Shuxin Ge

Pricing strategy is crucial for improving the revenue of mobility on-demand (MoD) systems by achieving supplydemand equilibrium across different city zones. Modern MoD systems commonly utilize order ridesharing and vehicle repositioning to improve the order completion rate while supporting this equilibrium, thereby improving the revenue. However, most existing pricing strategies overlook the effects of ridesharing and repositioning, resulting in supply-demand mismatch and revenue decline. To fill this gap, we propose a multi-agent reinforcement learning (MARL) based pricing strategy via a mutual attention mechanism, named R2Pricing, where the impact of ridesharing and repositioning is considered. First, we formulate the pricing with ridesharing and repositioning as an optimization problem toward maximum overall revenue. Then, we transform it into a MARL model, where the agent makes coupled decisions about order fare with ridesharing and vehicle income with repositioning for each zone. Next, the agents are clustered based on supplydemand observation and reward to train more efficiently. The pricing messages between agents are generated based on mutual information theory, which is then aggregated with an attention mechanism to estimate the impact of price differences among zones. Finally, simulations based on real-world data are conducted to demonstrate the superiority of R2Pricing over the benchmarks.

Vehicle repositioning is widely used in Mobility on-Demand (MoD) systems to address supply-demand imbalances and improve order completion rates. Existing methods typically offer repositioning recommendations focused on enhancing vehicle coordination toward supply-demand re-balance. However, these methods often overlook the possibility that drivers may not follow these recommendations due to their personal preferences, leading to recommendation-decision inconsistency and further disrupting the supply-demand balance. To address this issue, we propose a preference-aware vehicle repositioning recommendation strategy for MoD systems, named FREE, which is based on a Coulomb Force directed approach. The core idea is to strike a balance between vehicle coordination and consistency between recommendations and driver decisions. First, we introduce a Coulomb force-based representation (CFR) to model coordination among vehicles. In this model, the interactions between vehicles and orders are represented as forces that drive the repositioning of vehicles. Next, we develop a driver preference learning model that accurately captures drivers’ preferences using triplet and consistency loss. We then integrate these preferences with the CFR into a multi-agent deep reinforcement learning (MADRL) based repositioning algorithm to generate optimal recommendations. Finally, we validate the effectiveness of FREE through simulations using real-world data, demonstrating its superiority over existing benchmarks.

Mobility on-demand (MoD) systems widely use machine learning to estimate matching utilities of order-vehicle pairs to dispatch orders by bipartite matching. However, existing methods suffer from overestimation problems due to the complex interactions among order-vehicle pairs in the global bipartite graph, leading to low overall revenue and order completion rate. To fill this gap, we propose a multi-agent deep reinforcement learning (MADRL) based order dispatching method with bipartite splitting, named SplitMatch. The key idea is to split the global bipartite graph into multiple sub-bipartite graphs to overcome the overestimation problem. First, we propose a bipartite splitting theorem and prove that the optimal solution of global bipartite matching can be achieved by solving multiple sub-bipartite matching problems when certain conditions are met. Second, we design a spatial-temporal padding prediction algorithm to generate sub-bipartite graphs that satisfy this theorem, where the spatial-temporal feature of orders and vehicles is captured. Next, we propose a MADRL framework to learn the matching utility, where multi-objective, e.g., immediate revenue and quality of service (QoS), are taken into account to deal with varying action space. Finally, a series of simulations are conducted to verify the superiority of SplitMatch in terms of overall revenue and order completion rate.

The ever-increasing quality of experience (QoE) demand for video streaming has prompted the integration of video super-resolution and multi-access edge computing networks (MEC). With super-resolution, the low-resolution frames can be reconstructed into high-resolution ones by edge node and end device collaboratively, which is beneficial in improving QoE. However, the existing works focus on designing video streaming strategies in single-user scenarios, which cannot be applied to multi-user scenarios due to the resource contention among users, as well as the huge solution space of coupled bitrate selection and workload share between edge-end. To fill this gap, we propose a collaborative video streaming strategy with super-resolution in multi-user MEC networks, named Co-Video, to maximize the average QoE by making optimal bitrate selection and workload share. We first formulate the problem as an optimization problem towards maximum average QoE, where the QoE incorporates playback delay, video quality, and smoothness. Then, we transform the optimization problem into a partially observable Markov decision process (POMDP) and exploit the Co-Video strategy based on the multi-agent soft actor-critic (MASAC) algorithm. Specifically, Co-Video utilizes the branching actor network to converge to good policy stably. Finally, trace-driven simulations on real-world bandwidth traces demonstrate that Co-Video outperforms the state-of-the-art baselines.

Multi-access edge computing (MEC)-enabled Internet of Things (IoT) has become a powerful solution to run computation-intensive applications on end devices. These applications are composed of multiple dependent tasks, which can be abstracted as directed acyclic graphs (DAGs). Moreover, applications can share partial intermediate data with each other based on dynamic network conditions to boost its performance, so-called soft cooperation. However, it is quite challenging to make optimal offloading decisions with external dependency between tasks of different DAGs introduced by soft cooperation, as well as the subsequent huge continuous solution space caused. In this paper, we propose a DAG-based dependent tasks offloading method with soft cooperation in MEC-enabled IoT. First, we formulate the problem as a Markov decision process (MDP), aiming to minimize the application latency and energy consumption, and to maximize the cooperation gain simultaneously. Then, we propose a branch soft actor-critic (BSAC) algorithm to make optimal decisions under dynamic network conditions, including the offloaded tasks, the CPU frequency of end devices, and the sharing ratio of intermediate data. Specifically, BSAC uses multiple branch networks to reduce the solution space. Finally, a series of simulations are conducted to establish the superiority of the BASC algorithm over state-of-the-art solutions.

Mobility on demand (MoD) systems utilize ridesharing, i.e., multiple orders with high associating utility share a single vehicle, to reduce carbon footprint and alleviate traffic pressure. Existing methods mainly promote ridesharing by flocking multiple orders to the minimum required vehicles. However, supply-demand variations may aggregate undersupply in the long run and affect the order completion rate. Meanwhile, it is difficult to accurately estimate associating utility among ridesharing orders with lane-level features, such as traffic flow. To fill this gap, we propose ERShare, an elastic order dispatching algorithm to maximize the order completion rate in the MoD system. First, the ridesharing order dispatching problem is formulated as an offline optimization problem, and then it is proved that the order completion rate is maximized when the MoD system achieves long-term supply-demand equilibrium. Next, a dummy order/vehicle generation method is proposed to generate dummies as a spinner to achieve supply-demand equilibrium elastically. Also, a lane-level ridesharing rule is designed to accurately estimate the associating utility based on an order association graph. Subsequently, a dummy-based order dispatching algorithm is proposed to find the optimal dispatching decisions. Finally, the simulations on real-world data validate the superiority of ERShare over state-of-the-art solutions regarding order completion rate.

In Multi-access Edge Computing (MEC) systems, service migration has been extensively applied to ensure service quality by migrating services to follow mobile users. The existing migration methods mainly focus on optimizing service response latency and migration costs by predicting user’s movements. However, some malicious adversaries can learn auxiliary knowledge, i.e., users’ mobility model and service migration trajectory, and launch location inference attacks to infer user locations. This leads to serious personal security threats, like malvertising, fraud and kidnapping. In this paper, we propose a location privacy-aware service migration method to against adversaries’ location inference attacks in multi-user MEC systems. First, we adopt an entropy-based location privacy metric to accurately measure user’s location privacy leakage risk. Then, we formulate the service migration progress as a joint optimization problem that minimizes service response latency and location privacy leakage risk. To cope with inter-user interference, we developed a multi-agent soft actor-critic (MASAC) algorithm to help users collaboratively make service migration decisions. Finally, simulations based on real-world user movement trajectories were conducted to demonstrate the superiority of the proposed method. Evaluation and analysis results showed that our proposed method can effectively protect user location privacy while maintaining a low service response latency.

Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.

Vehicular Fog Computing (VFC) is a promising technique to enable ultra low service latency by exploiting the computation and storage resources of both Roadside Units (RSUs) and Serving Vehicles (SVs) such as buses and trams with rich resources. To tackle with the mobility of vehicles, the services are usually migrated between RSUs and SVs, i.e., follow the vehicle, to maintain the benefits of VFC. However, making optimal service migration decisions in VFC is challenging due to the mobility of SVs and the interference between vehicles. In this paper, we investigate multi-vehicle service migration problem in VFC. We propose an efficient online algorithm, called FEE, to optimize the service migration for each vehicle in each time slot, where the latency in the current time slot, the expected latency in future time slots, and the interference among vehicles are minimized. The expected latency in future times slots is obtained by trajectory prediction based on hidden Markov model, and the interference is measured based on the server load. Finally, a series of simulations based on real-world mobility traces of Rome taxis are conducted to verify the superior performance of the proposed FEE algorithm as compared with the state-of-the-art solutions.