Poster Abstract: MagoNode++ - A Wake-Up-Radio-Enabled Wireless Sensor Mote for Energy-Neutral Applications

The combination of low-power design, energy harvesting and ultra-low-power wake-up radios is paving the way for perpetual operation of Wireless Sensor Networks (WSNs). In this work we present the MagoNode++, a novel WSN platform supporting energy harvesting and radio-triggered wake ups for energy- neutral applications. The MagoNode++ features an energy- harvesting subsystem composed by a light or thermoelectric harvester, a battery manager and a power manager module. It further integrates a state-of-the-art RF Wake-Up Receiver (WUR) that enables low-latency asynchronous communication, virtually eliminating idle listening at the main transceiver. Experimental results show that the MagoNode++ consumes only 2.8uA with the WUR in idle listening and the rest of the platform in sleep state, making it suitable for energy-constrained WSN scenarios and for energy-neutral applications

EYES - Energy Efficient Sensor Networks

The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It will address the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This document gives an overview of the EYES project

Wake-up radio-based data forwarding for green wireless networks

This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively

OptoCOMM and SUNSET to enable large data offloading in Underwater Wireless Sensor Networks

In this paper we present the initial implementation of an integrated optical and acoustic system that can enable large data transfer between mobile and static nodes in Underwater Wireless Sensor Networks (UWSNs). The proposed system is based on the OptoCOMM optical modem and on the SUNSET Software Defined Communication Stack (S-SDCS) framework. The OptoCOMM modem allows to overcome the limits of maximum data rate and bandwidth imposed by the use of acoustic communication by providing a data rate of 10Mbps. SUNSET SDCS instead has been used to provide networking and fragmentation capabilities to efficiently offload large data in UWSNs. The performance of the proposed approach has been evaluated through in lab experiments where large files with arbitrary sizes have been optically transferred. The results achieved show that our system is able to transfer up to 1.5 GBytes of data in short time

Channel-aware routing for underwater wireless networks

Abstract-This paper presents a new cross layer routing protocol for underwater wireless sensor networks. The solution, termed CARP for Channel-aware Routing Protocol, exploits link quality information for cross layer relay determination. Nodes are selected as relays if they have a (recent) history of successful transmissions to their neighbors. CARP combines link quality with simple topology information (hop count), thus being able to route around connectivity voids and shadow zones. The protocol is also designed to take advantage of power control for selecting robust links. The performance of CARP has been evaluated through ns2-based simulations, and compared to the performance of two previously proposed routing protocols, namely, FBR and DBR. Our results show that CARP robust relay selection mechanism enables it to achieve throughput efficiency that is up to twice the throughput of FBR and almost three times that of DBR. CARP also obtains remarkable performance improvements over FBR and DBR with respect to end-to-end packet latency and energy consumption. Index Terms-Underwater acoustic networks, cross layer design, MAC and routing protocols

FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes

AURORA, a multi-sensor dataset for robotic ocean exploration

The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, this paper presents a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, sidescan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition metadata to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservation Zone (MCZ) of the U.K in 2015