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 rapid development of edge computing technology, edge-assisted unmanned aerial vehicle (UAV) networks have become popular, helping with fast and cost-effective data collection in mobile crowdsensing (MCS) environments. This paper investigates the online data collection problem for MCS over an edge-assisted UAV network architecture, where UAVs work to collect the data required by tasks at different on-ground point-of-interests (PoIs) in an autonomous and cooperative manner. Different from conventional edge-assisted UAV networks, edge nodes in our paper help distribute, aggregate, share, and update the task state information (TSI, e.g., if a task has been completed, or if it still requires more data), to support efficient and cost-effective data collection, e.g., avoiding repetitive and ineffective task execution. In particular, a UAV can exchange TSI with an edge node when it is within the signal coverage of the edge node, while making decisions on PoI selection and path planning in an online and decentralized manner. To address the issue of edge-assisted UAV data collection, we propose a multi-agent deep reinforcement learning-based algorithm using personalized training with decentralized executing (PTDE) architecture. Different from the traditional centralized training with decentralized executing (CTDE) architecture, our considered architecture adopts the agent-specific state for critic networks instead of the joint observation, thus achieving effective utilization of environmental information. Furthermore, we propose an observation enhancement algorithm based on artificial potential field (APF). Extensive simulation results demonstrate that our proposed algorithm greatly outperforms baseline algorithms in terms of total profit, data collection ratio, geographical fairness, and energy efficiency.

With the rapid growth of Internet of Things and proliferation of handheld smart devices, mobile crowdsensing has been regarded as an effective sensing paradigm due to its high scalability, low cost, and wide coverage. In this article, we study hybrid task assignment where semi-opportunistic and participatory users co-exist for task executions while tasks are delay sensitive and have heterogeneous qualities. The design objective is to maximize the total quality of completed tasks subject to a total budget shared by both types of users. We formulate this problem as an integer programming problem. We propose an efficient hybrid users-based task assignment algorithm (referred to as HU-TSA), which works in an iterative way as follows. It first selects the top $n$ (initially, $n=1$ ) semi-opportunistic users in terms of quality-cost ratio for task assignment. It then clusters the remaining tasks into different regions based on their closeness and then performs utility-based optimized user-region binding and standardized task density-based path planning for the participatory users. It repeats the above process over all possible values of $n$ to seek an optimal budget splitting between the two types of users for improved performance. We present the detailed design description of HU-TSA and deduce its computational complexity. Extensive simulations are carried out and the results show the effectiveness of HU-TSA by comparing with existing algorithms.

C loud- a ided m obile e dge n etworks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an o verbooking-driven a forehand c ross- l ayer m atching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services; and an o n s ite c ross- l ayer m atching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare.