Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.Comment: 15 pages, 10 figures, in review, 201

Precise vehicle positioning by cooperative feature association and tracking in vehicular networks

In cooperative intelligent transportation systems, precise vehicle positioning is a critical requirement that cannot be met by stand-alone Global Positioning Systems (GPSs). This paper proposes a distributed Bayesian data association and localization method, called Implicit Cooperative Positioning with Data Association (ICP-DA, where connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GPS accuracy. Results show that ICP-DA significantly outperforms GPS, with negligible performance loss compared to ICP with perfect data association knowledge

A wireless cloud network platform for industrial process automation: critical data publishing and distributed sensing

Wireless technologies combined with advanced computing are changing industrial communications. Industrial wireless networks can improve the monitoring and the control of the entire system by jointly exploiting massively-interacting communication and distributed computing paradigms. In this paper, we develop a wireless cloud platform for supporting critical data publishing and distributed sensing of the surrounding environment. The cloud system is designed as a selfcontained network that interacts with devices exploiting the Time Synchronized Channel Hopping protocol (TSCH), supported by WirelessHART (IEC 62591). The cloud platform augments industry-standard networking functions as it handles the delivery (or publishing) of latency and throughput-critical data by implementing a cooperative-multihop forwarding scheme. In addition, it supports distributed sensing functions through consensus-based algorithms. Experimental activities are presented to show the feasibility of the approach in two real industrial plant sites representative of typical indoor and outdoor environments. Validation of cooperative forwarding schemes shows substantial improvements compared with standard industrial solutions. Distributed sensing functions are developed to enable the autonomous identification of recurring co-channel interference patterns

Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance

Distributed Sensing of Interference Pattern in Dense Cooperative Wireless Networks

In this paper we consider the problem of distributed spectrum sensing in multiple selforganizing networks sharing the same timefrequency resources. Each of the networks allocates autonomously radio resources so as to minimize mutual interference. Interference sensing is part of this cognitive framework where sensing devices, or secondary users (SUs), exchange local estimates to cooperatively recognize and track the overall time-varying interference patterns caused by primary users (PUs). PUs are assumed to perform periodic transmission over pre-defined (but unknown to SUs) time-frequency hopped resources. Detection by the SUs is based on local processing and iterated exchanges of local decision with neighbors, so as to enable global fusion of sensed data as for an equivalent centralized approach. We propose a weighted-average consensus algorithm nested within a decision-directed procedure for distributed Bayesian detection of the PU spectrum occupancy. The distributed approach provides the estimate of the complete interference pattern to each SU regardless of the incomplete visibility at each node. Performance analysis is carried out both on simulated and real scenarios with mixed coexisting WiFi and ZigBee devices

Consensus-based Algorithms for Distributed Network-State Estimation and Localization

Recent advances of hardware design and radio technologies have opened the way for an emerging category of network-enabled smart physical devices as a result of convergence in computing and wireless communication capabilities. Inspired by biological interactions, distributed processing of data collected by individual devices is now becoming crucial to let the nodes self-learn relevant network-state information and self-organize without the support of a central unit. Focus of this paper is twofold. First, a novel network channel model tailored for dense deployments is developed and validated on real data. The model describes relevant channel features that are representative of site-specific static/dynamic multipath fading and are shared by all links of a network. Second, a new class of distributed weighted-consensus strategies is introduced to support distributed network calibration and localization in device-to-device networks. Network calibration allows the devices to self-learn the common channel parameters, by successive refinements of local estimates and peer-to-peer information exchange. Network-localization enables each node to acquire augmented information about the whole network topology, by distributed learning from local channel observations. The proposed distributed algorithms guarantee a fast convergence and can replace conventional centralized schemes. An experimental case study is discussed in a representative indoor environment for the purpose of system validation. Experimental results show that the proposed method can significantly improve the performance of conventional solutions

Device-to-Device Resource Scheduling by Distributed Interference Coordination

Device-to-device (D2D) communication overlaying a cellular network has been proved to augment the flexibility and enable new applications (e.g., content distribution) in cellular networks. In this context, it is crucial to design a radio resource management system that allocates the time-frequency (TF) resources to the D2D links in a distributed way, i.e. without any coordination by the cellular network, so as to guarantee the quality of service (QoS), particularly in heterogeneous traffic conditions. Interference-aware resource allocation has the capability to adapt the resource management to a context where multiple D2D links coexist in the same spectrum. In this paper, a distributed scheduling approach is proposed where each D2D link reacts to the locally sensed interference by self-adapting its own TF allocation. Each node autonomously trades the QoS request in term of packet service with the resource availability by inflating/deflating the spectrum allocation based on the sensed interference level. The change of the interference pattern perceived in turn by other D2D links serves as interlink signaling of the need/release of TF resources. Each node optimizes the allocation by iterated local adjustments, till an equilibrium with other D2D links is reached. The paper shows that the proposed scheduling algorithm is able to maximize the total throughput in a fully distributed way, arranging efficiently the radio resources over the TF domain so as to satisfy the QoS requirements for each node

Enhanced vehicle positioning in cooperative ITS by joint sensing of passive features

Satellite-based navigation systems, such as Global Positioning System (GPS) or Galileo, are the most common and accessible techniques for vehicle positioning. However, in dense urban areas, even if combined with vehicle on-board sensors, they lead to large localization errors due to multipath and signal blockage. In recent years, Cooperative Intelligent Transportation Systems (C-ITSs) have gained increasing attention as they allow vehicles to cooperate and broadcast safety-related information to the neighbors through Vehicle-to-Vehicle (V2V) communications. In this paper, a novel cooperative positioning method is developed by exploiting V2V communications without using explicit V2V ranging. Vehicles localize, in a distributed way, a set of jointly sensed non-cooperative features (e.g., people, traffic lights) and use them as common noisy reference points to implicitly enhance their own position accuracy. Distributed belief propagation is combined with consensus-based estimation of the features' positions to enable cooperative localization of vehicles. Numerical results show that the proposed method is able to significantly enhance the GPS-based vehicle location accuracy, especially in scenarios with dense feature deployments