18-02-2011, 11:38 AM
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UNDER WATER WIRELESS SENSOR NETWORK TECHNOLOGY
ABSTRACT
Underwater wireless sensor networks consist of a certain number of sensors and vehicles that interact to collect data and perform collaborative tasks. Designing energy-efficient routing protocols for this type of networks is essential and challenging because sensor nodes are powered by batteries, which are difficult to replace or recharge, and because underwater communications are severely affected by network dynamics, large propagation delays and high error probability of acoustic channels. The goal of this paper is to analyze the total energy consumption in underwater acoustic sensor networks considering two different scenarios: shallow water and deep water. We propose different basic functioning principles for routing protocols in underwater wireless sensor networks (relaying, direct transmission and clustering) and analyze the total energy consumption for each case, establishing a comparison between them.
1. INTRODUCTION
In last several years, underwater sensor network (UWSN) has found an increasing use in a wide range of applications, such as coastal surveillance systems, environmental research, autonomous underwater vehicle (AUV) operation, to name a few. By deploying a distributed and scalable sensor network in a 3-dimensional underwater space, each underwater sensor can monitor and detect environmental parameters and events locally.
Under water sensor network is wireless communication system that consist of battery- powered vessels , sensors node and variety of devices. Underwater sensor networks will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, tactical surveillance, and mine reconnaissance. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative missions. The enabling technology for underwater applications is acoustic wireless networking. Underwater Wireless Communication Networks (UWCNs) consist of sensors and autonomous vehicles that are deployed to perform collaborative monitoring tasks over a given volume of water. Although there exist recently developed network protocols for wireless sensor networks, the unique characteristics of the underwater acoustic communication channel, such as limited bandwidth capacity and high and variable propagation delays, require further very efficient and reliable new communication protocols, in particular the recent work is mainly focused on underwater sensor networks with only data traffic including our own research the last five years. To our knowledge, there is no solution for multimedia (audio, video, data, still image) integrated traffic applications in underwater sensor networks.
In this project, a cross-layer communication solution is introduced, for both data (delay-tolerant) and audio/ video (delay-sensitive) applications. The proposed solution addresses the unique challenges posed by the underwater channel. The ultimate objective of this research is to develop a reliable cross-layer solution tailored for multimedia traffic (i.e., video and audio streams, still images, and scalar sensor data) that integrates different communication functionalities to achieve high performance channel access, routing, event transport reliability, and data flow control. Furthermore, we investigate the performance of existing long delay TCP solutions, and develop a novel reliable underwater transport protocol based on our research results. Finally, to verify the validity and evaluate the performance of the proposed solutions, a new simulation test bed is developed, and a set of experiments are conducted.
Hence, compared with remote sensing, UWSNs provide a better sensing and surveillance technology to acquire better data to understand the spatial and temporal complexities of underwater environments. Clearly, efficient underwater communication among units or nodes in a UWSN is one of the most Acoustic communication is the most versatile and widely used technique in underwater environments due to the low attenuation (signal reduction) of sound in water. This is especially true in thermally stable, deep water settings. On the other hand, the use of acoustic waves in shallow water can be adversely affected by temperature gradients, surface ambient noise, and multipath propagation due to reflection and refraction. The much slower speed of acoustic propagation in water, about 1500 m/s (meters per second), compared with that of electromagnetic and optical waves, is another limiting factor for efficient communication and networking. Nevertheless, the currently favorable technology for underwater communication is upon acoustics and critical issues in the whole network system design.
2. OBJECTIVES
It provides better sensing and surveillance technology to acquire better data to understand spatial and temporal complexity of underwater environment.
UWWSNs consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants.
The development of underwater wireless sensor networks was motivated by navy applications such as battleship (ship war)& submarines surveillance (watching)and are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, aquatic applications, and sea traffic control.
locating and docking with modules
placing, retrieving, and organizing modules
cooperative navigation with docked AUVs
Data communication mulling over a deployed sensor node.
Under Water Acoustic Sensor Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.
Fields like include environmental monitoring and data collection, disaster early warning, tactical surveillance, military target detection, unmanned off-shore exploration, and underwater construction can benefit from UWSN.
3.DESCRIPTION OF THE TECHNOLOGY
UWWSNs provide the means for real-time, accurate and energy-efficient monitoring of seas/oceans. Such networks include a large number of underwater sensors with acoustic modems on-board and limited number of autonomous surface sinks/stations which can collect data reported from those sensors and communicate with on-shore stations through radio communication. While sensors can be deployed both on the water surface and underwater to collect ambient data, autonomous sinks/stations typically stay on the surface to receive data from the sensors.
Typical applications of UWWSNs include but not limited to underwater tactical surveillance to detect enemy submarines, small delivery vehicles, mines and divers, detection of pollution in coastal areas, performing in-situ oceanic studies of bird/fish migration, detection of terrorist threats to ships in ports, detection of tsunamis and sending warnings, etc.
As opposed to terrestrial WSNs, UWWSNs are deployed in 3- D environments which introduce new challenges in terms of connectivity, coverage and mobility. While maximizing the total network coverage is necessary for being able to sense information at every spot of the region, maintaining connectivity is crucial for continuous data gathering from the sensors. Achieving these goals is closely related to proper self-deployment of nodes in 3-D environments. While a lot of research has been done for node deployment and self-organization in terrestrial
WSNs, there is still much to do for self-deployment of the nodes in UWWSNs.
This stems from the observation that current node deployment in UWWSNs is mostly manual and centralized. Typically; sensors are placed manually with tethers (pahunch ) from the surface stations or anchors from the ground. However, such deployment scenarios may not be feasible in some applications where the deployment region is not accessible due to enemy threats or existence of mines in underwater tactical surveillance applications. In addition, such manual deployment requires additional Time and cost particularly when the volume of the monitored region is larger. In such applications, a large number of underwater sensors should be placed manually to the pre-determined locations which usually require a lot of human intervention. As a consequence, in some applications, it might be inevitable to drop the sensors from a flying vehicle or from a fixed ship to the area of interest. In such a case, the sensors need to self organize from such random deployment in order to improve the overall coverage and provide connectivity to the surface base station.
Application level features with minimized human intervention in a distributed manner. While such self-organization of the nodes is possible in terrestrial WSNs through controlled mobility, such idea has not been employed in UWWSNs.
In this a fully distributed and localized technique for self re-configuration of UWWSNs that will improve the initial coverage is proposed. The idea is to adjust the depths of the sensor nodes after their initial deployment at the bottom (or surface) of the ocean assuming that sensors can only be moved in vertical direction. Based on the redundancy that exists in 2- D region, the nodes within a neighborhood computes a certain depth which will minimize the coverage overlap among themselves.
The redundancy can be determined by one of the nodes which will be referred as the leader within a certain neighborhood by utilizing a vertex coloring problem formulation. The process of depth adjustment continues until there is no room for improving the coverage for a sensor. We also determine the necessary radio/sensing range ratio in order to provide connectivity with the surface base-station. We show how our distributed approach for node deployment behaves under different scenarios and compare it with random, semi-distributed and optimal solutions. Assuming a fixed number of sensors, we illustrate the optimal solutions in terms of coverage based on the volume.
The monitored region. The random solution was also implemented in a distributed manner. The simulation results show that with certain configurations, our distributed scheme can perform very close to optimal solution in terms of connected coverage and outperforms the random and semi-distributed approaches.
UNDER WATER WIRELESS SENSOR NETWORK TECHNOLOGY
ABSTRACT
Underwater wireless sensor networks consist of a certain number of sensors and vehicles that interact to collect data and perform collaborative tasks. Designing energy-efficient routing protocols for this type of networks is essential and challenging because sensor nodes are powered by batteries, which are difficult to replace or recharge, and because underwater communications are severely affected by network dynamics, large propagation delays and high error probability of acoustic channels. The goal of this paper is to analyze the total energy consumption in underwater acoustic sensor networks considering two different scenarios: shallow water and deep water. We propose different basic functioning principles for routing protocols in underwater wireless sensor networks (relaying, direct transmission and clustering) and analyze the total energy consumption for each case, establishing a comparison between them.
1. INTRODUCTION
In last several years, underwater sensor network (UWSN) has found an increasing use in a wide range of applications, such as coastal surveillance systems, environmental research, autonomous underwater vehicle (AUV) operation, to name a few. By deploying a distributed and scalable sensor network in a 3-dimensional underwater space, each underwater sensor can monitor and detect environmental parameters and events locally.
Under water sensor network is wireless communication system that consist of battery- powered vessels , sensors node and variety of devices. Underwater sensor networks will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, tactical surveillance, and mine reconnaissance. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative missions. The enabling technology for underwater applications is acoustic wireless networking. Underwater Wireless Communication Networks (UWCNs) consist of sensors and autonomous vehicles that are deployed to perform collaborative monitoring tasks over a given volume of water. Although there exist recently developed network protocols for wireless sensor networks, the unique characteristics of the underwater acoustic communication channel, such as limited bandwidth capacity and high and variable propagation delays, require further very efficient and reliable new communication protocols, in particular the recent work is mainly focused on underwater sensor networks with only data traffic including our own research the last five years. To our knowledge, there is no solution for multimedia (audio, video, data, still image) integrated traffic applications in underwater sensor networks.
In this project, a cross-layer communication solution is introduced, for both data (delay-tolerant) and audio/ video (delay-sensitive) applications. The proposed solution addresses the unique challenges posed by the underwater channel. The ultimate objective of this research is to develop a reliable cross-layer solution tailored for multimedia traffic (i.e., video and audio streams, still images, and scalar sensor data) that integrates different communication functionalities to achieve high performance channel access, routing, event transport reliability, and data flow control. Furthermore, we investigate the performance of existing long delay TCP solutions, and develop a novel reliable underwater transport protocol based on our research results. Finally, to verify the validity and evaluate the performance of the proposed solutions, a new simulation test bed is developed, and a set of experiments are conducted.
Hence, compared with remote sensing, UWSNs provide a better sensing and surveillance technology to acquire better data to understand the spatial and temporal complexities of underwater environments. Clearly, efficient underwater communication among units or nodes in a UWSN is one of the most Acoustic communication is the most versatile and widely used technique in underwater environments due to the low attenuation (signal reduction) of sound in water. This is especially true in thermally stable, deep water settings. On the other hand, the use of acoustic waves in shallow water can be adversely affected by temperature gradients, surface ambient noise, and multipath propagation due to reflection and refraction. The much slower speed of acoustic propagation in water, about 1500 m/s (meters per second), compared with that of electromagnetic and optical waves, is another limiting factor for efficient communication and networking. Nevertheless, the currently favorable technology for underwater communication is upon acoustics and critical issues in the whole network system design.
2. OBJECTIVES
It provides better sensing and surveillance technology to acquire better data to understand spatial and temporal complexity of underwater environment.
UWWSNs consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants.
The development of underwater wireless sensor networks was motivated by navy applications such as battleship (ship war)& submarines surveillance (watching)and are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, aquatic applications, and sea traffic control.
locating and docking with modules
placing, retrieving, and organizing modules
cooperative navigation with docked AUVs
Data communication mulling over a deployed sensor node.
Under Water Acoustic Sensor Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.
Fields like include environmental monitoring and data collection, disaster early warning, tactical surveillance, military target detection, unmanned off-shore exploration, and underwater construction can benefit from UWSN.
3.DESCRIPTION OF THE TECHNOLOGY
UWWSNs provide the means for real-time, accurate and energy-efficient monitoring of seas/oceans. Such networks include a large number of underwater sensors with acoustic modems on-board and limited number of autonomous surface sinks/stations which can collect data reported from those sensors and communicate with on-shore stations through radio communication. While sensors can be deployed both on the water surface and underwater to collect ambient data, autonomous sinks/stations typically stay on the surface to receive data from the sensors.
Typical applications of UWWSNs include but not limited to underwater tactical surveillance to detect enemy submarines, small delivery vehicles, mines and divers, detection of pollution in coastal areas, performing in-situ oceanic studies of bird/fish migration, detection of terrorist threats to ships in ports, detection of tsunamis and sending warnings, etc.
As opposed to terrestrial WSNs, UWWSNs are deployed in 3- D environments which introduce new challenges in terms of connectivity, coverage and mobility. While maximizing the total network coverage is necessary for being able to sense information at every spot of the region, maintaining connectivity is crucial for continuous data gathering from the sensors. Achieving these goals is closely related to proper self-deployment of nodes in 3-D environments. While a lot of research has been done for node deployment and self-organization in terrestrial
WSNs, there is still much to do for self-deployment of the nodes in UWWSNs.
This stems from the observation that current node deployment in UWWSNs is mostly manual and centralized. Typically; sensors are placed manually with tethers (pahunch ) from the surface stations or anchors from the ground. However, such deployment scenarios may not be feasible in some applications where the deployment region is not accessible due to enemy threats or existence of mines in underwater tactical surveillance applications. In addition, such manual deployment requires additional Time and cost particularly when the volume of the monitored region is larger. In such applications, a large number of underwater sensors should be placed manually to the pre-determined locations which usually require a lot of human intervention. As a consequence, in some applications, it might be inevitable to drop the sensors from a flying vehicle or from a fixed ship to the area of interest. In such a case, the sensors need to self organize from such random deployment in order to improve the overall coverage and provide connectivity to the surface base station.
Application level features with minimized human intervention in a distributed manner. While such self-organization of the nodes is possible in terrestrial WSNs through controlled mobility, such idea has not been employed in UWWSNs.
In this a fully distributed and localized technique for self re-configuration of UWWSNs that will improve the initial coverage is proposed. The idea is to adjust the depths of the sensor nodes after their initial deployment at the bottom (or surface) of the ocean assuming that sensors can only be moved in vertical direction. Based on the redundancy that exists in 2- D region, the nodes within a neighborhood computes a certain depth which will minimize the coverage overlap among themselves.
The redundancy can be determined by one of the nodes which will be referred as the leader within a certain neighborhood by utilizing a vertex coloring problem formulation. The process of depth adjustment continues until there is no room for improving the coverage for a sensor. We also determine the necessary radio/sensing range ratio in order to provide connectivity with the surface base-station. We show how our distributed approach for node deployment behaves under different scenarios and compare it with random, semi-distributed and optimal solutions. Assuming a fixed number of sensors, we illustrate the optimal solutions in terms of coverage based on the volume.
The monitored region. The random solution was also implemented in a distributed manner. The simulation results show that with certain configurations, our distributed scheme can perform very close to optimal solution in terms of connected coverage and outperforms the random and semi-distributed approaches.
