LAUSR

User-initiated mobile data trading, where mobile devices trade their mobile data quota via personal hotspots, is a promising approach to improve resource utilization. Most existing works only consider the data size, while ignoring the data rate and temporal requirements. To fill this void, we propose a novel data trading marketplace, where mobile users trade Internet access continuously for a time period with a specific data rate with neighboring mobile devices. Each request is characterized by an arrival time, departure time, the demanded data rate, and a value for getting services. To achieve the most system efficiency, we formulate an integer linear programming problem to maximize the total social welfare, which takes the data rate and temporal requirements into account. We next consider two request models: 1) a homogeneous request model and 2) a heterogeneous request model. In the homogeneous request model, all the requests demand the overall system lifetime, and we propose a computationally efficient auction that makes allocation decisions for all the requests simultaneously. In the heterogeneous request model, all the requests require different Internet access periods and dynamic arrive. Upon requests’ arrival, the system must make real-time allocations without the availability of future information. To jointly deal with requesters’ multidimensional private information (i.e., the arrival/departure time, demanded data rate, and the value), and the uncertainty about future arrival requests, we propose a multi-round online auction. Theoretical analysis shows that both the auctions satisfy the desired properties, including individual rationality, truthfulness, and computational efficiency. Simulation results show the efficiency of the proposed auctions.