Blockchain can provide a dependable environment for the Internet of Things (IoT), while the high computing power and energy required by blockchain hinder its applications in IoT. Offloading the computation at the resource-limited IoT devices to a cloud/edge computing service provider (CESP) is a feasible solution to the execution of computation-intensive blockchain tasks. The CESP provides computing resources to IoT users with a cloud and multiple edge servers that work collaboratively such that the users are able to perform mobile blockchain services. Resource allocation and pricing of computing resources at the cloud/edges have a significant impact on the revenues of CESP and users. Most of the existing works on the cooperative edge-cloud for computation offloading assumes that a user is mapped to a prespecified edge server or the cloud. However, the CESP may choose a server from either the edge servers or the cloud to run the offloaded tasks by jointly considering the cost and income of the service provisioning. In this article, we formulate a Stackelberg game with CESP as the leader and users as the followers for cloud/edge computing resource management. We prove the existence of Stackelberg equilibrium and analyze the equilibrium. We then model the resource allocation and pricing at the CESP as a mixed-integer programming problem (MIP) with the objective to optimize the CESP's revenue and propose an efficient iterative greedy-and-search-based resource allocation and pricing algorithm (IGS). The algorithm solves two subproblems comprising the CESP's revenue optimization problem: resource allocation under a given resource price and resource pricing based on a specified resource allocation scheme. The first subproblem evaluates where to execute the computing tasks via a greedy-and-search-based approach, whereas the second subproblem estimates the resource price through golden section search. We conduct experiments through simulations. Simulation results show that the proposed algorithm can effectively improve the revenue of both the CESP and the IoT terminals.

To deal with the problem of energy shortage in wireless sensor networks, wireless power transfer is a promising technology. Using a mobile device (MD) to charge multiple sensor nodes simultaneously is a more efficient way to meet the requirements from a large-scale network. In this article, a periodic multinode charging and data collection scheme based on MD is proposed to maintain a perpetual network operation, in which the network is divided into multiple cells, and the MD periodically traverses each cell to charge and collect data for the sensor nodes with the objective of maximizing the amount of data collected by the unit energy of the MD. To realize a multinode charging, an anchor optimization strategy to optimize the position of anchor point in the cell is proposed. Then, in order to solve the path planning problem of MD, a discrete fireworks algorithm (DFWA) based on population entropy (PE-FWA) is proposed. Compared with MD scheduling algorithm and DFWA, experimental results show that PE-FWA has better performance.