25-05-2012, 05:20 PM
MOBILE sensor NETWORK
[attachment=23065]
INTRODUCTION
Location awareness is important for wireless sensor networks since many applications such as environment monitoring, vehicle tracking and mapping depend on knowing the locations of sensor nodes. In addition, location-based routing protocols can save significant energy by eliminating the need for route discovery and improve caching behavior for applications where requests may be location dependent. Security can also been enhanced by location awareness (for example, preventing wormhole attacks. However, putting GPS receivers in every node or manually configuring locations is not cost effective for most sensor network applications. Recently some localization techniques have been proposed to allow nodes to estimate their locations using information transmitted by a set of seed nodes that know their own locations (for example, because they have GPS receivers).
They all suffer from one or both of these problems:
1. Dependence on special hardware.
Techniques that depend on measuring ranging information from signal Strength, time of arrival, time difference of arrival or angle of arrival requires hardware that is typically not available on sensor nodes. Adding the required hardware increases the cost and size of the nodes.
2. Requirement for particular network topologies.
Most techniques require seed nodes to be numerous and evenly distributed so they can cover the whole network. But prior deployment of seeds is not possible in many sensor network applications (for example, sensor nodes dropped from plane over a hostile territory). Hop count based techniques avoid the need for a large number of seeds, but instead require dense and uniform node distribution.
We are interested in performing localization in a more general network environment where no special hardware for ranging is available, the prior deployment of seed nodes is unknown, the seed density is low, the node distribution is irregular, and where nodes and seeds can move uncontrollably. Although mobility makes other localization techniques increasingly less accurate, our technique takes Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Advantage of mobility to improve accuracy and reduce the number of seeds required. We consider a network composed of nodes with unknown locations and seeds that know their locations.
We are interested in three kinds of scenarios:
(a). Nodes are static, seeds are moving.
For example, a military application where nodes are dropped from a plane onto land, and transmitters attached to soldiers or animals in the area are used as moving seeds. Each Node’s location estimate should become more accurate as time passes and it receives information from more seeds.
(b). Nodes are moving, seeds are static.
One example would be nodes floating in currents along a river and Seeds at fixed locations on the river banks. A more concrete, but less worldly, example is NASA’s Mars Tumbleweed project. It proposes a low cost way to explore large areas on Mars by having rovers with sensors that are blown over the surface by the wind, with minimal or no control over their movement. Of course, GPS does not work on Mars, but it may be possible to establish fixed landmark seeds or positioning from orbiters. For these scenarios, each node’s location estimate will fluctuate around its current actual location: as time passes, old location information becomes inaccurate since the node has moved, but as new seed information is received the location estimate is revised.
©. Both nodes and seeds are moving.
This is the most general situation. It applies to any application where the nodes and seeds are both deployed in an ad hoc way, and move either because of the environment they are in (wind, currents, etc.) or because they have actuators for motion. Next we provide background on previous localization work.
BACKGROUND
Extensive research has been done on localization for wireless networks. Here, we provide only a brief survey focusing only on localization techniques suitable for ad hoc sensor networks. The approaches taken to achieve localization in sensor networks differ in their assumptions about the network deployment and the hardware’s capabilities. Centralized localization techniques depend on sensor nodes transmitting data to a central location, where computation is performed to determine the location of each node. Centralized technique using convex optimization to estimate positions based only on connectivity constraints given some nodes with known Positions improve on these results by using a multidimensional scaling approach, but still require centralized computation. Requiring central computation would be infeasible for mobile applications because of the high communication costs and inherent delay; hence we focus on distributed localization techniques.
Distributed localization methods do not require centralized computation, and rely on each node determining its location with only limited communication with nearby nodes. These methods can be classified as range-based and range-free.
1. Range Based Techniques:
Range-based techniques use distance estimates or angle estimates in location calculations. Range-based approaches have exploited time of arrival, received signal strength time difference of arrival of two different signals (TDOA), and angle of arrival (AOA). Though they can reach fine resolution, but the required hardware is expensive (ultrasound device for TDOA, antenna arrays for AOA) or the results depend on other unrealistic assumptions about signal propagation (for example, the actual received signal strengths of radio signals can vary when the surrounding environment changes).Because of the hardware limitations of sensor devices, range-free localization algorithms are a cost effective alternative to more expensive range-based approaches.
2. Range –Free Techniques:
Range-free solution depends only on the contents of received messages. There are two main types of range-free localization algorithms that have been proposed for sensor networks. Local techniques that rely on a high density of seeds so that every node can hear several seeds, and hop counting techniques that rely on flooding a network.
(a) Local Techniques.
i) In the Centroid method, each node estimates its location by calculating the center of the locations of all seeds it hears. If seeds are well positioned, location error can be reduced, but this is not possible in ad hoc deployments.
ii) The APIT method isolates the environment into triangular regions between beaconing nodes, and uses a grid algorithm to calculate the maximum area in which a node will likely reside. Since APIT typically assumes a larger radio range for seed nodes and hence has high seed density, it is not reasonable to compare it to our technique directly.
[attachment=23065]
INTRODUCTION
Location awareness is important for wireless sensor networks since many applications such as environment monitoring, vehicle tracking and mapping depend on knowing the locations of sensor nodes. In addition, location-based routing protocols can save significant energy by eliminating the need for route discovery and improve caching behavior for applications where requests may be location dependent. Security can also been enhanced by location awareness (for example, preventing wormhole attacks. However, putting GPS receivers in every node or manually configuring locations is not cost effective for most sensor network applications. Recently some localization techniques have been proposed to allow nodes to estimate their locations using information transmitted by a set of seed nodes that know their own locations (for example, because they have GPS receivers).
They all suffer from one or both of these problems:
1. Dependence on special hardware.
Techniques that depend on measuring ranging information from signal Strength, time of arrival, time difference of arrival or angle of arrival requires hardware that is typically not available on sensor nodes. Adding the required hardware increases the cost and size of the nodes.
2. Requirement for particular network topologies.
Most techniques require seed nodes to be numerous and evenly distributed so they can cover the whole network. But prior deployment of seeds is not possible in many sensor network applications (for example, sensor nodes dropped from plane over a hostile territory). Hop count based techniques avoid the need for a large number of seeds, but instead require dense and uniform node distribution.
We are interested in performing localization in a more general network environment where no special hardware for ranging is available, the prior deployment of seed nodes is unknown, the seed density is low, the node distribution is irregular, and where nodes and seeds can move uncontrollably. Although mobility makes other localization techniques increasingly less accurate, our technique takes Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Advantage of mobility to improve accuracy and reduce the number of seeds required. We consider a network composed of nodes with unknown locations and seeds that know their locations.
We are interested in three kinds of scenarios:
(a). Nodes are static, seeds are moving.
For example, a military application where nodes are dropped from a plane onto land, and transmitters attached to soldiers or animals in the area are used as moving seeds. Each Node’s location estimate should become more accurate as time passes and it receives information from more seeds.
(b). Nodes are moving, seeds are static.
One example would be nodes floating in currents along a river and Seeds at fixed locations on the river banks. A more concrete, but less worldly, example is NASA’s Mars Tumbleweed project. It proposes a low cost way to explore large areas on Mars by having rovers with sensors that are blown over the surface by the wind, with minimal or no control over their movement. Of course, GPS does not work on Mars, but it may be possible to establish fixed landmark seeds or positioning from orbiters. For these scenarios, each node’s location estimate will fluctuate around its current actual location: as time passes, old location information becomes inaccurate since the node has moved, but as new seed information is received the location estimate is revised.
©. Both nodes and seeds are moving.
This is the most general situation. It applies to any application where the nodes and seeds are both deployed in an ad hoc way, and move either because of the environment they are in (wind, currents, etc.) or because they have actuators for motion. Next we provide background on previous localization work.
BACKGROUND
Extensive research has been done on localization for wireless networks. Here, we provide only a brief survey focusing only on localization techniques suitable for ad hoc sensor networks. The approaches taken to achieve localization in sensor networks differ in their assumptions about the network deployment and the hardware’s capabilities. Centralized localization techniques depend on sensor nodes transmitting data to a central location, where computation is performed to determine the location of each node. Centralized technique using convex optimization to estimate positions based only on connectivity constraints given some nodes with known Positions improve on these results by using a multidimensional scaling approach, but still require centralized computation. Requiring central computation would be infeasible for mobile applications because of the high communication costs and inherent delay; hence we focus on distributed localization techniques.
Distributed localization methods do not require centralized computation, and rely on each node determining its location with only limited communication with nearby nodes. These methods can be classified as range-based and range-free.
1. Range Based Techniques:
Range-based techniques use distance estimates or angle estimates in location calculations. Range-based approaches have exploited time of arrival, received signal strength time difference of arrival of two different signals (TDOA), and angle of arrival (AOA). Though they can reach fine resolution, but the required hardware is expensive (ultrasound device for TDOA, antenna arrays for AOA) or the results depend on other unrealistic assumptions about signal propagation (for example, the actual received signal strengths of radio signals can vary when the surrounding environment changes).Because of the hardware limitations of sensor devices, range-free localization algorithms are a cost effective alternative to more expensive range-based approaches.
2. Range –Free Techniques:
Range-free solution depends only on the contents of received messages. There are two main types of range-free localization algorithms that have been proposed for sensor networks. Local techniques that rely on a high density of seeds so that every node can hear several seeds, and hop counting techniques that rely on flooding a network.
(a) Local Techniques.
i) In the Centroid method, each node estimates its location by calculating the center of the locations of all seeds it hears. If seeds are well positioned, location error can be reduced, but this is not possible in ad hoc deployments.
ii) The APIT method isolates the environment into triangular regions between beaconing nodes, and uses a grid algorithm to calculate the maximum area in which a node will likely reside. Since APIT typically assumes a larger radio range for seed nodes and hence has high seed density, it is not reasonable to compare it to our technique directly.