CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism

An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications

abstract: Internet of Things (IoT) is emerging as part of the infrastructures for advancing a large variety of applications involving connections of many intelligent devices, leading to smart communities. Due to the severe limitation of the computing resources of IoT devices, it is common to offload tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, this offloading method suffers from both high latency and network congestion in the IoT infrastructures. Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks. In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Security and Privacy in Dynamic Spectrum Access: Challenges and Solutions

abstract: Dynamic spectrum access (DSA) has great potential to address worldwide spectrum shortage by enhancing spectrum efficiency. It allows unlicensed secondary users to access the under-utilized spectrum when the primary users are not transmitting. On the other hand, the open wireless medium subjects DSA systems to various security and privacy issues, which might hinder the practical deployment. This dissertation consists of two parts to discuss the potential challenges and solutions. The first part consists of three chapters, with a focus on secondary-user authentication. Chapter One gives an overview of the challenges and existing solutions in spectrum-misuse detection. Chapter Two presents SpecGuard, the first crowdsourced spectrum-misuse detection framework for DSA systems. In SpecGuard, three novel schemes are proposed for embedding and detecting a spectrum permit at the physical layer. Chapter Three proposes SafeDSA, a novel PHY-based scheme utilizing temporal features for authenticating secondary users. In SafeDSA, the secondary user embeds his spectrum authorization into the cyclic prefix of each physical-layer symbol, which can be detected and authenticated by a verifier. The second part also consists of three chapters, with a focus on crowdsourced spectrum sensing (CSS) with privacy consideration. CSS allows a spectrum sensing provider (SSP) to outsource the spectrum sensing to distributed mobile users. Without strong incentives and location-privacy protection in place, however, mobile users are reluctant to act as crowdsourcing workers for spectrum-sensing tasks. Chapter Four gives an overview of the challenges and existing solutions. Chapter Five presents PriCSS, where the SSP selects participants based on the exponential mechanism such that the participants' sensing cost, associated with their locations, are privacy-preserved. Chapter Six further proposes DPSense, a framework that allows the honest-but-curious SSP to select mobile users for executing spatiotemporal spectrum-sensing tasks without violating the location privacy of mobile users. By collecting perturbed location traces with differential privacy guarantee from participants, the SSP assigns spectrum-sensing tasks to participants with the consideration of both spatial and temporal factors. Through theoretical analysis and simulations, the efficacy and effectiveness of the proposed schemes are validated.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Study on incentive mechanisms of smes crowdsourcing contest innovation.

Lighterness, Paul - Associate SupervisorDealing with insufficient resources is a common challenge yet practical reality for many project managers working within SMEs. With the rise of Web 2.0, crowdsourcing contest innovation (CCI) it is now possible for project managers to use online platforms as a way to collaborate with external agents to fill this resource gap and thus improve innovation. This research uses agent-based modelling to prognosticate the efficacy of crowdsourcing contest innovation with a particular focus on the project manager ‘seeker’ within an SME initiating competitive crowdsourced contest teams made up of individual ‘solver’ participants. The contribution of knowledge will benefit the open innovation community to better understand the main motivational incentives to obtain maximum productivity of a team with limited project management resources. In pursuit of this, the social exchange theory is challenged, this thesis explores the motivation factors that influence solvers to participate in SMEs CCI from the perspectives of benefit perception and cost perception. The results found that non-material factors such as knowledge acquisition and sharing, reputation can stimulate solvers to participate in SMEs CCI more than material (physical money) rewards. Meanwhile, risks such as intellectual property risks and waste of resources are significant participation obstacles. Based on this, the principal- agent theory is used to design the models of team collaboration material incentive mechanism, dynamic reputation incentive mechanism and knowledge sharing incentive mechanism, and the performance of each incentive mechanism is analysed. At last, according to the principles of sample selection, Zbj.com, the China’s most successful crowdsourcing platform of which the main clients are SMEs, is chosen as the research object, and the effectiveness of the incentive mechanisms designed in this thesis is verified. It is found that the material and non-material incentives have been partially applied on the platform, and the explicit, implicit and synergistic effects of incentives are preliminarily achieved. According to the research results, it is suggested that the guarantee measures of the incentive mechanisms should be further developed, such as optimising pricing services and refining task allocation rules.PhD in Water, including Desig