254 research outputs found
Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges
In recent years, blockchain has gained widespread attention as an emerging
technology for decentralization, transparency, and immutability in advancing
online activities over public networks. As an essential market process,
auctions have been well studied and applied in many business fields due to
their efficiency and contributions to fair trade. Complementary features
between blockchain and auction models trigger a great potential for research
and innovation. On the one hand, the decentralized nature of blockchain can
provide a trustworthy, secure, and cost-effective mechanism to manage the
auction process; on the other hand, auction models can be utilized to design
incentive and consensus protocols in blockchain architectures. These
opportunities have attracted enormous research and innovation activities in
both academia and industry; however, there is a lack of an in-depth review of
existing solutions and achievements. In this paper, we conduct a comprehensive
state-of-the-art survey of these two research topics. We review the existing
solutions for integrating blockchain and auction models, with some
application-oriented taxonomies generated. Additionally, we highlight some open
research challenges and future directions towards integrated blockchain-auction
models
Incentive Mechanism Design in Mobile Crowdsensing Systems
In the past few years, the popularity of Mobile Crowdsensing Systems (MCSs) has been greatly prompted, in which sensory data can be ubiquitously collected and shared by mobile devices in a distributed fashion. Typically, a MCS consists of a cloud platform, sensing tasks, and mobile users equipped with mobile devices, in which the mobile users carry out sensing tasks and receive monetary rewards as compensation for resource consumption ( e.g., energy, bandwidth, and computation) and risk of privacy leakage ( e.g., location exposure). Compared with traditional mote-class sensor networks, MCSs can reduce the cost of deploying specialized sensing infrastructures and enable many applications that require resources and sensing modalities beyond the current mote-class sensor processes as today’s mobile devices (smartphones (iPhones, Sumsung Galaxy), tablets (iPad) and vehicle-embedded sensing devices (GPS)) integrate more computing, communication, and storage resources than traditional mote-class sensors. The current applications of MCSs include traffic congestion detection, wireless indoor localization, pollution monitoring, etc . There is no doubt that one of the most significant characteristics of MCSs is the active involvement of mobile users to collect and share sensory data.
In this dissertation, we study the incentive mechanism design in mobile crowdsensing system with consideration of economic properties.
Firstly, we investigate the problem of joining sensing task assignment and scheduling in MCSs with the following three considerations: i) partial fulfillment, ii) attribute diversity, and iii) price diversity. Then, we design a distributed auction framework to allow each task owner to independently process its local auction without collecting global information in a MCS, reducing communication cost. Next, we propose a cost-preferred auction scheme (CPAS) to assign each winning mobile user one or more sub- working time durations and a time schedule-preferred auction scheme (TPAS) to allocate each winning mobile user a continuous working time duration.
Secondly, we focus on the design of an incentive mechanism for an MCS to minimize the social cost. The social cost represents the total cost of mobile devices when all tasks published by the MCS are finished. We first present the working process of a MCS, and then build an auction market for the MCS where the MCS platform acts as an auctioneer and users with mobile devices act as bidders. Depending on the different requirements of the MCS platform, we design a Vickrey-Clarke-Groves (VCG)-based auction mechanism for the continuous working pattern and a suboptimal auction mechanism for the discontinuous working pattern. Both of them can ensure that the bidding of users are processed in a truthful way and the utilities of users are maximized. Through rigorous theoretical analysis and comprehensive simulations, we can prove that these incentive mechanisms satisfy economic properties and can be implemented in reasonable time complexcity.
Next, we discuss the importance of fairness and unconsciousness of MCS surveillance applications. Then, we propose offline and online incentive mechanisms with fair task scheduling based on the proportional share allocation rules. Furthermore, to have more sensing tasks done over time dimension, we relax the truthfulness and unconsciousness property requirements and design a (ε, μ)-unconsciousness online incentive mechanism. Real map data are used to validate these proposed incentive mechanisms through extensive simulations.
Finally, future research topics are proposed to complete the dissertation
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing
The sharing-economy-based business model has recently seen success in the
transportation and accommodation sectors with companies like Uber and Airbnb.
There is growing interest in applying this model to energy systems, with
modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based
Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and
Battery Swapping Technology (BST). In this work, we exploit the increasing
diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly
enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits
spatial crowdsourcing, reinforcement learning, and reverse auction theory.
Specifically, the platform uses reinforcement learning to understand the
drivers' preferences towards different ride-sharing and energy-sharing tasks.
Based on these preferences, a personalized list is recommended to each driver
through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid
on their preferred tasks in their list in a reverse auction fashion. Then
e-Uber solves the task assignment optimization problem that minimizes cost and
guarantees V2G energy requirement. We prove that this problem is NP-hard and
introduce a bipartite matching-inspired heuristic, Bipartite Matching-based
Winner selection (BMW), that has polynomial time complexity. Results from
experiments using real data from NYC taxi trips and energy consumption show
that e-Uber performs close to the optimum and finds better solutions compared
to a state-of-the-art approachComment: Preprint, under revie
Understanding human-machine networks: A cross-disciplinary survey
© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends
Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling
Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.
Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating.
In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023
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