Harnessing HyDRO: Harvesting-aware Data ROuting for Underwater Wireless Sensor Networks

We demonstrate the feasibility of long lasting underwater networking by proposing the smart exploitation of the energy harvesting capabilities of underwater sensor nodes. We define a data routing framework that allows senders to select the best forwarding relay taking into account both residual energy and foreseeable harvestable energy. Our forwarding method, named HyDRO, for Harvesting-aware Data ROuting, is also configured to consider channel conditions and route-wide residual energy, performing network wide optimization via local information sharing. The performance of our protocol is evaluated via simulations in scenarios modeled to include realistic underwater settings as well as energy harvesting based on recorded traces. HyDRO is compared to state-of-the-art forwarding protocols for underwater networks. Our results show that jointly considering residual and predicted energy availability is key to achieve lower energy consumption and latency, while obtaining much higher packet delivery ratio.

[1]  Shusen Yang,et al.  Distributed Networking in Autonomic Solar Powered Wireless Sensor Networks , 2013, IEEE Journal on Selected Areas in Communications.

[2]  Mario Gerla,et al.  VAPR: Void-Aware Pressure Routing for Underwater Sensor Networks , 2013, IEEE Transactions on Mobile Computing.

[3]  Stefano Basagni,et al.  Wireless Sensor Networks with Energy Harvesting , 2013, Mobile Ad Hoc Networking.

[4]  Michele Magno,et al.  Poster Abstract: MagoNode++ - A Wake-Up-Radio-Enabled Wireless Sensor Mote for Energy-Neutral Applications , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[5]  Yunsi Fei,et al.  QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[6]  Stefano Basagni,et al.  Finding MARLIN: Exploiting multi-modal communications for reliable and low-latency underwater networking , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[7]  Kay Römer,et al.  Perpetual Data Collection with Energy-Harvesting Sensor Networks , 2014, TOSN.

[8]  Dongkyun Kim,et al.  DFR: an efficient directional flooding-based routing protocol in underwater sensor networks , 2012, Wirel. Commun. Mob. Comput..

[9]  Milica Stojanovic,et al.  Underwater sensor networks: applications, advances and challenges , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Roberto Petroccia,et al.  CARP: A Channel-aware routing protocol for underwater acoustic wireless networks , 2015, Ad Hoc Networks.

[11]  R. Masiero,et al.  Field experiments for Dynamic Source Routing: S2C EvoLogics modems run the SUN protocol using the DESERT Underwater libraries , 2012, 2012 Oceans.

[12]  Alessandro Casavola,et al.  Long lasting underwater wireless sensors network for water quality monitoring in fish farms , 2017, OCEANS 2017 - Aberdeen.

[13]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[14]  José-Fernán Martínez,et al.  A Survey on Underwater Acoustic Sensor Network Routing Protocols , 2016, Sensors.

[15]  Azzedine Boukerche,et al.  On the design of green protocols for underwater sensor networks , 2016, IEEE Communications Magazine.

[16]  Michele Zorzi,et al.  Routing in multi-modal underwater networks: A throughput-optimal approach , 2017, 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[17]  Ting Wang,et al.  Adaptive Routing for Sensor Networks using Reinforcement Learning , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[18]  John R. Potter,et al.  The SUNSET framework for simulation, emulation and at-sea testing of underwater wireless sensor networks , 2015, Ad Hoc Networks.

[19]  Faisal Karim Shaikh,et al.  Underwater Sensor Network Applications: A Comprehensive Survey , 2015, Int. J. Distributed Sens. Networks.

[20]  Cherry Wakayama,et al.  Utilizing kinematics and selective sweeping in reinforcement learning-based routing algorithms for underwater networks , 2015, Ad Hoc Networks.

[21]  Li Xu,et al.  Fault-Tolerant Algorithms for Connectivity Restoration in Wireless Sensor Networks , 2016, Sensors.

[22]  Hwee Pink Tan,et al.  Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications , 2014, IEEE Communications Surveys & Tutorials.

[23]  Yunsi Fei,et al.  An adaptive routing protocol based on connectivity prediction for underwater disruption tolerant networks , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[24]  A. Montecucco,et al.  Autonomous Underwater Vehicle Thermoelectric Power Generation , 2013, Journal of Electronic Materials.

[25]  A. El Saddik,et al.  Ant Colony-Based Reinforcement Learning Algorithm for Routing in Wireless Sensor Networks , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[26]  Ronald W. Yeung,et al.  Piezoelectric devices for ocean energy: a brief survey , 2015 .

[27]  Michele Zorzi,et al.  World ocean simulation system (WOSS): a simulation tool for underwater networks with realistic propagation modeling , 2009, WUWNet.

[28]  T. Boyer,et al.  Global Ocean Currents Database , 2016 .

[29]  P.H. Chou,et al.  Efficient Charging of Supercapacitors for Extended Lifetime of Wireless Sensor Nodes , 2008, IEEE Transactions on Power Electronics.

[30]  Chiara Petrioli,et al.  Online Energy Harvesting Prediction in Environmentally Powered Wireless Sensor Networks , 2016, IEEE Sensors Journal.