Delay Tolerant Networking over the Metropolitan Public Transportation

We discuss MDTN: a delay tolerant application platform built on top of the Public Transportation System (PTS) and able to provide service access while exploiting opportunistic connectivity. Our solution adopts a carrier-based approach where buses act as data collectors for user requests requiring Internet access. Simulations based on real maps and PTS routes with state-of-the-art routing protocols demonstrate that MDTN represents a viable solution for elastic nonreal-time service delivery. Nevertheless, performance indexes of the considered routing policies show that there is no golden rule for optimal performance and a tailored routing strategy is required for each specific case

Modeling and Performance Evaluation of Advanced Diffusion with Classified Data in Vehicular Sensor Networks

International audienceIn this paper, we propose a newly distributed protocol called ADCD to manage information harvesting and distribution in Vehicular Sensor Networks (VSN). ADCD aims at reducing the generated overhead avoiding network congestions as well as long latency to deliver the harvested information. The concept of ADCD is based on the characterization of sensed information (i.e. based on its importance, location and time of collection) and the diffusion of this information accordingly. Furthermore, ADCD uses an adaptive broadcasting strategy to avoid overwhelming users with messages in which they have no interest. Also, we propose in this paper a new probabilistic model for ADCD based on Markov chain. This one aims at optimally tune the parameters of ADCD, such as the optimal number of broadcaster nodes. The analytical and simulation results based on different metrics, like the overhead, the delivery ratio, the probability of a complete transmission and the minimal number of hops, are presented. These results illustrate that ADCD allows to mitigate the information redundancy and its delivery with an adequate latency while making the reception of interesting data for the drivers (related to their location) more adapted. Moreover, the ADCD protocol reduces the overhead by 90% compared to the classical broadcast and an adapted version of MobEyes. The ADCD overhead is kept stable whatever the vehicular density

Smart Vehicle- to-Vehicle Communication with 5G Technology

Vehicle-to-vehicle (V2V) communication aim to avoid vehicular accident by providing advisory to the driver as less congestion, mishap warning, road investigation, etc. Technological developments in electronics, computing, identifying, robotics, control, signal processing, and communications makes this things possible. Different technology like WAVE) is based on the IEEE 802.11p standard , DSRC,CALM standard mainly used in v2v communication. One major technical challenge in all technology is to achieve low-latency in delivering emergency warnings in various road situations. 5G technology with its specific potential feature e.g. End to End latency -1ms,High data rate, mobility, traffic density makes V2V communication smart DOI: 10.17762/ijritcc2321-8169.150515

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Distributed and Communication-Efficient Continuous Data Processing in Vehicular Cyber-Physical Systems

Processing the data produced by modern connected vehicles is of increasing interest for vehicle manufacturers to gain knowledge and develop novel functions and applications for the future of mobility.Connected vehicles form Vehicular Cyber-Physical Systems (VCPSs) that continuously sense increasingly large data volumes from high-bandwidth sensors such as LiDARs (an array of laser-based distance sensors that create a 3D map of the surroundings).The straightforward attempt of gathering all raw data from a VCPS to a central location for analysis often fails due to limits imposed by the infrastructure on the communication and storage capacities. In this Licentiate thesis, I present the results from my research that investigates techniques aiming at reducing the data volumes that need to be transmitted from vehicles through online compression and adaptive selection of participating vehicles. As explained in this work, the key to reducing the communication volume is in pushing parts of the necessary processing onto the vehicles\u27 on-board computers, thereby favorably leveraging the available distributed processing infrastructure in a VCPS.The findings highlight that existing analysis workflows can be sped up significantly while reducing their data volume footprint and incurring only modest accuracy decreases. At the same time, the adaptive selection of vehicles for analyses proves to provide a sufficiently large subset of vehicles that have compliant data for further analyses, while balancing the time needed for selection and the induced computational load