Improving data driven decision making through integration of environmental sensing technologies

Coastal and estuarine zones contain vital and increasingly exploited resources. Traditional uses in these areas (transport, fishing, tourism) now sit alongside more recent activities (mineral extraction, wind farms). However, protecting the resource base upon which these marine-related economic and social activities depend requires access to reliable and timely data. This requires both acquisition of background (baseline) data and monitoring impacts of resource exploitation on aquatic processes and the environment. Management decisions must be based on analysis of collected data to reduce negative impacts while supporting resource-efficient, environmentally sustainable uses. Multi-modal sensing and data fusion offer attractive possibilities for providing such data in a resource efficient and robust manner. In this paper, we report the results of integrating multiple sensing technologies, including autonomous multi-parameter aquatic sensors with visual sensing systems. By focussing on salinity measurements, water level and freshwater influx into an estuarine system; we demonstrate the potential of modelling and data mining techniques in allowing deployment of fewer sensors, with greater network robustness. Using the estuary of the River Liffey in Dublin, Ireland, as an example, we present the outputs and benefits resulting from fusion of multi-modal sensing technologies to predict and understand freshwater input into estuarine systems and discuss the potential of multi-modal datasets for informed management decisions

Making Wireless Sensor Network Simulators Cooperate

CONE

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

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