Pervasive and Mobile Computing最新文献

Sensor-based human activity recognition (HAR), with the ability to recognise human activities from wearable or embedded sensors, has been playing an important role in many applications including personal health monitoring, smart home, and manufacturing. The real-world, long-term deployment of these HAR systems drives a critical research question: how to evolve the HAR model automatically over time to accommodate changes in an environment or activity patterns. This paper presents an online continual learning (OCL) scenario for HAR, where sensor data arrives in a streaming manner which contains unlabelled samples from already learnt activities or new activities. We propose a technique, OCL-HAR, making a real-time prediction on the streaming sensor data while at the same time discovering and learning new activities. We have empirically evaluated OCL-HAR on four third-party, publicly available HAR datasets. Our results have shown that this OCL scenario is challenging to state-of-the-art continual learning techniques that have significantly underperformed. Our technique OCL-HAR has consistently outperformed them in all experiment setups, leading up to 0.17 and 0.23 improvements in micro and macro F1 scores.

Wireless power transfer (WPT) techniques are emerging as a fundamental component of next-generation energy management in mobile networks. In this context, the use of UAVs opens many possibilities, either using them as mobile energy storage devices to recharge IoT nodes, or to prolong their operation time via smart charging themselves at ground stations. This paper surveys the recent literature on WPT as it applies to UAVs and identifies several open research challenges for the future. As a first step, we tessellate the related research corpus in four fundamental categories (architectures, power and communications enabling technologies, optimization with respect to spatial concepts, optimization of operational aspects). Second, for each category, we provide a critical review of the recent WPT UAV approaches with respect to the way they specialize the general concept of WPT and the extent of their applicability. The survey presents the latest advances in WPT UAV methodologies and related energy-centric services, spanning all the way from the communications aspects deep in the small- and large-scale deployments, up to the operational and applications aspects. Finally, motivated by the rich conclusions of this critical analysis, we identify open challenges for future research. Our approach is horizontal, as the selected publications were drawn from across all vertical areas of research on UAVs. This paper can help the readers to deeply understand how WPT is currently applied to UAVs, and select interesting open research opportunities to pursue.

In the world of cloud technologies, serverless computing has now settled as a stable and promising resident. This gives a cloud provider the flexibility to provide its users with both Platform-as-a-Service (PaaS), i.e., the back-end application runs in a dedicated container, or Function-as-a-Service (FaaS), i.e., the back-end logic is offered as elementary functions that are invoked by the client applications. In parallel, edge computing has attracted a significant interest, due its enticing promises of reducing the outbound traffic of telco operators, while at the same time cutting down the user latency. As a result, in the near future, PaaS and FaaS containers are going to cohabit in a versatile computation infrastructure spanning from the far edge up to the cloud. In this paper we propose a mathematical formulation of a resource allocation problem that optimizes the assignment of both types of containers and can be solved efficiently by an edge orchestrator. We evaluate the proposed solution via extensive simulation experiments, which show that our approach, which takes into account the characteristics of PaaS vs. FaaS, provides significant performance benefits compared to less sophisticated strategies, despite its relatively low run-time complexity.

In this paper, a cooperative MEC system with multi-UAV and a ground access point (AP) is considered, in which UAVs can act as both a computing platform to help Internet of Things devices (IoTDs) deal with their computing tasks and a relay platform to offload some of the task data from IoTDs to the AP with higher computing ability. We aim to minimize the weighted overall energy consumption of UAVs and IoTDs by jointly optimizing connection scheduling, CPU frequency, task offloading bits and the flight trajectory of UAVs. The formulated problem is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is hard to solve. To tackle this problem, we divided it into three sub-problems and resolved them iteratively by the Lagrangian dual method and succession convex approximation (SCA) technique. Finally, an alternately iterative optimization algorithm is proposed. The numerical results show that our proposed algorithm has better performance compared to other benchmark algorithms.

Groundwater contamination poses serious threats to public health and environmental sustainability. In this paper, we explore smart groundwater contamination sensing, which aims to accurately estimate the nitrate concentration in groundwater via a crowdsensing approach. Existing solutions often require professional groundwater collection and high-quality measurement of groundwater properties, making the data collection process time-consuming and unscalable. In this work, we leverage the approximate nitrate concentration measured by crowd sensors (i.e., participants from well-dependent communities) to accurately estimate nitrate concentration in groundwater samples. Three critical challenges exist in developing the crowdsensing-based groundwater contamination estimation solution: (i) the spatial irregularity of the crowdsensing groundwater contamination data, (ii) the hidden temporal dependency of groundwater contamination in the anthropogenic context, and (iii) the uncertainty of crowdsensing nitrate measurements from crowd sensors. To address the above challenges, we develop CrowdWaterSens, an uncertainty-aware graph neural network framework that explicitly examines the uncertainty and spatial irregularity of the crowdsensing groundwater contamination data and its relevant anthropogenic context to accurately estimate groundwater nitrate concentration. We evaluate the CrowdWaterSens framework through two real-world case studies in well-dependent communities in Northern Indiana, United States. The evaluation results not only show the effectiveness of CrowdWaterSens in accurately estimating nitrate concentration, but also demonstrate the viability of crowdsensing for community-level groundwater quality monitoring.

Internet of Things (IoT) products provide over-the-net capabilities such as remote activation, monitoring, and notifications. An associated mobile app is often provided for more convenient usage of these capabilities. The perceived quality of these companion apps can impact the success of the IoT product. We investigate the perceived quality and prominent issues of smart-home IoT mobile companion apps with the aim of deriving insights to: (i) provide guidance to end users interested in adopting IoT products; (ii) inform companion app developers and IoT producers about characteristics frequently criticized by users; (iii) highlight open research directions. We employ a mixed-methods approach, analyzing both quantitative and qualitative data. We assess the perceived quality of companion apps by quantitatively analyzing the star rating and the sentiment of 1,347,799 Android and 48,498 iOS user reviews. We identify the prominent issues that afflict companion apps by performing a qualitative manual analysis of 1,000 sampled reviews. Our analysis shows that users’ judgment has not improved over the years. A variety of functional and non-functional issues persist, such as difficulties in pairing with the device, software flakiness, poor user interfaces, and presence of issues of a socio-technical impact. Our study highlights several aspects of companion apps that require improvement in order to meet user expectations and identifies future directions.