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Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve themore »number of active users and the task completion rate by 9.8% and 11%, on average.« less

Collaborative perception enables autonomous driving vehicles to share sensing or perception data via broadcast-based vehicle-to-everything (V2X) communication technologies such as Cellular-V2X (C-V2X), hoping to enable accurate perception in face of inaccurate perception results by each individual vehicle. Nevertheless, the V2X communication channel remains a significant bottleneck to the performance and usefulness of collaborative perception due to limited bandwidth and ad hoc communication scheduling. In this paper, we explore challenges and design choices for V2X-based collaborative perception, and propose an architecture that lever-ages the power of edge computing such as road-side units for central communication scheduling. Using NS-3 simulations, we show the performance gap between distributed and centralized C-V2X scheduling in terms of achievable throughput and communication efficiency, and explore scenarios where edge assistance is beneficial or even necessary for collaborative perception.

With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing Deep Neural Network (DNN) models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, we propose a novel online Co-Scheduling framework based on deep REinforcement Learning (DRL), which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages emerging Deep Reinforcement Learning (DRL) to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate COSREL, we conduct extensive experiments on an off-the-shelf Android smartphone with widely-used DNN models to compare it withmore »three commonly-used baselines. Our experimental results show that 1) COSREL consistently and significantly outperforms all the baselines in terms of both throughput and latency; and 2) COSREL is generally superior to all the baselines in terms of energy efficiency.« less