Accurate traffic forecasting is critical for intelligent transportation systems. While recent spatiotemporal Graph Neural Networks (GNNs) have shown strong performance by modeling spatiotemporal dependencies, they primarily capture correlational patterns and lack a causal foundation. This limits their interpretability and robustness, especially under partial observability and structural changes in traffic dynamics. We propose CausalGRIT, a causal spatiotemporal forecasting framework that integrates observational learning with intervention-aware reasoning. Grounded in Structural Causal Models (SCMs), our method constructs dynamic causal graphs that encode directed cause-effect relationship across space and time. To handle latent confounding from incomplete sensor data, we introduce a variational belief encoder with planar flows for uncertainty-aware inference. To further enhance robustness, we develop an Edge Generation via Counterfactuals (EGC) module that simulates interventions to reveal and regularize weak or spurious dependencies during training. On three California PeMS (Performance Measurement System) datasets, CausalGRIT reduces RMSE by 7.2% relative to the next best baseline in average and shows far greater robustness: under perturbations, MAE increases by only 0.8%, compared with 8.8% for non-causal models.

Collaborative Perception enables multiple agents, such as autonomous vehicles and infrastructure, to share sensor data via vehicular networks so that each agent gains an extended sensing range and better perception quality. Despite its promising benefits, realizing the full potential of such systems faces significant challenges due to inherent imperfections in underlying system layers, consisting of network layer imperfections and hardware-level noises. Such imperfections and noises include packet loss in vehicular networks, localization errors from GPS measurements, and synchronization errors caused by clock deviation and network latency. To address these challenges, we propose a novel end-to-end collaborative perception framework, SCORPION, that harnesses the AI co-design of the application layer and system layer to tackle the aforementioned imperfections. SCORPION consists of three main components: lost bird’s eye view feature reconstruction (L-BEV-R) recovers lost spatial features during lossy V2X communication, while deformable spatial cross attention (DSCA) and temporal alignment (TA) compensate for localization and synchronization errors in feature fusion. Experimental results on both synthetic and real-world collaborative 3D object detection datasets demonstrate that SCORPION advances the state-of-the-art collaborative perception methods by 5.9 - 13.2 absolute AP on both standard and noisy scenarios.

This paper aims to provide a sub-meter localization and tracking capability for vehicle key fob detection in challenging outdoor environments. We take an experimentation-oriented approach and design a UWB-based real-time localization and tracking system called ULTRA. We introduce two novel algorithmic components to improve the accuracy and reliability: (i) We first generate a set of potential locations based on their geometric features, then propose an algorithm based on A-DBSCAN (adaptive density-based spatial clustering of applications with noise) to dynamically form clusters, estimate sensor trustworthiness and the respective probabilities. (ii) We design a Bayesian algorithm without requiring extra motion sensors, which seamlessly balances the real-time measurements with history-based motion tracking. Thanks to these novel algorithms, ULTRA is able to better handle signal blockage, uncertain ranging noises caused by non-line-of-sight (NLOS) caused by nearby vehicles and parking structures. Using a combination of vehicle testbed systems and high-fidelity emulation platforms, we experimentally demonstrate that ULTRA has a localization accuracy of ≤30cm, and achieves a directional angle error of ≤ 5° under challenging outdoor environments, outperforming the state-of-the-art linear/nonlinear least squares algorithms, as well as other unsupervised-learning-based approaches (i.e., K-means clustering, SVM unsupervised outlier detection).

On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.

Vehicle function offloading has been an active research topic in the connected and automated vehicles (CAV) domain. Mobile edge computing can execute sophisticated algorithms on abundant computing resources, leading to superior accuracy for vehicle system state estimation. On the other hand, cellular network latency causes edge-computed information to be obsolete.In this paper, by focusing on camera-based object tracking applications, we develop a novel state fusion framework that not only achieves the benefits (enhanced detection accuracy) but also mitigates the disadvantages (unpredictable network latency) of edge computing. This is achieved by carefully managing the system state consistency between the vehicle onboard system and the remote edge system through our novel backward-and-forward algorithms. We evaluate our system by comparing our method with the edge-only and onboard-only counterparts through extensive empirical experiments. The presented framework improves the accuracy of camera-based perception by at least 2x compared to traditional techniques, with an average response time of 48.13 ms (strictly less than the mission-critical latency threshold of 100 ms [1]).

This is Part II of the two-part Special Issue (SI) on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV). The SI aims at bringing together contribution from both academia and industry to highlight the recent progress in various aspects of positioning systems. We have included 30 original contributions in this two-parts SI. We kindly refer readers to Part I of this SI for a comprehensive overview written by the Guest Editorial Team.

The ongoing large-scale deployment of 5G (3GPP New Radio) cellular systems makes it attractive to use their signals for “opportunistic” bistatic radar sensing, where the base stations serve as transmitters and user equipment (UEs) as receivers. Such 5G based radar sensing has the potential to complement and improve existing advanced driver assistance systems (ADAS) and future self-driving cars, e.g., in terms of increased sensing range. However, realizing a radar sensing system using strictly standards-compliant 5G-NR signals is a complicated task, mainly because of the signal inconsistency across time and frequency. This paper first analyzes the different reference signals (RSs) defined in the NR standard and which combinations thereof are suitable for vehicular radar applications. We then present several signal processing approaches to overcome difficulties arising from the standardized signals; in particular two-dimensional interpolation between non-regularly spaced locations in the time-frequency plane, where the classical Nyquist-Shannon sampling breaks down, as well as serial interference cancellation(SIC) for determination of the peaks in the delay-Doppler domain with better-than-Fourier resolution. We finally demonstrate the use of payload data for improving the radar estimates. Simulation results demonstrate the validity of our approach.

Cooperative perception significantly enhances the perception performance of connected autonomous vehicles. Instead of purely relying on local sensors with limited range, it enables multiple vehicles and roadside infrastructures to share sensor data to perceive the environment collaboratively. Through our study, we realize that the performance of cooperative perception systems is limited in real-world deployment due to (1) out-of-sync sensor data during data fusion and (2) inaccurate localization of occluded areas. To address these challenges, we develop RAO, an innovative, effective, and lightweight cooperative perception system that merges asynchronous sensor data from different vehicles through our novel designs of motion-compensated occupancy flow prediction and on-demand data sharing, improving both the accuracy and coverage of the perception system. Our extensive evaluation, including real-world and emulation-based experiments, demonstrates that RAO outperforms state-of-the-art solutions by more than 34% in perception coverage and by up to 14% in perception accuracy, especially when asynchronous sensor data is present. RAO consistently performs well across a wide variety of map topologies and driving scenarios. RAO incurs negligible additional latency (8.5 ms) and low data transmission overhead (10.9 KB per frame), making cooperative perception feasible.

The advancement of connected intelligent transportation systems (C-ITS) and connected automated vehicles (CAV) has brought about a growing need for precise positioning solutions. Positioning technologies play a crucial role in many use cases such as emergency call systems, disaster rescue operations, automated robotics, and more. To ensure the availability, reliability, and quality of location systems both indoors and outdoors, the evolution of cellular technology, particularly in the form of 5G/6G networks, promises to provide a new pathway towards achieving high precision positioning.

Nowadays, Mobile Crowdsourcing (MCS) systems, which leverage crowdsourced data to enable large-scale sensing, has become a popular research area. A persistent question has been whether the number of deployed sensing assets is sufficient to provide enough spatial and temporal coverage in particular areas and whether there is sufficient data to enable individual crowdsensing use cases within an area. We present a framework to provide such analysis. We also propose a pipeline for processing millions of vehicle telemetry data points into coverage assessment results. A set of novel geospatial algorithms for data processing are presented as well. Our analysis models support multiple types of coverage analysis including Segment mode, Node mode, Unique Mileage mode, and Categorized Road mode. Analysis table results and heatmap results are presented in the Demonstration section.

In this paper, we develop a set of normalized spatial temporal properties from which to assess road performance and detect abnormal traffic stream environments. Data is provided by non-instrumented retail vehicle telemetry data, which captures timestamps, GPS locations, and velocity provided every 3 seconds. Open Street Map (OSM) roadways are subdivided into 500-meter segments. Vehicles traveling across those segments are crowd-sensed every 15 minutes. The demonstration compares two traffic streams over the same segments and time periods exactly two weeks apart and illustrates the effectiveness of the approach. The novelty includes methods to create comparable spatial temporal frames and properties by transformations of telemetry data into driver experiences. The outcome of our approach generates a space time grid of cells from which to detect, describe, and diagnose traffic environments.

Vehicular applications are becoming increasingly complex and resource hungry (e.g. autonomous driving). Today, they run entirely on the vehicle, which is a costly solution that also imposes undesirable resource constraints. This paper uses Simultaneous Localization and Mapping (SLAM) as an example application to explore how these applications can instead leverage edge clouds, utilizing their inexpensive and elastic resource pool. This is challenging as these applications are often latency-sensitive and mission-critical. They also process high-bandwidth sensor data streams and maintain large, complex data structures. As a result, traditional offloading techniques generate too much traffic, incurring high delay. To overcome these challenges, we designed CloudSLAM. It partitions SLAM between the vehicle and the edge. To manage the complex, replicated SLAM state, we propose a new consistency model, Output-driven Consistency, that allows us to maintain a level of consistency that is sufficient for accurate SLAM output while minimizing network traffic. This paper motivates and describes our offloading design and discusses the results of an extensive performance evaluation of a CloudSLAM prototype based on ORB-SLAM.

Collaborative computing, leveraging resource on multiple wireless-connected devices, enables complex applications that a single device cannot support individually. However, the problem of assigning tasks over devices becomes challenging in the dynamic environments encountered in real-world settings, considering that the resource availability and channel conditions change over time in unpredictable ways due to mobility and other factors. In this paper, we formulate the task assignment problem as an online learning problem using an adversarial multi-armed bandit framework. We propose MABSTA, a novel algorithm that learns the performance of unknown devices and channel qualities continually through exploratory probing and makes task assignment decisions by exploiting the gained knowledge. The implementation of MABSTA, based on Gibbs Sampling approach, is computational-light and offers competitive performance in different scenarios on the trace-data obtained from a wireless IoT testbed. Furthermore, we prove that MABSTA is 1-competitive compared to the best offline assignment for any dynamic environment without stationarity assumptions, and demonstrate the polynomial-time algorithm for the exact implementation of the sampling process. To the best of our knowledge, MABSTA is the first online learning algorithm tailored to this class of problems.

Driver assistance and vehicular automation would greatly benefit from uninterrupted lane-level vehicle positioning, especially in challenging environments like metropolitan cities. In this paper, we explore whether the WiFi Fine Time Measurement (FTM) protocol, with its robust, accurate ranging capability, can complement current GPS and odometry systems to achieve lane-level positioning in urban canyons. We introduce Wi-Go, a system that simultaneously tracks vehicles and maps WiFi access point positions by coherently fusing WiFi FTMs, GPS, and vehicle odometry information together. Wi-Go also adaptively controls the FTM messaging rate from clients to prevent high bandwidth usage and congestion, while maximizing the tracking accuracy. Wi-Go achieves lane-level vehicle positioning (1.3 m median and 2.9 m 90-percentile error), an order of magnitude improvement over vehicle built-in GPS, through vehicle experiments in the urban canyons of Manhattan, New York City, as well as in suburban areas (0.8 m median and 3.2 m 90-percentile error).

The fifth generation (5G) wireless communication networks are expected to support communications with high mobility, e.g., with a speed up to 500 km/h. Hence 5G communications will have numerous applications in high mobility scenarios, such as high speed railways (HSRs), vehicular ad hoc networks, and unmanned aerial vehicles (UAVs) communications [1]-[3]. The 5G systems will provide advanced communication platforms enabling reliable transmission for the Wireless Train Backbone (WLTB) or Wireless Train Control & Management System (WTCMS) [4], [5]. They will also enable new services or enhancements for vehicular communications in Intelligent Transportation System (ITS) [6]-[9]. The coordination and swarming control for UAVs will also benefit from 5G capabilities, as UAV-based 5G infrastructure modeling and improvement have begun receiving attention [10]. In general, high mobility communication is not only about how large is the maximum speed, it is more about the challenges caused by mobility. In high mobility scenarios, a wireless channel is rapidly time varying, Doppler shifts and spreads can be much larger than those in cellular communications, and if modeled statistically, the channel will be non-wide-sense stationary (non-WSS) over a short time period. In addition, network topology can change quickly, and switching among base stations (BSs) and/or peer nodes can be more frequent, not forgetting 5G challenges in cross-border mobility [11].

Automated driving and advanced driver assistance systems benefit from complete understandings of traffic scenes around vehicles. Existing systems gather such data through cameras and other sensors in vehicles but scene understanding can be limited due to the sensing range of sensors or occlusion from other objects. To gather information beyond the view of one vehicle, we propose and explore FusionEye - a connected vehicle system that allows multiple vehicles to share perception data over vehicle-to-vehicle communications and collaboratively merge this data into a more complete traffic scene. FusionEye uses a self-adaptive topology merging algorithm based on bipartite graph. We explore its network bandwidth requirements and the trade-off with merging accuracy. Experimental results show that FusionEye creates more complete scenes and achieves a merging accuracy of 88% with 5% packet drop rate and transmission latency around 200ms. We show that richer vehicle descriptors offer only marginal accuracy improvements compared to lower communication overhead options.

Disaster and emergency response operations require rapid situational assessment of the affected area for timely and efficient rescue operations. A 3D map, collected after a disaster, can provide such awareness, but constructing this map quickly is a significant challenge. In this paper, we explore the design of a capability called QuickSketch that rapidly builds 3D representations of an unknown environment using crowdsourcing. QuickSketch employs multiple vehicles equipped with 3D sensors (stereo cameras) to explore different areas of an unknown territory and then combines 3D data from all the vehicles to build a single 3D map. QuickSketch annotates the 3D map with important landmarks and enables rapid contextualization of visual intelligence (photos) received from first responders and disaster victims to guarantee timely backup and rescue operations. Our evaluation results show that QuickSketch can stitch a 3D map for a large campus with sub-meter mapping accuracy under certain conditions, position landmarks an order of magnitude more accurately than other image matching techniques, and contextualize visual intelligence accurately.

Thanks to the increased popularity of Global Position System (GPS) devices (such as smartphones and GPS navigators), the amount of GPS data that can be collected is increasing tremendously. This paper aims to develop novel methods of inferring and updating road topology maps from a large amount of crowd-sourced GPS data. We explore map inference using a three-stage approach, which incorporates a novel Multi-Source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first estimation theory method used for map inference. In particular, our approach explicitly leverages the nature of GPS error models, and addresses the unique challenges of vehicular GPS data (asynchronous, varying sampling rate, and under-sampled); as a result, our MSVR approach can better handle inherent GPS errors, reconstruct road shapes more accurately, and better deal with variable GPS data density in empirical environments. The maps inferred from this data are compared to Open Street Map (OSM) maps as ground truth. We evaluate our method using the Tsinghua University's Beijing Taxi Dataset and Shanghai Jiao Tong University's SUVnet Dataset.

Academic and industry research has argued for supporting WiFi time-of-flight measurements to improve WiFi localization. The IEEE 802.11-2016 now includes a Fine Time Measurement (FTM) protocol for WiFi ranging, and several WiFi chipsets offer hardware support albeit without fully functional open software. This paper introduces an open platform for experimenting with fine time measurements and a general, repeatable, and accurate measurement framework for evaluating time-based ranging systems. We analyze the key factors and parameters that affect the ranging performance and revisit standard error correction techniques for WiFi time-based ranging system. The results confirm that meter-level ranging accuracy is possible as promised, but the measurements also show that this can only be consistently achieved in low-multipath environments such as open outdoor spaces or with denser access point deployments to enable ranging at or above 80 MHz bandwidth.

Autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These 3D sensors are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to wirelessly share visual information with other nearby vehicles, but requires the design of novel relative positioning techniques, new perspective transformation methods, approaches to isolate and predict the motion of dynamic objects in order to hide latency, and adaptive transmission strategies to cope with wireless bandwidth variability. We show that AVR is feasible using off-the-shelf wireless technologies, and it can qualitatively change the decisions made by autonomous vehicle path planning algorithms. Our AVR prototype achieves positioning accuracies that are within a few percent of car lengths and lane widths, and is optimized to process frames at 30fps.

As smart vehicles have become more ubiquitous, the capability now exists to detect environmental road features (e.g., potholes, road incline angle, etc.) from their embedded sensor data. By aggregating data from multiple vehicles, crowdsourcing can be leveraged to detect environmental information with improved accuracy. We focus on using such data to detect and localize potholes on multi-lane roads. Extracting information from aggregated vehicle data is challenging due to undersampling sensors, sensor mobility, asynchronous sensor operation, sensor noise, vehicle and road heterogeneity, and GPS position error. GPS position error is particularly problematic in multi-lane environments since the position error is generally larger than standard lane widths. In this paper, we investigate these issues and develop a crowdsourced system to detect and localize potholes in multi-lane environments using accelerometer data from embedded vehicle sensors. Our crowdsourced system reduces the required network bandwidth by determining road incline and bank angle information in each vehicle to filter acceleration components that do not correspond to pothole conditions. We evaluate our system on simulated and real-world data, analyze tradeoffs in the number of vehicles and the amount of bandwidth required for accurate detection, and compare the results to the simpler single lane detection scenario.

With mobile devices increasingly able to connect to cloud servers from anywhere, resource-constrained devices can potentially perform offloading of computational tasks to either save local resource usage or improve performance. It is of interest to find optimal assignments of tasks to local and remote devices that can take into account the application-specific profile, availability of computational resources, and link connectivity, and find a balance between energy consumption costs of mobile devices and latency for delay-sensitive applications. We formulate an NP-hard problem to minimize the application latency while meeting prescribed resource utilization constraints. Different from most of existing works that either rely on the integer programming solver, or on heuristics that offer no theoretical performance guarantees, we propose Hermes, a novel fully polynomial time approximation scheme (FPTAS). We identify for a subset of problem instances, where the application task graphs can be described as serial trees, Hermes provides a solution with latency no more than (1 + ε) times of the minimum while incurring complexity that is polynomial in problem size and 1/ε. We further propose an online algorithm to learn the unknown dynamic environment and guarantee that the performance gap compared to the optimal strategy is bounded by a logarithmic function with time. Evaluation is done by using real data set collected from several benchmarks, and is shown that Hermes improves the latency by 16 percent compared to a previously published heuristic and increases CPU computing time by only 0.4 percent of overall latency.

The connected vehicles have been considered as a remedy for modern traffic issues, potentially saving hundreds of thousands of lives every year worldwide. The Dedicated Short-Range Communications (DSRC) technology is an essential building block of this promising vision. DSRC faces volatile vehicular environments, where not only wireless propagation channels but also network topologies vary rapidly. Moreover, traffic congestions during rush hours may lead to an unprecedentedly high density of broadcasting radios, resulting in compromised reliability, efficiency and fairness of DSRC. In order to optimize the performance of DSRC, we develop a novel Online Control Approach of power and Rates (OnCAR). Supported by systematic control theories, OnCAR performs stably even in the dynamic and unpredictable vehicular environments. To the best of our knowledge, OnCAR is the first solution to address the strong coupling between communication variables. It adopts a multi-variable control model to synchronously adjust transmission power and data rates, which are two major variables determining the performance of DSRC. In addition, OnCAR leverages receiver-side measurements of performance metrics to strike a balance between overall performance and fairness. Compared with the state of the art, OnCAR enhances the overall reliability and efficiency of DSRC by 23.7% and 30.1%, respectively. Meanwhile, these numbers are achieved with a 40.1% improvement in fairness.

Crowdsourcing data from multiple sensing agents has become a fundamental mechanism for extracting information in large-scale cyber-physical systems (CPS). However, there has been little attention paid to the difficulties crowdsourcing presents when the desired information is represented by continuous signals. Crowdsourcing can result in unique types of noise in the aggregated data sample set. Errors from clock synchronization, GPS location determination, or sensor heterogeneity result in biased and correlated errors in the sampled data from each individual sensor. Furthermore, in CPS sensors are often mobile and operate asynchronously. This affects the spatial sampling rates among sensors and results in nonuniform sample spacing. We refer to these sampling issues as the noisy multi-source, variable-rate (MSVR) sampling problem. This work introduces and investigates signal reconstruction algorithms given MSVR sampling conditions. These signal reconstructions are investigated for vehicular applications, using a joint road inclination and bank angle signal estimation algorithm that has access to MSVR sampled vehicle GPS and accelerometer data. The signal reconstruction and angle determination algorithms are validated on simulated and real-world vehicle data, where we reconstruct a road elevation signal with 0.89 m root-mean-square error.

Video streaming has been dominating the mobile bandwidth, and is still expanding drastically. Its tremendous economic benefits have driven the automobile industry to equip vehicles with video streaming capacity. As a result, the new in-cabin Wi-Fi systems have been deployed, enabling each vehicle as a streaming hotspot on the wheels. A built-in Access Point (AP) bridges the communications between Wi-Fi devices inside and cellular networks outside. Distinct advantages offered by this system include a more powerful antenna array to improve multimedia quality, a constant energy source to power the streaming, etc. However, there exist two challenging features that may jeopardize the system performance. (1) The in-cabin Wi-Fi hotspots are mostly deployed on private vehicles, and thus are completely decentralized. (2) Video packets need to be delivered before their deadlines with small delays. Due to these features, existing algorithms may fail to efficiently schedule the in-cabin Wi-Fi video streaming. To fill the gap, we propose the Delay-awaRe dIstributed Video schedulING (DRIVING) framework. Being fully distributed and delay-aware, DRIVING not only increases the streaming goodput, but also reduces the delivery latency and deadline missing ratio. %In order to optimize this new framework, we establish cross-layer analytical models, which help us tune the framework parameters for better performance. In a typical scenario, DRIVING increases the goodput by up to 27.0%, while reducing the queueing delay and the deadline missing ratio by up to 40.0% and 38.4%, respectively.

The TV white spaces (TVWS) enabled infostation has received significant attention due to its wide area coverage for cost-effective and media-rich content dissemination. In this paper, we engineer WhiteFi infostation, which is dedicated for Internet-based vehicular media streaming by leveraging geolocation database. After demonstrating the empirical observations of unique TVWS features and analyzing the real-world TVWS data collected from geolocation database, we first propose an optimal TVWS network planning to deploy WhiteFi infostation with the objective of maximizing network-wide throughput. The proposed TVWS network planning jointly considers the multi-radio configuration and the channel-power tradeoff, which can be realized by decentralized Markov approximation. Furthermore, we introduce a location-aware contention-free multi-polling access scheduling scheme for vehicular media streaming, which considered both the realistic vehicular applications and dynamics of wireless channel conditions. Through extensive simulations with real-world empirical TVWS data and urban vehicular traces, we demonstrate that our WhiteFi infostation solution can well support both the delay-sensitive and delay-tolerant vehicular media streaming services.

Enabling high-throughput and cost-effective vehicular communications is important for many emerging vehicular applications, such as safety applications, traffic management, and mobile Internet access. However, dedicated short-range communications (DSRC), as the sole solution so far, would meet significant challenges in the foreseeable future for supporting diverse vehicular applications simply due to the spectrum scarcity. To address this issue, in this paper, we propose an adaptive vehicular data piping framework, which is assisted by a geolocation database, for the joint utilization of DSRC and TV white space (TVWS) spectrum; in this framework, three types of vehicular data pipes (DSRC, TVWS, and cellular) are considered, while the cellular data pipe is only used as a control-plane link in coordinating the dynamic DSRC and TVWS spectrum sharing happened in the data-plane operations. In order to guarantee the optimal dynamic vehicular access to the geolocation database, we first propose a log-sum-exp (LSE) approximation-based TVWS geolocation database access approach, named LSE-WS algorithm. We formulate the adaptive vehicular data piping problem for dynamic DSRC/TVWS spectrum sharing as a coalitional formation game, and it is shown that the proposed coalitional formation approach reaches the optimal and Nash-stable vehicular data pipe selection partition in a distributed way. Through extensive simulations, we demonstrate that not only the proposed LSE-WS algorithm satisfies the dynamic vehicular geolocation database access requirement but also the adaptive multi-radio vehicular data piping approach for dynamic DSRC/TVWS spectrum sharing significantly outperforms the traditional DSRC solution.

Networking research benefits from controlled, repeatable experimentation using simulation and emulation systems. Making simulations realistic is a challenge for wireless systems and is especially difficult for vehicular networks. This paper presents a general framework for modeling and reproducing environment-specific channel properties. We show that one can estimate localized environment information, and adding such information to state-of-the art channel models significantly increases accuracy. We describe the proposed framework and validate it for fading and line-of-sight effects in vehicle-to-vehicle channels. While suitable models can provide a close approximation of channel conditions, accuracy is limited by the quality of the input information about the environment being modeled. We present a systematic approach to estimating location-specific scattering properties using aerial photography. Using signal-level channel emulation, we show that the improved fading models produce more accurate results at the packet/link level. The error rates of the improved model are 45 and 22 percent lower than using previous state of the art, for Doppler spectrum similarity and packet delivery ratio, respectively. The proposed models-and their implementation-have been designed to minimize run-time complexity while preserving accuracy. The implementation is efficient gh to allow concurrent real-time simulation of many of channels.

With mobile devices increasingly able to connect to cloud servers from anywhere, resource-constrained devices can potentially perform offloading of computational tasks to either improve resource usage or improve performance. It is of interest to find optimal assignments of tasks to local and remote devices that can take into account the application-specific profile, availability of computational resources, and link connectivity, and find a balance between energy consumption costs of mobile devices and latency for delay-sensitive applications. Given an application described by a task dependency graph, we formulate an optimization problem to minimize the latency while meeting prescribed resource utilization constraints. Different from most of existing works that either rely on an integer linear programming formulation, which is NP-hard and not applicable to general task dependency graph for latency metrics, or on intuitively derived heuristics that offer no theoretical performance guarantees, we propose Hermes, a novel fully polynomial time problem approximation scheme (FPTAS) algorithm to solve this problem. Hermes pros vides a solution with latency no more than (1 + ε) times of the minimum while incurring complexity that is an polynomial in problem size and //ε We evaluate the performance by using real data set collected from several benchmarks, and show that Hermes improves the latency by 16% (36% for larger scale application) compared to a previously published heuristic and increases CPU computing time by only 0.4% of overall latency.

We study the presence of social communities in mobility traces from vehicular fleets. By analyzing publicly available sets of fleet vehicle mobility traces obtained from two real-world deployments — consisting of more than 2000 taxis in Shanghai and Beijing respectively, we confirm the existence of small numbers of distinct social communities in vehicular networks, which is in direct contrast to the general belief that vehicular networks are best modeled as a relatively homogeneous system. We examine the spatio-temporal characteristics of social communities, gaining the insight that they are driven primarily by social proximity induced by geographic locality. We then develop a parsimonious multi-community ordinary differential equation (ODE) model, which uses the heterogeneous structure introduced by social communities to model information dissemination. We show through simulations that this approach dramatically outperforms the conventional homogeneous ODE model in capturing the dynamics of the dissemination process. We further demonstrate that the use of the ODE model to optimize seeding of an initial set of vehicles results in improved utility for information dissemination compared to seed-optimization using a homogeneous model.

The increased availability of embedded vehicle sensors allows for the detection of road features such as potholes. Despite being a promising approach, current vehicle embedded sensors operate at low frequencies and undersample sensor signals, thus degrading detection accuracy. One emerging solution is to crowdsource such undersampled sensor data from multiple vehicles to increase the detection accuracy. Aggregating sensor data from multiple vehicles, nonetheless, is a challenging task given the heterogeneity among vehicles, asynchronous sensor operation, GPS error, and sensor noise. Additionally, there may be bandwidth restrictions in vehicular networks which limit the amount of data available for aggregation. We investigate these issues by focusing on the problem of pothole detection. To quantify the detection accuracies and effects of real-world limitations, we design and evaluate three crowdsourcing pothole detection schemes involving vehicles and the Cloud. We also address the issue of lack of extensive model training data by demonstrating that a detection model applicable to real-world systems can be derived using simulated data. We validate our pothole detection methods using 38.1 km of real-world data collected from driving on roads in Warren, Michigan.

Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lane-level positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location of estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.

Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lane-level positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location of estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.

IEEE 802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. One major challenge of IEEE 802.11p technology development ensuring its communication reliability in highly dynamic Vehicle-to-Vehicle (V2V) environments. In this paper, by investigating the characteristics of V2V channels through empirical measurements, we show that the decreased reliability of V2V communication is because the IEEE 802.11p design does not have sufficient number of training symbols in time domain and pilot subcarriers in frequency domain to enable estimation of the fast-changing V2V channels accurately. To tackle this challenge, we propose a new approach called Tentpoles scheme, which utilizes a small subset of data symbols and subcarriers protected by a strong Error Correction Code (ECC) to track and estimate V2V channel variations. Since this subset of data symbols and subcarriers provide dual-use, our Tentpoles scheme not only improves the accuracy of channel estimation and communication reliability, but has only a small impact on effective throughput. Through extensive simulations in synthetic V2V channels and emulation experiments using empirical measurements, our proposed Tentpoles scheme is shown to significantly outperform other V2V channel estimation and equalization methods in terms of communication reliability, at the cost of only moderate loss of throughput.

We formulate and address mathematically the fundamental problem of resource allocation in the form of helper nodes in disseminating multiple content in a hybrid intermittently connected mobile network under a general stochastic homogeneous contact process. We consider and solve two variations of the problem - one in which the goal is to maximize the expected demands satisfied and another in which the goal is to minimize the time taken to disseminate the contents. Besides the global optimization perspective, we also examine the problem from a game theoretic perspective in which a central agent auctions the storage to competing content providers, and show how self-interested decisions impact the social welfare.

Automotive apps can improve efficiency, safety, comfort, and longevity of vehicular use. These apps achieve their goals by continuously monitoring sensors in a vehicle, and combining them with information from cloud databases in order to detect events that are used to trigger actions (e.g., alerting a driver, turning on fog lights, screening calls). However, modern vehicles have several hundred sensors that describe the low level dynamics of vehicular subsystems, these sensors can be combined in complex ways together with cloud information. Moreover, these sensor processing algorithms may incur significant costs in acquiring sensor and cloud information. In this paper, we propose a programming framework called CARLOG to simplify the task of programming these event detection algorithms. CARLOG uses Datalog to express sensor processing algorithms, but incorporates novel query optimization methods that can be used to minimize bandwidth usage, energy or latency, without sacrificing correctness of query execution. Experimental results on a prototype show that CARLOG can reduce latency by nearly two orders of magnitude relative to an unoptimized Datalog engine.

With the increasing popularity of mobile devices, drivers and passengers will naturally want to connect their devices to their cars. Malicious entities can and likely will try to attack such systems in order to compromise other vehicular components or eavesdrop on privacy-sensitive information. It is imperative, therefore, to address security concerns from the onset of these technologies. While guaranteeing secure wireless vehicle-to-mobile communication is crucial to the successful integration of mobile devices in vehicular environments, usability is of equally critical importance. With MVSec, we propose novel approaches to secure vehicle-to-mobile communication tailored specifically for vehicular environments. We present novel security protocols and provide complete implementation and user study results.

Disseminating shared information to many vehicles could incur significant access fees if it relies only on unicast cellular communications. We consider the problem of efficient content dissemination over a vehicular network, in which vehicles are equipped with two kinds of radios: a high-cost low-bandwidth, long-range cellular radio, and a free high-bandwidth short-range radio. We formulate and solve an optimization problem to maximize content dissemination from the infrastructure to vehicles within a predetermined deadline while minimizing the cost associated with communicating over the cellular connection. We examine numerically the tradeoffs between cost, delay and system utility in the optimum regime. We find that, in the optimum regime, (a) system utility is more sensitive to the cost budget when the allowed delay for the dissemination is not large, (b) the system requires relatively smaller cost budget as more vehicles participate and more delay is allowed, (c) when the cost is very important, it is better not to spread the content if it needs small delay. We also develop a polynomial-time algorithm to obtain the optimal discrete solution needed in practice. Finally, we verify our analysis using real GPS traces of 632 taxis in Beijing, China.

We investigate the benefits of distributed storage using erasure codes for file sharing in vehicular networks through both analysis and realistic trace-based simulations. We show that the key parameter affecting the on-demand file download latency is the ratio of file size to download bandwidth. When this ratio is small so that a file can be communicated in a single encounter, we find that coding techniques offer very little benefit over simple file replication. However, we analytically show that for large ratios, for a memoryless contact model, distributed erasure coding yields a latency benefit of N/α over uncoded replication, where N is the number of vehicles and α the redundancy factor. Effectively, in this regime, coding yields the same performance as replicating all the files at all other vehicles, but using much less storage. We also evaluate the benefits of coded storage using large real vehicle traces of taxis in Beijing and buses in Chicago. These simulations, which include a realistic radio link quality model for a IEEE 802.11p dedicated short range communication (DSRC) radio, validate the observations from the analysis, demonstrating that coded storage dramatically speeds up the download of large files in vehicular networks.

In this paper, we investigate the asymptotic performance limits (throughput capacity and average packet delay) of social-proximity vehicular networks. The considered network involves N vehicles moving and communicating on a scalable grid-like street layout following the social-proximity model: Each vehicle has a restricted mobility region around a specific social spot and transmits via a unicast flow to a destination vehicle that is associated with the same social spot. Moreover, the spatial distribution of the vehicle decays following a power-law distribution from the central social spot toward the border of the mobility region. With vehicles communicating using a variant of the two-hop relay scheme, the asymptotic bounds of throughput capacity and average packet delay are derived in terms of the number of social spots, the size of the mobility region, and the decay factor of the power-law distribution. By identifying these key impact factors of performance mathematically, we find three possible regimes for the performance limits. Our results can be applied to predict the network performance of real-world scenarios and provide insight on the design and deployment of future vehicular networks.

Cooperative medium access control (MAC) protocols have been proposed for improving communication reliability and throughput in wireless networks. In a recent study, a cooperative MAC scheme called Cooperative ADHOC MAC (CAH-MAC) has been proposed to increase the network throughput by reducing the wastage of time slots under a static network scenario. Particularly, neighbor nodes cooperate to increase the transmission reliability by utilizing unreserved time slots for retransmission of failed packets. In this paper, we focus on a mobile networking scenario and study the effects of time slot reservation on the performance of CAH-MAC under highly dynamic vehicular environments. We find out that the introduction of time slot reservation results in cooperation collisions, degrading the system performance. To tackle this challenge, we present an enhanced CAH-MAC (eCAH-MAC) that is able to avoid cooperation collisions and thus efficiently utilize a time slot. In eCAH-MAC, the cooperative relay transmission phase is delayed, so that cooperation collisions can be avoided and time slots can be efficiently reserved. Through extensive simulations, we demonstrate that eCAH-MAC uses time slot more efficiently than CAH-MAC in direct and/or cooperative transmissions and in reserving time slots in the presence of relative mobility among nearby nodes.

The effective inter-vehicle transmission of content files, e.g., images, music and video clips, is the basis of media communications in vehicular networks, such as social communications and video sharing. However, due to the presence of diverse node velocities, severe channel fadings and intensive mutual interferences among vehicles, the inter-vehicle or vehicle-to-vehicle (V2V) communications tend to be transient and highly dynamic. Content transmissions among vehicles over the volatile and spotty V2V channels are thus susceptible to frequent interruptions and failures, resulting in many fragment content transmissions which are unable to finish during the connection time and unusable by on-top media applications. The interruptions of content transmissions not only lead to the failure of media presentations to users, but the transmission of the invalid fragment contents would also result in the significant waste of precious vehicular bandwidth. On addressing this issue, in this work we target on provisioning the integrity-oriented inter-vehicle content transmissions. Given the initial distance and mobility statistics of vehicles, we develop an analytical framework to evaluate the data volume that can be transmitted upon the short-lived and spotty V2V connection from the source to the destination vehicle. Provided the content file size, we are able to evaluate the likelihood of successful content transmissions through the model. Based upon this analysis, we propose an admission control scheme at the transmitters, that filters the suspicious content transmission requests which are unlikely to be accomplished over the transient inter-vehicle links. Using extensive simulations, we demonstrate the accuracy of the developed analytical model, and the effectiveness of the proposed admission control scheme. In the simulated scenario, with the proposed admission control scheme applied, it is observed that about 30% of the network bandwidth can be saved for effective content transmissions.

In this paper, we investigate to achieve low Packet Error Rate (PER) for Vehicle-to-Vehicle (V2V) communications within the IEEE 802.11p standard. We first present the best achievable PER with the assumption of perfect channel knowledge. Then we propose to approach the best achievable PER with a relaxed assumption of partial channel knowledge, i.e., knowing a few separated channels in a packet. In order to obtain partial channel knowledge in an 802.11p standard-compliant way, we propose a sub-packet decoding processing in which the successful decoding of the previous sub-packet will provide partial channel knowledge for decoding the following sub-packet. Then we design a double decoding scheme to achieve low PER in a standard-compliant way. The double decoding scheme utilizes the error detection information provided by CRC check in medium-access (MAC) layer and combines two different decoding methods in physical (PHY) layer. The results show that comparing to previous work, the proposed double decoding can improve PER significantly.

Achieving accurate and effective modeling of the vehicle-to-vehicle (V2V) communication channel has proven to be a challenging task, particularly owing to the highly dynamic nature of vehicular environments. V2V channels generally may have contributions from the line-of-sight path, reflections from large stationary and moving objects such as bridges and other vehicles, and a diffuse base from large numbers of small stationary objects in the environment. We propose a new geometrical model for the diffuse component based on scattering objects distributed along the roadside, and use this model to predict the Doppler spectrum and angle-of-arrival distribution associated with this component for various V2V scenarios. In contrast with previous roadside scattering models that sum the contributions from large numbers of randomly-generated scattering objects, our model assumes a uniform linear distribution along the roadside. This permits a computationally efficient, closed-form model. Comparisons with on-road measurement data as well as the double ring model demonstrate the validity and effectiveness of the proposed model.

Access infrastructure, such as Wi-Fi access points and cellular base stations (BSs), plays a vital role in providing pervasive Internet services to vehicles. However, the deployment costs of different access infrastructure are highly variable. In this paper, we make an effort to investigate the capacity-cost tradeoffs for vehicular access networks, in which access infrastructure is deployed to provide a downlink data pipe to all vehicles in the network. Three alternatives of wireless access infrastructure are considered, i.e., cellular BSs, wireless mesh backbones (WMBs), and roadside access points (RAPs). We first derive a lower bound of downlink capacity for each type of access infrastructure. We then present a case study based on a perfect city grid of 400 km2with 0.4 million vehicles, in which we examine the capacity-cost tradeoffs of different deployment solutions in terms of capital expenditures (CAPEX) and operational expenditures (OPEX). The rich implications from our results provide fundamental guidance on the choice of cost-effective access infrastructure for the emerging vehicular networking.

In vehicular ad hoc networks (VANETs), improving uploading efficiency is crucial to enabling the copious applications such as reporting sensed data for traffic management or environment monitoring. Depending on the applications, the contents to be uploaded can be of large volumes. Therefore, there exist the fundamental demands of the delivery with high throughput. In this paper, we derive the achievable throughput capacity scaling law for such applications in VANETs as Θ(1/log n), with the number of road-side units scaling as Θ(n/log n). Furthermore, by exploring the mobility diversity among vehicles, we propose a novel two-hop forwarding scheme to improve the throughput performance approaching the throughput capacity. Specifically, the source vehicle distributes the contents to multiple relay vehicles with the largest mobility diversity so that the number of concurrent transmissions can be increased. The simulation results demonstrate the effectiveness of the proposed transmission scheme in terms of the increased throughput performance.

With the advent of the new IEEE 802.11p DSRC/WAVE radios, Vehicle-to-Vehicle (V2V) communications is poised for a dramatic leap. A canonical application for these future vehicular networks is the detection and notification of anomalous road events (e.g., potholes, bumps, icy road patches, etc.). We present the Road Information Sharing Architecture (RISA), the first distributed approach to road condition detection and dissemination for vehicular networks. RISA provides for the in-network aggregation and dissemination of event information detected by multiple vehicles in a timely manner for improved information reliability and bandwidth efficiency. RISA uses a novel Time-Decay Sequential Hypothesis Testing (TD-SHT) approach in which event information from multiple sources is combined with time-varying beliefs. We describe our implementation of RISA which has been deployed and tested on a fleet of vehicles on-site at the GM Warren Technical Center in Michigan. We further provide a comprehensive evaluation of the aggregation mechanism using emulation of the RISA code on real vehicular mobility traces.

In this paper, the asymptotic capacity and delay performance of social-proximity urban vehicular networks with inhomogeneous vehicle density are analyzed. Specifically, we investigate the case of N vehicles in a grid-like street layout while the number of road segments increases linearly with the population of vehicles. Each vehicle moves in a localized mobility region centered at a fixed social spot and communicates to a destination vehicle in the same mobility region via a unicast flow. With a variant of the two-hop relay scheme applied, we show that social-proximity urban networks are scalable: a constant average per-vehicle throughput can be achieved with high probability. Furthermore, although the throughput and delay of a unicast flow may degrade in a high density area, almost constant per-vehicle throughput Ω(1/log (N)) and almost constant delay O(log2(N)) (except for the polylogarithmic factor) are still achievable with high probability. By identifying the key impact factors of performance mathematically, our results should provide insight on the design and deployment of future vehicular networks.

We investigate the benefits of distributed storage using erasure codes for file sharing in vehicular networks through realistic trace-based simulations. We find that coding offers substantial benefits over simple replication when the file sizes are large compared to the average download bandwidth available per encounter. Our simulations, based on a large real vehicle trace from Beijing combined with a realistic radio link quality model for a IEEE 802.11p dedicated short range communication (DSRC) radio, demonstrate that coding provides significant cost reduction in vehicular networks.

This paper studies the problem of bulk data transfer between a pair of moving vehicles as they encounter, using IEEE 802.11p (Dedicated Short Range Communication, or DSRC) radios. Comparing to the well-studied problem of “Drive-Through Internet”, enabling an efficient, reliable and fair data transfer between two fast-moving vehicles could be even more challenging because the effective link duration is halved, vehicle-to-vehicle channel is unpredictable and DSRC is a new technology. We design an Encounter Transfer Protocol (ETP) which is suitable for this vehicle encounter case. We introduce two new components which are able to improve the data transfer performance in such a challenging environment: (1) we advocate an enhanced window adjustment policy called BIBD(Bimodal Increase, Bimodal Decrease) which is able to quickly adapt to fast-changing channel conditions and stabilize afterwards. We experimentally demonstrate the BIBD policy outperforms other policies, including commonly used AIMD policy. (2) We also suggest a variety of enhancement techniques that not only fully utilize the precious link duration as vehicles encounter but also aggressively compensate packet drops caused by fading channel. Using a small fleet of DSRC-equipped vehicles, we experimentally demonstrate that ETP is able to at least double the throughput of TCP as both vehicles are moving, and improve performance by about 20-50% as only one vehicle is moving. Our experiments also show that ETP fairly allocates bandwidth resource under stressful network congestion scenarios.

Digital signatures are one of the fundamental security primitives in Vehicular Ad-Hoc Networks (VANETs) because they provide authenticity and non-repudiation in broadcast communication. However, the current broadcast authentication standard in VANETs is vulnerable to signature flooding: excessive signature verification requests that exhaust the computational resources of victims. In this paper, we propose two efficient broadcast authentication schemes, Fast Authentication (FastAuth) and Selective Authentication (SelAuth), as two countermeasures to signature flooding. FastAuth secures periodic single-hop beacon messages. By exploiting the sender's ability to predict its own future beacons, FastAuth enables 50 times faster verification than previous mechanisms using the Elliptic Curve Digital Signature Algorithm. SelAuth secures multi-hop applications in which a bogus signature may spread out quickly and impact a significant number of vehicles. SelAuth pro- vides fast isolation of malicious senders, even under a dynamic topology, while consuming only 15%--30% of the computational resources compared to other schemes. We provide both analytical and experimental evaluations based on real traffic traces and NS-2 simulations. With the near-term deployment plans of VANET on all vehicles, our approaches can make VANETs practical.

The copious social and user generated contents, like Facebook and Youtube, are re-shaping the way people share, access, and digest information. Although flourishing in Internet, content sharing services are still considered expensive and not ready for mobile users of vehicular networks. In this paper, we propose VTube, an autonomous and cost-effective infrastructure, to facilitate the localized content publish/subscribe in an urban area. VTube relies on the distributed low-cost light-weight storage buffers, namely roadside buffer, installed in the city facilities, such as stores, museums, cafeteria, etc., to cache and publish contents for mobile users. The contents at different storage buffers are then transported to different locations by moving vehicles and cached collaboratively in both vehicles and storage buffers across the city. In this work, we unfold the design of VTube by first presenting the detailed design principles and practices of VTube. Given the content availability and capacity of the buffer storage, we then develop a mathematical model to evaluate the mean download delay of mobile users. Using the delay as an input, we formulate the content replication problem in roadside buffers as a stochastic programming problem to attain the mean system-wide minimum download delay. Finally, we propose a fully distributed random walk based algorithm to solve the optimization problem. Extensive simulations demonstrate that VTube can minimize the download delay of users because of the exploitation of vehicle mobility and distributed buffer storage at different locations.

Vehicular ad hoc networks (VANETs) have emerged as a serious and promising candidate for providing ubiquitous communications both in urban and highway scenarios. Consequently, nowadays it is widely believed that VANETs will be able to support both safety and non-safety applications. For both classes of applications, since a zero-infrastructure is the typical premise assumed, it is crucial to understand the dynamics of network connectivity when one operates without relying on any telecommunications infrastructure. Using the key metrics of interest (such as link duration, connection duration, and re-healing time) we provide a comprehensive framework for network connectivity of urban VANETs. Our study, in addition to extensive simulations based on a new Cellular Automata Model for mobility, also provides a comprehensive analytical framework. This analytical framework leads to closed form results which facilitate physical insight into the impact of key system parameters on network connectivity. The predictions of our analytical framework also shed light on which type of safety and non-safety applications can be supported by urban VANETs.

One of the most important problems studied in data harvesting wireless sensor networks (WSNs) is the optimization of the tradeoff between the accuracy of the reconstructed field data and the resource consumption. In order to optimize the resource consumption, whilst not compromising the accuracy of the reconstructed field data, an ecologically inspired marginal value theorem strategy (MVTS) is proposed for a mobile agent for choosing the next sensor node to be visited in the data acquisition process. The proposed MVTS can adaptively gain new knowledge during the process of collecting observations from a WSN comprising of static sensor nodes. Therefore, only the relatively important sensor observations will be colleted by the agent according to the variety of the background environmental data. This is thought as an efficient way to reserve the resources, such as energy and bandwidth, because only the important observations are collected. Illustrated analytical and simulation results confirm the above achievements.

IEEE 802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. Here, using a large set of empirical measurement data taken in a rich variety of realistic driving environments, we attempt to characterize communication properties of DSRC as well as to analyze the causes of communication loss. Specifically, from a perspective of vehicular network engineers, the fundamental characteristic of DSRC communications is Packet Delivery Ratio (PDR). We investigate the impact of both uncontrollable environmental factors and controllable radio parameters on DSRC characteristics. Moreover, we also examine temporal correlation, spatial correlation and symmetric correlation of DSRC characteristics under realistic vehicular environments.

In this paper, inspirations from behavioral ecology are applied for mobile agent assisted data collection in a Wireless Sensor Networks (WSNs). With the help of the marginal value theorem based strategy (MVTS), each observation (Λ), which is gathered by a given sensor node, is considered as a marginal information source with a relative entropy H(Λ). The mobile agent exploits the correlation and chooses the next sensor node to be visited, which is deemed that the information contribution of the contained observation is not smaller than a predefined threshold (TH). Therefore, understanding the correlation models can benefit the WSNs system from two aspects. On one hand, the resource consumption could be reduced during the acquisition process of the observation; on the other hand, the accuracy of the reconstructed field data is least compromised, due to relatively critical observations being collected by the mobile agent over a dynamically changing environmental. The resource consumption such as energy and bandwidth, is proportional to the number of visited sensors. With MVTS, the resource consumption is optimized through bypassing the sensors with relatively unimportant observations. Illustrated analytical and simulation results confirm the above achievements.

Traditional data aggregation methods use data originating from all sensor nodes of the wireless sensor network (WSN). Unfortunately, the use of all available sensors is not a resource efficient approach for such environments. Therefore, in this paper, we propose a novel aggregation procedure based on a hypothesis testing, where a subset of reporting nodes is intermittently selected. Neyman-Pearson lemma is used to select reporting nodes and the critical value, which is used in this aggregation procedure. Furthermore, a performance evaluation is discussed to illustrate how the proposed aggregation procedure outperforms the existing detection methods, and the dependency of the aggregation procedure and network attributes.

Vehicular ad hoc networks (VANETs) require a mechanism to help authenticate messages, identify valid vehicles, and remove malevolent vehicles. A public key infrastructure (PKI) can provide this functionality using certificates and fixed public keys. However, fixed keys allow an eavesdropper to associate a key with a vehicle and a location, violating drivers' privacy. In this work we propose a VANET key management scheme based on temporary anonymous certified keys (TACKs). Our scheme efficiently prevents eavesdroppers from linking a vehicle's different keys and provides timely revocation of misbehaving participants while maintaining the same or less overhead for vehicle-to-vehicle communication as the current IEEE 1609.2 standard for VANET security.

Modeling complicated vehicular traffic behavior and the associated network connectivity in urban areas has been a challenging task for the past several years. In this paper, we study how intersections and two-dimensional road topology affect the connectivity behavior of traffic in urban areas. To better understand this phenomenon, we investigate both static and dynamic aspects of network connectivity in urban areas and analyze several characteristics such as network connectivity, path redundancy, re-healing time, etc. Based on a comprehensive study, we make several key observations and provide insights into the behavior of network connectivity whereby critical routing problems are identified and suggestions are made for the key components of an appropriate routing protocol for different vehicular ad hoc network (VANET) applications in urban areas.

An experimental study of the Doppler coherence time properties of Vehicle-to-Vehicle (V2V) wireless channels at 5.9 GHz in both rural and highway environments is presented. Simultaneous RF and mobility measurements were conducted in environments near Pittsburgh, Pennsylvania. The average Doppler spread was observed to depend linearly on effective speed, defined as the square root of the sum of the squares of the ground speeds of the two vehicles. Sample spectrum is analyzed and comparisons with the double-ring models are presented. The coherence time was observed to vary inversely with effective speed, as expected. In addition, the coherence time was observed to decrease with vehicle separation out to about 100 m, followed by a relative peak at about 200 m. A possible interpretation of this peak in terms of a two-ray propagation model is presented. The observed Doppler spread should not be a problem for proposed V2V OFDM signal transmission formats, but the channel coherence time is much shorter than a typical packet suggesting that present equalization schemes may not be adequate.

Development of data aggregation techniques is thought as an effective way to save energy in order to prolong the lifetime of wireless sensor networks (WSNs). Particular characteristics of data gathered from spatial-temporal domain may represent certain level of correlation among data values. Based on this observation, we start with analyzing the optimal sampling rate in temporal correlation model to find out the best sleep time of sensor nodes. Then we propose an aggregation technique which exploits the spatial-temporal correlation using a discrete cosine transform (DCT). It transfers the spatial-temporal data into uncorrelated frequency domain coefficients. The WSN is split into several clusters. Original data are aggregated at an aggregation point which acts as a cluster head. The 3D-Zigzag sorting algorithm makes sure that the aggregation point transmits the frequency coefficients from lower frequencies which contain the main energy of the original data to higher frequencies. Simulation results show only a few coefficients are enough to recover original data under high correlation model within the user tolerable distortion rate.

This paper aims at proposing a network structure to minimize the energy consumption in cluster-based wireless sensor networks (WSNs), which is directly related to the lifetime of the network. In large scale wireless sensor networks, data aggregation is known as an effective technique to save the energy by a trade off between the complexity of local processing and the amount of data transmitted in networks. Normally, in cluster-based WSNs, the sensor node which is elected as the cluster head will become the aggregation point which has the responsibility to aggregate the data from cluster members. In this paper, we analyze the aggregation characteristic in a correlated data field, and then find out the optimal cluster radius for each sensor node in the case of that node has been elected as cluster head. Furthermore, we propose a novel network structure called Unbalanced Clustering (UBC) and compare it to a LEACH-like cluster based network and an equal-sized cluster based network to see how much energy UBC can save and the potentiality of making the energy dissemination among the sensor nodes more evenly.

Broadband sounding of the vehicle-to-vehicle channel is reported in suburban, rural, and highway environments. A direct sequence spread spectrum waveform based on zero correlation zone sequences was used with an instantaneous bandwidth of about 40 MHz. Cumulative distribution functions are presented for the maximum excess delay, RMS delay spread, and coherence bandwidth using a threshold of 15 dB below the peak. The highway environment showed the largest median RMS delay spread (about 110 ns), the largest median maximum excess delay (about 600 ns) and the smallest median 90% coherence bandwidth (about 900 kHz). In general, the distributions of these quantities for the suburban environment were narrower than those for the rural and highway environments. This is interpreted in terms of the more restricted range of distances to scattering objects in the suburban environment.

Vehicular ad hoc wireless network (VANET) exhibits a bipolar behavior in terms of network topology: fully connected topology with high traffic volume or sparsely connected topology when traffic volume is low. In this work, we develop a statistical traffic model based on the data collected on 1-80 freeway in California in order to study key performance metrics of interest in disconnected VANETs, such as average re-healing time (or the network restoration time). Our results show that, depending on the sparsity of vehicles, the network re-healing time can vary from a few seconds to several minutes. This suggests that, a new ad hoc routing protocol will be needed as the conventional ad hoc routing protocols such as dynamic source routing (DSR) and ad hoc on-demand distance vector routing (AODV) will not work with such long re-healing times.

This study presents narrow-band measurements of the mobile vehicle-to-vehicle propagation channel at 5.9 GHz, under realistic suburban driving conditions in Pittsburgh, Pennsylvania. Our system includes differential Global Positioning System (DGPS) receivers, thereby enabling dynamic measurements of how large-scale path loss, Doppler spectrum, and coherence time depend on vehicle location and separation. A Nakagami distribution is used for describing the fading statistics. The speed-separation diagram is introduced as a new tool for analyzing and understanding the vehicle-to-vehicle propagation environment. We show that this diagram can be used to model and predict channel Doppler spread and coherence time using vehicle speed and separation.

A vehicular ad hoc network (VANET) may exhibit a bipolar behavior, i.e., the network can either be fully connected or sparsely connected depending on the time of day or on the market penetration rate of the wireless communication devices. In this paper, we use empirical vehicle traffic data measured on 1-80 freeway in California to develop a comprehensive analytical framework to study the disconnected network phenomenon and its network characteristics. These characteristics shed light on the key routing performance metrics of interest in disconnected VANETs, such as the average time taken to propagate a packet to disconnected nodes (i.e., the re-healing time). Our results show that, depending on the sparsity of vehicles or the market penetration rate of cars using Dedicated Short Range Communication (DSRC) technology, the network re-healing time can vary from a few seconds to several minutes. This suggests that, for vehicular safety applications, a new ad hoc routing protocol will be needed as the conventional ad hoc routing protocols such as Dynamic Source Routing (DSR) and Ad Hoc On-Demand Distance Vector Routing (AODV) will not work with such long re-healing times. In addition, the developed analytical framework and its predictions provide valuable insights into the VANET routing performance in the disconnected network regime.

Routing protocols developed for ad hoc wireless networks use broadcast transmission to either discover a route or disseminate information. More specifically, reactive routing protocols has to flood the network with a route request (RREQ) message in order to find an optimal route to the destination. Several applications developed for vehicular ad hoc wireless networks (VANET), which is a subset of MANET, rely on broadcast to propagate useful traffic information to other vehicles located within a certain geographical area. However, the conventional broadcast mechanism may lead to the so-called broadcast storm problem. In this paper, we explore how serious the broadcast storm problem is in both MANET and VANET by examining how broadcast packets propagate in a 2-dimensional open area and on a straight road or highway scenarios. In addition, we propose three novel distributed broadcast suppression techniques; i.e., weighted p-persistence, slotted 1-persistence, and slotted p- persistence schemes. Our simulation results show that the proposed schemes can achieve up to 90% reduction in packet loss rate while keeping the end-to-end delay at acceptable levels for most VANET applications. They can also be used together with the route discovery process to guide the routing protocols to select routes with fewer hop counts.

Vehicular networks are being developed for efficient broadcast of safety alerts, real-time traffic congestion probing and for distribution of on-road multimedia content. In order to investigate vehicular networking protocols and evaluate the effects of incremental deployment it is essential to have a topology-aware simulation and test-bed infrastructure. While several traffic simulators have been developed under the intelligent transport system initiative, their primary motivation has been to model and forecast vehicle traffic flow and congestion from a queuing perspective. GrooveNet is a hybrid simulator which enables communication between simulated vehicles, real vehicles and between real and simulated vehicles. By modeling inter-vehicular communication within a real street map-based topography it facilitates protocol design and also in-vehicle deployment. GrooveNet's modular architecture incorporates mobility, trip and message broadcast models over a variety of link and physical layer communication models. It is easy to run simulations of thousands of vehicles in any US city and to add new models for networking, security, applications and vehicle interaction. GrooveNet supports multiple network interfaces, GPS and events triggered from the vehicle's on-board computer. Through simulation, we are able to study the message latency, and coverage under various traffic conditions. On-road tests over 400 miles lend insight to required market penetration

One of the main challenges in ad hoc networks research is understanding the effect of mobility on the performance of routing protocols. In a previous study, we have shown why mobility affects the performance of routing protocols. In this study, we further extend our approach to analyze the interplay between mobility and the protocol mechanistic building blocks. Through this approach we hope to explain how performance varies with mobility by decomposing the protocol into parameterized mechanistic building blocks based on their functionalities. Then, we apply this approach to reactive MANET routing protocols like AODV and DSR, which enables us to build a common building block architecture that encompasses these reactive protocols. The effect of mobility on each building block is evaluated. We are specifically interested in understanding the contribution of each building block to the overall protocol performance. Through simulations, several lessons on protocol design are learnt. For example, in both AODV and DSR, flooding and caching seem to have a great effect on performance, while salvaging in DSR barely seems to have an effect on the protocol performance.

Sensor networks would be usually used for the collection of measured data. In many cases, a tree structure is formed for data query, data dissemination and other operations. As sensor nodes fail, this underlying tree structure is impacted or, at the worst case, disabled. In this paper, we propose and compare three algorithms to restore the tree structure for sensor network under network dynamics. Two of the algorithms use global information at the base station (or sink), while the third uses only local information. Through simulations, we observe that the localized algorithm outperforms the two global algorithms in terms of communication cost, corresponding energy and latency. In addition, we also gain a deeper insight into the performance tradeoff for algorithms in sensor network. We clearly identify energy-latency tradeoff for the global algorithms, as well as energy-accuracy (optimality) tradeoff for the localized algorithm.

We develop a detailed approach to study how mobility impacts the performance of reactive mobile ad hoc network routing protocols. In particular, we examine how the statistics of path durations including probability density functions vary with the parameters such as the mobility model, relative speed, number of hops, and radio range. We find that at low speeds, certain mobility models may induce multimodal distributions that reflect the characteristics of the spatial map, mobility constraints and the communicating traffic pattern. However, this paper suggests that at moderate and high velocities the exponential distribution with appropriate parameterizations is a good approximation of the path duration distribution for a range of mobility models. Analytically, we show that the reciprocal of the average path duration has a strong linear relationship with the throughput and overhead of dynamic source routing (DSR), which is also confirmed by simulation results. In addition, we show how the mathematical expression obtained for the path duration distribution can also be used to prove that the nonpropagating cache hit ratio in DSR is independent of velocity for the freeway mobility model. These two case studies illustrate how various aspects of protocol performance can be analyzed with respect to a number of significant parameters including the statistics of link and path durations.

A mobile ad hoc network (MANET) is a collection of wireless mobile nodes forming a temporary network without using any existing infrastructure. Since not many MANETs are currently deployed, research in this area is mostly simulation based. Random waypoint is the commonly used mobility model in these simulations. Random waypoint is a simple model that may be applicable to some scenarios. However, we believe that it is not sufficient to capture some important mobility characteristics of scenarios in which MANETs may be deployed. Our framework aims to evaluate the impact of different mobility models on the performance of MANET routing protocols. We propose various protocol independent metrics to capture interesting mobility characteristics, including spatial and temporal dependence and geographic restrictions. In addition, a rich set of parameterized mobility models is introduced including random waypoint, group mobility, freeway and Manhattan models. Based on these models several 'test-suite' scenarios are chosen carefully to span the metric space. We demonstrate the utility of our test-suite by evaluating various MANET routing protocols, including DSR, AODV and DSDV. Our results show that the protocol performance may vary drastically across mobility models and performance rankings of protocols may vary with the mobility models used. This effect can be explained by the interaction of the mobility characteristics with the connectivity graph properties. Finally, we attempt to decompose the routing protocols into mechanistic "building blocks" to gain a deeper insight into the performance variations across protocols in the face of mobility.

We develop a detailed approach to study how mobility impacts the performance of reactive MANET routing protocols. In particular we examine how the statistics of path durations including PDFs vary with the parameters such as the mobility model, relative speed, number of hops, and radio range. We find that at low speeds, certain mobility models may induce multi-modal distributions that reflect the characteristics of the spatial map, mobility constraints and the communicating traffic pattern. However, our study suggests that at moderate and high velocities the exponential distribution with appropriate parameterizations is a good approximation of the path duration distribution for a range of mobility models. The reciprocal of the average path duration is analytically shown to have a strong linear relationship with the throughput and overhead that is confirmed by the simulation results for DSR.