Learning to Communicate Underwater

WUWNet'17 1570384846 1 2 3 4 5 Learning to Communicate Underwater 6 An Exploration of Limited Mobility Agents 7 8 9 Steven Porretta Michel Barbeau Carleton University, School of Computer Science Carleton University, School of Computer Science 10 11 Ottawa, Ontario Ottawa, Ontario 12 Joaquin Garcia-Alfaro Evangelos Kranakis 13 SAMOVAR, TELECOM SudParis Carleton University, School of Computer Science 14 CNRS, Paris-Saclay University Ottawa, Ontario 15 91011 Evry, France 16 17 ABSTRACT with changes in the acoustic environment [9]. An existing problem 18 Underwater environmental parameters vary with time and can neg- in underwater communication is the fact that the acoustic qualities 19 atively impact the quality of communication. The adaptive control of oceanic media have a tendency to change both seasonally and 20 system suggested attempts to improve communication in underwa- with local weather phenomena [1, 11]. Although, these acoustic 21 ter networks where environmental conditions are stochastic and properties are unavoidable, it is the goal of this work to demon- 22 time-variant. Adaptive depth control is explored in limited mobility strate that it may be possible for depth-varying anchored Limited 23 underwater acoustic sensor networks. The adaptive control seeks Mobility Agents (LMAs) to utilize adaptive rule learning strategies 24 to leverage the acoustic properties along the thermocline sensors to alter their depth in order to improve communication. Currently, 25 anchored to the seabed in order to improve link stability in under- many of the sensor networks which allow for variable depth of an- 26 water networks. The sensor is allowed two directions of movement, choring rely on human interaction to change node depths, a costly process which results in few movements of the sensor with respect 27 either surface or dive, in order to avoid physical phenomena that to sensor operating lifespan. Replacing that human interaction with 28 cause faults. The algorithm presented is capable of adapting to the a motorized component would allow limited mobility to network 29 optimal performance depth in unimodal stochastic stationary and non-stationary environments. elements which can be leveraged for sensing tasks and communica- 30 tions alike. Ultimately, the work herein seeks to provide a basis for 31 ACM Reference Format: leveraging acoustic sound speed changes along the thermocline of Steven Porretta, Michel Barbeau, Joaquin Garcia-Alfaro, and Evangelos 32 an underwater acoustic environment with the intent of improving 33 Kranakis. 2017. Learning to Communicate Underwater: An Exploration of Limited Mobility Agents. In WUWNET’17: WUWNET’17: International link stability in an Underwater Acoustic Sensor Network (UASN). 34 Conference on Underwater Networks & Systems, November 6–8, 2017, Halifax, A learning strategy that allows LMAs to adapt to the best depth 35 NS, Canada. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/ of operation may not only be a tactic for fault avoidance in UASN, 36 3148675.3148709 but could also be an acceptable strategy to avoid collisions in UASNs, 37 since an adaptive strategy would not discriminate against the cause 38 1 INTRODUCTION of faults in the network. A significant motivator of using learn- 39 Mobile Ad-hoc Networks (MANETs) are a prominent topic of re- ing strategies to avoid faults in UASN is the fact that the agents 40 search in terrestrial networks. The applications possible for MANETs in UASNs are economically expensive. The cost of UASN agents inspire network sparsity, and it is this agent sparsity which moti- 41 in underwater environments is vast and can range from naval se- vates a scheme that can avoid faults, or link failures, in underwater 42 curity to seabed mining operations. Currently, underwater applications of MANETs are limited, in part due to the physical difficulties communication. 43 Partan et al. [10] describes a series of physical limitations to 44 in establishing wireless communication networks underwater. The stochastic nature of underwater acoustic environments creates underwater acoustic communication, among which are concerns 45 including shadow zones, multipath interference, and near surface 46 difficulties for communication1 [ ]. Much of the existing work in the routing of underwater communications focuses on fixed rule bubble cloud regions. These physical properties not only cause link 47 algorithms for transmission that do not leverage the mobility of failure in UASNs, but are demonstrably characteristic of a time- 48 agents, or utilize the opportunity to change their rule structure varying stochastic environment [2, 3]. Although the observation of 49 stochastic features like bubble clouds, caustic regions, and shadow 50 Permission to make digital or hard copies of all or part of this work for personal or regions seems trivial, it is an important observation when consider- classroom use is granted without fee provided that copies are not made or distributed ing the work of Narendra et al. into learning automata techniques to 51 for profit or commercial advantage and that copies bear this notice and the full citation 52 on the first page. Copyrights for components of this work owned by others than ACM improve communication in terrestrial telephone networks [5, 6, 8]. 53 must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, Narendra et al. [5, 6, 8] use a Mean Action Learning Automaton (M- to post on servers or to redistribute to lists, requires prior specific permission and/or a automaton) to create adaptive rule routing in a stochastic demand 54 fee. Request permissions from [email protected]. 55 WUWNET’17, November 6–8, 2017, Halifax, NS, Canada telephone network and demonstrate improvements in performance © 2017 Association for Computing Machinery. over traditional fixed rule routing. In a similar way, M-automaton 56 ACM ISBN 978-1-4503-5561-2/17/11...$15.00 can be leveraged in 3D underwater network topologies where agent 57 https://doi.org/10.1145/3148675.3148709 60 61 62 63 64 65 1 WUWNET’17, November 6–8, 2017, Halifax, NS, Canada Steven Porretta, Michel Barbeau, Joaquin Garcia-Alfaro, and Evangelos Kranakis depth can be varied as described by Akyildiz et al. [1] in order to to control the depth of an underwater LMA a M-automaton would leverage the properties of acoustics along the thermocline of a body have possible action probabilities correspond to control states and of water. At this time, the authors of this work are unaware of any the environmental response acts as a predictor of the next action. publication which seeks to take advantage of such properties of In a fixed-rule control system, such a LMA may have two states, underwater environments. dive and surface. However, the adaptive control counterpart would In the discussion of 3D underwater networks Akyildiz et al. [1] have three control states: dive, surface, and idle in order to allow proposes to anchor the depth varying network agents on the seabed for a surjective relationship between control states and LA actions, to improve surreptitious monitoring capabilities or to reduce the as discussed in subsequent sections. hazard of collision with passing vessels. A further alteration to this The application of reinforcement learning in adaptive control network architecture would be to allow the agents to autonomously of the operating depth of LMAs in an UASN requires that the LA alter their depth of operation while anchored to the seabed. In other determines rewards by the successful reception of a confirmation words, allow the node itself to take action regarding the depth of message, a type of message that must originate from a Fusion operation. This way a network agent may avoid faults caused by Centre (FC), and is propagated to all agents in the network. A physical phenomena, like caustics, air bubbles, and sound speed pro- penalty, then, is determined by a timeout failure. Timeout counters file changes along the thermocline [4, 11]. This level of autonomous are only initiated after a data message is sent by an agent, in this control is achieved by equipping each agent in the network with way it does not matter which agent has originated the message. A a M-automaton. This application of a M-automaton can perform further discussion of the algorithm which utilizes reinforcement up to three distinct tasks simultaneously. First, a M-automaton learning in this manner is found in Section 3. operates by determining mean actions, therefore it can maintain a probability vector of the approximate mean correlation between the depth of sensor operation and the probability of link existence. Secondly, the probability vector inherently contains information for the depth or depths corresponding to the highest probability of link existence. Finally, the M-automaton is able to avoid faults without 2.1 The Linear Reward-Penalty Scheme ever requiring change to the routing protocol used by the network There are many schemes for LA and the scheme being used in this since it manipulates the link-state, thus reducing implementation work is a type of Variable-Structure Stochastic Automata (VSSA) cost. The Maximum Award Stationary Transmission (MaSt) algo- known as a Linear Reward-Penalty (LR;P ) scheme. This scheme rithm, solves the best depth location, but does not maintain an is chosen for several reasons, primarily because it is ergodic. It accurate probability vector of the approximate mean correlation allows the collection of useful metrics that can implicitly map the between depths and link existence, instead it maintains a vector of environment as a probability vector; something that fixed-structure the confidence in the best depth with respect to time. automata schemes could not provide. Since the LR;P scheme is The theory of Learning Automata (LA) is discussed in Section 2. ergodic it allows adaptation to changing environments including A network topology consisting of stationary network elements and seasonal changes, or the natural drift of FCs and LMA caused by LMAs is discussed in Section 3.

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