Low latency in establishing a communication link is a key to maximize the performance of vehicle-to-vehicle wireless LANs (V2V WLANs), as contact duration of vehicles is often limited due to their fast mobility. However, previous works have mainly addressed Vehicle-to-Infrastructure (V2I) link setup latency on legacy Wi-Fi standards (IEEE 802.11b/g). Moreover, there has been a lack of performance evaluation regarding V2V link setup latency with current Wi-Fi technology and IEEE 802.11ai standard defines Fast Initial Link Setup (FILS), which simplifies the link setup process to reduce the latency. In this paper, we investigate the characteristics of V2V link setup latency with regular Extensible Authentication Protocol (EAP) authentication (EAP-TLS) and FILS with current Wi-Fi technology. The evaluation was conducted in general vehicular scenarios with different combinations of inter-vehicle distance, vehicle speed and type of roads (i.e., urban roads vs highways). FILS achieved the average link setup latency of 0.32 seconds, outperforming EAP-TLS that resulted in the average latency of 2.35 seconds. The link setup latency was stable in most of the scenarios we tested regardless of inter-vehicle distance, speed and Wi-Fi signal strength. The only exception was on highways, where significantly longer link setup latency was observed when the relative speed and distance changed rapidly, line-of-sight (LoS) was fully blocked by other vehicles and there were no other objects (e.g., buildings and vegetation) that form indirect signal propagation paths.

Decentralized content distribution over vehicle-to-vehicle (V2V) WLANs holds promise to mitigate load on cellular networks. The delay in establishing a communication link is a key to maximize V2V data transfer opportunity, as contact duration of vehicles is often limited due to their fast mobility. IEEE 802.11ai standard defines Fast Initial Link Setup (FILS), which simplifies the link setup process to reduce latency. In this paper, we investigate the benefit and limitations of FILS in V2V WLANs. We develop a testbed involving multiple vehicles, and compare the link setup latency of FILS with regular EAP authentication. Counterintuitively, regular EAP achieved equivalent performance in V2V WLANs due to architectural constraints of FILS.

Network simulations play an important role in the evolution of V2X (Vehicle-to-Everything) communication services. Large-scale simulations involving a huge number of connected vehicles and/or heavy network traffic, however, typically take a long period of time to complete. In this poster, we explore a mechanism to accelerate network simulations (in terms of simulation time processed per unit wall-clock time) by the means of parallel and distributed simulations (PADS). Multiple instances of network simulators are run in parallel and loosely synchronized to process simulation events in a distributed manner. We develop a proof-of-concept implementation of the framework to showcase its feasibility. The results indicate that the proposed framework achieves up to 10 times faster simulation speed with eight parallel network simulation instances.

We present the performance analysis of TCP CUBIC/BBR over V2V Wi-Fi (IEEE 802.11ac). Our measurements focus on three static parking scenarios with different distances at the office area. The results reveal the impact of TCP CUBIC and BBR on data transfer time and TCP metrics. (i) There are two major reasons of fluctuated TCP throughput. The first reason is narrow available bandwidth over V2V Wi-Fi. The second reason is delayed TCP connection establishment due to delayed SYN+ACK and SYN packet retransmission. (ii) The bytes in-flight of TCP CUBIC are dynamically changed by packet retransmission events on V2V Wi-Fi. The loss-based congestion control is not promising the high throughput. We believe that our analysis results provide implications for efficient data transfer over V2V Wi-Fi.

Vehicular Micro Cloud (VMC) is a group of connected vehicles where vehicles collaborate on a task over the vehicular network. A potential use case of VMC is that micro cloud members transfer data to each other via Vehicle-to-Vehicle (V2V) links, and the data is collaboratively uploaded to remote server (e.g., data center) when the connected vehicles are connected to a Wi-Fi network. In this paper, we focus on this use case and propose collaborative upload by VMC. We demonstrated the feasibility of the proposed method through the large-scale urban simulation (Los Angeles downtown traffic model). Our simulation results showed that the proposed method can reduce the upload data of traditional cellular network-based data upload by 50%.

A Vehicular Micro Cloud (VMC) is a group of connected vehicles where vehicles collaborate on tasks through vehicular networks. VMCs may overlap with other vehicular micro clouds in certain regions (e.g., intersections), known as overlapping zones. In overlapping zones, micro cloud members may have to switch between multiple VMCs back and forth, which degrades the functionality of the VMC. In this paper, we address this problem and propose to regroup micro cloud members when VMCs overlap. VMCs share their information with a remote server. The remote server controls and swaps members among VMCs in overlapping zones. We test the feasibility of the proposed approach through a simulation study. Extensive simulations in different settings demonstrate that controlled membership swaps can reduce the member switches among VMCs in overlapping zones by about 85%.

In the field of vehicular networks, multiple approaches have been proposed to share and reuse data acquired by participating hosts. In this context, vehicular micro clouds extend the concept of Mobile Edge Computing (MEC) and bring data storage and processing to the vehicles, solving application tasks that need to be done in real-time. A critical point in shared computing tasks and storage is to keep all nodes synchronized and to maintain consistency. For the first time, we study the relevance of data versions in micro clouds offering intersection management service at four-way stop intersections and we investigate how different versions of data affect both road traffic and wireless communications. Our results validate the intuition that an increase in the amount of different data versions in the micro cloud has negative effects on both road traffic and wireless channel usage. Yet, the choice of the data sharing algorithm can make a huge difference and reduce these effects on a large scale. We found that synchronicity of data versions can be increased by up to 20% through small changes, e.g., keeping small amounts of data history in the applied algorithm.

It is estimated that the data volume between connected vehicles and edge/cloud server(s) will be about 100 petabytes per month by 2025. The networking framework we have, on the other hand, is the existing cellular network in which the most connected vehicles function today. However, such a network suffers from several issues and may not work under this predicted data demand. To address such a dilemma, a new paradigm, Vehicular Knowledge Networking (VKN), is recently introduced. In VKN, the data is transformed into knowledge and it is distributed with various lifetimes/relevance. To benefit from the knowledge, on the other hand, it should be placed intelligently such that a high number of vehicles can access and consume it. In this paper, we tackle this issue and propose mobility-aware smart knowledge placement. In the proposed method, vehicle mobility is analyzed to measure the centrality degree of a region. The computed centrality degrees are then further analyzed to identify the most central zones. The knowledge is placed on these zones to increase availability. We demonstrate the benefits of the proposed method through a simulation. Our preliminary result has shown that the mobility-aware smart knowledge placement makes knowledge accessible from vehicles over short range communication. Through such short-range availability of knowledge, vehicles can use the free spectrum to download it which decreases the cellular communication cost significantly.

Vehicular Micro Clouds (VMCs) are an emerging development in the domain of vehicular networks posed to provide local services to users without the need for external infrastructure. This can significantly improve the user experience, in particular due to the low latencies that such systems can achieve. Due to the distributed nature of such a VMC, effective local coordination is important while using minimal communication resources. To this end, it is important to know, how long vehicles will be participating in, and contributing to a VMC. In this work, we investigate, how previous, heuristic-based approaches can be improved by incorporating local, learning-based techniques. Our analysis indicates a potential improvement of the accuracy of the prediction, and resulted in an improved simulation environment within which the learning-based approach can be deployed.

Cellular networks usually support non-safety-critical V2X services using Vehicle-to-Network (V2N) connections. However, the flexibility and capabilities of 5G have triggered interest in analyzing whether 5G could also support advanced V2X services using Vehicle-to-Network-to-Vehicle (V2N2V) connections instead of direct Vehicle-to-Vehicle (V2V) connections. V2N2V requires the integration of the 5G network with computing platforms for processing the V2X packets. The flexibility introduced by 5G facilitates the integration with multiple computing platforms such as Multi-access Edge Computing (MEC), edge cloud, shared data center or central cloud. This results in alternative 5G network deployments with the computing platform installed at different locations between the base station and the Internet. These deployments can have important technical implications for supporting V2X services. In this study, we analyze the impact of different 5G and computing platform deployments on the end-to-end (E2E) latency of V2N2V communications under multi-MNO (Mobile Network Operator) scenarios since vehicles may be served by different operators. We also identify which deployment strategies are more suitable to meet the latency requirements of V2X services for connected and automated driving.

In this paper, we use the Perceived Safety Analysis Framework (PSAF) to assess the awareness of vehicles performing an unprotected left turn at unsignalized intersections. PSAF is an analytical method developed to quantify the awareness of vehicles to surrounding safety-critical road users in traffic. We derive safety conditions for unprotected left turns using surrogate safety measures and right of way rules, and determine which road users are safety-critical to the left-turning ego vehicle. Then, we evaluate the Perceived Safety Error based on whether the ego vehicle can detect critical road users (CRUs) using sensors (such as camera or radar) and via vehicle-to-everything (V2X) communication. We demonstrate that for intersections with sparse traffic, vehicle-to-vehicle (V2V) communication may be insufficient for left-turning vehicles to get full awareness of CRUs, and vehicle-to-infrastructure (V2I) communication helps to eliminate awareness gaps.

5G and multi-access edge computing (MEC) are being considered to support V2X services demanding low latency and highly reliable communications using V2N2V (Vehicle-to-Network-to-Vehicles) communications instead of direct or sidelink V2V (Vehicle-to-Vehicle). Guaranteeing V2X service continuity using V2N2V is a challenging task in multi-Mobile Network Operator (MNO) deployments where vehicles are supported by different MNOs. MEC federations have been proposed to address some of these challenges. A MEC federation is a federated model of MEC systems enabling shared usage of MEC services and applications. Through MEC federations, vehicles can seamlessly access V2X applications independently of whether they are hosted on their MNO’s MEC, or on the MEC of a different (but federated) MNO. This paper presents the first study that analyses the impact of MEC federation on the end-to-end (E2E) latency when supporting V2X services using 5G V2N2V in multi-MNO scenarios. The paper also evaluates the feasibility to support the latency requirements of advanced V2X services in these scenarios, and the benefits introduced by MEC federation. This study considers the V2Xbased cooperative lane merge service as a case study.

The riskiness of the roadway environment needs to be known in advance to improve driving safety. Such knowledge brings strong benefit to drivers and could be used to reduce the risk of collision. For example, vehicles can support a driver with guidance before arriving at the risky zones. In this paper, we focus on this use case. We propose risk avoidance by Vehicular Knowledge Networking (VKN). The proposed method mines the maneuver conflicts to determine risky zones. According to identified zones, guidance (e.g., speed and lane change suggestions) is shared with vehicles to help drivers pass these risky regions smoothly. Extensive simulations in different settings have shown that risk avoidance by VKN could decrease the collision risk by approximately 50%.

Vehicular edge computing is enabling a variety of new services that better assist safety and comfort of driving. However, vehicles cannot offload an unlimited amount of computational tasks and input sensor data to a remote edge server because of the limitations in network bandwidth. In this paper, we design a learning-based task of floading mechanism that selects a small subset of input sensor data, which are expected to improve the application performance if processed by a rich and resource-intensive algorithm, hosted by the edge server. As a case study, we apply this framework to a vision-based object tracking application. The simulation results show that the proposed solution significantly improves object tracking accuracy with the same amount of resource consumption.

5G offers high flexibility at the radio, transport and core networks to support various services of critical verticals such as connected and automated driving. At the Radio Access Network (RAN), 5G defines a New Radio (NR). 5G NR utilizes different subcarrier spacing, slot durations, modulations and channel coding schemes. This flexibility offers the possibility to support automotive services with different and demanding requirements, such as Advanced Driver-Assistance System (ADAS), cooperative driving, and remote driving. Previous studies showed that 5G NR can be configured to achieve latencies below 2 ms. However, existing studies are generally restricted to scenarios with a limited number of users and unlimited bandwidth. Therefore, it is important to analyze whether 5G NR can effectively support these services as the network scales under limited spectrum allocations. This study advances the current state of the art to demonstrate that the capability of 5G NR RAN to support advanced V2X services depends on the RAN configuration (subcarrier spacing, slot duration and error protection) and network load.

Vehicular micro cloud is a promising solution in which connected vehicles offer their resources as services to others and collaborate on tasks through vehicular networks. The properties of the vehicular micro cloud are generally set when it is formed at the core area and they remain static. However, a change in the environment could leave the micro cloud unable to respond adequately as its static properties become obsolete. Reacting to changes in the environment, on the other hand, requires dynamic updates on the properties of the vehicular micro cloud. In this paper, we address this dilemma and propose a chain of interdependent vehicular micro clouds. To respond to the changes in the environment, interdependent micro clouds are formed around the first vehicular micro cloud. Guidance information is transmitted before members of the dependent micro clouds arriving at the core area of the first micro cloud. Extensive simulations in different vehicular micro cloud settings show that the chain of interdependent vehicular micro clouds significantly improves the ability to respond to changes within the first micro cloud. Through the provided guidance information, members of dependent micro clouds are collectively prepared before they arrive at the core area.

The live availability of parking spots is a key enabler of a variety of intelligent parking solutions. A body of research has explored the possibility of using connected vehicles as mobile sensors to measure parking availability. It removes the need for dedicated roadway sensors, achieving wide sensing coverage in a cost-efficient manner. However, the existing crowd-sourcing solutions typically rely on on-board ranging sensors such as radars, lidars and/or sonars to measure locations of the surrounding parked vehicles. In order to lower the barrier for connected vehicles to participate in the crowd-sourced parking availability sensing, this paper investigates a range-free approach to identifying spot-level parking availability. We employ a monocular RGB camera installed in a vehicle and a computer vision-based object tracking mechanism to perceive the surrounding parked vehicles. Analyzing the time series of object tracking results and matching them with a digital map of a parking facility, the system identifies availability of each spot without requiring depth information. The simulation results show that the proposed range-free parking availability sensing system can detect spot-level parking availability with the average accuracy of 83%.

In this work, we propose the use of hybrid offloading of computing tasks simultaneously to edge servers (vertical offloading) via LTE communication and to nearby cars (horizontal offloading) via V2V communication, in order to increase the rate at which tasks are processed compared to local processing. Our main contribution is an optimized resource assignment and scheduling framework for hybrid offloading of computing tasks. The framework optimally utilizes the computational resources in the edge and in the micro cloud, while taking into account communication constraints and task requirements. While cooperative perception is the primary use case of our framework, the framework is applicable to other cooperative vehicular applications with high computing demand and significant transmission overhead. The framework is tested in a simulated environment built on top of car traces and communication rates exported from the Veins vehicular networking simulator. We observe a significant increase in the processing rate of cooperative perception sensor frames when hybrid offloading with optimized resource assignment is adopted. Furthermore, the processing rate increases with V2V connectivity as more computing tasks can be offloaded horizontally.

Signal Phase and Timing (SPaT) refers to the current signal state of an intersection and the time duration that the state will last for each lane. Learning SPaT information constitutes a building block for many connected vehicle applications such as light duration advisory and start-stop control. However, retrieving SPaT information is not an easy task. One alternative approach to learn SPaT information could be the analysis of vehicles' mobility patterns. This paper investigates the design and feasibility of such an approach and proposes a system namely Virtual SPaT (V-SPaT). In V-SPaT, a group of connected vehicles form a Vehicular Cloud (VC) and collaborate to act as a virtual infrastructure at an intersection. Cloud members not only analyze their mobility patterns to predict the current phase and a residual time of that phase but also keep the SPaT information through collaborative data storage so that vehicles approaching the intersection can obtain this information over Vehicle-to-Vehicle (V2V) networks. The simulation results show that V-SPaT can identify the current phase and estimate the residual time by about 85% accuracy under a certain degree of V2V communications penetration rates.

Sensor-based perception on vehicles are becoming prevalent and important to enhance road safety. Autonomous driving systems use cameras, LiDAR and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. By using deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular networks and enhances the communication reliability. To design, test and verify the practical and resource-efficient cooperative perception framework, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform where we integrate three software components: a traffic simulator, a vehicle simulator, and an object classifier. The simulation platform constitutes a unified framework to evaluate a traffic model, vehicle model, communication model, and object classification model. Simulation results show that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.

Vehicles are expected to generate and consume an increasing amount of data, but how to perform risk reasoning over relevant data is still not yet solved. Location, time of day and driver behavior change the risk dynamically and make risk assessment challenging. This paper introduces a new paradigm, transferring information from raw sensed data to knowledge and explores the knowledge of risk reasoning through vehicular maneuver conflicts. In particular, we conduct a simulation study to analyze the driving data and extract the knowledge of risky road users and risky locations. We use knowledge to facilitate reduced volume and share it through a Vehicular Knowledge Network (VKN) for better traffic planning and safer driving.

The growing penetration of on-board communication units is enabling intelligent vehicles to share their sensor data with cloud computing platforms as well as with other vehicles. Although this unlocks the possibility of a variety of emerging applications, the massive amount of data traffic in vehicular networks is expected to pose a big challenge in the long term. In this paper, we shed light on the potential of value-anticipating networking to tackle this issue. A vehicle sending a piece of information first anticipates the value of that information for potential receivers. When the network is congested, the sender may defer or even cancel transmissions of less valuable information, so that important information can be delivered to receivers more reliably. We investigate the applicability of this concept to cooperative perception, where vehicles exchange processed sensor data over vehicle-to-vehicle (V2V) networks to collaboratively improve coverage and accuracy of environmental perception. Through simulations based on realistic road traffic, we show that value-anticipating V2V communications can significantly improve the performance of cooperative perception under heavy network load.

This paper investigates the design and feasibility of Co-Park, where a group of connected vehicles in a parking facility collaborate with each other to find available parking spots in a timely fashion. Vehicles measure occupancy of the surrounding parking spots by their on-board sensors, and share the occupancy information with other group members over vehicular networks. Based on prediction of the short-term occupancy variation and coordination among group members, the vehicles strategically plan efficient paths to search for an available spot. Simulation results show that the system can significantly reduce the trip time in a parking facility.

Vehicular micro clouds are considered a prime building block for next generation Intelligent Transportation Systems (ITS)also supporting a variety of Information and Communication Systems (ICT)applications in smart cities. Such micro clouds are established by multiple cars equipped with communication, storage, and computational resources. We recently presented the concept of hierarchical vehicular cloud computing, which is meant to extend on Mobile Edge Computing (MEC). Based on clustering algorithms, we can set up and maintain such micro clouds and eventually make use of the distributed resources. Looking at the high mobility of cars, it is very difficult to reliably maintain data collected by cars at a given location in space that is geographically relevant, e.g., at intersections. In this paper, we propose a new protocol which encourages coordination between neighboring micro clouds to help keeping local data current, i.e., cars moving out of the micro cloud may take data to neighboring clouds, hand data over to cars moving towards the original micro cloud, and, thus, returning the data to its original geographical location. We evaluate the performance of the protocol with different vehicle densities in a Manhattan Grid scenario and our results show the benefits of our proposed inter micro cloud coordination protocol.

Recently, the millimeter wave (mmWave) band has been investigated as a means to support the foreseen extreme data rate demands of emerging automotive applications, which go beyond the capabilities of existing technologies for vehicular communications. However, this potential is hindered by the severe isotropic path loss and the harsh propagation of high-frequency channels. Moreover, mmWave signals are typically directional, to benefit from beamforming gain, and require frequent realignment of the beams to maintain connectivity. These limitations are particularly challenging when considering vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of the vehicular scenarios, and pose new challenges for proper vehicular communication design. In this paper, we conduct simulations to compare the performance of IEEE 802.11p and the mmWave technology to support V2V networking, aiming at providing insights on how both technologies can complement each other to meet the requirements of future automotive services. The results show that mmWave-based strategies support ultra-high transmission speeds, and IEEE 802.11p systems have the ability to guarantee reliable and robust communications.

The emerging concept of vehicle cloudification is a promising solution to deal with ever-growing computational and communication demands of connected vehicles. A key idea is to have connected vehicles in the vicinity form a cluster, called vehicular micro cloud, and collaborate with other cluster members over vehicle-to-vehicle (V2V) networks to offer data processing, data storage, sensing and communication services. It allows us to use vehicles as virtual edge servers that complement traditional cloud and physical edge servers in the backbone network. In this paper, we design a mechanism to intelligently schedule where and when to form such vehicular micro clouds. A remote server maintains statistics of the amount of available on-board computational resources spatio-temporally, and analyzes these statistics to identify the locations where vehicles can consistently offer a sufficient amount of resources for service provisioning. The results from our proof-of-concept simulations show that our system can significantly reduce the risk of resource scarcity in vehicular micro clouds.

One of the most recent applications in the vehicular networking domain is distributed data processing using cars as sensors of information. In recent work, the concept of vehicular cloud computing has been explored to provide the necessary scalability and to improve the communication between clusters of cars being called vehicular micro clouds and other participants (cars, bicyclists, pedestrians). In order to provide a bigger picture and also to interconnect such micro clouds, data centers or cloud servers are considered bridging the gap. We study the uplink capabilities from connected cars to such data centers. Options include direct LTE uplinks from all cars, selected use of Roadside Units (RSUs) with back-end connectivity or LTE uplinks from the vehicular micro clouds, and finally hybrid solutions taking network quality and available channel resources into account. Our findings clearly show the advantages of such hybrid solutions both in terms of throughput as well as of optimizing operational costs.

Vehicular networks need to make optimal use of their limited radio resources to achieve sufficient performance and reliability. However, the scale and dynamicity make it very challenging to optimize this use and to meet the strict requirements. In this paper, we propose a centralized scheme that allocates resources to geographic locations to address this problem. The scheme can improve the overall network performance by leveraging wide-scale information instead of the restricted local views used in distributed approaches.

Fusion of infrastructure-based pedestrian tracking systems and embedded sensors on mobile devices holds promise for providing accurate positioning in large public buildings. However, privacy concerns regarding handling of sensitive user location data potentially disrupt the adoption of such systems. This paper presents AnonyCast, a novel privacy-aware mechanism for delivering precise location information measured by crowd-tracking systems to individual pedestrians' smartphones. AnonyCast uses sparsely placed Bluetooth Low Energy transmitters to advertise location-dependent, time-varying keys. Using location measurements, AnonyCast estimates a subset of keys that each pedestrian's phone receives along its path. By combining a cryptography scheme called CP-ABE with a novel greedy algorithm for key selection, it encrypts each path before publishing, allowing users to decrypt only their own trajectories. The results from field experiments show that AnonyCast delivers accurate locations over 84% of time, bounding probability of unauthorized access to one's location below 1%.

In this paper, we design a mobile augmented reality (AR) system called TweetGlue, which overlays text messages (i.e., tweets) that are posted to a local social networking service onto live view images from cameras in mobile/wearable devices. By displaying the tweets at the current positions of the users who posted them, it supports social interaction between the users. Accurate pose tracking of mobile devices is an essential building block of such mobile AR applications. While visual features that are extracted from scenes are commonly used for vision-based pose estimation, it would not be suitable for such a mobile social AR application because the features may be often occluded by human bodies nearby. To cope with the problem, we leverage an external pedestrian tracking system using a small number of laser-based distance measurement sensors (i.e., LRS sensors) to utilize the surrounding human bodies as virtual markers for pose estimation. The mobile devices periodically analyze images from the embedded camera sensor to estimate relative positions of pedestrians in the images. By matching the estimated relative positions with accurate human location measurements by the LRS sensors, the system robustly identifies location and horizontal orientation of the devices. Through simulation experiments, we show that the TweetGlue system can accurately identify pose of mobile devices in 83% of the simulated scenarios.

Data collection from a crowd of mobile devices is an essential building block of emerging mobile sensing systems. In this paper, we propose an efficient information diffusion protocol for sensor data collection via an opportunistic network. The proposed method detects groups of pedestrians based on the history of radio connectivity between the nodes and maintains a local network (i.e., a cluster) among the detected group members. By collaboratively performing neighbor discovery and link management with the cluster members, it enhances energy-efficiency of the neighbor discovery and minimizes the information delivery delay. Simulation results show that the proposed method can improve the message delivery performance by 16%–83% with equivalent contact probing intervals.

Despite recent advances in localization technology for mobile devices, to provide real-time position information to people indoors is still a big challenge; usually there is a trade-off between localization accuracy and infrastructural costs (e.g., dense anchor deployment). A possible solution would be employing cooperative approaches which utilize estimated positions of surrounding mobile nodes to complement a small number of anchors. However, it often results in poor estimation accuracy since a temporary large position error due to node mobility easily propagates to neighbor nodes. This paper presents a novel cooperative localization algorithm that addresses this problem by focusing on “stop-and-go behavior” of indoor pedestrians. The key idea is to collaboratively find movement state (moving or static) of each node based on peer-to-peer distance measurement which is inherently necessary for cooperative localization, and use only static nodes as reference points for localization to avoid potential accuracy deterioration. Also, nodes in static state can reduce localization frequency to conserve battery power, keeping the tracking quality. Through extensive simulations, we have demonstrated the performance of our method in terms of accuracy and energy efficiency. The effectiveness in a real application scenario has been also confirmed using a measurement-based sensor model and real mobility traces.

In this paper, we propose a novel approach to positioning mobile phone users in a crowd of people. We assume such a situation that people in specific space (e.g., an exhibition hall) have mobile phones and they periodically probe proximity between the surrounding users via short-range wireless communication (e.g., Bluetooth). We also assume that laser range scanners (LRSs) are deployed in the space to track positions of those mobile phone users. By fusing the accurate, but anonymous trace information obtained by LRSs and the identified phone-to-phone proximity information, our method derives accurate identified traces of each mobile phone user, which are essential for pedestrian navigation and other emerging location/situation-aware applications. Through extensive simulations, we show that the tracking error quickly converges below 1m in most cases.

This paper presents a cooperative localization approach for mobile nodes using wireless and ranging devices. We consider scenarios where node mobility follows stop-and-go behavior; we can then utilize the different movement states of nodes as an input to our localization approach. In the proposed method, each node autonomously finds among its surrounding nodes the ones that do not seem to move, and treats them as static nodes. Only nodes that are deemed static are then used as reference points for position estimation. Furthermore, each node adjusts its localization frequency automatically according to its estimated velocity. Performance evaluation results based on a realistic sensor model and actual mobility traces show that our method could achieve sufficient accuracy and efficiency for an exhibition scenario where people need to be tracked.