Considering the problems of low-enthusiasm workers and task expiration in the mobile crowd sensing system, a task assignment algorithm based on initial cost and soft time window was proposed. As the corresponding task assignment problem belongs to the category of NP-hard problems and the computationally efficient optimal algorithm cannot be found, thus, an algorithm was developed based on Discrete Cuckoo Search Algorithm (DCSA). Firstly, the corresponding global search process and local search process were designed respectively according to the problem characteristics. Secondly, to derive the better solution, the priorities of tasks with respect to the distance between tasks and workers' starting positions as well as the size of time windows were analyzed. Finally, feasible operations were executed to guarantee that the related constraints were satisfied by each task assignment. Compared with genetic algorithm and greedy algorithm, the simulation results show that DCSA-based task assignment algorithm can improve the enthusiasm of workers to participate, solve the problem of task expiration, and ultimately reduce the total system cost.

Due to the limited resources, nodes in Delay Tolerant Network (DTN) behave selfishly, i.e. nodes refuse to help forward message for others. In order to improve the cooperative behavior of nodes, and enhance the overall network performance, a new incentive mechanism of node behavior based on Evolutionary Game Theory (EGT) was proposed. In the proposed mechanism, the prisoner's dilemma model was employed to establish payoff matrix between the node and its neighbors. Then, based on the degree centricity, social authority of the node was defined. Further, when designing the strategy update rule, the influence of social authority was considered. That is, nodes with higher social authority were selected from the current neighborhood to imitate and learn. Finally, on the basis of real dynamic network topology, the simulation experiments were conducted by the Opportunistic Network Environment (ONE) simulator. The simulation results show that, compared with the Fermi update rule which chooses neighbors randomly, the strategy update rule which considers the social authority can promote the cooperative behavior, accordingly, improve the overall performance of the network.