Non-emergency municipal services, e.g., city 311 systems, have been widely implemented across cities in Canada and the United States to enhance residents' quality of life. These systems enable residents to report issues, e.g., noise complaints, missed garbage collection, and potholes, via phone calls, mobile applications, or webpages. However, residents are often given limited information about when their service requests will be addressed, which can reduce transparency, lower resident satisfaction, and increase the number of follow-up inquiries. Predicting the service time for municipal service requests is challenging due to several complex factors: (i) dynamic spatial-temporal correlations, (ii) underlying interactions among heterogeneous service request types, and (iii) high variation in service duration even within the same request category. In this work, we propose MuST2-Learn: a Multi-view Spatial-Temporal-Type Learning framework designed to address the aforementioned challenges by jointly modeling spatial, temporal, and service type dimensions. In detail, it incorporates an inter-type encoder to capture relationships among heterogeneous service request types and an intra-type variation encoder to model service time variation within homogeneous types. In addition, a spatiotemporal encoder is integrated to capture spatial and temporal correlations in each request type. The proposed framework is evaluated with extensive experiments using two real-world datasets. The results show that MuST2-Learn reduces mean absolute error by at least 32.5%, which outperforms state-of-the-art methods.

Shared micromobility (e.g., shared bikes and electric scooters), as a kind of emerging urban transportation, has become more and more popular in the world. However, the blooming of shared micromobility vehicles brings some social problems to the city (e.g., overloaded vehicles on roads, and the inequity of vehicle deployment), which deviate from the city regulator's expectation of the service of the shared micromobility system. In addition, the multi-operator shared micromobility system in a city complicates the problem because of their non-cooperative self-interested pursuits. Existing regulatory frameworks of multi-operator vehicle rebalancing generally assume the intrusive control of vehicle rebalancing of all the operators, which is not practical in the real world. To address this limitation, we design REALISM, a regulatory framework for coordinated scheduling in multi-operator shared micromobility services that incorporates the city regulator's regulations in the form of assigning a score to each operator according to the city goal achievements and operators' individual contributions to achieving the city goal, measured by Shapley value. To realize the fairness-aware score assignment, we measure the fairness of assigned scores and use them as one of the components to optimize the score assignment model. To optimize the whole framework, we develop an alternating procedure to make operators and the city regulator interact with each other until convergence. We evaluate our framework based on real-world e-scooter usage data in Chicago. Our experiment results show that our method achieves a performance gain of at least 39.93% in the equity of vehicle usage and 1.82% in the average demand satisfaction of the whole city.

Speaker diarization refers to identifying who speaks what in a conversation. It is critical in sensitive settings like psychological counseling and legal consultations. However, traditional approaches, such as microphone or video, raise privacy concerns and cause discomfort to participants due to their noticeable deployment. To address this, we propose a non-intrusive speaker diarization system via mmWave sensing. Our approach leverages the spatial diversity of signals from multiple objects to distinguish speakers. Specifically, it isolates speech-induced vibrating objects signals and extracts speaker-related features through a two-stage feature extraction process. Our system achieves over 93% accuracy in real-world scenarios, demonstrating its effectiveness in reliably distinguishing speakers.

Heterogeneous devices in edge computing bring challenges as well as opportunities for edge computing to utilize powerful and heterogeneous hardware for a variety of complex tasks. In this paper, we propose a service function chain placement strategy considering the heterogeneity of devices in deviceless edge computing environments. The service function chain system utilizes lightweight virtualization technologies to manage resources, considering the heterogeneity of devices to support various complex tasks, and offer low latency services to user requests. We propose an optimal service function chain placement problem minimizing the service delay and formulate it into a quasi-convex problem. We implement different edge applications that can be served by function chains and conduct extensive experiments over real heterogeneous edge devices. Results from the experiments and simulations show that our proposed service function chain scheme is applicable in edge environments, and perform well over services latency, resource utilization as well as the power consumption of edge devices.

As electric vehicles (EVs) gradually replace fuel vehicles and provide transportation services in cities, e.g., electric taxi fleets, solar-powered charging stations with energy storage systems have been deployed to provide charging services for EV fleets. The mixture of solar-powered and traditional charging stations brings efficiency challenges to charging stations and reliability challenges to power systems. In this article, we explore e-taxis’ mobility and charging demand flexibility to co-optimize service quality of e-taxi fleets and system cost of charging infrastructures, e.g., solar power under-utilization and reliability issues of power distribution networks due to reverse power flow. We propose SAC, an e-taxi coordination framework to dispatch e-taxis for charging or serving passengers under spatial-temporal dynamics of renewable energy and passenger mobility, which integrates the renewable power generation estimation from a forecast system. Moreover, we extend our design to a stochastic Model Predictive Control problem to handle the uncertainty of solar power generation, aiming to fully utilize generated solar power. Our data-driven evaluation shows that SAC significantly outperforms existing solutions, enhancing the usage rate of solar power by up to 172.6%, while maintaining e-taxi service quality with very small overhead, i.e., reducing the supply-demand ratio by 2.2%.

Shared electric micromobility has surged to a popular model of urban transportation due to its efficiency in short-distance trips and environmentally friendly characteristics compared to traditional automobiles. However, managing thousands of shared electric micromobility vehicles including rebalancing and charging to meet users’ travel demands still has been a challenge. Existing methods generally ignore human preferences in vehicle selection and assume all nearby vehicles have an equal chance of being selected, which is unrealistic based on our findings. To address this problem, we design PERCEIVE, a human preference-aware rebalancing and charging framework for shared electric micromobility vehicles. Specifically, we model human preferences in vehicle selection based on vehicle usage history and current status (e.g., energy level) and incorporate the vehicle selection model into a robust adversarial reinforcement learning framework. We further utilize conformal prediction to quantify human preference uncertainty and fuse it with the reinforcement learning framework. We evaluate our framework using two months of real-world electric micromobility operation data in a city. Experimental results show that our method achieves a performance gain of at least 4.02% in the net revenue and offers more robust performance in worst-case scenarios compared to state-of-the-art baselines.

Information-centric Networking in edge computing environments exhibits the potential to significantly enhance the efficiency, reliability, and security of data transmission, making it a promising technology for future network deployments. However, the differences from traditional networks require applications to actively redevelop and redeploy onto edge devices, incurring additional costs for the proliferation of ICN applications. In this paper, we propose a system to adapt QUIC protocol over ICN networks in multi-access edge networks. The system aims to expand the application repertoire for ICN by providing a smooth transition of applications using QUIC to run on ICN networks. We design an ICN-QUIC conversion layer to manage the transmission of data from QUIC-based applications. We implement and evaluate the designed system. The experiment results show that, compared to existing networks, our system can enhance the transmission efficiency, i.e., up to 20 times better goodput in multicast situations, and achieves comparable results in unicast scenarios. We test the scalability of our system in real edge and wireless environments. We also deploy the real applications over the proposed system to demonstrate its compatibility in ICN and MEC environments.

Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.

Future mobile edge computing (MEC) is envisioned to provide federated intelligence to delay-sensitive learning tasks with multimodal data. Conventional horizontal federated learning (FL) suffers from high resource demand in response to complicated multi-modal models. Multi-modal FL (MFL), on the other hand, offers a more efficient approach for learning from multi-modal data. In MFL, the entire multi-modal model is split into several sub-models with each tailored to a specific data modality and trained on a designated edge. As sub-models are considerably smaller than the multi-modal model, MFL requires fewer computation resources and reduces communication time. Nevertheless, deploying MFL over MEC faces the challenges of device mobility and edge heterogeneity, which, if not addressed, could negatively impact MFL performance. In this paper, we investigate an Service Migration-assisted Mobile Multi-modal Federated Learning (SM3FL) framework, where the service migration for sub-models between edges is enabled. To effectively utilize both communication and computation resources without extravagance in SM3FL, we develop the optimal strategies of service migration and data sample collection to minimize the wall-clock time, defined as the required training time to reach the learning target. Our experiment results show that the proposed SM3FL framework demonstrates remarkable performance, surpassing other state-of-art FL frameworks via substantially reducing the computing demand by 17.5% and dramatically decreasing the wall-clock time by 25.3%.

With the rapid development of cities, heterogeneous urban cyber-physical systems are designed to improve citizens’ experience, e.g., navigation and delivery service. However, the integration of services is not designed for disruptive events, an oversight that has rippling effects on service quality. For example, urban transportation systems consist of multiple transport modes that have complementary characteristics of capacities, speeds, and costs, facilitating smooth passenger transfers by planned schedules. Such integration may experience significantly increased delays during disruptions. Current solutions rely on a substitute service to transport passengers from and to affected areas using ad-hoc schedules and static routes, which are inefficient and do not utilize mobility patterns of mobile systems, e.g., dynamic passenger demand. To coordinate heterogeneous transportation systems under disruptions, we design a service to automatically select and integrate part of three systems (subway, bus, and taxi) using systems’ mobility patterns, e.g., predicted supply and demand. The service is presented in a normal version, eRoute, considering both subway and bus, and in a version taking taxis into account, called enhanced eRoute. We implement and evaluate eRoute with datasets including subway, bus and taxi, and a fare collection system. The data-driven evaluation results show that eRoute improves the ratio of served passengers per time interval by up to 11.5 times and reduces the average traveling time by up to 82.1 percent compared with existing solutions.

With increasing deployment of electric vehicles in urban mobility-on-demand systems, electric taxis (e-taxi) drivers need to compete with each other not only for passengers but also for limited charging points due to frequent and time-consuming charging activities. This paper focuses on two crucial research questions in this context: (1) What is the strategy of each e-taxi driver for charging and searching passengers in a non-cooperative environment, and what is the collective system outcome of competing e-taxis? (2) How can the mobility-on-demand service platforms (e.g., Uber and Lyft) push self-interested e-taxi drivers to improve the overall system efficiency. Technically, we study the non-cooperative mobility-on-demand system consisting of e-taxis from a game theoretic perspective. We formulate a mobility-on-demand system with competition among drivers as a stochastic game, analyze the Nash Equilibrium (NE) of the game, and design an approximation algorithm to obtain the NE. Moreover, we show that the NE is not necessarily efficient for the platform and propose a pricing scheme from the platform's perspective which induces the new NE to be efficient. We use a trace-driven simulation to evaluate the design based on datasets consisting of more than 7,000 fuel vehicles and nearly 700 e-taxis, 37 working charging stations, and more than 60,000 passenger trips per day. We show that, compared with the state-of-the-art which optimizes the system efficiency by coordinating e-taxis but is not an equilibrium, the NE achieves a system efficiency of merely 73.5% of that of the cooperative state-of-the-art, and the designed pricing scheme improves the price of anarchy to 95.5 %.

As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-step self-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large dataset consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution.

Electric taxis (e-taxis) have been increasingly deployed in metropolitan cities due to low operating cost and reduced emissions. Compared to conventional taxis, e-taxis require frequent recharging and each charge takes half an hour to several hours, which may result in unpredictable number of working taxis on the street. In current systems, E-taxi drivers usually charge their vehicles when the battery level is below a certain threshold, and then make a full charge. Although this charging strategy directly decreases the number of charges and the time to visit charging stations, our study reveals that it also significantly reduces the availability of number of taxis during busy hours with our data driven analysis. To meet dynamic passenger demand, we propose a new charging strategy: proactive partial charging (p2Charging), which allows an e-taxi to get partially charged before its remaining battery level is running too low. Based on this strategy, we propose a charging scheduling framework for e-taxis to meet dynamic passenger demand in spatial-temporal dimensions as much as possible while minimizing idle time to travel to charging stations and waiting time at charging stations. This work implements and evaluate our solution with large datasets that consist of (i) 7,228 regular internal combustion engine taxis and 726 e-taxis, (ii) an automatic taxi payment transaction collection system with total 62,100 records per day, (iii) charging station system, including 37 working charging stations over the city. The evaluation results show that p2Charging improves the ratio of unserved passengers by up to 83.2% on average and increases e-taxi utilization by up to 34.6% compared with ground truth and existing charging strategies.