Mobile devices with dynamic refresh rate (DRR) switching displays have recently become increasingly common. For power optimization, these devices switch to lower refresh rates when idling, and switch to higher refresh rates when the content displayed requires smoother transitions. However, the security and privacy vulnerabilities of DRR switching have not been investigated properly. In this paper, we propose a novel attack vector called RefreshChannels that exploits DRR switching capabilities for mobile device attacks. Specifically, we first create a covert channel between two colluding apps that are able to stealthily share users' private information by modulating the data with the refresh rates, bypassing the OS sandboxing and isolation measures. Second, we further extend its applicability by creating a covert channel between a malicious app and either a phishing webpage or a malicious advertisement on a benign webpage. Our extensive evaluations on five popular mobile devices from four different vendors demonstrate the effectiveness and widespread impacts of these attacks. Finally, we investigate several countermeasures, such as restricting access to refresh rates, and find they are inadequate for thwarting RefreshChannels due to DDR's unique characteristics.

We present a method for inferring dense depth from a camera image and a sparse noisy radar point cloud. We first describe the mechanics behind mmWave radar point cloud formation and the challenges that it poses, i.e. ambiguous elevation and noisy depth and azimuth components that yields incorrect positions when projected onto the image, and how existing works have overlooked these nuances in camera-radar fusion. Our approach is motivated by these mechanics, leading to the design of a network that maps each radar point to the possible surfaces that it may project onto in the image plane. Unlike existing works, we do not process the raw radar point cloud as an erroneous depth map, but query each raw point independently to associate it with likely pixels in the image – yielding a semi-dense radar depth map. To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map. We test our method on the NuScenes benchmark and show a 10.3% improvement in mean absolute error and a 9.1% improvement in root-mean-square error over the best method. Code: https://github.com/nesl/radar-camera-fusion-depth.

In this article, we demonstrate that by using a temporal sequence of applied brake signals collected from a vehicle, attackers can still possibly infer the vehicle’s route over the period, even though brake-signal data does not reveal any specific location information. Our route inference is basically composed of three steps. At first, we categorize brake-signal subsequences into four different driving maneuvers (i.e., stopping from a certain speed, reducing speed to adjust with the traffic flow, and taking left and right turns). Second, we estimate the number of intersections traversed by the vehicle using the applied brake signals and their corresponding maneuvers. Finally, we design a graph-based route-selection algorithm to find a list of (paths) routes from the regional map using the predicted driving maneuvers and the speed profile. We evaluate our approach using over 450 km of transportation data, which has been collected from 25 individuals. The experimental results demonstrate that, by resorting to our solution, 92.04% of the original drivers’ trajectory can be successfully recovered from their brake data regardless of driver and vehicle models.

Sensing and data collection platforms are the crucial components of high-quality datasets that can fuel advancements in research. However, such platforms usually are ad-hoc designs and are limited in sensor modalities. In this paper, we discuss our experience designing and deploying a multi-modal multi-node sensor data collection platform that can be utilized for various data collection tasks. The main goal of this platform is to create a modality-rich data collection platform suitable for Internet of Things (IoT) applications with easy reproducibility and deployment, which can accelerate data collection and downstream research tasks.

Video scene analysis is a well-investigated area where researchers have devoted efforts to detect and classify people and objects in the scene. However, real-life scenes are more complex: the intrinsic states of the objects (e.g., machine operating states or human vital signals) are often overlooked by vision-based scene analysis. Recent work has proposed a radio frequency (RF) sensing technique, wireless vibrometry, that employs wireless signals to sense subtle vibrations from the objects and infer their internal states. We envision that the combination of video scene analysis with wireless vibrometry form a more comprehensive understanding of the scene, namely "rich scene analysis". However, the RF sensors used in wireless vibrometry only provide time series, and it is challenging to associate these time series data with multiple real-world objects. We propose a real-time RF-vision sensor fusion system, Capricorn, that efficiently builds a cross-modal correspondence between visual pixels and RF time series to better understand the complex natures of a scene. The vision sensors in Capricorn model the surrounding environment in 3D and obtain the distances of different objects. In the RF domain, the distance is proportional to the signal time-of-flight (ToF), and we can leverage the ToF to separate the RF time series corresponding to each object. The RF-vision sensor fusion in Capricorn brings multiple benefits. The vision sensors provide environmental contexts to guide the processing of RF data, which helps us select the most appropriate algorithms and models. Meanwhile, the RF sensor yields additional information that is originally invisible to vision sensors, providing insight into objects' intrinsic states. Our extensive evaluations show that Capricorn real-timely monitors multiple appliances' operating status with an accuracy of 97%+ and recovers vital signals like respirations from multiple people. A video (https://youtu.be/b-5nav3Fi78) demonstrates the capability of Capricorn.

Intelligent systems commonly employ vision sensors like cameras to analyze a scene. Recent work has proposed a wireless sensing technique, wireless vibrometry, to enrich the scene analysis generated by vision sensors. Wireless vibrometry employs wireless signals to sense subtle vibrations from the objects and infer their internal states. However, it is difficult for pure Radio-Frequency (RF) sensing systems to obtain objects' visual appearances (e.g., object types and locations), especially when an object is inactive. Thus, most existing wireless vibrometry systems assume that the number and the types of objects in the scene are known. The key to getting rid of these presumptions is to build a connection between wireless sensor time series and vision sensor images. We present Capricorn, a vision-guided wireless vibrometry system. In Capricorn, the object type information from vision sensors guides the wireless vibrometry system to select the most appropriate signal processing pipeline. The object tracking capability in computer vision also helps wireless systems efficiently detect and separate vibrations from multiple objects in real time.

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.

The current autonomous vehicles are equipped with onboard deep neural network (DNN) models to process the data from different sensor and communication units. In the connected autonomous vehicle (CAV) scenario, each vehicle receives time-series driving signals (e.g., speed, brake status) from nearby vehicles through the wireless communication technologies. In the CAV scenario, several black-box adversarial attacks have been proposed, in which an attacker deliberately sends false driving signals to its nearby vehicle to fool its onboard DNN model and cause unwanted traffic incidents. However, the previously proposed black-box adversarial attack can be easily detected. To handle this problem, in this paper, we propose a Suspicion-free Boundary Black-box Adversarial (SBBA) attack, where the attacker utilizes the DNN model's output to design the adversarial perturbation. First, we formulate the attack design problem as a goal satisfying optimization problem with constraints so that the proposed attack will not be easily detectable by detection methods. Second, we solve the proposed optimization problem using the Bayesian optimization method. In our Bayesian optimization framework, we use the Gaussian process to model the posterior distribution of the DNN model, and we use the knowledge gradient function to choose the next sample point. We devise a gradient estimation technique for the knowledge gradient method to reduce the solution searching time. Finally, we conduct extensive experimental evaluations using two real driving datasets. The experimental results show that SBBA outperforms the previous adversarial attacks by 56% higher success rate under detection methods, 238% less time to launch the attacks, and 76% less perturbation (to avoid being detected), and 257% fewer queries (to the DNN model to verify the attack success).

Connected and autonomous vehicles have been introduced to increase roadway safety and traffic flow efficiency, where an autonomous vehicle shares its current and near-future driving maneuver information using different CAN-bus signals (e.g., speed, brake pedal pressure, and so on) with its nearby vehicles using wireless communication technologies. In order to classify these driving maneuver data, deep neural network (DNN) models are widely utilized as DNN models result in high prediction accuracy. However, the existing DNN-based classification models are not suitable for the driving maneuver time-series datasets, which are often imbalanced (i.e., different number of instances for different types of maneuvers). The raw driving signals also carry (univanted) traffic context information (e.g., congestions, signs, curvatures, etc.), which can significantly reduce the driving maneuver classification accuracy. This paper presents a Deep Driving Maneuver Classification (DeepDMC) framework to classify different driving maneuvers more accurately. There are three modules in the proposed framework: maneuver classification, context discrimination, and maneuver generation. First, the maneuver classification module takes both labeled and unlabeled data to predict different maneuvers. Second, the context discriminator module detects different traffic context information from the data, and then reduces traffic contexts’ impacts to classify different driving maneuvers. Third, the maneuver generation module generates synthetic maneuver data to handle the imbalanced data problem. To better understand the proposed framework’s robustness, we evaluate the proposed framework with other existing DNN-based classification models using two real driving datasets. From the experimental evaluations, we find that the proposed DeepDMC framework outperforms other existing classification models by up to 42.82% and 37.53% in terms of precision and recall values, respectively.

Deep Neural Network (DNN) models are expected to be widely used in self-driven autonomous vehicles to understand surrounding environments and enhance driving safety. In this paper, we propose a Fast Black-box Adversarial (FBA) attack for time-series DNN models in connected autonomous vehicle (CAV) scenarios. In this attack, an attacker sends false driving signals to a vehicle to misclassify its DNN model (e.g., maintaining speed is misclassified to stopping). Though different black-box adversarial attacks have been proposed previously, they are mainly for image classification, which cannot be directly adopted in the CAV scenarios due to two challenges. First, the attack needs to be generated in near real time. Second, it should not be noticeable based on the driving time-series signals. To handle these two challenges, FBA consists of two steps for the adversarial signal generation: offline and online. First, based on our real data analysis observation that each driving maneuver has maneuver-specific similar patterns (in the time-series) regardless of drivers or vehicles, FBA finds the influential input portion for each maneuver as the offline adversarial signal portion. Second, given a benign driving signal input, FBA replaces its influential input portion with the offline adversarial signal portion and smooths the signals, and uses this input as the initial solution to find the optimal perturbation (that leads to successful attack while minimizing the perturbation values) online using the zeroth-order gradient descent method. It significantly reduces the time to find the optimal perturbation since the initial solution is closer to the optimal solution. Our experiments based on real-driving datasets show the effectiveness of FBA in dealing with the two challenges compared with the existing black-box adversarial attacks.

In a connected autonomous vehicle (CAV) scenario, each vehicle utilizes an onboard deep neural network (DNN) model to understand its received time-series driving signals (e.g., speed, brake status) from its nearby vehicles, and then takes necessary actions to increase traffic safety and roadway efficiency. In the scenario, it is plausible that an attacker may launch an adversarial attack, in which the attacker adds unnoticeable perturbation to the actual driving signals to fool the DNN model inside a victim vehicle to output a misclassified class to cause traffic congestion and/or accidents. Such an attack must be generated in near real-time and the adversarial maneuver must be consistent with the current traffic context. However, previously proposed adversarial attacks fail to meet these requirements. To handle these challenges, in this paper, we propose a Context- aware Black-box Adversarial Attack (CBAA) for time-series DNN models in CAV scenarios. By analyzing real driving datasets, we observe that specific driving signals at certain time points have a higher impact on the DNN output. These influential spatio-temporal factors differ in different traffic contexts (a combination of different traffic factors (e.g., congestion, slope, and curvature)). Thus, CBAA first generates the perturbation only on the influential spatio-temporal signals for each context offline. In generating an attack online, CBAA uses the offline perturbation for the current context to start searching the minimum perturbation using the zeroth-order gradient descent method that will lead to the misclassification. Limiting the spatio-temporal searching scope with the constraint of context greatly expedites finding the final perturbation. Our extensive experimental studies using two different real driving datasets show that CBAA requires 43% fewer queries (to the DNN model to verify the attack success) and 53% less time than existing adversarial attacks.

Many usage-based automotive insurance programs do not directly use any GPS-based location tracking devices. Instead, CAN-bus data, such as brake signal data, can be collected by these programs to evaluate drivers’ driving habits and vehicle usage policies. In this paper, we demonstrate that by using a temporal sequence of applied brake signals collected from a vehicle, attackers can still possibly infer the vehicle’s route over the period, even though brake signal data does not reveal any specific location information. Our route inference is basically composed of three steps: At first, we categorize brake signal subsequences into four different driving maneuvers (i.e., stopping from a certain speed, reducing speed to adjust with the traffic flow, and taking left and right turns). Second, we estimate the number of intersections traversed by the vehicle using the applied brake signals and their corresponding maneuvers. Particularly, we also estimate the overall speed profile based on the magnitude and interval of different brake signals. From the estimated speed profile, we infer the distances, traveling time, and traffic signs corresponding to the candidate edges. Finally, we design a graph-based route-selection algorithm to find a list of (paths) routes from the regional map using the predicted driving maneuvers and the speed profile. We use a score function based on three factors (i.e., distance, traveling time, and traffic signs) to identify a candidate edge. We evaluate our approach using over 450km of transportation data, which has been collected from 24 individuals. The experimental results demonstrate that, by resorting to our solution, 89% of the original drivers’ trajectory can be successfully recovered from their brake data regardless of driver and vehicle models.

Driving route recommendation systems have been becoming popular due to high demands on such systems and their high socio-economic impacts. Existing route recommendation systems cannot provide a well-balanced route by considering the user preference on multiple criteria or make route recommendation in a short time. This paper presents a multi-objective route recommendation system considering three different attributes (i.e., fuel consumption, travel time, and air quality). The proposed route recommendation system uses the Q-learning based reinforcement learning algorithm to leverage the available datasets to make route recommendations in a timely manner. First, we build a road network graph using a publicly available map service (i.e., OpenStreetMap) and other real-world datasets on traffic, weather, and air substances. Second, we utilize the existing predictors for air quality, travel time, and fuel consumption estimations to update the road network graph periodically. Third, we design the route recommendation system using the Q-learning reinforcement learning approach considering the given user's preference for travel time, fuel consumption, and air quality. To evaluate the proposed approach's performance, we conduct experimental evaluations based on the real-world datasets with publicly available map service.

Connected and autonomous vehicles (CAV) have been introduced to increase roadway safety and traffic flow efficiency. In CAV scenarios, an autonomous vehicle shares its current and near-future driving maneuvers in terms of different driving signals (e.g., speed, brake pedal pressure) with its nearby vehicles using wireless communication technologies. Deep neural network (DNN) models are usually used to process the driving maneuver time-series data over other machine learning algorithms due to the high prediction accuracy of DNN models. In this scenario, an attacker can send false driving maneuver signals to fool the DNN model to misclassify an input. The existing black-box adversarial attacks (which are for image datasets) require many queries to the DNN model to check if a generated attack will be successful (hence long time) or high amount of perturbation (low imperceptibility), and thus cannot be applied to the time-sensitive CAV scenarios featured by multi-dimensional time series driving data. In this paper, we present an Advanced black-box Adversarial Attack (A3) for the deep driving maneuver classification models. We first formulate an optimization problem for the attack generation with continuous search space to reduce the search time. To solve the optimization problem, A3innovatively combines the binary search and optimization algorithm to improve the time-efficiency of searching the optimal solution. It first uses a binary partition technique to reduce the perturbation search space in solving the problem to improve time-efficiency. It then uses the zeroth-order stochastic gradient descent approach, which is featured by searching a solution faster for high-dimensional datasets, thus further improving time-efficiency. We evaluate the proposed A3attack in terms of different metrics using two real driving datasets. The experimental results show that the A3attack requires up to 84.12% fewer queries and 57.67% less perturbation with 94.87% higher success rates than the existing black-box adversarial attacks.

Nowadays, smart building infrastructures are equipped with hundreds of sensors to monitor building environments and provide smart solutions for occupant comfortability and energy efficiency. Ideally, an automated system can predict and adjust the physical features (e.g., lighting, air quality, temperature, and so on) in a person’s office based on his/her personalized preferences and activities. However, since the data is from one person, there may not be sufficient data for machine learning model training, and the data’s quality may be low (e.g., with noises). Then, it is a challenge to conduct accurate predictions to provide personalized environment adjustment. To handle this problem, in this paper, we propose a smart building assistance system consisting of different sensor data analysis approaches and a deep neural network (DNN)-based prediction model to make a more accurate prediction despite low-quality sensor data. First, we collected a year-long smart building dataset from four different data sources (i.e., sensors, calendar, weather, and survey). Second, we perform different feature engineering approaches (i.e., concretization, one-hot encoding, and multiple feature combination) on the data as inputs for the prediction models. Third, we identify a support vector regression-based prediction model and propose a hybrid DNN model consisting of several recurrent neural network blocks and a feed-forward DNN block to predict different preferred physical features considering different activities of a person (e.g., meeting, lunch, research activities). Finally, we conduct experimental studies to evaluate the performance of the proposed prediction models compared to other existing machine learning models in terms of accuracy. Our predicted preferred physical features match the occupant’s preferred ranges of different physical features during a specific activity. We also open-sourced our code on GitHub.

As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. Also, as velocity optimization is for individual vehicles, previous methods cannot avoid rear-end collisions. That is, a vehicle following its optimal velocity profile may experience rear-end collisions with its frontal vehicle on the road. In this article, for the first time, we propose a velocity optimization system that enables EVs to immediately pass green traffic lights without delay and to avoid rear-end collisions to ensure driving safety when EVs follow optimal velocity profiles on the road. We collected real driving data on road sections of US-25 highway (with two driving lanes in each direction and relatively low traffic volume) to conduct extensive trace-driven simulation studies. Results show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time. Also, it helps EVs to avoid possible collisions compared with existing collision avoidance methods.

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to their potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies; human factors; and information-aware controller design. First, the different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factors, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, the different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, the critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, the promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.

Always keeping a certain distance between vehicles in a platoon system is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle's limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. By leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its channel simply based on its distance to the leader vehicle. We also devise a strategy, in which a succeeding vehicle uses its stored common velocity profile when it is disconnected from its preceding vehicle and then adjusts its velocity once the connection is built. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods and implement a real-world prototype by equipping vehicles with Android mobile devices. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks.

In recent years, driving route recommendation has attracted growing interest from researchers and industries. However, previously proposed route recommendation systems cannot jointly consider different factors (e.g., fuel consumption, travel time, air quality) in parallel with different weights entered by users. In addition, as users set the weights based on their own evaluation (e.g., much higher weight on air quality than fuel consumption), which may lead to a very unbalanced route (e.g., worst travel time and worst fuel consumption) that actually is not what the users desire. To handle these issues, in this paper, we propose a routing recommendation system, called Vehicle Routing Trifecta (VRT), which can jointly blend different considered factors with different weights entered by users while still producing well-balanced routes that conform user normal desire. VRT consists of two innovative components. First, we establish three different predictors for air quality, travel time, and fuel consumption estimations of each road segment in the road network. Second, we design an optimal route selector, which consists of the solution of a multi-criteria optimization problem based on the given user preference on three different aspects (e.g., air quality, travel time, and fuel consumption). We conduct extensively simulation studies based on the real-world, geo-tagged datasets to evaluate VRT. The comparative studies with other existing routing recommendation systems show the superior performance of VRT in terms of recommending routes that meet user entered preference.

In spite of many shuffle-heavy jobs in current commercial data-parallel clusters, few previous studies have considered the network traffic in the shuffle phase, which contains a large amount of data transfers and may adversely affect the cluster performance. In this paper, we propose a network-aware scheduler (NAS) that handles two main challenges associated with the shuffle phase for high performance: i) balancing cross-node network load, and ii) avoiding and reducing cross-rack network congestion. NAS consists of three main mechanisms: i) map task scheduling (MTS), ii) congestion-avoidance reduce task scheduling (CA-RTS) and iii) congestion-reduction reduce task scheduling (CR-RTS). MTS constrains the shuffle data on each node when scheduling the map tasks to balance the cross-node network load. CA-RTS distributes the reduce tasks for each job based on the distribution of its shuffle data among the racks in order to minimize cross-rack traffic. When the network is congested, CR-RTS schedules reduce tasks that generate negligible shuffle traffic to reduce the congestion. We implemented NAS in Hadoop on a cluster. Our trace-driven simulation and real cluster experiment demonstrate the superior performance of NAS on improving the throughput (up to 62%), reducing the average job execution time (up to 44%) and reducing the cross-rack traffic (up to 40%) compared with state-of-the-art schedulers.

Task scheduling and preemption are two important functions in data-parallel clusters. Though directed acyclic graph task dependencies are common in data-parallel clusters, previous task scheduling and preemption methods do not fully utilize such task dependency to increase throughput since they simply schedule precedent tasks prior to their dependent tasks or neglect the dependency. We notice that in both scheduling and preemption, choosing a task with more dependent tasks to run allows more tasks to be runnable next, which facilitates to select a task that can more increase throughput. Accordingly, in this paper, we propose a Dependency-aware Scheduling and Preemption system (DSP) to achieve high throughput. First, we build an integer linear programming model to minimize the makespan (i.e., the time when all jobs finish execution) with the consideration of task dependency and deadline, and derive the target server and start time for each task, which can minimize the makespan. Second, we utilize task dependency to determine tasks' priorities for preemption. Finally, we propose a method to reduce the number of unnecessary preemptions that cause more overhead than the throughput gain. Extensive experimental results based on a real cluster and Amazon EC2 cloud service show that DSP achieves much higher throughput compared to existing strategies.

As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. In this paper, for the first time, we propose a velocity optimization system which enables EVs to immediately pass green traffic lights without delay. We collected real driving data on a 4.0 km long road section of US-25 highway to conduct extensive trace-driven simulation studies. The experimental results from Matlab and Simulation for Urban MObility (SUMO) traffic simulator show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time.

Electric vehicles (EVs) have great potential to reduce dependency on fossil fuels. The recent surge in the development of online EV (OLEV) will help to address the drawbacks associated with current generation EVs, such as the heavy and expensive batteries. OLEVs are integrated with the smart grid of power infrastructure through a wireless power transfer system (WPT) to increase the driving range of the OLEV. However, the integration of OLEVs with the grid creates a tremendous load for the smart grid. The demand of a power grid changes over time and the price of power is not fixed throughout the day. There should be some congestion avoidance and load balancing policy implications to ensure quality of services for OLEVs. In this paper, first, we conduct an analysis to show the existence of unpredictable power load and congestion because of OLEVs. We use the Simulation for Urban MObility tool and hourly traffic counts of a road section of the New York City to analyze the amount of energy OLEVs can receive at different times of the day. Then, we present a game theory based on a distributed power schedule framework to find the optimal schedule between OLEVs and smart grid. In the proposed framework, OLEVs receive the amount of power charging from the smart grid based on a power payment function which is updated using best response strategy. We prove that the updated power requests converge to the optimal power schedule. In this way, the smart grid maximizes the social welfare of OLEVs, which is defined as mixed consideration of total satisfaction and its power charging cost. Finally, we verify the performance of our proposed pricing policy under different scenarios in a simulation study.

This paper aims to improve wireless network throughput by suppressing duplicate data transmissions from network links. It has been demonstrated that IP-layer Redundancy Elimination (RE) with content overhearing can significantly improve the goodput and utilization of wireless channels in wireless environment. However, the integration of IP-layer RE and wireless overhearing introduces a challenge. That is, probabilistic wireless overhearing and the possibility of a receiver overhearing from multiple transmitters cause the caches of a sender and a receiver far from synchronization, which can disrupt IP-layer RE's correctness and degrade its performance. The previous work deals with this challenge by the overhearing probability estimation, which however is not efficient or scalable. In this paper, we propose a Prediction-based Redundancy Elimination with Content Overhearing method (PRECO) to address this challenge. By exploiting prediction-based RE, PRECO does not require cache synchronization and overhearing probability estimation, which enables its efficient and scalable deployment. Based on PRECO, we exploit the benefits of deploying sub-packet level RE as a primitive IP-layer service on all nodes in wireless mesh networks by proposing a redundancy-aware routing protocol. Trace-driven performance evaluation shows the effectiveness and efficiency of PRECO compared with other RE methods.

Electric vehicles (EVs) will become a component of the future generation intelligent transportation system. Because of EVs' limited battery power, the wireless power transfer (WPT) system has drawn much attention in recent years. The WPT system charges EVs in motion when they pass the charging lanes installed in roads without requiring physical contact between utility power supply and vehicle battery. A charging lane has limited power that can be transferred to EVs on the charging lane. A challenge here is how to allocate the limited power to the EVs so that they have sufficient power to arrive at the next charging lane or their destinations (when there are no charging lanes ahead). In this paper, we study this power distribution scheduling problem.We provide solutions to handle this challenge and also achieve each of the following goals as much as possible: i) balancing the state of charge (SOC) of the EVs, ii) balancing the amount of stored power of the EVs, and iii) minimizing the total power charged. This paper is the first work that handles such a power distribution scheduling problem in WPT systems. Our extensive experiments on MatLab and Simulation for Urban MObility (SUMO) show the effectiveness of our scheduling solutions in achieving the different goals compared with other scheduling methods including first-come- first-serve and equal share.

To minimize the fuel consumption for driving, several methods have been proposed to calculate vehicles' optimal velocity profiles on a remote cloud. Considering the traffic dynamism, each vehicle needs to keep updating the velocity profile, which requires low latency for information uploading and profile calculation. However, these proposed methods cannot satisfy this requirement due to (1) high queuing delay for information uploading caused by a large number of vehicles, and (2) the neglect of the traffic light and high computation delay for velocity profile. For (1), considering the driving features of close vehicles on a road, e.g., similar velocity and interdistances, we propose to group vehicles within a certain range and let the leader vehicle in each group to upload the group information to the cloud, which then derives the velocity of each vehicle in the group. For (2), we propose spatial-temporal DP (ST-DP) that additionally considers the traffic lights. We innovatively find that the DP process makes it well suited to run on Spark (a fast parallel cluster computing framework) and then present how to run ST-DP on Spark. Finally, we demonstrate the superiority of our method using both trace-driven simulation (NS-2.33 simulator and MATLAB) and real-world experiments.

Maximizing data locality in task scheduling is critical for the performance of MapReduce job execution. Manyexisting works on MapReduce scheduling decide the placementof map and reduce tasks on a coarse granularity of locationsmeasured by located machines and racks. They do not explicitlyconsider the network topology and data transmission cost, whichmay cause task straggling and degrade the job performance. Inorder to improve MapReduce job performance, in this paper, we consider the task placement with the goal of minimizing theoverall data transmission cost for a job execution while balancingthe transmission cost reduction and resource utilization. Wepropose a probabilistic network-aware scheduling algorithm thatselects a task (map task or reduce task) to be scheduled on a givenavailable task slot that leads to the minimum transmission costamong the task candidates, and then schedule the selected taskon the slot with a probability determined by its transmission cost, a lower expected transmission cost leads to a higher probabilityand vice versa. We also propose a method to more accuratelyestimate the intermediate data size based on the progress ofmap tasks, which is needed to calculate the transmission cost ofreduce tasks but is unknown at the time of reduce task scheduling. We implement our probabilistic network-aware schedulingalgorithm on Apache Hadoop and conduct experiments on ahigh-performance computing platform. The experimental resultsshow that our scheduling algorithm outperforms the previousapproaches in terms of job completion time and cluster resource utilization.

As an alternate form in the road transportationsystem, electric vehicle (EV) can help reduce the fossil-fuelconsumption. However, the usage of EVs is constrained by thelimited capacity of battery. Wireless Power Transfer (WPT) can increase the driving range of EVs by charging EVs inmotionwhen they drive through a wireless charging laneembedded in a road. The amount of power that can be suppliedby a charging lane at a time is limited. A problem here iswhen a large number of EVs pass a charging lane, how toefficiently distribute the power among different penetrationslevels of EVs? However, there has been no previous researchdevoted to tackling this challenge. To handle this challenge, wepropose a system to balance the State of Charge (called BSoC) among the EVs. It consists of three components: i) fog-basedpower distribution architecture, ii) power scheduling model, and iii) efficient vehicle-to-fog communication protocol. The fogcomputing center collects information from EVs and schedulesthe power distribution. We use fog closer to vehicles ratherthan cloud in order to reduce the communication latency. Thepower scheduling model schedules the power allocated to eachEV. In order to avoid network congestion between EVs and thefog, we let vehicles choose their own communication channelto communicate with local controllers. Finally, we evaluateour system using extensive simulation studies in NetworkSimulator-3, MatLab, and Simulation for Urban MObilitytools, and the experimental results confirm the efficiency ofour system.

Always keeping a certain distance between vehicles in a platoon is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle's limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its own velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. Directly applying current channel allocation methods for interference avoidance leads to high communication cost and delay in vehicle joins and departures (i.e., vehicle dynamics). As a result, a challenge is how to reduce the communication delay and cost for channel allocation in decentralized platoon networks? To handle this challenge, by leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its own channel simply based on its distance to the leader vehicle. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks and of FLA over previous channel allocation methods in platoons.

Reversible logic transforms logic signal in a way that allows the original input signals to be recovered from the produced outputs, has attracted great attention because of its application in many areas. Traditional silicon computers consume much more power compared to computing systems based on Deoxyribonucleic Acid (DNA). In addition, DNA-based logic gates are stable and reusable. In this paper, we propose a new approach for designing DNA-based reversible adder/subtractor circuit; it's possible to perform addition and subtraction operations using single circuit representation. We first merge the properties of addition and subtraction operations. Then, we demonstrate reversible DNA-based addition and subtraction operations. Our proposed DNA-based reversible addition/subtraction circuit is faster than the conventional one due to parallelism and replication properties of DNA strands. It also requires less space because of compactness of DNA strands. In addition, the DNA-based adder/subtractor circuit needs low power as the formation of DNAs consumes a small amount of energy. Finally, the comparative results show that the proposed DNA-based system requires m+3.2nDNA signals, but in existing system, it requires m.2n, where m is the size of extra tags and n is the total number of bits. Besides, the run time complexity of proposed system has O(1) while the existing system has O(mln2n).

In this paper, we realize the reversible multiplier circuit using Deoxyribonucleic Acid (DNA). Due to reversible logic's emerging characteristics, it has drawn great attention in recent years. As multiplication operation consists of several shift and addition operations, we use shifter and adder circuits as building blocks to construct multiplication circuit. We also present an algorithm for depicting overall procedures of multiplication operation using an example. The proposed circuit is faster, required less space and power due to parallelism, replication properties, compactness and formation of DNA strands, respectively. Additionally, the run time complexity of our proposed system is O(m) instead of O(m(ln2n)2) in existing DNA-based system, m and n are the bit length of multiplier and multiplicand. Also, proposed system needs u+3.2nDNA signals while the existing system needs u.2n, u is the extra tag.

In this paper, DNA-based Toffoli gate has been modeled using the formation of DNA characteristics. After that, the logics of the basic (AND, OR, NOT) gates and EX-OR gates have been realized using our DNA-based Tofffoli gate. The proposed DNA-based Tofolli gate is faster due to parallelism and replication properties of DNA strands. The proposed gate also requires less space because of the compactness of DNA strands. Moreover, the DNA-based Tofolli gate requires low power as the formation of DNA consumes a small amount of energy. Reversible DNA-based composite logic has also been accomplished by a reversible Half-Adder circuit. Finally, a comparative study between the existing Toffoli gate and our proposed DNA-based Toffili gate has been done.