Power management in battery operated sensor networks, is a hot topic addressed by many ongoing researches. One of the most commonly employed technique consists in turning on and of the power of the radio unit to reduce the power consumption. In this paper we exploit the modelling power of Interacting Markovian agent to evaluate the performance of four different on-off strategies. A Markovian agent (MA) is an entity whose behaviour is described by a continuous time Markov chain (CTMC) and that is able to interact with other MA's by sending and receiving messages. A perceived message may induce a state transition in an MA according to a perception function than can depend on the geographical location of the MA's, on the message routing strategy and on the transmission property of the medium. We represent each sensor by an MA and we show how MA's can interact to produce the collective behaviour of the sensor network.

Analysis of On-off policies in Sensor Networks Using Interacting Markovian Agents

Collision warning is one of the most important functions of a vehicle safety system. The emergence and expansion of the applications of positioning techniques and dedicated short-range communication (DSRC) have promoted the collision warning system evolution from a simple ranging-sensor-based system to a cooperative system. Differing from prior work that relied heavily on the e-Map, high-accuracy differential Global Positioning System (DGPS), or advanced car features like the controller area network (CAN) bus, this paper proposes Forwards, i.e., a map-free intersection collision-warning system for all road patterns with lower requirement and lower cost accessories. Forwards employs a triple Kalman filter (tri-KF)-based estimator that integrates GPS and external inertial sensor measurement to provide calibrated motion state information (MSI) such as position, velocity, and acceleration of the vehicle. Each vehicle then adaptively broadcasts its own MSI via the DSRC-based protocol. Using the steady-state maneuvering model, short-term trajectories of local and neighboring vehicles are further predicted, based on their current MSI. Collision-detection algorithms are then designed based on the model of finding the minimum distance of vehicles' future trajectories, and hierarchical warnings are given upon different criteria. Simulation results show that our approach outperforms the referenced approach in successful warning ratio and requires far fewer accessories and external conditions than the other referenced approaches.

Forwards: A Map-Free Intersection Collision-Warning System for All Road Patterns

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy. 

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A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Data has become an important currency in today's world economy. Ephemeral and real-time data from Twitter, Facebook, Google, urban sensors, weather stations, and the Web contain hidden patterns of the city that are useful for informing architectural and urban design. However, often data required for informing a particular building or urban project is not available. In order to gather local and real-time data of the city, sensor devices, which are now embedded in today's urban infrastructures, buildings, vehicles, and mobile phones, have become useful tools for probing streets and the built environment. The proliferations of low-cost microcontrollers that leverage physical computing have also made sensor devices more accessible and easier to configure. The wealth of data from these sensors, if aggregated, synthesized, and analyzed, has the potential to increase our understanding of human and social behaviors in the city. This paper presents a number of projects which use ambient or community sensing to pro...

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Probing Streets and the Built Environment with Ambient and Community Sensing

In recent years, research on Intelligent Transportation System (ITS) was done to solve many traffic problems Considering the problem of traffic safety at blind crossing, a vehicle collision warning system based on Internet-Connected Vehicles was proposed in order to prevent a vehicle collision before it occurs Internet-Connected Vehicles plays an important role in realizing the next generation ITS Under Internet-Connected Vehicles environment, the vehicles install communication equipments and can exchange their driving information, including their position, velocity, heading angle and so on On this basis, physical and logical frameworks of the collision warning system were designed after analyzing the system function Then time to collision (TTC) was introduced to judge the vehicle collision and a judgment algorithm was proposed based on inter-vehicle communication Furthermore, time to avoidance (TTA) was introduced to be compared with TTC in order to define the severity of collision and the system could alert the driver according to the level of danger Finally, the simulation was carried out and results proved that the warning algorithm of the system is effective to give the an driver alert

Vehicle Collision Warning System and Algorithm at Intersection under Internet-Connected Vehicles Environment

Embedded with sensors and appropriate computational entities, a road intersection can be viewed as a pervasive computing environment. The crash rate in road intersections demonstrates the need for a fast and accurate collision detection system. We suggest that an intersection collision detection system should be able to adapt to different types of intersections for faster collision detection. Moreover, a real-time application-level communication protocol to warn affected drivers is required. An intersection agent that takes vehicular status information from vehicle agents and learns, detects and warns collisions at a road intersection is proposed. The issues, challenges, and cost of a multiagent collision avoidance system are discussed. A communication protocol that is designed specifically with intersection safety in mind is presented here.

Licheng Cai

Papers

Road Intersections as Pervasive Computing Environments: Towards a Multiagent Real-Time Collision Warning System