Mobile crowd sensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data. Many incentive mechanisms for mobile crowd sensing have been proposed. However, none of them takes into consideration the cooperative compatibility of users for multiple cooperative tasks. In this paper, we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users. We study two bid models and formulate the Social Optimization Compatible User Selection (SOCUS) problem for each model. We also define three compatibility models and use real-life relationships from social networks to model the compatibility relationships. We design two incentive mechanisms, $MCT - M$ and $MCT - S$ , for the compatibility cases. Both of $MCT - M$ and $MCT - S$ consist of two steps: compatible user grouping and reverse auction. We further present a user grouping method through neural network model and clustering algorithm. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality and truthfulness. Moreover, $MCT - M$ can output the optimal solution. By using neural network and clustering algorithm for user grouping, the proposed incentive mechanisms can reduce the social cost and overpayment ratio further with less grouping time.

Wireless Power Transmission (WPT) has been widely used to replenish energy for wireless rechargeable sensor networks. This paper concerns the fundamental issue of robust fault-tolerant placement of wireless chargers for directional charging. Following the general directional charging model, we formulate the Charger Placement for Robust Coverage (CPRC) problem, which has continuous and infinite constraints, for resisting the wireless charger failure. We transform the problem to the equivalent integer program problem without performance loss by area partition and dominating strategy extraction. We show that the greedy algorithm achieves the logarithmic approximation ratio. We further formulate the Charger Placement for Robust Utility (CPRU) problem for resisting the sensor node failure. This problem also has continuous and infinite constraints. We transform the problem to the combinational optimization problem with finite strategy space through the techniques of charging power approximation, area discretization and dominating strategy extraction. We present the algorithm, which utilizes the combination of binary search and greedy algorithm, to solve the CPRU problem. We conduct both simulations and field experiments to validate our theoretical results. The simulation results show that the proposed algorithms for CPRC and CPRU can outperform comparison algorithms by at least 17.48% and 21.15%, respectively.

The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating the minimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

Charger placement is an important problem in improving the quality of service in wireless rechargeable sensor networks. This paper studies the problem of W ireless Ch A rger Placeme N t with Multiple ( D irectional) A ntennas (WANDA). The problem is described as follows: given a set of wireless chargers equipped with multiple directional antennas and a set of wireless rechargeable sensors, determine the chargers’ positions and orientations to maximize the overall charging utility. According to the relative positional relationship between the antennas, the problem is classified into Relative Orientation Fixed (WANDA-ROF) and Relative Orientation Unfixed (WANDA-ROU) situations. To address WANDA, we present a piecewise constant function to approximate the nonlinearity of charging power and propose an area discretization technique to reduce the infinite solution space to a limited one without performance loss. Then, we prove the monotonic submodularity of WANDA, and present a $\frac{1}{2}-\epsilon$ approximation algorithm for the ROF situation and a $\frac{1}{6}-\epsilon$ approximation algorithm for the ROU situation, all run in polynomial time. Finally, we conduct extensive simulation and experiments to show that our algorithms outperform comparison algorithms by at least 16% for ROF situation and 12% for ROU situation.

The multi-hop wireless charging technology has attracted a lot of attention, as it largely extends the charging range of chargers. Different from the existing work with single cost optimization, the objective of this article is to optimize the comprehensive cost, which is the combination of energy cost and deployment cost. We decompose the target problem into two sub-problems. The first sub-problem aims to minimize the deployment cost with energy capacity constraints. The proposed algorithm follows the greedy strategy, where the subset of sensor nodes for any charger is determined by finding the capacitated minimum spanning tree. The second sub-problem, which aims to maximize the reduction of comprehensive cost by adding chargers to the solution of the first sub-problem, is proved to be an unconstrained submodular set function maximization problem and can be solved by a 1/2-approximation randomized linear time algorithm for its equivalent problem. Through extensive simulations, we demonstrate that the proposed solution can reduce the comprehensive cost by 57.55% comparing with the benchmark algorithms.