Mobile Crowdsourcing (MCS) is now an important source of information for smart cities, especially with the help of unmanned aerial vehicles (UAVs) and driverless cars. They are equipped with different kinds of high-precision sensors, and can be scheduled/controlled completely during data collection, which will make MCS system more robust. However, they are limited to energy constraint, especially for long-term, long-distance sensing tasks, and cities are almost too crowded to set stationary charging station. Towards this end, in this paper we propose to leverage emerging Deep Reinforcement Learning (DRL) techniques for enabling model-free unmanned vehicles control, and present a novel and highly effective control framework, called "DRL-RVC". It utilizes the powerful Convolutional Neural Network (CNN) for feature extraction of the necessary information (including sample distribution, traffic flow, and etc.), then makes decisions under the guidance of the Deep Q Network (DQN). That is, UAVs will cruise in the city without control and collect most required data in the sensing region, while mobile unmanned charging station will reach the charging point in the shortest possible time. Finally, we validate and evaluate the proposed framework via extensive simulations based on a real dataset in Rome. Extensive simulation results well justify the effectiveness and robustness of our approach.

Mobile crowd sensing (MCS) with human participants has been proposed as an efficient way of collecting data for smart cities applications. However, there often exist situations where humans are not able or reluctant to reach the target areas, due to for example traffic jams or bad road conditions. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a task allocation method, called “UMA” ( U AV-assisted M ulti-task A llocation method) to optimize the sensing coverage and data quality. The method incentivizes human participants to contribute sensing data from nearby points of interest (PoIs), with a limited budget. Meanwhile, the method jointly considers the optimization of task assignment and trajectory scheduling. It schedules the trajectories of UAVs, considering the locations of human participants, other UAVs and PoIs which are rarely visited by human participants. In detail, UAVs take care of two tasks in our proposal. One is to calibrate the data collected by the human participants whom the UAVs come across along their trajectories. The other is to collect data from the PoIs which are not covered by other UAVs or human participants. We apply deep reinforcement learning to schedule UAVs moving trajectories and sensing activities in order to minimize the overall energy cost. We evaluate the proposed scheme via simulation using two real data sets. The results show that our proposal outperforms the compared methods, in terms of coverage completed ratio, calibrating ratio, energy efficiency, and task fairness.

Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and share various types of sensing data in urban environments. However, new challenges arise: one is how to evaluate the quality of data each user potentially capable of providing; another is how to allocate satisfactory yet profitable amount of reward to keep them participating in future tasks. In this paper, we first introduce a mathematical model for characterizing quality of sensing data to be contributed by mobile users. Then, we present a utility function and formulate an optimization problem to maximize the amount of high quality data, given a limited task budget. We next present an effective quality-aware incentive mechanism to solve this problem for online scenarios where participants may arrive or leave at any time. Moreover, the proposal allows the platform to provide selected participants extra bonus according to task completion level and their previous performance to motivate them further. We formally showed the proposed mechanism has the desirable properties of truthfulness, individual rationality, budgetary feasibility and computational efficiency. We compare the proposed scheme with existing methods using a real dataset. Extensive simulation results well justify the effectiveness and robustness of the proposed approach, e.g, compared with another online method "OMG", the gap to the optimum for our proposed Online-QIM approach is reduce by 33.3% when budget $B = 1000$ .

As a key enabling technology in intelligent heterogeneous Internet of Things(IoT), edge caching provides important support for reducing core network load and improving network service efficiency, especially for high bandwidth demand services represented by multimedia applications. However, external time-varying information is hard to be obtained comprehensively in a complicated heterogeneous IoT environment. Meanwhile, there exists the substitutability of content (e.g., videos with different bitrates), which is difficult to make caching decisions online in real-time to achieve fast feedback with low latency and avoid useless deployment. To this end, this paper designs a transcoding-enabled online cache scheme for IoT video service with cloud-edge-terminal collaboration. Firstly, we design a variable bitrate video routing strategy to dynamically retrieve content from cloud/edge according to user demands. Furthermore, the video caching problem is considered as an online convex optimization problem to learn utility gradient and determine the optimal caching strategy in real-time without any prior information. On this basis, we extend the problem to elastic networks with dynamic available resources, and prove the sublinear regret and sublinear constraint violation. Finally, we summarized 5 video request datasets, and carried out differentiated multiple verifications based on different request habits and content requirements. Compared with the most advanced algorithms in terms of delay, we evaluated the performance advantages of the proposed scheme.

Mobile Crowd Sensing (MCS) acts as a key component of Internet of Things (IoTs), which has attracted much attention. In an MCS system, participants play an important role, since all the data is collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high quality data. In this paper, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants’ profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations which relatively few data has so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality and truthfulness. We evaluate the proposed scheme via simulation using three real datasets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with other two methods.

The rapid advancement in robotics technology has catalyzed the emergence of Internet of the Robotic Things crowdsourcing, a novel paradigm in the digital economy era. However, the escalating complexity of crowdsourcing tasks, coupled with the constrained resources of robot nodes and the diverse demands of stakeholders, has resulted in inefficiency, which poses a significant challenge to the burgeoning robot crowdsourcing market in Internet of Things. To address these challenges, we develop a comprehensive analytical model that encapsulates the interests of both task scheduler nodes (TNs) and robotic nodes (RNs) within Internet of Things, which integrates various factors, such as communication, computation, mobility, latency, and energy, thereby tailoring utility functions for different stakeholders. Second, we propose a task-driven robotic crowdsourcing strategy based on hierarchical game (TRC-HG), which conceptualizes TNs and RNs as rational entities with sequential actions. At the first layer, the interaction between participants is transformed into a Stackelberg game. This aids TNs in determining optimal pricing strategies while guiding RNs toward the most efficient task-completion strategies. Furthermore, we delve into the collaborative dynamics within the RNs, a cooperative coalition formation strategy at the second layer is established, which iteratively determines node responsibilities and coalition members. The Nash stability and optimality of coalition are ensured, thereby maximizing the utility of RNs. Finally, we validate the performance through a series of high-fidelity simulation experiments. These experiment results, benchmarked against classical methods, highlight significant improvements in terms of latency, energy consumption, and cooperative efficiency.