Pervasive computing environments are characterized by a plethora of sensing and communication-enabled devices that diffuse themselves among different users. Built-in sensors and telecommunication infrastructure allow co-presence detection. In turn, co-presence detection enables context-aware applications, like those for social networking among close-by users, and for modeling human behavior. We aim to support developers building better context-aware applications by a deepened understanding of which set of context information is appropriate for co-presence detection. We have gathered a multimodal dataset for proximity sensing, including several proximity verification sets, like Bluetooth, Wi-Fi, and GSM encounters, to be able to associate sensor's data with a spatial granularity. We show that sensor modalities are suitable to recognize the spatial adjacency of users with different spatio-temporal granularity. We find that individual user mobility has only a minor, negligible effect on co-presence detection. In contrast, the heterogeneity of device's sensor hardware has a major negative impact on co-presence detection. To reveal energy pitfalls with respect to usability, we perform an energy analysis pertaining to the usage stemming from different sensors for co-presence detection.

For indoor IoT environments, spontaneous device associations are of particular interest where users establish a connection in an ad-hoc manner to enable serendipitous interaction. For instance, between a user's personal device and devices the user encounters in the surrounding environment. Our system for device grouping named DevLoc takes advantage of ubiquitous light sources around us to perform continuous device grouping based on the similarity of light signals. To control the spatial granularity of user's proximity, we provide a configuration framework to manage the lighting infrastructure through customized visible light communication. We support two modes of device associations to achieve a binding between different entities: device-to-device and device-to-area allowing either proximity-based or location-based services. Our device grouping includes several methods where in general the machine learning based signal similarity performs best compared to distance and correlation metrics.

Visible Light Communication (VLC)emerges as a communication technology for Internet of Things (IoT)services with appealing benefits not present in existing radio-based communication. However, current VLC designs commonly require dedicated LED lights to emit modulated light beams which entail high energy overhead and unpleasant visual experiences due to the perceptible light blinking effects for end users. This greatly limits the deployment and applicable scenarios of VLC. In this paper, we design and develop LocalVLC, a practical and low-cost VLC system that can be used as a standard light source to augment smart IoT services. LocalVLC introduces a novel Morse-code inspired modulation scheme that can operate on off-the-shelf LEDs with low energy overhead. It can effectively overcome the light flickering by encoding data into high frequency light pulses without requiring extra processing hardware such as FPGA or micro-controller. We have implemented and evaluated a full-fledged system prototype based on LocalVLC design. Under practical settings, our LocalVLC prototype can support up to 10 meters of range, and attain reasonable throughput (up to 1.4 Kbps)with low error rate and energy consumption. Comparing with the widely adopted Manchester encoding, LocalVLC yields 8x improvement on both throughput and energy consumption. In addition, we demonstrate the practicality of LocalVLC through two IoT use cases where we developed two lightweight LocalVLC-based solutions using low-cost off-the-shelf hardware to exemplify the usage of LocalVLC for indoor service discovery and smart home key management.

The number of deployed Internet of Things (IoT) devices is steadily increasing to manage and interact with community assets of smart cities, such as transportation systems and power plants. This may lead to degraded network performance due to the growing amount of network traffic and connections generated by various IoT devices. To tackle these issues, one promising direction is to leverage the physical proximity of communicating devices and inter-device communication to achieve low latency, bandwidth efficiency, and resilient services. In this work, we aim at enhancing the performance of indoor IoT communication (e.g., smart homes, SOHO) by taking advantage of emerging technologies such as visible light and ultrasound. This approach increases the network capacity, robustness of network connections across IoT devices, and provides efficient means to enable distance-bounding services. We have developed communication modules using off-the-shelf components for visible light and ultrasound and evaluate their network performance and energy consumption. In addition, we show the efficacy of our communication modules by applying them in a practical indoor IoT scenario to realize secure IoT group communication.

Towards private proximity services we realized a set of proximity services at different spatial resolutions. For small-scale (0.5 m) securing remote access to smart homes and for mid-scale (10-20 m) to manage nearby Internet of Things (IoT) devices and offer fine-grained service discovery in indoor environments. Regarding large-scale services (100 m) we implemented a device grouping via similarity of light patterns ambient sound Wi-Fi signals and ultrasound communication which is naturally restricted by spatial barriers. To improve user's privacy from a system point of view we analyzed different security mechanisms in the domain of device-to-device (D2D) communication such as access control location privacy. Based on visible light communication (VLC) we are implemented and tested a system for private indoor service discovery and distance-bounding authorization. Furthermore we examined the feasibility of homomorphic encryption for time-series data like visible light patterns.

Future computing environments are embedded with many sensors for applications like augmented reality. Much of the deployed Internet of Things (IoT) technology is designed to be invisible. To support user's privacy awareness, a map of surrounding sensing devices is beneficial to determine the nature of data collection taking place in any given area. Moreover, security and governance issues are among the challenges IoT poses to organizations which might not know exactly which IoT devices are connected to their network. For instance, many employees bringing their own devices to the workplace. We explore the feasibility to use small COTS drones to create indoor maps of wireless devices. These comprehensive device maps serve as basis for device localization and monitoring to enhance user privacy and network security. We analyze the impact of our device management platform at the drone's energy consumption and evaluate the device detection rate, explored area, and localization error. Due to the restricted battery capacity of the drone, we simulate larger areas with a varying number of IoT devices to highlight the limits of our drone-based device management platform regarding area exploration and reachable IoT devices.