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

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Routing And Communication Path Mapping In VANETS

Vehicular ad-hoc network (VANET) has quickly become an important aspect of the intelligent transport system (ITS), which is a combination of information technology, and transport works to improve efficiency and safety through data gathering and dissemination. However, transmitting data over an ad-hoc network comes with several issues such as broadcast storms, hidden terminal problems and unreliability; these greatly reduce the efficiency of the network and hence the purpose for which it was developed. We therefore propose a system of utilising information gathered externally from the node or through the various layers of the network into the access layer of the ETSI communication stack for routing to improve the overall efficiency of data delivery, reduce hidden terminals and increase reliability. We divide route into segments and design a set of metric system to select a controlling node as well as procedure for data transfer. Furthermore we propose a system for faster data delivery based on priority of data and density of nodes from route information while developing a map to show the communication situation of an area. These metrics and algorithms will be simulated in further research using the NS-3 environment to demonstrate the effectiveness