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EEIRA: An Energy Efficient Interference and Route Aware Protocol for Underwater WSNs
Abstract
In this paper, an energy efficient, interference and route aware (EEIRA) protocol is proposed for underwater wireless sensor networks (UWSNs). The protocol combines the direct and relay forwarding mechanisms in transmitting the packets from source to destination. The relaying process involves selection of the best relay from a set of relay nodes. A relay node having the least distance from source to destination and the minimum number of neighbor nodes qualifies the criterion of the best relay. The direct transmission is used when the best relay is not within the transmission range of the source node. From top to bottom, the network is divided into three different zones, destination, relay and source zone, respectively. The relay zone has the greatest area to have maximum choices of selecting the best relay from the relay nodes residing in it. Nodes in all the three regions can sense the attribute and send the data to the sink. The destination nodes send data directly to sink. The relay nodes send the packets to the sink either directly or through the relaying process. The source nodes in the bottom send the data to sink via the best relay node or directly. Based on simulation results, the proposed protocol outperforms the depth based routing (DBR) scheme in terms of energy efficiency by selecting the best relay, reducing number of hops and following the shortest path to reduce channel losses.
... Hence the optimal energy consumption design is the key issue due to limited energy supply of underwater relay nodes in practice. Much work has been done on how to design the optimal routing for this special application in underwater environments, including the traditional routing protocols [5]- [15] and the artificial intelligence (AI) algorithm-based routing protocols [16]- [23]. In general, for a given source-destination pair in multi-hop UASNs, the optimal routing protocol will always be the one with the lowest energy consumption, which is usually composed of the same relay nodes for each transmission task. ...
... Based on the DBR, Mohammadi et al. in [10] proposed the Fuzzy DBR (FDBR) which selects forwarding nodes based on the number of hops, depth, and energy information, improving the energy efficiency and reducing the end-to-end delay. There are also many similar routing protocols, such as the VARP [11], GEDAR [12], iIA-EEDBR [13], EECOR [14], EEIRA [15], and so on. Similarly, the underwater acoustic routing design based on cooperative communication, such as CoDBR [42], Co-UWSN [43], SPARCO [44], RBCMIC [45], S-DCC [4], etc., also mostly considers end-to-end delay, network lifetime, and energy consumption. ...
... When the distance d ij between node i and node j is determined, the propagation attenuation U (d ij ) can be calculated by Eqs. (6) to (9), and then the transmitting power P can be calculated by [4] P = P 0 · U d ij (15) where P 0 is the lowest power level at which the packet can be successfully decoded (error-free) by the receiver node on path (i, j). Assume the maximum importance rating of data N rank is 5, the number of ants N ant is 3, and the number of iteration N iter is 32. ...
For a given source-destination pair in multi-hop underwater acoustic sensor networks (UASNs), an optimal route is the one with the lowest energy consumptions that usually consists of the same relay nodes even under different transmission tasks. However, this will lead to the unbalanced payload of the relay nodes in the multi-hop UASNs and accelerate the loss of the working ability for the entire system. In this paper, we propose a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission. The proposed PB-ACR protocol is a relay node energy consumption balanced scheme, which considers both data priority and residual energy of each relay node, aiming to reduce the occurrence of energy holes and thereby prolong the lifetime of the entire UASNs. We compare the proposed PB-ACR protocol with the existing ant colony algorithm routing (ACAR) protocol to verify its performances in multi-hop UASNs, in terms of network throughput, energy consumption, and algorithm complexity. The simulation results show that the proposed PB-ACR protocol can effectively balance the energy consumption of underwater sensor nodes and hence prolong the network lifetime.
... The Energy Efficient Interference and Route Aware Protocol combines direct and relay forwarding mechanism to transmit the DPSN from source node to sink node [14]. The Energy Efficient And Balanced Energy Consumption Cluster-Based Routing Protocol (EBECRP) improves the stability period and network lifetime. ...
... Sensor nodes are deployed randomly in 3D environment. The depth distances for different regions are shown in Table 1 [14]. All the nodes in the N/w are energy constrained except sink. ...
... An energy efficient, interference and route aware protocol (EEDBR) [10] reduces the number of transmitting nodes and prolongs the existence and utilization of the network. ...
In this paper, a trade-off between the energy consumption and network lifetime is considered. This paper proposes an optimal routing protocol called Energy Dynamic Adaptive Routing (EDAR) protocol. The DAR protocol maintains a tradeoff between the reliability or packet delivery ratio (PDR) of sensor nodes and Bit Error Ratio (BER) using optimal dynamic adaptive routing approach. The proposed approach operates on three different phases, namely, initialization, dynamic routing and transmission. During initial phase, all the nodes in the UWSN share location and residual energy information among all the nodes in the network. During dynamic routing phase, an optimal Directed Acyclic Graph (DAG) based route selection is exploited to select the neighbor and successor nodes. This facilitates the successive routing to transmit the packets from one node to another. Here, the cost function with directed acyclic graph is utilized for better transmission of packets. The experimental results show that proposed method encounters the issues raised in conventional protocol and improves the reliability of packets with higher BER.
... Apart from this, there are several energy efficient algorithm that operates on routing algorithm. This includes energy efficient depth-based routing and depth-based routing (Mahmood et al., 2014), dynamic sink mobility equipped depth-based routing , scalable and efficient data gathering routing protocol (Ilyas et al., 2015), ARCUN (Ahmed et al., 2015), the error control and adjustment method (Han et al., 2018), an energy efficient chain-based routing protocol (Rani et al., 2017), the slotted CSMA-based reinforcement learning approach (Jin and Huang, 2013), a fault-resilient localisation scheme (Das and Thampi, 2017), a proactive opportunistic forwarding mechanism (Liu et al., 2017), cross-layer protocol stack development (Dhongdi et al., 2017), multi-hop mechanisms , data aggregation protocols (Goyal et al., 2017), a topology control algorithm for signal irregularity (Liu et al., 2015), constrained surface-level gateway placement (Li et al., 2010), highly selective and fitness protocols (Zenia et al., 2016), forward error correction (Domingo and Vuran, 2012), maximum coverage algorithms (Akkaya and Newell, 2009), a diagonal and vertical routing protocol (Ali et al., 2014), a connected dominating set (Senel et al., 2015), energy efficiency distributed time synchronisation algorithms (Li et al., 2013), level-based adaptive geo-routing (Du et al., 2014), adaptive reliable transport (Cai et al., 2013b), integer linear programming (Ibrahim et al., 2013), the node architecture low-cost realisation method (Lu et al., 2008) and vector-based forwarding-network coding (Cai et al., 2013a) and an energy efficient, interference and route aware protocol (Khan et al., 2016) which forwards the data to its neighbouring node to increase the network lifetime. All of the above methods provide data reliability, energy efficiency and secured routing in underwater sensor networks. ...
... Apart from this, there are several energy efficient algorithm that operates on routing algorithm. This includes energy efficient depth-based routing and depth-based routing (Mahmood et al., 2014), dynamic sink mobility equipped depth-based routing , scalable and efficient data gathering routing protocol (Ilyas et al., 2015), ARCUN (Ahmed et al., 2015), the error control and adjustment method (Han et al., 2018), an energy efficient chain-based routing protocol (Rani et al., 2017), the slotted CSMA-based reinforcement learning approach (Jin and Huang, 2013), a fault-resilient localisation scheme (Das and Thampi, 2017), a proactive opportunistic forwarding mechanism (Liu et al., 2017), cross-layer protocol stack development (Dhongdi et al., 2017), multi-hop mechanisms , data aggregation protocols (Goyal et al., 2017), a topology control algorithm for signal irregularity (Liu et al., 2015), constrained surface-level gateway placement (Li et al., 2010), highly selective and fitness protocols (Zenia et al., 2016), forward error correction (Domingo and Vuran, 2012), maximum coverage algorithms (Akkaya and Newell, 2009), a diagonal and vertical routing protocol (Ali et al., 2014), a connected dominating set (Senel et al., 2015), energy efficiency distributed time synchronisation algorithms (Li et al., 2013), level-based adaptive geo-routing (Du et al., 2014), adaptive reliable transport (Cai et al., 2013b), integer linear programming (Ibrahim et al., 2013), the node architecture low-cost realisation method (Lu et al., 2008) and vector-based forwarding-network coding (Cai et al., 2013a) and an energy efficient, interference and route aware protocol (Khan et al., 2016) which forwards the data to its neighbouring node to increase the network lifetime. All of the above methods provide data reliability, energy efficiency and secured routing in underwater sensor networks. ...
... To address interference during packet forwarding, an energy-efficient interference-and route-aware (EEIRA) protocol is proposed in [59]. The protocol selects forwarder nodes based on the lowest depth and the least number of neighbors of the forwarder nodes. ...
Recent research in underwater wireless sensor networks (UWSNs) has gained the attention of researchers in academia and industry for a number of applications. They include disaster and earthquake prediction, water quality and environment monitoring, leakage and mine detection, military surveillance and underwater navigation. However, the aquatic medium is associated with a number of limitations and challenges: long multipath delay, high interference and noise, harsh environment, low bandwidth and limited battery life of the sensor nodes. These challenges demand research techniques and strategies to be overcome in an efficient and effective fashion. The design of routing protocols for UWSNs is one of the promising solutions to cope with these challenges. This paper presents a survey of the routing protocols for UWSNs. For the ease of description, the addressed routing protocols are classified into two groups: localization-based and localization-free protocols. These groups are further subdivided according to the problems they address or the major parameters they consider during routing. Unlike the existing surveys, this survey considers only the latest and state-of-the-art routing protocols. In addition, every protocol is described in terms of its routing strategy and the problem it addresses and solves. The merit(s) of each protocol is (are) highlighted along with the cost. A description of the protocols in this fashion has a number of advantages for researchers, as compared to the existing surveys. Firstly, the description of the routing strategy of each protocol makes its routing operation easily understandable. Secondly, the demerit(s) of a protocol provides (provide) insight into overcoming its flaw(s) in future investigation. This, in turn, leads to the foundation of new protocols that are more intelligent, robust and efficient with respect to the desired parameters. Thirdly, a protocol can be selected for the appropriate application based on its described merit(s). Finally, open challenges and research directions are presented for future investigation.
Owing to the harsh and unpredictable behavior of the sea channel, network protocols that combat the undesirable and challenging properties of the channel are of critical significance. Protocols addressing such challenges exist in literature. However, these protocols consume an excessive amount of energy due to redundant packets transmission or have computational complexity by being dependent on the geographical positions of nodes. To address these challenges, this article designs two protocols for underwater wireless sensor networks (UWSNs). The first protocol, depth and noise-aware routing (DNAR), incorporates the extent of link noise in combination with the depth of a node to decide the next information forwarding candidate. However, it sends data over a single link and is, therefore, vulnerable to the harshness of the channel. Therefore, routing in a cooperative fashion is added to it that makes another scheme called cooperative DNAR (Co-DNAR), which uses source-relay-destination triplets in information advancement. This reduces the probability of information corruption that would otherwise be sent over a single source-destination link. Simulations-backed results reveal the superior performance of the proposed schemes over some competitive schemes in consumed energy, packet advancement to destination, and network stability.
This paper presents two new energy balanced routing protocols for Underwater Acoustic Sensor Networks (UASNs); Efficient and Balanced Energy consumption Technique (EBET) and Enhanced EBET (EEBET). The first proposed protocol avoids direct transmission over long distance to save sufficient amount of energy consumed in the routing process. The second protocol overcomes the deficiencies in both Balanced Transmission Mechanism (BTM) and EBET techniques. EBET selects relay node on the basis of optimal distance threshold which leads to network lifetime prolongation. The initial energy of each sensor node is divided into energy levels for balanced energy consumption. Selection of high energy level node within transmission range avoids long distance direct data transmission. The EEBET incorporates depth threshold to minimize the number of hops between source node and sink while eradicating backward data transmissions. The EBET technique balances energy consumption within successive ring sectors, while, EEBET balances energy consumption of the entire network. In EEBET, optimum number of energy levels are also calculated to further enhance the network lifetime. Effectiveness of the proposed schemes is validated through simulations where these are compared with two existing routing protocols in terms of network lifetime, transmission loss, and throughput. The simulations are conducted under different network radii and varied number of nodes.
Increasing attention has recently been devoted to underwater sensor networks (UWSNs) because of their capabilities in the ocean monitoring and resource discovery. UWSNs are faced with different challenges, the most notable of which is perhaps how to efficiently deliver packets taking into account all of the constraints of the available acoustic communication channel. The opportunistic routing provides a reliable solution with the aid of intermediate nodes' collaboration to relay a packet toward the destination. In this paper, we propose a new routing protocol, called opportunistic void avoidance routing (OVAR), to address the void problem and also the energy-reliability trade-off in the forwarding set selection. OVAR takes advantage of distributed beaconing, constructs the adjacency graph at each hop and selects a forwarding set that holds the best trade-off between reliability and energy efficiency. The unique features of OVAR in selecting the candidate nodes in the vicinity of each other leads to the resolution of the hidden node problem. OVAR is also able to select the forwarding set in any direction from the sender, which increases its flexibility to bypass any kind of void area with the minimum deviation from the optimal path. The results of our extensive simulation study show that OVAR outperforms other protocols in terms of the packet delivery ratio, energy consumption, end-to-end delay, hop count and traversed distance.
Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS) to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics.
Underwater Sensor Network (UWSN) is a representative three-dimensional wireless sensor network. Due to the unique characteristics of underwater acoustic communication, providing energy-efficient and low-latency routing protocols for UWSNs is challenging. Major challenges are water currents, limited resources, and long acoustic propagation delay. Network topology of UWSNs is dynamic and complex as sensors have always been moving with currents. Some proposed protocols adopt geographic routing to address this problem, but three-dimensional localization is hard to obtain in underwater environment. As depth-based routing protocol (DBR) uses depth information only which is much more easier to obtain, it is more practical for UWSNs. However, depth information is not enough to restrict packets to be forwarded within a particular area. Packets may be forwarded through multiple paths which might cause energy waste and increase end-to-end delay. In this paper, we introduce underwater time of arrival (ToA) ranging technique to address the problem above. To maintain all the original advantages of DBR, we make the following contributions: energy-efficient depth-based routing protocol that reduces redundancy energy cost in some blind zones; low-latency depth-based routing protocol that is able to deliver a packet through an optimal path. The proposed protocols are validated through extensive simulations.
Performance enhancement of Underwater Wireless Sensor Networks (UWSNs)
in terms of throughput maximization, energy conservation and Bit Error Rate (BER)
minimization is a potential research area. However, limited available bandwidth, high
propagation delay, highly dynamic network topology, and high error probability leads to
performance degradation in these networks. In this regard, many cooperative communication
protocols have been developed that either investigate the physical layer or the Medium
Access Control (MAC) layer, however, the network layer is still unexplored. More
specifically, cooperative routing has not yet been jointly considered with sink mobility.
Therefore, this paper aims to enhance the network reliability and efficiency via dominating
set based cooperative routing and sink mobility. The proposed work is validated via
simulations which show relatively improved performance of our proposed work in terms
the selected performance metrics.
Efficient routing protocols for data packet delivery are crucial to underwater sensor networks (UWSNs). However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. Network coding is a promising technique for efficient data packet delivery thanks to the broadcast nature of acoustic channels and the relatively high computation capabilities of the sensor nodes. In this work, we present GPNC, a novel geographic routing protocol for UWSNs that incorporates partial network coding to encode data packets and uses sensor nodes' location information to greedily forward data packets to sink nodes. GPNC can effectively reduce network delays and retransmissions of redundant packets causing additional network energy consumption. Simulation results show that GPNC can significantly improve network throughput and packet delivery ratio, while reducing energy consumption and network latency when compared with other routing protocols.
The area of three-dimensional (3D) underwater wireless sensor networks (UWSNs) has attracted significant attention recently due to its applications in detecting and observing phenomena that cannot be adequately observed by means of two-dimensional UWSNs. However, designing routing protocols for 3D UWSNs is a challenging task due to stringent constraints imposed by acoustic communications and high energy consumption in acoustic modems. In this paper, we present an ultrasonic frog calling algorithm (UFCA) that aims to achieve energy-efficient routing under harsh underwater conditions of UWSNs. In UFCA, the process of selecting relay nodes to forward the data packet is similar to that of calling behavior of ultrasonic frog for mating. We define the gravity function to represent the attractiveness from one sensor node to another. In order to save energy, different sensor nodes adopt different transmission radius and the values can be tuned dynamically according to their residual energy. Moreover, the sensor nodes that own less energy or locate in worse places choose to enter sleep mode for the purpose of saving energy. Simulation results show the performance improvement in metrics of packet delivery ratio, energy consumption, throughput, and end-to-end delay as compared to existing state-of-the-art routing protocols.
This survey aims to provide a comprehensive overview of the current researchon underwater wireless sensor networks, focusing on the lower layers of the communicationstack, and envisions future trends and challenges. It analyzes the current state-of-the-art onthe physical, medium access control and routing layers. It summarizes their security threadsand surveys the currently proposed studies. Current envisioned niches for further advances inunderwater networks research range from efficient, low-power algorithms and.
This report summarizes our research directions in un- derwater sensor networks. We highlight potential applica- tions to off-shore oilelds for seismic monitoring, equipment monitoring, and underwater robotics. We identify research directions in short-range acoustic communications, MAC, time synchronization, and localization protocols for high- latency acoustic networks, long-duration network sleeping, and application-level data scheduling.
This paper explores applications and challenges for underwater sensor networks. We highlight potential applications to off-shore oilfields for seismic monitoring, equipment monitoring, and underwater robotics. We identify research directions in short-range acoustic communications, MAC, time synchronization, and localization protocols for high-latency acoustic networks, long-duration network sleeping, and application-level data scheduling. We describe our preliminary design on short-range acoustic communication hardware, and summarize results of high-latency time synchronization
Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined.
This article reviews progress in the area of underwater acoustic modeling techniques since 1980. Emphasis is placed on developments of general interest to the Navy sonar modeling community. They include: model refinements, bottom, interaction modeling, model operating systems and trainers, and specialized modeling applications in shallow water and Arctic environments.
In this paper, a reliable and energy-efficient routing (RER) protocol is proposed for underwater acoustic sensor networks to enhance the communication reliability. In the route discovery phase of the proposed method, a node with the minimum packet transmission delay to the current node is selected as its next hop node. After a route is set up, other nodes in the network will also send packets to the sink through the already established route, which greatly reduces the amount of time and energies spent to build a new route to send the packets. As a result, the real-time data will arrive at the destination earlier and the network lifetime will be extended. To handle the case of node failure on the established route, the proposed RER protocol includes a route recovery scheme that a new node will be selected instead of the failure node to continue the packet transmission. Through simulations, the proposed RER protocol is shown to outperform the traditional routing protocols in terms of average end-to-end delay, packet delivery ratio, and average energy consumption.
Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. Usage of cheap and small-sized wireless sensors allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Cooperative routing exploits the broadcast nature of wireless medium and transmits cooperatively using nearby sensor nodes as relays. It is a promising technique that is a mixture of a routing protocol and cooperative communication to improve the communication quality of single-antenna sensor nodes. In this paper, we propose a cooperative transmission scheme for UnderWater Sensor Networks (UWSNs) to enhance the network performance. Cooperative diversity has been introduced to combat fading. Cooperative UWSN (Co-UWSN) is proposed, which is a reliable, energy efficient and high throughput routing protocol for UWSN. Destination and potential relays are selected from a set of neighbor nodes that utilize distance and signal-to-noise ratio computation of the channel conditions as cost functions. This contributes to sufficient decrease in path-losses occurring in the links connecting sensors in UWSN and transferring of data with much reduced path-loss. March 11, 2015 DRAFT 2 Simulation results show that Co-UWSN protocol performs better in terms of end-to-end delay, energy consumption and network lifetime. Selected protocols for comparison are non-cooperative routing protocols Energy-Efficient Depth-Based Routing (EEDBR) and Improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD) and a cooperative routing protocol for UWSN, Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth).
Underwater sensor networks are used to detect the objects and the environmental pattern under the sea water. Some of its limiting features are high latency, low bandwidth, and high energy consumption. Some of the applications of under sensor networks are oceanographic data collection, pollution monitoring and disaster prevention. The issues which have to be resolved in the underwater sensor networks are high energy consumption and low delivery rate. Since the sensor devices are placed under the sea water it is difficult to change the batteries frequently. So maintaining the life time of the sensor nodes is the biggest challenge. There are several methods available to overcome the issue namely VBR, Depth Based Forwarding and Focus Beam Routing protocol. In the existing system, Q-Learning based tracking technique is used to find the next forwarding node based on the residual energy of the individual node. So that number of forwarding will be reduced and energy consumption of the sensor nodes will be reduced. In my proposed work the buffer size is also considered for finding the next forwarder, so that the dropping on the packets will be reduced.
Ocean-bottom seismic systems are emerging as superior information-acquisition methods in seismic monitoring of petroleum reservoirs beneath ocean beds. These systems use a large network of sensor nodes that are laid on the ocean floor to collectively gather and transmit seismic information. In particular, underwater wireless sensor networks are gaining prominence in continuous seismic monitoring of undersea oilfields. They are autonomous and use wireless acoustic transmission for transferring data. However, the deployment period of such networks extends well beyond the battery lifetimes of the nodes. Hence, to ensure continuous monitoring from all node locations, it is required to replace the energy-depleted nodes on the ocean floor. Replacing these nodes at remote undersea locations is very expensive, and hence, the total node replacement cost in large seismic node networks is extremely high. In this paper, we develop effective joint policies involving routing and node replacement decisions to minimize the replacement costs per unit time. Our routing approach is simple and suitable for this application, and it strives for energy efficiency at all times. We propose a few node-replacement policies and compare their performances when they are combined with our proposed routings.
Between 10 and 1000 kHz the absorption of sound in sea water can be considered as the sum of contributions from pure water and magnesium sulfate. Near 10 kHz there is also a small contribution from boric acid. In this paper, the contribution from MgSo4 is treated extensively using the authors’ measurements of absorption in the ocean to construct a quantitative equation for absorption as a function of temperature, salinity, and depth. The frequency region below 10 kHz, where the boric acid contribution predominates, will be reviewed later in Part II which includes an equation for the total absorption in the frequency range 400 Hz to 1 MHz.
A simple yet accurate nine‐term, eight‐variable equation for sound speed in the oceans, suitable for programmable pocket calculators, is presented. Ranges of validity encompass: temperature −2° to 30° C, salinity 30° to 40°/°°, and depth 0 to 8000 m. Good quality oceanographic data at 15 worldwide stations, including two in the Mediterranean, were utilized for the computations. The Del Grosso and Mader [J. Acoust. Soc. Am. 52, 961–974 (1972)] sound‐speedequation was employed as ’’truth’’ after comparisons among Wilson [J. Acoust. Soc. Am. 32, 641–644. 1357 (1960)] and others. A precise method of computing pressure is detailed. Practical differences in critical depth, excess depth, conjugate depth, deep sound‐speed gradients, and echo‐sounder corrections are shown to be small between computations based on Wilson or Del Grosso and Mader sound‐speedequations.
The absorption of sound in seawater is considered as the sum of three contributions: those from pure water, magnesium sulfate, and boric acid. Contributions from other reactions are small and are not included. The pure water and magnesium sulfate contributions obtained from analyses of extensive oceanic measurements, including many in the Arctic, were discussed in Part I. In Part II, an analysis is made of all reported measurements in the low-frequency region (0. 2-10 kHz) to evaluate contribution of boric acid. This is done by subtracting the pure water and magnesium sulfate contributions determined in Part I from the total absorption to give a more accurate estimate of the boric acid contribution than previously obtained. The three contributions are then combined to form an equation with both a theoretical basis and a satisfactory empirical fit that will be useful to researchers and engineers in the field of underwater sound. The equation applies to all oceanic conditions and frequencies from 200 Hz to 1 MHz.
Introduction. Acoustical Oceanography. Propagation I. Observations and Physical Models. Propagation II. Mathematical Models (Part One). Propagation II. Mathematical Models (Part Two). Noise I. Observations and Physical Models. Noise II. Mathematical Models. Reverberation I. Observations and Physical Models. Reverberation II. Observations and Physical Models. Sonar Performance Models. Model Evaluation. Simulation.
A simple empirical formula is presented for computing the reflection coefficient of a rough-sea surface using windspeed, angle of incidence, and frequency as parameters. The formula closely mimics the Beckmann-Spizzichino loss model, from which is was derived. Error contours are presented, which illustrate the closeness of fit between the two approaches.< >
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