Top PDF Intelligent and Secure Underwater Acoustic Communication Networks

Intelligent and Secure Underwater Acoustic Communication Networks

Intelligent and Secure Underwater Acoustic Communication Networks

In this chapter, we investigated signal alignment for secure underwater CoMP trans- missions. Exploiting the low sound speed in water and the spatial diversity of DAEs, transmission secrecy was achieved by overlapping signals at the eavesdropper while keeping them free of collision at the legitimate user. Practical designs of the above signal alignment concept were pursued. The eavesdropper’s interception capability was minimized through jointly optimizing relevant transmission parameters, including the transmit DAE set, and the transmission schedule and power level of each DAE, under a lower bound constraint of the received SNR at the legitimate user. Taking OFDM as the underlying modulation, both simulation and emulated experimental results showed that the proposed method has much higher data confidentiality than a benchmark method. From an information-theoretic perspective, we further derived the secrecy capacity and the secure d.o.f. of the signal alignment method, which
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A hybrid based MAC protocol for underwater 
		acoustic sensor networks

A hybrid based MAC protocol for underwater acoustic sensor networks

contribution for transmission and miniature average waiting time is the colossal asset for aquatic communication. The RR mechanism makes a sensor node to wait less than the propagation of the network for their transmission, which is advantageous to improving the channel efficiency. The second priority task (P2) is carried out at background process by First Come First Served (FCFS) scheduling. The adverse of FCFS, waiting time can be extensive if terse request behind the longest ones and not suited for time sharing applications. Indeed, most existing WSN applications use FCFS schedulers that process data in the sequence of their arrival times at the ready queue. Fitly, these are the added advantages to the low priority task schedule for acoustics communication. The emergency data goes to high priority queue (P1) are processed using RR and secondary data packets that arrive at low priority queue (P2) are processed by FCFS. Queue size is contingent on the application necessity and high priority event is barely crop ups.
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Challenges for Efficient Communication in Underwater Acoustic Sensor Network

Challenges for Efficient Communication in Underwater Acoustic Sensor Network

transmitter to communicate with the onshore sink (os-sink) or to a surface sink (s-sink).Sensors can be connected to uw-sinks via direct links or through multi-hop paths. In the former case, each sensor directly sends the gathered data to the selected uw-sink. This is the simplest way to network sensors, but it may not be the most energy efficient, since the sink may be far from the node and the power necessary to transmit may decay with powers greater than two of the distance. Furthermore, direct links are very likely to reduce the network throughput because of increased acoustic interference due to high transmission power. In case of multi-hop paths, as in terrestrial sensor networks [5], the data produced by a source sensor is relayed by intermediate sensors until it reaches the uw-sink. This results in energy savings and increased network capacity but increases the complexity of the routing functionality as well. In fact, every network device usually takes part in a collaborative process whose objective is to diffuse topology information such that efficient and loop free routing decisions can be made at each intermediate node. This process involves signaling and computation. Since, as discussed above, energy and capacity are precious resources in underwater environments, in UWASNs the objective is to deliver event features by exploiting multi-hop paths and minimizing the signaling overhead necessary to construct underwater paths at the same time.
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Safety Aspects of Enhanced Underwater Acoustic Sensor Networks

Safety Aspects of Enhanced Underwater Acoustic Sensor Networks

The following safety requirements should be considered in UASNs. Authentication is the proof that the data was sent by a legitimate sender that is essential in military and safety-critical applications of UASNs. During the authentication and key establishment between two or more, the nodes verify each other’s authenticity and can establish one or more secret keys over the open acoustic channel for exchange information securely. This procedure should be capable of recognizing the characteristics of the underwater channel. Such a procedure has proposed that requires only a threshold detector, lightweight computation, and communication costs [7]. It utilizes reciprocity, deep fades (strong destructive interference), randomness extractor, and robust secure fuzzy information re-conciliators. In this way, a suitable communication path is generated using the characteristics of the underwater channel and is safe against the opponents who know the number of deep fades but not their exact scenario.
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A discovery process for initializing ad hoc underwater acoustic networks

A discovery process for initializing ad hoc underwater acoustic networks

In [11], another centrally-controlled network-layer discovery and routing protocol was proposed for underwater acoustic networks. It relies on a master node to discover the topology of the nodes that comprise the network. Topology discovery is done by the transmission of a probe by the master node to its nearest neighbors as shown in Figure 3. A probe is a topology discovery message (TDM) broadcast. The transmit level of the probe is set to a predetermined signal strength to limit the range of the probe. Upon receipt of a TDM, each neighbor appends its node ID to it and relays it to the next “ring of nodes”, so that the probe propagates outward from the master. In addition, each neighbor selects a communication channel from a set of channels not already allocated. Therefore, the probe contains node IDs of nodes it traversed, as well as channel allocation for each of those nodes that relayed the TDM.
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Simulation of Multimodal Optical and Acoustic Communications in Underwater Networks

Simulation of Multimodal Optical and Acoustic Communications in Underwater Networks

However, the missions that underwater networks of au- tonomous static and mobile assets may be required to carry out are diverse in nature, and may embrace periodic low-rate telemetry as well as fast, event-based data transfers, up to intense two-way point-to-point communications intended to, e.g., control Remotely Operated Vehicles (ROVs) [4] without the use of umbilical cables. As a consequence, today there is a growing interest in communicating through mediums other than the acoustic channel, by means of optical [5]–[7] or radio- frequency [8], [9] hardware. In this context, it is of great interest to simulate the behavior of underwater networks where nodes may carry out multi-modal communications via differ- ent kinds of communication technologies. This may involve not only optical, acoustic and RF communications, but also different implementations of either technology, e.g., multiple
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Optimization of Multiple Gateway Deployment for Underwater Acoustic Sensor Networks

Optimization of Multiple Gateway Deployment for Underwater Acoustic Sensor Networks

Abstract. This research aims to develop novel technologies to efficiently integrate wireless communication networks and Underwater Acoustic Sensor Networks (UASNs). Surface gateway deployment is one of the key techniques for connecting two networks with different channels. In this work, we propose an optimization method based on the genetic algorithm for surface gateway deployment, design a novel transmission mechanism—simultaneous transmission, and realize two efficient routing algorithms that achieve minimal delay and payload balance among sensor nodes. We further develop an analytic model to study the delay, energy consumption and packet loss ratio of the network. Our simulation results verify the effectiveness of the model, and demonstrate that the technique of multiple gateway deployment and the mechanism of simultaneous transmission can effectively reduce network delay, energy consumption and packet loss rate.
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Need and Role of Underwater Acoustic Sensor Networks

Need and Role of Underwater Acoustic Sensor Networks

The controller performs tasks, processes data and controls the functionality of other components in the sensor node. While the most common controller is a microcontroller, other alternatives that can be used as a controller are: a general purpose desktop microprocessor, digital signal processors, FPGAs and ASICs. A microcontroller is often used in many embedded systems such as sensor nodes because of its low cost, flexibility to connect to other devices, ease of programming, and low power consumption. A general purpose microprocessor generally has a higher power consumption than a microcontroller, therefore it is often not considered a suitable choice for a sensor node. Digital Signal Processors may be chosen for broadband wireless communication applications, but in Wireless Sensor Networks the wireless communication is often modest: i.e., simpler, easier to process modulation and the signal processing tasks of actual sensing of data is less complicated. Therefore the advantages of DSPs are not usually of much importance to wireless sensor nodes. FPGAs can be reprogrammed and reconfigured according to requirements, but this takes more time and energy than desired.
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A reverse localization scheme for underwater acoustic sensor networks

A reverse localization scheme for underwater acoustic sensor networks

Since acoustic communication is employed as convenient choice for underwater links, the localization schemes suffer from many constraints of acoustic channel. The limited bandwidth and low data rates are two closely related features which put some requirements on designing the localization protocols such as avoiding extensive messaging and huge communication overhead. A practical solution for achieving more data rate is using short-range communications which is required more sensor nodes to attain a certain level of connectivity and coverage. So, the existing small-scale localization schemes are not proper for large-scale UWSNs (Heidemann et al., 2006). In addition, the mobility feature of water currents may create the lower accuracy. Since almost often existing underwater localization techniques achieve low accuracy, highly precise localization is desired for a localization protocol. Beside the stringent resource limitation of underwater wireless sensor networks, high accurate localization scheme is specially challenging. Moreover, the speed of sound is slow (approximately 1500 m/s) yielding large propagation delay. Last, collecting beacons information required for localization is a time consuming process which is most likely the movement of underwater sensor nodes to new places during the collection time.
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Multi-node processing for asymmetrical communications in underwater acoustic networks

Multi-node processing for asymmetrical communications in underwater acoustic networks

It is first shown that, due to the spatial diversity that result by the use of several nodes, the MISIMA multi-node combining, that results from the weighed summation of the outputs of each node, reduces the ISI. Those predictions have been illustrated on high rate underwater communication real data for which multi-node processing resulted in sensible gains in both MSE and BER, namely when environmental stationarity can be assumed. Also, an interesting ’at least equal to the best’ behavior of the multi-node combining has been observed. Depict MISIMA was developed for a network of nodes running a pTR equalizer it can be readily applied to any network where nodes are running other equalizers as a DFE, FSE, etc. In such context the main advantage of MISIMA relays in its ability to preserves the efficiency of the network as one node encounters severe difficulties as it happens e.g. in a pTR equalizer when the IRs estimate by the probe loss validity or in a DFE equalizer when due to numerical errors the equalizer starts to diverge. However, in a non stationary environment, multi-node weighted combining stems from the ability to continuously adapt the weighted coefficients to channel variations. Considering that a ”physical-model´´ can be extracted from the closed form of the coefficients (11) a Kalman filter approach is suggested for future work.
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CFDAMA-SRR: A MAC Protocol for Underwater Acoustic Sensor Networks

CFDAMA-SRR: A MAC Protocol for Underwater Acoustic Sensor Networks

Poisson data traffic is a traditional traffic model that has been for decades the first choice for evaluating communication protocol performance. The main feature of this model is that the inter-arrival times between packets can be modelled as independent exponentially distributed random events at each source. In spite of some arguments made in a number of studies claiming that the Poisson model is not suitable for many applications, e.g. [29], [30], it is still widely used for simulation-based studies as a tool allowing comparison with relatively tractable theoretical analysis. This applies also to UASNs, e.g. [10], [11], [12]. Many UASN applications can be characterised by periodic data traffic models, particularly for applications associated with environmental monitoring tasks [7]. In such tasks, the network is configured in a way that every node transmits a packet periodically containing a sensor reading to a base station or a gateway node, e.g. [31]. In [13], it is found that statistics of data traffic generated by event-based wireless sensor applications are found to obey the Pareto ON/OFF distribution very well. Two distinct traffic models (Poisson OFF and Pareto ON/OFF) have been developed in RM for the evaluation of CFDAMA-SRR per- formance in this paper.
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A New Localization Algorithm for Underwater Acoustic Sensor Networks

A New Localization Algorithm for Underwater Acoustic Sensor Networks

In this article, an intellectual range-free localization method is introduced for sound sensor networks under water. In the improved method, the sensor nodes do not need extra hardware in order to achieve the distance or data such as AOA, TOA, RSS. The sensor nodes can achieve their position through the resulted average step and the connected anchor nodes. A neural network plays a main role in the improved method. In this method localization in considered as an issue and by neural network the accurate position of the sensor node is achieved. The method is stimulated in Matlab environment. During the simulation it was clarified that the improved method has a much better function than the available method ASP and decreases the localization error in a considerable manner and raises the accuracy of localization. Simulation results in two distributed and centralized techniques are shown that improved algorithm include high accuracy, fast convergence therefore, since nodes may drift due to water currents, the localization procedure should be fast so that it reports the actual location when data is sensed, wide coverage, low communication costs and good scalability features because all the network nodes are localized.
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Hybrid Optimization Approach for Node Localization in Underwater Acoustic Networks

Hybrid Optimization Approach for Node Localization in Underwater Acoustic Networks

Abstract: The underwater acoustic network is the type of network which is deployed under the deep sea to sense ocean conditions like pressure etc. Due to the presence of limited bandwidth, higher multi-path, higher fading, huge time-variations as well as Doppler shifts, it is difficult to perform high-speed communication within underwater acoustic channels. Within the sea waters, the propagation of electromagnetic waves is very poor. Originally for the terrestrial wired and wireless channels, the communication techniques were designed. Thus, in order to make them appropriate for underwater channels, there is a need to modify these techniques. In the previous research, the fry fly algorithm is applied for the node localization. In the fry fly algorithm the optimal value is calculated for the node localization. In this research work, distance based technique is applied for the node localization. The proposed and existing algorithms are implemented in MATLAB. The simulation results show that proposed algorithm performs well in terms of certain parameters.
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Energy Efficient 4-Dimensional Heterogeneous Communication Architecture For Underwater Acoustic Wireless Sensor Networks

Energy Efficient 4-Dimensional Heterogeneous Communication Architecture For Underwater Acoustic Wireless Sensor Networks

Underwater sensor nodes are usually equipped with acoustic modems to wirelessly communicate with each other. This is because high-frequency radio waves are intensely absorbed in water medium and optical signals undergo heavy scattering and are restricted to low-range-LoS applications [6]. The sound speed in UWSNs is around 1500 m/s near the shallow ocean which is five times slower than the speed of light. Also, it depends on water temperature, depth and salinity [7]. The UWSN acoustic communication has a limited bandwidth and it depends on both signal range and water column depth. The acoustic bandwidth varies from 500 Hz to 10 kHz for wide- range communications, 10 to 100 kHz for mid-range and 100 to 500 kHz for short range communications [8]. The communication range also influences the data rate in UWSNs. The data rate varies from 10 kbps to 100 kbps respectively for long-range and short-range communications. Underwater acoustic communications are getting influenced by signal interference, noise, multipath, Doppler Effect and high propagation latency. All these factors establish the temporal and spatial variability of the acoustic channel. The path loss in UWSNs is mainly due to signal spreading and attenuation. During Spreading, the acoustic signal will spread over a wider surface area, and so the wave energy in each unit surface area becomes smaller. Attenuation causes the signal energy to transform into other forms like heat energy and get absorbed by the medium. Ocean noise is another factor that has a severe impact on the communication in underwater acoustic channel. The ambient noises in UWSNs can be categorized into turbulence noise, shipping noise, wind noise and thermal noise. In UWSNs, the multipath effect is predominant than that in WSNs. Multiple arrival of the same signal at a destination contributes to the changes in the channel’s frequency response termed as Doppler effect. The effect of Doppler shift is proportional to the relative speed between source and destination of a particular signal [9]. Furthermore, the sharing of an underwater acoustic channel can passively intercept the attackers to disrupt the transmission.
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Information-Centric Design and Implementation for Underwater Acoustic Networks

Information-Centric Design and Implementation for Underwater Acoustic Networks

a lookup table for where an interest packet should be forwarded based on its named content. This table is populated through name related routing protocols. Once corresponding data is found that matches the interest packet, this newly created data packet will return to the consumer using the reverse path taken to find the data on the network. Since NDN packets do not carry host information, interest and data routes are handled by keeping state information at each router hop. The PIT stores all unsatisfied interests and their associated application information. By maintaining this table we are able to collapse duplicate content interests and only forward the first interest packet towards a data source. Once an interest entry within the PIT is satisfied (data packet arrives with a matching name) NDN will remove the associated entry and multicast the data packet to all previous requesters. There are many areas of ongoing research for routing strategies in NDN. Many different routing applications have been proposed [51, 52] as well as different forms to populate lookup tables [53]. Since NDN is targeted at data awareness and in-network caching, the architecture also uses the CS to cache recently received information from data packets. Many different forms of data management have been proposed to improve and specialize how routers can cache content for future requests [54, 55]. Furthermore, this storing component is meant to reduce redundant data sending that can be seen in certain scenarios of IP networking. For example, if a mobile device is requesting information while changing its Internet access point during communication, this can lead to additional networking issues such as requesting duplicate copies of information.
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A Review on Underwater Acoustic Sensor Networks: Perspective of Internet of Things

A Review on Underwater Acoustic Sensor Networks: Perspective of Internet of Things

 Abstract: In the progressively used terrestrial and air-based Wireless Sensor Networks, Radiofrequency aids in the transmission of data and information. Nonetheless, from sensing followed by transmission in an underwater environment demands a completely contrasting technique to perform underwater communication. The oceans being the least unexploited, covering over 70% of the Earth’s surface is a source of vast resources. Still, the underwater world is unaffected less by the recent progressions in this field of WSN and their impact on the research and development. Growing practical difficulties burden the shifting of the many terrestrial and aerial WSNs state-of-the-art to the aquatic world advancements. Acoustics with sensors forms the key for many underwater deployments to tackle with the stringent environmental conditions of the oceans. However, different underwater environment demands different communication approaches for sensing and transmission. This paper emphases in broadening the scope of sensing in acoustic sensing incorporated multimodal sensors, paving the way to the progress of robust Internet of Things (IoT) applications and the key idea of this detailed review is to see UW-ASN from the perspective of IoT. A detailed overview and niche future research openings in UW-ASN and its deployments is presented aiming for novel architectures and protocols in the future, covering the bigger picture of Underwater Acoustic Sensor Network through better research and industrial advancements.
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Cooperative Authentication in Underwater Acoustic Sensor Networks

Cooperative Authentication in Underwater Acoustic Sensor Networks

The focus of this paper is the authentication of a packet received by a sink node with the support of trusted nodes. Authentication mechanisms allow a node to prove that it is a legitimate member of a network, so that controller nodes or sinks can trust the data sent by the node. This step is of great importance, especially in underwater monitoring tasks and tactical scenarios. The sink’s objective is to determine whether the packet is coming from either the legitimate node or the attacker. Conversely, the attacker’s objective is to let its own packet be recognized as authentic by the sink. The authentication process is based on the acoustic communication channel features, rather than on cryptographic techniques. Trusted nodes cooperate to the authentication process without knowing its outcome: each trusted node independently sends authentication data to the sink, which does not broadcast the authenticity decision, in order to increase the system’s spectral efficiency and avoid additional security risks. We remark that the trusted nodes do not need to exchange data in order to complete the authentication process.
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An acoustic communication based AQUA-GLOMO simulator for underwater networks

An acoustic communication based AQUA-GLOMO simulator for underwater networks

Global Mobile Information System Simulator (GloMoSim) [1,12] provides the simula- tion environment for large wireless/wired communication networks. It is good for simulating mobile networks. It works with C-based parallel discrete-event simulation language PARSEC (Parallel Simulation Environment for Complex Systems). It can work efficiently in a parallel environment; this property distinguishes it from most other sensor network simulators. It is an open-source and an extensible tool as all other protocols is implemented as modules in its library. Its main limitation is that it only presently supports protocols for a purely radio wireless network; not acoustic.
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Enhanced hop-by-hop routing algorithms for underwater acoustic sensor networks

Enhanced hop-by-hop routing algorithms for underwater acoustic sensor networks

This research is to address the problem of high energy consumption in the information distribution phase due to uncontrolled flooding or unnecessary traffic flow in building up routes. In underwater networks multiple channel impairments together with node mobility result in unstable links. Selection of such links for data communication based on improper quality estimation results in multiple re- transmission attempts that lead to higher end-to-end delay. Therefore, sending data over communication links, requires proper quality estimation of the link between the two neighboring nodes. This research therefore, mainly focuses on next hop neighbor selection based on composite metric (link quality and distance) in data forwarding phase to reduce end-to-end delay. This research also address the issue of inefficient energy balancing schemes that allow repeated use of same nodes for data communication. Such schemes leads to the death of nodes due to frequent usage and reduce lifetime of the network. Therefore, the problem on how to efficiently provide energy balancing has been taken into consideration to enhance the lifetime of a underwater network.
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Design and Simulation of a Secure and Robust Underwater Acoustic Communication System in the Persian Gulf

Design and Simulation of a Secure and Robust Underwater Acoustic Communication System in the Persian Gulf

Bandwidth-efficient digital underwater acoustic com- munications can be achieved by employing equalization of quadrature amplitude modulation (QAM) and phase shift keying (PSK) signals. The receiver structure that has been found useful in many applications is a mul- tichannel decision-feedback equalizer. Due to the nature of the propagation channel, the required signal process- ing is often prohibitively complex. Reduction in compu- tational complexity can be achieved by using efficient adaptive algorithms, such as the low-complexity LMS algorithms with improved tracking properties [3,4], and by reducing the number of adaptively adjusted receiver parameters [4-6].
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