The extensive use of mobile intelligent devices, such as smart phones and tablets, induces new opportunity and challenge for computation offloading. Task offloading is an important issue in a system consisting of multiple types of devices, such as mobile intelligent devices, local edge hosts and a remote cloud server. In this paper, we study the offloading assignment of multiple applications, each one comprising several dependent tasks, in such a system. To evaluate the total cost in the offloading process, a new metric is introduced to take into account features of different devices. The remote server and local hosts are more concerned about their processors utilization, while mobile devices pay more attention to their energy. Therefore, this metric uses relative energy consumption to denote the cost of mobile devices, and evaluates the cost of the remote server and local hosts by the processor cycle number of task execution. We formulate the offloading problem to minimize the system cost of all applications within each application's completed time deadline. Since this problem is NP-hard, the heuristic algorithm is proposed to offload these dependent tasks. At first, our algorithm arranges all tasks from different applications in a priority queue considering both completed time deadline and task-dependency requirements. Then, based on the priority queue, all tasks are initially assigned to devices to protect mobile devices with low energy and make them survive in the assignment process as long as possible. At last, to obtain a better schedule realizing lower system cost, based on the relative remaining energy of mobile devices, we reassign tasks from high-cost devices to low-cost devices to minimize the system cost. Simulation results show that our proposed algorithm increases the successfully completed probability of whole applications and reduces the system cost effectively under time and energy constraints. INDEX TERMS Offloading, dependent tasks, mobile, cost.
Cooperative spectrum sensing which enhances the sensing accuracy is an important research issue for cognitive radio networks, especially in complicated environment. Considering the extensive use of mobile intelligent terminals such as smart phones and tablets, crowdsourced spectrum sensing, which assigns spectrum sensing tasks to mobile terminals, can take advantage of mobile terminals' cooperation and obtain the accurate sensing results. In this paper, crowdsourced spectrum sensing is studied to propose assignment scheme of spectrum sensing tasks in large geographical areas. There may be several kinds of terrains affecting sensing in large-scale regions. Hence, according to the terrains, we divide a large region into several sub-regions and introduce sensing effect function to evaluate the sensing accuracy based on the number of sensing sub-regions. Furthermore, considering energy consumption is an important issue which mobile terminals focus on, we use the relative energy consumption to evaluate the cost of mobile terminals during spectrum sensing. Then, we formulate the crowdsourced sensing problem to minimize the total cost while keeping sensing effect not lower than the predefined threshold to maintain sensing accuracy. Since the problem is NP-hard, a heuristic algorithm is proposed to solve the crowdsourced sensing problem. At first, our algorithm arranges all sensing tasks in a priority queue based on their urgency. Then, sensing tasks are sequentially assigned to terminals with higher energy to prolong their survival time under makespan and energy constraints. To obtain the lowest system cost, we introduce remaining time and reassign sensing tasks from high-cost terminals to low-cost terminals based on the remaining time. Simulation results show our algorithm achieves higher performance than the other algorithms. INDEX TERMS Cost, crowdsourced, spectrum sensing.
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