Organizations around the world are increasingly using multiple clouds for critical workloads, which makes the accurate prediction of cloud workload a collaborative task across multiple cloud providers. Our experimental studies on four large-scale cloud workload datasets demonstrate that the workloads are highly heterogeneous, and different cloud providers have highly heterogeneous model preferences, which makes the above task challenging. Accordingly, we propose a joint privacy-preserving cloud workload prediction framework based on Federated Learning (FL), which enables the collaborative training of a workload prediction model across cloud providers with heterogeneous workloads and model preferences without exposing private workload data. To handle workload heterogeneity without privacy leakage, we first design a Generative Adversarial Network (GAN) based virtual workload dataset generation method that incorporates workloads’ temporal and resource-usage-plan-related features via temporal-aware feature calibration. Then, to handle model heterogeneity, we design a FL approach based on a hybrid local model composed of a homogeneous public model and a heterogeneous personal model. Finally, we design a Knowledge Distillation (KD) based approach to post-train the personal model with both private workload knowledge and shared workload knowledge learned from the trained global public model. Extensive experiments on various real-world workloads and actual implementations demonstrate that compared with the state-of-the-art, our framework improves workload prediction accuracy by 57.7% in average over all cloud providers.

To comply with privacy regulations and self-protection from cloud outages, increasingly more users are leveraging multiple cloud providers for service deployment. However, due to the isolation of highly Non-Identically and Independently Distributed (Non-IID) workload datasets and deficiencies of existing methods as reflected in our experimental studies, no cloud providers can single-handedly capture the workload patterns of such users for accurate workload prediction. Accordingly, we proposeP3Forecast, aPersonalizedPrivacy-Preserving Cloud workload prediction framework based on Federated Generative Adversarial Networks (GANs), which allows cloud providers with Non-IID workload data to collaboratively train workload prediction models as preferred while protecting privacy. We first design a data synthesis quality assessment method based on Dynamic Time Warping (calledpattern-aware DTW), which is insusceptible to time series length and reliable for the comparison of temporal patterns. By usingpattern-aware DTWas the model aggregation weights, we adopt the Federated Learning (FL) of a GAN model for the augmentation of IID workload training datasets per cloud provider. Then, we further design a post-training method of local workload prediction models, which consists of a query mechanism based on comprehensive evaluation of data synthesis informativeness and an adaptive learning rate adjustment strategy for stable convergence. Extensive experiments driven by real-world workloads demonstrate that compared with the state-of-the-art,P3Forecastimproves workload prediction accuracy by 23.7%-64.5% on average over all cloud providers, while ensuring the fastest convergence in Federated GAN training.

The workload data generated in large-scale cloud environments is becoming increasingly complex, making collaborative training a promising approach for developing more efficient task schedulers. Considering privacy security and transfer costs, Federated Reinforcement Learning (FRL) emerges as a promising solution. However, our exploratory experiments demonstrate that the environmental heterogeneity contributes to performance degradation in FRL, which makes the above issue challenging. Accordingly, we propose a Personalized FRL method based on Dual-critic networks and Multi-head attention aggregator (PFRL-DM), which achieves the optimal scheduling policies by collaborative training on diverse workload data in heterogeneous environments without exposing private data. We initially introduce a novel Reinforcement Learning (RL) environment modeling, serving as a foundation for the collaborative training of the cloud scheduling agents. Then, we implement a dual-critic network Proximal Policy Optimization (PPO) algorithm for each client, effectively balancing the influence between global and local models on the agents. Furthermore, we integrate multi-head attention weights into the server-side aggregator to implement personalization. Extensive experiments on various real-world workloads have demonstrated that, compared to state-of-the-art algorithm MFPO, the proposed algorithm exhibits faster convergence, shorter response and completion times, and achieves the highest resource utilization. Additionally, the PFRL-DM algorithm constructs personalized models for each client, enabling greater adaptability in heterogeneous and hybrid workload environments. The codes for this paper can be found at https://github.com/liyan2015/PFRL-DM.

To comply with privacy regulations and selfprotection from cloud outages, increasingly more users are leveraging multiple cloud providers for service deployment. However, due to the isolation of highly Non-Identically and Independently Distributed (Non-IID) workload datasets and deficiencies of existing methods as reflected in our experimental studies, no cloud providers can single-handedly capture the workload patterns of such users for accurate workload prediction. Accordingly, we propose$\boldsymbol{P}^{3}$Forecast, a Personalized Privacy-Preserving Cloud workload prediction framework based on Federated Generative Adversarial Networks (GANs), which allows cloud providers with Non-IID workload data to collaboratively train workload prediction models as preferred while protecting privacy. We first design a data synthesis quality assessment method based on Dynamic Time Warping (called pattern-aware DTW), which is insusceptible to time series length and reliable for the comparison of temporal patterns. By using pattern-aware DTW as the model aggregation weights, we adopt the Federated Learning (FL) of a GAN model for the augmentation of IID workload training datasets per cloud provider. Then, we further design a post-training method of local workload prediction models, which consists of a query mechanism for extracting the most informative synthesized data for training dataset augmentation and a learning rate adjustment strategy for stable convergence. Extensive experiments driven by real-world workloads demonstrate that compared with the state-of-the-art,$P^{3}$Forecast improves workload prediction accuracy by$19.5 \%-46.7 \%$in average over all cloud providers, while ensuring the fastest convergence in Federated GAN training. The codes can be found at https://github.com/liyan2015/P3Forecast.

Effective dispatching of rescue teams under flooding disasters is crucial. However, previous methods are either incapable of handling flooding disaster situations, or cannot accurately estimate the distribution of rescue requests and accordingly adjust the search of the rescue teams. We proposeMobiRescue, a humanMobility basedRescueteam dispatching system, which aims to maximize the total number of rescued people, minimize the rescue delay and the number of serving rescue teams. We studied a city-scale human mobility dataset collected under the Hurricane Florence, and observed that several natural and demographic factors are closely related to impact severity, and road segment passability must be considered. Accordingly, we first propose a Support Vector Machine based method to predict the distribution of rescue requests considering the disaster-related factors. Then, we design an Euler path based method to determine the search paths for rescue team dispatching. Subsequently, we develop a Reinforcement Learning based method to guide the search of the rescue teams. Finally, we design a multi-objective optimization problem based method to adapt to the changed road segment passability. Our experiments demonstrate that compared with the other methods,MobiRescueincreases the total number of timely served rescue requests by 43.4% in average.

Federated Learning (FL) is a technique that allows multiple participants to co-train machine learning models, while also enhancing privacy by avoiding the exposure of local data. However, it is important to note that despite its effectiveness, there is still a potential risk of leaking users’ private information through weight analysis during FL updates. Local Differential Privacy (LDP) is a technique used to prevent individual information leakage by adding noise to the user's model parameters. However, FL based on LDP lacks dynamic optimization and adaptation considering privacy and data utility, especially regarding noise constraints. This paper investigates FL under the scenario of noise optimization with LDP. Specifically, given a certain privacy budget, we design the adaptive LDP method via a noise scaler, which adaptively optimizes the noise size of every client. Second, we dynamically tailor the model direction after adding noise by the designed a direction matrix, to overcome the model drift problem caused by adding noises to the client model. Finally, our method achieves higher accuracy than some existing works with the same privacy level and the convergence speed is significantly improved.

More and more application providers (APs) start to deploy their geo-distributed services in multiple cloud environments, such as JointCloud, federated clouds and InterCloud. Thus, massive cross-cloud traffic is generated from the services of APs, who need to pay Internet service providers (ISPs) for using their bandwidth. As such, an effective cross-cloud link bandwidth allocation and pricing mechanism is needed between APs and ISPs. Existing fixed-price scheme lacks market efficiency. Thus, we propose a truthful padding-based auction mechanism (TPAM) for cross-cloud bandwidth, which introduces the padding method and well-designed pricing strategy to ensure desirable properties. This mechanism is flexible enough to allow each AP to win the whole request, or win the specified proportional request, or lose and get nothing. Specifically, we first devise a linear-program-based method to calculate the padding vector for each candidate AP. Next, we design a padding-based method to determine the winning APs and match them with ISPs who offer the cheapest bandwidth. Finally, we design a critical-value-based pricing strategy and a marginal-cost-based pricing strategy for APs and ISPs to achieve truthfulness and budget balance. Theoretical analyses prove that TPAM achieves truthfulness, budget balance, individual rationality, asymptotic efficiency and computational tractability. Trace-driven simulation results also validate the effectiveness and efficiency of TPAM.

Cross-device federated learning (FL) enables the privacy-preserving and collaborative training of machine learning models across heterogeneous clients. To prevent gradient information leakage, homomorphic encryption (HE) has been widely utilized due to its strong protection without losing accuracy. However, our experiments demonstrate that for clients with heterogeneous data and system capabilities, previous plain HE methods (i.e., encryption applied per gradient) and batch HE methods (i.e., encryption applied per batch of gradients) either significantly prolong training time or suffer from accuracy loss due to gradient quantization. We propose an adaptive batch HE framework for cross-device FL, which determines cost-efficient and sufficiently secure encryption strategies for clients with heterogeneous data and system capabilities. By leveraging the sparsity of convolutional neural networks for privacy-preserving similarity measurement of clients’ data, we first split the clients with similar data into their respective clusters. Then, we develop a fuzzy logic-based method to determine a cost-efficient and sufficiently secure HE key size for each client corresponding to its system capability, and arrange the clients with identical key size to the same groups. Finally, we design an efficient batch encryption approach for accuracy-lossless model aggregation. Extensive experiments on multiple heterogeneity scenarios demonstrate that our framework achieves comparable accuracy to plain HE, while reducing training time by$3\times $–$31\times $, and communication cost by$45\times $–$66\times $.

Autonomous robotic boats equipped with chargers or swappable batteries can serve as Mobile Energy Disseminators (MED) and proactively maintain the State-of-Charge of electric vessels. However, previous methods are either incapable of keeping the vessels continuously driving without recharge downtime, or not directly applicable for the scheduling of chargers on city-scale transportation networks. We proposeRoboCharger: aRoboticChargerboat scheduling system that adaptively determines the number of serving MEDs, and the optimal routes of the MEDs according to vessel traffic. We studied a metropolitan-scale vessel mobility dataset provided by MarineTraffic and analyzed the spatial-temporal characteristics of vessel movements in Amsterdam's canals. Based on the analysis insights, we developed a MinHash and spatial-temporal similarity comparison based method for vessel traffic estimation, a Chinese Postman Problem based method for determining the cruising routes of the MEDs, and formulated and solved a multi-objective optimization problem to determine the number of serving MEDs, the driving route of each MED and maintain the SoC of the vessels above zero. Our trace-driven experiments demonstrate that compared with previous methods,RoboChargerincreases the average SoC of vessels over all time slots throughout a day by almost 42%, and the number of charges of vessels by almost 53%.

With the advancement of dynamic wireless charging for Electric Vehicles (EVs), Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to MED deployment, are not directly applicable for city-scale EV-to-EV dynamic wireless charging. We presentMobiCharger:aMobile wirelessChargerguidance system that determines the number of serving MEDs, and their optimal routes. We studied a metropolitan-scale vehicle mobility dataset, and found: most vehicles have routines, and the number of driving EVs changes over time, which means MED deployment should adaptively change as well. We combine EVs' current trajectories and routines to estimate EV density and the cruising graph for MED coverage. Then, we develop an offline MED deployment method that utilizes multi-objective optimization to determine the number of serving MEDs and the driving route of each MED, and an online method that utilizes Reinforcement Learning to adjust the MED deployment when the real-time vehicle traffic changes. Our trace-driven experiments show that compared with previous methods,MobiChargerincreases the medium State-of-Charge of all EVs by 50% during all time slots, and the number of charges of EVs by almost 100%.

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure.

Previous methods for passenger demand inference are unable to capture the effect of all possible random factors (e.g., accident and weather), hence resulting in insufficient accuracy. Moreover, due to the lack of charging optimization, existing taxicab dispatching methods cannot be applied to electric taxicabs directly. We propose CD-Guide, which provides Charging and Dispatching Guide for electric taxicabs based on customized selection and training of historical passenger demand data, multiobjective optimization, and reinforcement learning (RL). By analyzing a large-scale electric taxicab data set, we found that: 1) the histogram of passengers’ origin buildings is effective in illustrating the suitability of historical data for learning; 2) passenger demands in different regions vary a lot due to various random factors; and 3) charging time must be considered in dispatching electric taxicabs. We first develop a passenger demand inference model based on customized selection and training of suitable historical passenger demand data. Then, we develop two taxicab guidance methods that utilize multiobjective optimization and RL, respectively, to maximize the taxicab’s likelihood of finding passengers, maximally prevent the taxicab from missing passengers due to charging, and, meanwhile, maintain the continuous service of the taxicab. Extensive experiments on real-world data sets demonstrate that compared with the state of the art, CD-Guide increases the total number of served passengers by 100%, and the minimum State-of-Charge of all taxicabs by 75% during all time slots.

In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless power transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We proposeCatChargerto tackle this challenge. By analyzing a metropolitan-scale data set, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks), and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the kernel density estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multiobjective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate thatCatChargerincreases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost.

The Coronavirus disease 2019 (COVID-19) pandemic has severely impacted countries around the world with unprecedented mortality and economic devastation and has disproportionately and negatively impacted different communities—especially racial and ethnic minorities who are at a particular disadvantage. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information and machine learning (ML) to track the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepTrack, a geospatial and ML-based approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities and diets based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. We track determinants of nutrition and obesity through diet examination. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracking with a goal to improve pandemic health care.

Charger lanes, which are road segments equipped with in-motion wireless chargers, are expected to keep Electric Vehicles (EVs) continuously driving without recharging downtime. To maximize the service efficiency of the in-motion wireless chargers, we must properly coordinate the traffic of the EVs to avoid the generation of congestion at the charger lanes and on the road segments to them. In this article, we propose WPT-Opt , a game-theoretic approach for optimizing in-motion wireless charging efficiency, minimizing EVs’ driving time to the charger, and avoiding traffic congestion at the charger lanes to fulfill this task. We studied a metropolitan-scale dataset of public transportation EVs and observed the EVs’ spatial and temporal preference in selecting chargers, competition for chargers during busy charging times, the relationship between vehicle density and driving velocity on a road segment, the normal distribution of travel time of road segments, and the fact that vehicles have similar frequently driven trajectories. Based on the observations, a central controller estimates the vehicle density of the road segments by measuring the vehicles’ trajectory travel time, the friendship among the vehicles, and the vehicles’ routing choice given the presence of charger lanes. Then, we formulate a non-cooperative Stackelberg game between all the EVs and the central controller , in which each EV aims at minimizing its charging time cost to its selected target charger, while the central controller tries to maximally avoid the generation of congestion on the way through the in-motion wireless chargers. Our trace-driven experiments on SUMO demonstrate that WPT-Opt can maximally reduce the average charging time cost of the EVs by approximately 200% during different hours of a day.

So far, there is no file replication method that tries to reduce data loss in correlated node failures, which however are common in Disruption Tolerant Networks (DTNs). In this paper, we propose a distributed file replication method (called MobileCopy) in DTNs, which aims to achieve low probability of totally losing a file at the expense of having a high number of impacted files in an individual large-scale correlated node failure. MobileCopy is designed for community-based file sharing systems. It has two main components: i) data loss resistant and popularity aware file replication, and ii) distributed hash table (DHT)-based file replica indexing. MobileCopy considers file popularity to determine the number of replicas of a file in each community. Through limiting the possible combination of candidate replica holders, MobileCopy greatly reduces the probability of node failures that will lead to data loss, i.e., losing all replicas of a file. Moreover, MobileCopy enables nodes to efficiently store and fetch the placement information of file replicas through competition based file replication and considering node mobility throughput among communities. Extensive trace-driven experiments demonstrate the effectiveness of MobileCopy against correlated node failures compared with previous methods.

To promote cost-effective task assignment in Spatial Crowdsourcing (SC), workers are required to report their location to servers, which raises serious privacy concerns. As a solution, geo-obfuscation has been widely used to protect the location privacy of SC workers, where workers are allowed to report perturbed location instead of the true location. Yet, most existing geo-obfuscation methods consider workers? mobility on a 2 dimensional (2D) plane, wherein workers can move in arbitrary directions. Unfortunately, 2D-based geo-obfuscation is likely to generate high traveling cost for task assignment over roads, as it cannot accurately estimate the traveling costs distortion caused by location obfuscation. In this paper, we tackle the SC worker location privacy problem over road networks. Considering the network-constrained mobility features of workers, we describe workers? mobility by a weighted directed graph, which considers the dynamic traffic condition and road network topology. Based on the graph model, we design a geo-obfuscation (GO) function for workers to maximize the workers? overall location privacy without compromising the task assignment efficiency. We formulate the problem of deriving the optimal GO function as a linear programming (LP) problem. By using the angular block structure of the LP's constraint matrix, we apply Dantzig-Wolfe decomposition to improve the time-efficiency of the GO function generation. Our experimental results in the real-trace driven simulation and the real-world experiment demonstrate the effectiveness of our approach in terms of both privacy and task assignment efficiency.

The effectiveness of dispatching rescue teams under a flooding disaster is crucial. However, previous emergency vehicle dispatching methods cannot handle flooding disaster situations, and previous rescue team dispatching methods cannot accurately estimate the positions of potential rescue requests or dispatch the rescue teams according to the real-time distribution of rescue requests. In this paper, we propose MobiRescue, a human Mobility based Rescue team dispatching system, that aims to maximize the total number of fulfilled rescue requests, minimize the rescue teams' driving delay to the rescue requests' positions and also the number of dispatched rescue teams. We studied a city-scale human mobility dataset for the Hurricane Florence, and found that the disaster impact severities are quite different in different regions, and people's movement was significantly affected by the disaster, which means that the rescue teams' driving routes should be adaptively adjusted. Then, we propose a Support Vector Machine (SVM) based method to predict the distribution of potential rescue requests on each road segment. Based on the predicted distribution, we develop a Reinforcement Learning (RL) based rescue team dispatching method to achieve the aforementioned goals. Our trace-driven experiments demonstrate the superior performance of MobiRescue over other comparison methods.

With ever increasing concerns on environmental issues caused by gasoline fuel based vehicles, electric vehicles (EVs) have attracted more and more attention from governments, industries, and customers [1] . The recent advancements in EVs have great potential to create a more environmentally friendly smart city. However, due to limited battery capacity, most current mainstream EVs still have quite limited driving range (e.g., 100 miles) [2] . How to ensure the continuous running of EVs on a large-scale road network (e.g., metropolitan city, interstate) becomes a major concern.

Previous passenger demand inference methods have insufficient accuracy because they fail to catch the influence of all random factors (e.g., weather, holiday). Also, existing taxicab dispatching methods are not directly applicable for electric taxicabs because they cannot optimize their charging. We present CD-Guide: an electric taxicab dispatching and charging approach based on customized training and Reinforcement Learning (RL). We studied a metropolitan-scale taxicab dataset, and found: histogram of passengers' origin buildings (i.e., where they come from) is useful for selecting suitable training data for inference model, passenger demand in different regions may be influenced by various unpredictable random factors, and taxicabs' charging time must be considered to avoid missing potential passengers. By saying suitable historical data, we mean the data that are under the influence of random factors similar as current time. Then, we develop a RL based method to guide a taxicab to maximize its probability of picking up a passenger, minimize the number of its missed passengers due to charging, and meanwhile avoid the taxicab from battery exhaustion. Our trace-driven experiments show that compared with previous methods, CD-Guide increases the total number of served passengers by 100%.

Previous Electric Vehicle (EV) charging scheduling methods and EV route planning methods require EVs to spend extra waiting time and driving burden for a recharge. With the advancement of dynamic wireless charging for EVs, Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to the deployment of MEDs, are not directly applicable for the scheduling of MEDs on city-scale road networks. We present MobiCharger: a Mobile wireless Charger guidance system that determines the number of serving MEDs, and the optimal routes of the MEDs periodically (e.g., every 30 minutes). Through analyzing a metropolitan-scale vehicle mobility dataset, we found that most vehicles have routines, and the temporal change of the number of driving vehicles changes during different time slots, which means the number of MEDs should adaptively change as well. Then, we propose a Reinforcement Learning based method to determine the number and the driving route of serving MEDs. Our experiments driven by the dataset demonstrate that MobiCharger increases the medium state-of-charge and the number of charges of all EVs by 50% and 100%, respectively.

Traffic congestion control is pivotal for intelligent transportation systems. Previous works optimize vehicle speed for different objectives such as minimizing fuel consumption and minimizing travel time. However, they overlook the possible congestion generation in the future (e.g., in 5 minutes), which may degrade the performance of achieving the objectives. In this article, we propose a vehicle Trajectory–based driving speed OPtimization strategy ( TOP ) to minimize vehicle travel time and meanwhile avoid generating congestion. Its basic idea is to adjust vehicles’ mobility to alleviate road congestion globally. TOP has a framework for collecting vehicles’ information to a central server, which calculates the parameters depicting the future road condition (e.g., driving time, vehicle density, and probability of accident). Based on the collected information, the central server also measures the friendship among the vehicles and considers the delay caused by red traffic signals to help estimating the vehicle density of the road segments. The server then formulates a non-cooperative Stackelberg game considering these parameters, in which when each vehicle aims to minimize its travel time, the road congestion is also proactively avoided. After the Stackelberg equilibrium is reached, the optimal driving speed for each vehicle and the expected vehicle density that maximizes the utilization of the road network are determined. Our real trace analysis confirms some characteristics of vehicle mobility to support the design of TOP . Extensive trace-driven experiments show the effectiveness and superior performance of TOP in comparison with other driving speed optimization methods.

With recent experience in multiple large-scale disasters, it has been widely confirmed that the severity of a disaster is greatly dependent on the effectiveness of ambulance dispatching during disaster phase. However, previous base station (i.e., temporary or permanent hospital) based ambulance redeployment methods and dynamic ambulance scheduling methods cannot handle the ambulance dispatching problem in catastrophic situations. In this paper, we present MobiAmbulance: a human Mobility based Ambulance dispatching system that aims to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delay of the fulfilled requests. We studied a state-scale human mobility dataset and found that the change of vehicle flow rate can be utilized to determine the connection status between road segments, and the distribution of people in catastrophic situations is drastically different from that in normal situations. Then, we develop a method to determine the road network connection status and the set of road segments that can still be driven through by ambulances after disaster. Based on the updated road network graph, we develop an ambulance dispatching method based on weighted driving route to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delays of the fulfilled requests. Our trace-driven experiments demonstrate the superior performance of MobiAmbulance over other comparison methods.

With the application of Wireless Power Transfer (WPT) techniques for Electric Vehicles (EVs), public transportation EVs are expected to be continuously operable without recharging downtime. A road segment equipped with an inmotion wireless charger is called a charger lane. To maximize the service efficiency of deployed in-motion wireless chargers without suffering from traffic congestion, we must properly manage the traffic of the EVs and coordinate their arrivals at the charger lanes to avoid the generation of traffic congestion at the charger lanes and on the road segments to them. In this paper, we propose WPT-Opt, a game theoretic approach for Optimizing in-motion wireless charging service efficiency, minimizing EVs' time spent on the way to the charger, and avoiding traffic congestion at the charger lanes, to fulfill this task. We studied a metropolitan-scale dataset of public transportation EVs, and observed the EVs' spatial and temporal preference in selecting chargers, competition for chargers during busy charging times, and the relationship between vehicle density and driving velocity on a road segment. Then, we formulate a non-cooperative Stackelberg game between all the EVs and a central controller, in which each EV aims at minimizing its charging time cost to its selected target charger, while the central controller tries to maximally avoid the generation of congestion on the in-motion wireless chargers and the road segments to them in the near future. Our tracedriven experiments on SUMO demonstrate that WPT-Opt can maximally reduce the average charging time cost of the EVs by approximately 200% during different hours of a day.

The future generation of transportation system will be featured by electrified public transportation. To fulfill metropolitan transit demands, electric vehicles (EVs) must be continuously operable without recharging downtime. Wireless Power Transfer (WPT) techniques for in-motion EV charging is a solution. It however brings up a challenge: how to deploy charging lanes in a metropolitan road network to minimize the deployment cost while enabling EVs' continuous operability. In this paper, we propose CatCharger, which is the first work that handles this challenge. From a metropolitan-scale dataset collected from multiple sources of vehicles, we observe the diversity of vehicle passing speed and daily visit frequency (called traffic attributes) at intersections (i.e., landmarks), which are important factors for charging lane deployment. To select landmarks for deployment, we first group landmarks with similar traffic attribute values using the entropy minimization clustering method, and choose better candidate landmarks from each group suitable for deployment. To determine the deployment locations from the candidate landmarks, we infer the expected vehicle residual energy at each landmark using a Kernel Density Estimator fed by the vehicles' mobility, and formulate and solve an optimization problem to minimize the total deployment cost while ensuring a certain level of expected residual energy of EVs at each landmark. Our trace-driven experiments demonstrate the superior performance of CatCharger over other methods.

The future generation transportation system will be featured by electrified public transportation. To fulfill metropolitan transit demands, electric vehicles (EVs) must be continuously operable without recharging downtime. Wireless Power Transfer (WPT) techniques for in-motion EV charging is a solution [1], [2]. It however brings up a challenge: how to deploy charging lanes in a metropolitan road network to minimize the deployment cost while enabling EVs' continuous operability.

Traffic congestion control is pivotal for intelligent transportation systems. Previous works optimize vehicle speed for different objectives such as minimizing fuel consumption and minimizing travel time. However, they overlook the possible congestion generation in the future (e.g., in 5mins), which may degrade the performance of achieving the objectives. In this paper, we propose a vehicle Trajectory based driving speed OPtimization strategy (TOP) to minimize vehicle travel time and meanwhile avoid generating congestion. Its basic idea is to adjust vehicles'mobility to alleviate road congestion globally. TOP has a framework for collecting vehicles' information to a central server, which calculates the parameters depicting the future road condition (e.g., driving time, vehicle density, and probability of accident). The server then formulates a non-cooperative Stackelberg game considering these parameters, in which when each vehicle aims to minimize its travel time, the road congestion is also proactively avoided. After the Stackelberg equilibrium is reached, the optimal driving speed for each vehicle and the expected vehicle density that maximizes the utilization of the road network are determined. Our real trace analysis confirms some characteristics of vehicle mobility to support the design of TOP. Extensive trace-driven experiments show the effectiveness and superior performance of TOP in comparison with other driving speed optimization methods.

Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.

In delay tolerant networks (DTNs), enabling a mobile node to search and find another interested mobile node is an important function in many applications. However, the movement of nodes in DTNs makes the problem formidable. Current node searching methods in disconnected networks mainly rely on fixed stations in the network and infrastructure-based communication to collect node position information, which is difficult to implement in DTNs. In this paper, we present DSearching, a distributed mobile node searching scheme for DTNs that requires no infrastructure. In DSearching, the entire DTN area is split into sub-areas, and each node summarizes its mobility information as both transient sub-area visiting record and long-term movement pattern. Upon arriving at a sub-area, a node generates a new visiting record for the sub-area and distributes it to nodes that are likely to stay in the previous sub-area, so that visiting records form a chain for the locators to trace the node. Each node also stores different parts of its long-term mobility pattern to long-staying nodes in different sub-areas for others to trace it when visiting records are absent. Considering that nodes in DTNs usually have limited resources, DSearching constrains the communication and storage cost in the information distribution while enabling efficient node searching. Advanced extensions that can further improve the searching efficiency is also proposed in this paper. Extensive trace-driven experiments with real traces demonstrate the high efficiency and high effectiveness of DSearching.

Node searching in delay tolerant networks is of great importance for different applications, in which a locator node finds a target node in person. In the previous distributed node searching method, a locator traces the target along its movement path from its most frequently visited location. For this purpose, nodes leave traces during their movements and also store their long-term movement patterns in their frequently visited locations (i.e., preferred locations). However, such tracing leads to a long delay and high overhead on the locator by long-distance moving. Our trace data study confirms these problems and provides the foundation of our design of a new node searching method, called target-oriented method (TSearch). By leveraging social network properties, TSearch aims to enable a locator to directly move toward the target. Nodes create encounter records (ERs) indicating the locations and times of their encounters and make the ERs easily accessible by locators through message exchanges or a hierarchical structure. In node searching, a locator follows the target's latest ER, the latest ERs of its friends (i.e., frequently meeting nodes), its preferred locations, and the target's possible locations deduced from additional information for node searching. Extensive trace-driven and real-world experiments show that TSearch achieves significantly higher success rate and lower delay in node searching compared with previous methods.

Distributed file searching in delay tolerant networks formed by mobile devices can potentially support various useful applications. In such networks, nodes often present certain social network properties of their holders in terms of contents (i.e., interests) and contacts. However, current methods in DTNs only consider either content or contact for file searching or dissemination, which limits the file sharing efficiency. In this paper, we first analyze real traces to confirm the importance and necessity of considering both content and contact in file search. We then propose Cont2, a social-aware file search method that exploits both node contents and contact patterns. First, considering people with common interests tend to share files and gather together, Cont2virtually groups common-interest nodes into a community to direct file search. Second, considering human mobility follows a certain pattern, Cont2exploits nodes' contact frequencies with a community to expedite file searching. To further improve the searching efficiency, Cont2also integrates sub-communities and parallel forwarding as optional components for file searching. Trace-driven experiments on the GENI testbed and NS-2 simulator show that Cont2can effectively improve the search efficiency compared to current methods.

Node searching in delay tolerant networks (DTNs) is of great importance for different applications, in which a locator node finds a target node in person. In the previous distributed node searching method, a locator traces the target along its movement path from its most frequently visited location. For this purpose, nodes leave traces during their movements and also store their long-term movement patterns in their frequently visited locations (i.e., preferred locations). However, such tracing leads to a long delay and high overhead on the locator by longdistance moving. Our trace data study confirms these problems and provides foundation of our design of a new node searching method, called target-oriented method (TSearch). By leveraging social network properties, TSearch aims to enable a locator to directly move towards the target. Nodes create encounter records (ERs) indicating the locations and times of their encounters and make the ERs easily accessible by locators through message exchanges or a hierarchical structure. In node searching, a locator follows the target's latest ER, the latest ERs of its friends (i.e., frequently meeting nodes), and its preferred locations in order. Extensive trace-driven and real-world experiments show that TSearch achieves significantly higher success rate and lower delay in node searching compared with previous methods.

Motivated by the growing popularity of mobile devices and their increasing capacities, file sharing in disruption tolerant networks (DTNs) has attracted significant attention recently. Since nodes are sparsely distributed in separated areas and are intermittently disconnected in DTNs, it is difficult to achieve high data availability in file sharing. Many previous methods enhance file availability in DTNs through file replication. However, there has been no file replication method that tries to reduce data loss in correlated node failures, which however are common in wireless networks. In this paper, we propose a distributed file replication method (called MobileCopy) in DTNs, which aims to achieve low probability of totally losing a file at the expense of having a high number of impacted files in an individual large-scale correlated node failure. MobileCopy is designed for community-based file sharing systems. It has two main components: i) data loss resistant and popularity aware file replication, and ii) distributed hash table (DHT)-based file replica indexing. MobileCopy considers file popularity to determine the number of replicas of a file in each community. Through limiting the possible combination of candidate replica holders, MobileCopy greatly reduces the probability of node failures that will lead to data loss, i.e., losing all replicas of a file. Moreover, MobileCopy enables nodes to efficiently store and fetch the placement information of file replicas for efficient file searching. Extensive trace-driven experiments show that MobileCopy is robust against correlated node failures and efficient in file sharing in comparison with previous methods.

In Delay Tolerant Networks (DTNs), nodes meet opportunistically and exchange packets only when they meet with each other. Therefore, routing is usually conducted in a store-carry-forward manner to exploit the scarce communication opportunities. As a result, different packet routing strategies, i.e., which packet to be forwarded or stored with priority, can lead to different routing performance objectives, such as minimal average delay and maximal hit rate. On the other hand, incentive systems are necessary for DTNs since nodes may be selfish and may not be cooperative on packet forwarding/storage. However, current incentive systems for DTNs mainly focus on encouraging nodes to participate in packet forwarding/storage but fail to further encourage nodes to follow a certain packet routing strategy to realize a routing performance objective. We name the former as the first aspect of cooperation and the latter as the second aspect of cooperation in DTN routing. Therefore, in this paper, we first discuss the routing strategy that can realize different performance objectives when nodes are fully cooperative, i.e., are willing to follow both aspects of cooperation. We then propose Multicent, a game theoretical incentive scheme that can encourage nodes to follow the two aspects of cooperation even when they are selfish. Basically, Multicent assigns credits for packet forwarding/storage in proportional to the priorities specified in the routing strategy. Multicent also supports adjustable Quality of Service (QoS) for packet routing between specific sources and destinations. Extensive trace-driven experimental results verify the effectiveness of Multicent.