Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus — a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by$\sim 4\times$and the energy consumption by$\sim$77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.

Bluetooth trackers, or tags, have quickly become ubiquitous and widely supported by multiple vendors. Beyond their original design of finding lost objects, these devices have the ability to extend the capabilities of current wireless smart devices. Since its launch in 2019, Apple’s FindMy enables any devices from their brand to be easily tracked by more than 1 billion active iPhones and iPads on the market. While convenient, these systems may even serve further uses, including as a result of this work, crowd sensing and a side channel for mobile communication. But they also raise privacy concerns for their users. In this paper, we demonstrate how Apple FindMy can be used as a privacy-friendly tool for crowd monitoring, and how it may inadvertently leak information on a person’s location in case of deliberate tracking. Additionally, we design and evaluate a proof of concept protocol, using the Apple FindMy and a crafted tag using a simple microcontroller. We show how such system could be used to transmit information at very low bit rates, while the devices transporting the information remain unaware of this covert channel, yielding an out of band communication channel.

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.

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology. We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.

Two major factors affecting mobile network performance are mobility and traffic patterns. Simulations and analytical-based performance evaluations rely on models to approximate factors affecting the network. Hence, the understanding of mobility and traffic is imperative to the effective evaluation and efficient design of future mobile networks. Current models target either mobility or traffic, but do not capture their interplay. Many trace-based mobility models have largely used pre-smartphone datasets (e.g., AP-logs), or much coarser granularity (e.g., cell-towers) traces. This raises questions regarding the relevance of existing models, and motivates our study to revisit this area. In this study, we conduct a multidimensional analysis, to quantitatively characterize mobility and traffic spatio-temporal patterns, for laptops and smartphones, leading to a detailed integrated mobility-traffic analysis. Our study is data-driven, as we collect and mine capacious datasets (with 30TB, 300k devices) that capture all of these dimensions. The investigation is performed using our systematic (FLAMeS) framework. Overall, dozens of mobility and traffic features have been analyzed. The insights and lessons learnt serve as guidelines and a first step towards future integrated mobility-traffic models. In addition, our work acts as a stepping-stone towards a richer, morerealistic suite of mobile test scenarios and benchmarks.