Mobile crowdsensing has been a promising and cost-effective sensing paradigm for the Internet of Things by exploiting the proliferation and prevalence of sensor-rich mobile devices held/worn by mobile users. In this paper, we study the task assignment and path planning problem in mobile crowdsensing, which aims to maximize total task quality under constraints of user travel distance budgets. We first formulate the problem mathematically when all task and user arrival information is known a priori and prove it to be NP-hard. Then, we focus on studying the scenarios where users and tasks arrive dynamically and accordingly design four online task assignment algorithms, including quality/progress-based algorithm (QPA), task-density-based algorithm (TDA), travel-distance-balance-based algorithm (DBA), and bio-inspired travel-distance-balance-based algorithm (B-DBA). All the four algorithms work online for task assignment upon arrival of a new user. The former three algorithms work in a greedy manner for assigning tasks, one task each time, where the QPA prefers the task leading to largest ratio of task quality increment to travel cost, the TDA tends to guide user to high-task-density areas, and the DBA further considers travel distance balance information. The last algorithm B-DBA integrates the travel-distance-balance-aware metric in the DBA and bio-inspired search for further improved task assignment performance. Complexities of the proposed algorithms are deduced. Simulation results validate the effectiveness of our algorithms; B-DBA has the best performance among the four algorithms in terms of task quality, and furthermore, it outperforms existing work in this area.

With the advancement of wireless communication technologies and rich embedded sensors in smart mobile devices, mobile crowdsensing has become an attractive paradigm for collecting sensing data from surrounding environments. Effective incentive mechanisms often play an important role in guaranteeing high quality of services and wide involvement of workers. Much work has been carried out in this aspect to encourage workers to strenuously and truthfully provide high quality data. However, task quality does not only depend on workers’ subjective effort or truthfulness, but also workers’ distribution and task quality growth law. In this paper, we consider a scenario where worker arrivals are unevenly distributed and the growth of task quality conforms to the law of diminishing margin of workers’ efforts. We propose a Quality-driven Online Task-Bundling-based incentive mechanism (QOTB). The design objective is to maximize the social welfare while maximally satisfying the task quality requirements. In QOTB, we introduce Mental accounting Theory to build accounts for task execution profit and bonus, respectively, which are then used to derive the participation willingness of workers. We adopt task bundling to stimulate workers to change their original travel schedules for balancing the task participations according to the popularities of task locations and also the traveling cost. We present the detailed mechanism design of QOTB. We prove that the QOTB mechanism has desired properties of willingness truthfulness, individual rationality, and computation efficiency. We conduct extensive simulations and the results show that QOTB can effectively improve the social welfare while satisfying the task quality requirements.

Incentive mechanism has been an important research direction in mobile crowdsensing. An effective incentive mechanism is critical to ensure the adequate number of participants/workers by providing them proper rewards. However, existing incentive mechanisms lack consideration on potential contributions of individual workers when recruiting new workers and retaining existing workers in the system. In this article, we propose a reverse auction-based incentive mechanism (RAIN), which considers participants' potential contributions when recruiting new workers, performing reverse auctions, and retaining existing workers. The design objective is to optimize the worker composition in the system while reducing the system cost. In RAIN, the potential contribution of a user to the system is measured as the degree at which the user's joining or staying in the system can remedy the inadequacy of workers for task auction/execution at the frequently visited locations of the user. We present design details of RAIN which includes selective worker recruitment, reverse auction based on biased bids, and selective retaining of auction losers, all based on individual users' potential contributions to the system. Extensive simulation results show that RAIN can effectively optimize the worker composition in a system and also effectively reduce the system cost.

To satisfy the explosive increasing demands of cellular traffic, content caching at the network edge (e.g., User Equipment) has become a promising paradigm in the next-generation cellular networks. Device-to-Device (D2D) communications can improve the content caching and fetching performance without deploying additional infrastructure. In this paper, we investigate the joint optimization of cooperative caching and fetching in dynamic D2D environment for minimizing the overall content fetching delay. We formulate it as a decentralized partially observable Markov game for finding the optimal policies at agents. To address this problem, we propose a Fully Decentralized Soft Multi-Agent Reinforcement Learning (FDS-MARL) algorithm, which extends the soft actor-critic framework to non-stationary multi-agent environment for fully decentralized learning and it contains the following major design components: Graph Attention Network based self-attention for cooperative inter-agent coordination, a consensus communication mechanism for effectively reducing the information loss and non-stationarity of the environment while keeping gradual global consensus, and an influence based transmission scheduling mechanism for effective credit assignment and also alleviation of potential transmission contentions among agents. Simulation results show that FDS-MARL can improve the content caching and fetching performance significantly compared with the representative work in the literature.

Fingerprint-based indoor localization is one of the most promising solutions for various Intelligent Internet of Things (IIoT) systems. However, recent studies show that the key design challenges of current fingerprint-based localization techniques come from the following three aspects: 1) temporal variation caused by various patterns of IIoT device operations and stochastic fluctuation of wireless signals; 2) spatial unevenness of collected received signal strength indicator (RSSI) samples due to complex multifloor environments; and 3) high feature sparsity of collected RSSI samples in large areas. To address these challenges, we present a localization architecture for multifloor indoor localization in multibuilding environment and accordingly propose a fingerprint-based localization method (referred to as GrowNetLoc) based on gradient boosting neural network (GrowNet) and long short-term memory (LSTM) network. Regarding building/floor identification, the gradient ensemble model GrowNet is utilized for extracting the mapping relationship between uneven RSSI samples and building/floor indices. Regarding location estimation, the LSTM network is adopted as one layer of base learner to extract temporal features of RSSI samples, and a gradient boosting strategy is further used for overcoming the sample sparsity issue and improving the location estimation performance. Extensive experiments are conducted on real datasets and the results demonstrate that GrowNetLoc has superior localization accuracy and robustness performance compared with the existing methods.

Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives. In this sensing paradigm, mobile users can make full use of such sensors-rich smart devices for task executions. Recently, social mobile crowdsensing (SMCS) has received a lot of attention and much work has been carried out. Many incentive mechanisms exploit the social relations among users/workers for improving the system performance. However, most existing work in this area focused on offline and socially-aware scenarios. In this paper, we study both online socially-aware and socially-unaware scenarios for maximizing the platform utility. We formulate the problem of worker selection for maximizing the platform utility and prove this problem is NP-hard. For the socially-aware scenario, we propose an incentive mechanism (called SA-WGRA), which adopts sociality and capability based clustering algorithm for Worker Group formation and uses Reverse Auction for worker selection. For the socially-unaware scenario, we propose an incentive mechanism (called SUA-CGRA), which adopts Coalitional Game combined with Reversed Auction for worker selection. We prove that both mechanisms achieve computational efficiency, individual rationality, and platform rationality. Moreover, for SUA-CGRA, we prove that its formed coalitions satisfy coalition rationality, and further each of its formed coalitions is convex and hence the Shapley value is in the core solutions for profit distribution in each formed coalition. Simulations results show that both SA-WGRA and SUA-CGRA can effectively improve the platform utility.