Mobile crowdsensing is a sensing paradigm using mobile users’ smart devices to perform sensing tasks, which has attracted much attention due to its low system cost, high flexibility, and wide coverage. In this paper, we study the hybrid sensing online task allocation problem for maximizing the total quality of completed tasks under given budget constraint. We formulate this problem as a 0-1 integer programming. To address this problem, we propose an efficient hybrid sensing based online task assignment algorithm (HSTA), which consists of two major components: Expected task completion quality based opportunistic user recruitment and participatory user recruiting and path planning. We present the detailed algorithm design of HSTA and deduce its computational complexity. Simulation results demonstrate the effectiveness of the proposed HSTA algorithm.

Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives. In this sensing paradigm, mobile users can make full use of such sensors-rich smart devices for task executions. Recently, social mobile crowdsensing (SMCS) has received a lot of attention and much work has been carried out. Many incentive mechanisms exploit the social relations among users/workers for improving the system performance. However, most existing work in this area focused on offline and socially-aware scenarios. In this paper, we study both online socially-aware and socially-unaware scenarios for maximizing the platform utility. We formulate the problem of worker selection for maximizing the platform utility and prove this problem is NP-hard. For the socially-aware scenario, we propose an incentive mechanism (called SA-WGRA), which adopts sociality and capability based clustering algorithm for Worker Group formation and uses Reverse Auction for worker selection. For the socially-unaware scenario, we propose an incentive mechanism (called SUA-CGRA), which adopts Coalitional Game combined with Reversed Auction for worker selection. We prove that both mechanisms achieve computational efficiency, individual rationality, and platform rationality. Moreover, for SUA-CGRA, we prove that its formed coalitions satisfy coalition rationality, and further each of its formed coalitions is convex and hence the Shapley value is in the core solutions for profit distribution in each formed coalition. Simulations results show that both SA-WGRA and SUA-CGRA can effectively improve the platform utility.

Mobile edge computing (MEC) is a promising computing paradigm and can effectively reduce the energy consumption and computing costs at mobile devices by offloading computation-intensive and latency-sensitive applications/tasks to edge servers. However, how to achieve cost-effective dependent task offloading and resource allocation subject to application completion time constraint and service configuration constraint at edge side in heterogeneous MEC environments remains a challenge. To address this challenge, in this paper, we study the multi-application dependent task offloading and resource allocation problem in heterogeneous MEC environments for jointly minimizing the energy consumption and computing cost. We first formulate this problem as a mixed integer nonlinear programming (MINLP) problem. We propose a two-stage alternating optimization algorithm. In the first stage, a genetic-based algorithm is proposed to determine an optimized task offloading profile for given transmit power matrix, a look ahead based task scheduling algorithm is designed to obtain an optimized task schedule for the profile. In the second stage, the transmit power allocation problem for a given offloading profile is solved using convex optimization techniques. Extensive simulation results show that the proposed algorithm can effectively reduce the total cost of task executions as compared with baseline algorithms.