Chongyu Zhou

QOATA: QoI-aware task allocation scheme for mobile crowdsensing under limited budget

The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowdsensing a promising approach for large-scale data collection. In mobile crowdsensing, a specific situation is that the service provider (SP) needs to recruit contributors to fulfill sensing tasks requested by consumers. There are several challenges for contributor selection and task allocation in mobile crowdsensing. Firstly, since some of the contributors may need to travel a certain distance to complete the sensing task, they may need to be compensated for their contributions, in proportion to the distance they need to travel. Secondly, the diversity in sensing devices and contributor behavior means that the sensing data from different contributors will have different Quality of Information (QoI). Moreover, the SP usually only has a limited budget to compensate the contributors. Thus, how to handle the trade-offs between traveling distances of the contributors and QoI of the sensing data in order to maximize the sensing revenue based on limited budget is an important consideration in mobile crowdsensing applications. In practice, the QoI from different contributors are usually unknown to the SP in advance, which makes contributor selection and task allocation even more challenging. In this paper, we propose a QoI-aware task allocation scheme (QOATA) for contributor selection and task allocation in mobile crowdsensing applications. Using an online learning approach to learn the QoI from different contributors, the scheme aims to achieve the highest sensing revenue under a limited budget. The effectiveness of QOATA is evaluated through extensive simulations.

Optimizing Graphical Model Structure for Distributed Inference in Wireless Sensor Networks

Graphical models have been widely applied in distributed network computation problems such as inference in large-scale sensor networks. While belief propagation (BP) based on message passing is a powerful approach to solving such distributed inference problems, one major challenge, in the context of wireless sensor networks, is how to systematically address the trade-off between energy efficiency and inference performance. Although various energy-efficient message passing algorithms based on a given graphical model have been proposed in the literature, little work has been done to optimize the graphical model structure to achieve good energy efficiency and inference performance at the same time. In this paper, we propose an efficient distributed algorithm for optimizing the graphical model structure in order to minimize the communication cost required by the inference algorithm without incurring significant performance loss. We first formulate the problem as a multi-objective constrained problem and prove its NP-hardness. Then, we propose an efficient heuristic to solve the problem in polynomial time. Through extensive simulations, using both real-world sensor network data and synthesized data, we empirically evaluate our proposed graphical model structure optimization framework. The simulation results demonstrate that the optimized graphical model efficiently balances the performance of the inference algorithm (measured by mean squared error) and the energy consumed by the inference algorithm (measured by energy used in communication). These highlight the advantages of our proposed framework.

A RESTful web networking framework for vital sign monitoring

The burgeoning cost of healthcare has forced industry experts and academics to rethink current healthcare systems. There is a compelling argument that the increasing costs are primarily due to the fundamentally reactionary approach used in healthcare today, with the focus being on treatment and cure rather than on prevention. Consequently, building a sustainable healthcare system requires a paradigm shift towards a proactive prevention based approach. A critical requirement of such a system is the ubiquitous collection of and access to patient physiological data. Both real-time and historical analysis of this data is the basis of a preventive healthcare system. To this end, we are developing a pervasive real-time health monitoring framework to seamlessly connect both patients and healthy people to healthcare professionals. In this paper, we present the design of this framework, which is intended to be lightweight, agile and scalable. The design primarily utilizes a resource-oriented architecture (ROA) based RESTful HTTP to connect wireless biosensors, wireless networks and a cloud computing platform. The paper also discusses a proof-of-concept implementation of the framework that demonstrates the effectiveness of the technology choices made in achieving the required design goals.

Auction Meets Queuing: Information-Driven Data Purchasing in Stochastic Mobile Crowd Sensing

The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowd sensing (MCS) a promising approach for large-scale event detection and collective knowledge formation. In a typical MCS system, the crowdsourcer purchases sensing data from some mobile phone users (i.e., contributors) and sells it to consumers for revenue. This kind of sensing data exchange has its unique challenges in practical MCS systems. On one hand, the crowdsourcer wants to maximize the information utility to get the most revenue under heterogeneous requests from the consumers while offering incentives to strategic contributors. On the other hand, the contributors need to make optimal real-time sensing and data selling decisions by considering their real-time sensing cost and quality of information, in order to maximize their own profit. In this paper, we propose a novel Information-driven Data Auction (IDA) scheme for data exchange in practical stochastic MCS systems, which offers optimal strategies for both the crowdsourcer and the contributors. By applying stochastic Lyapunov optimization and mechanism design theory, IDA is able to achieve a near-optimal time-averaged system-wide utility, while offering incentives to the contributors. Moreover, IDA achieves favourable economic properties including truthfulness, individual rationality, and budget balance. We demonstrate the efficacy of IDA through rigorous theoretical analysis and comprehensive simulations.