individuals to wear custom hardware. In this paper, we present an extensive evaluation of Bluetooth Low Energy (BLE) as a technology to monitor people proximity in the workplace. We examine the key parameters that affect the accuracy of the detected contacts and their impact on power consumption. We study how this system can be implemented on popular wearable devices (i.e., Android Wear and Tizen) and the resulting limitations. Through a real world deployment in a commercial organisation with 25 participants we evaluate the performances of a BLE-based proximity detection technique. Our results show the suitability of BLE for workplace interaction detection and give guidance to vendors and Operating System (OS) developers on the impact of the restrictions regarding the use of BLE on commodity wearables.

While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, can open doors for the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones.

Studies of automotive traffic have shown that on average 30% of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver's return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehicle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based positioning while still maintaining sufficient accuracy.

In this paper, we propose a robust multilateration algorithm for localizing sensor nodes in cluttered environments where the estimated distances between an unlocalized node and reference nodes with known coordinates may contain large errors due to non line of sight signal propagation. We show that the traditional least squares multilateration is severely affected even if one of the measured distances is erroneous whereas our approach functions properly even if half of the measured distances contain large errors due to non line of sight signals. Our algorithm is independent of the physical layer used to perform ranging and does not require the identification of direct and reflected signals or any prior information about the statistical properties of measurement errors or characterization of the environment where the sensor nodes are deployed.

In this paper, we propose an anchor free localization mechanism for wireless sensor networks. Our algorithm is based on a graph drawing approach and uses inter-node distances to localize sensor nodes in a local coordinate system up to a global translation, rotation and reflection without any absolute reference positions such as GPS or other anchor nodes. We show that it is possible to avoid folds and flips in the localized network layout by introducing long range constraints among non adjacent nodes which can be derived from inter-node distance measurements between adjacent nodes. We evaluate the effect of different parameters like network shape, scale, average neighbors and ranging noise on our algorithm and compare it with an anchor based and a number of anchor free schemes. We also present experimental results from an actual sensor network showing the accuracy of our approach.

The availability of simulators and emulators that are tailored for wireless sensor networks (WSNs) is a necessary step in allowing accurate evaluation, but simply having the right tools is not sufficient to ensure that the results of experiments actually correspond to what might be expected in realistic deployments. The critical issue is what models are available in these tools, and if these are used in an appropriate manner. In this paper we review the current approaches to WSN experimentation and identify a serious shortcoming in common simulation methodology, specifically in regard to the choice of network topologies and traffic models. We hypothesize that this mismatch has an important impact on the sensitivity of published sensor network simulations. We support our hypothesis using an analysis of the directed diffusion protocol