The past few years have witnessed the dramatic proliferation of portable mobile devices, including smartphones (e.g., iPhone and Google Nexus) and tablet computers (e.g., iPad). These small-sized mobile devices, embedded with diverse sensors, can provide abundant sensing data about the environment and the human society, thus offering great opportunities to carry out crowdsensing. One primary objective of this project is to develop a mobile crowdsensing framework with fair pricing and task allocation. To this end, a key step is to resolve conflicting interests of different parties involved: 1) mobile users aim to maximize the profit for performing sensing tasks; 2) task owners strive to get their sensing tasks performed with high quality of sensing, at a cost as small as possible; and 3) the platform would desire social welfare maximization. Intellectual Merit. Appealing to the theory for Exchange Economy, the proposed research employs the notion of Walrasian Equilibrium as the overall metric, at which there exists a price vector for mobile users and an allocation for task owners such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). Under the common theme of joint pricing and task scheduling with constraints, the focus of this project is on devising algorithms that can achieve a Walrasian Equilibrium for mobile crowdsensing, for both cases where sensing tasks are either divisible or indivisible. Thrust I studies joint pricing and allocation for crowdsensing with divisible sensing tasks, via a strategic bargaining approach. The existence of a Walrasian Equilibrium will be investigated first, together with a centralized scheme used as a benchmark. Then, based on multi-lateral bargaining theory, decentralized algorithms will be devised where mobile users and task owners negotiate with each other to determine the pricing and allocation, and the convergence of the bargaining game output to a Walrasian Equilibrium will be investigated thoroughly. Thrust II will be devoted to joint pricing and allocation for crowdsensing with indivisible sensing tasks. One challenge in this more sophisticated setting is that there may not exist a Walrasian Equilibrium. In view of this, the notion of Combinatorial Walrasian Equilibrium (a relaxation of Walrasian Equilibrium) will be applied to characterize an optimal state. Since this relaxation may give rise to some inefficiency issues, the Tatonnement based approach will be taken to quantify the corresponding performance, in terms of the ratios to approximate the optimal social welfare and individual revenue. Further, decentralized solutions will be developed to achieve a Combinatorial Walrasian Equilibrium. Broader Impacts. This project serves as an excellent example for exploring innovative research on the interplay among engineering, economics and operation research. It will spur a new line of thinking for large-scale cyber-physical system applications. Another major task of this project is to integrate research into educational activities. Graduate students participating in this project will be trained on a variety of subjects, ranging from wireless networks to exchange economics, from game theory to bargain theory, and from network optimization to protocol design. The PI is strongly committed to promoting diversity by providing research opportunities to women and under-represented students. Key Words: Integrated Sensing, Crowdsensing, Pricing, Task Allocation, Walrasian Equilibrium
|Effective start/end date||8/1/14 → 7/31/18|
- National Science Foundation (NSF): $350,000.00
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