21-08-2012, 01:27 PM
A wireless sensor network (WSN)
A wireless sensor.docx (Size: 789.62 KB / Downloads: 66)
INTRODUCTION
Wireless Communication System
A wireless sensor network (WSN) is a collection of nodes organized into a co-operative network [1]. Each node consists of processing capability (one or more micro controllers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single omnidirectional antenna), have a power source (e.g., batteries or an embedded form of energy harvesting), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. These nodes in real time environment work as spatially distributed autonomous sensors which are used to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, enabling also to control the activity of the sensors. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes"(demo video) of genuine microscopic dimensions have yet to be created it is believed these systems are as much efficient the others. The cost of sensor nodes is also similarly variable, ranging from hundreds of dollars to a few pennies, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth.
Evolution of Wireless Communication Systems
First-generation wireless systems were developed to allow a user to maintain continuous access to telephone connections within the service area of a network provider. These systems were characterized by analog transmission of voice signal, and a nonexistent inter-operability between existing standards. Examples of first-generation systems include the total access communication system (TACS) used in the United Kingdom, and the advanced mobile system (AMPS) of the United States [9]. The need for more efficient spectrum use, as well as an interest to exploit Improved transmission performance offered by digital encoding and modulation techniques, led to the migration towards second-generation systems. Examples of second-generation systems include the United States Digital Cellular (USDC) standards IS-136 and IS-54, as well as Europe’s Global System for Mobile (GSM) communication [9]. Third-generation wireless systems are developed to provide an integrated wide-band communication network capable of providing excellent performance while supporting large numbers of mobile equipment. Design efforts on third-generation standards are presently on going, and different standards organizations are working together to define universally accepted specifications.The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.
Requirements of Wireless Communication Systems
The goal of the wireless communication provider is to deliver communication links with efficiency comparable to that of wire line systems, at reasonable costs and convenience to as many subscribers as possible. A system with good performance should be able to deliver clear, uncorrupted data/voice/video connection with very little latency, and minimal power consumption. In digital systems, the ability to achieve uncorrupted data transmission is directly proportional to a system’s probability of bit error, which is in turn inversely proportional to the power of data transmission. One of the major drives in wireless communication is the effort to increase power efficiency, thus delivering reliable links at lower and lower power levels. In order for wireless system to serve efficiently, there is a need to maximize the capacity of the systems so that all the subscribers can be efficiently served as possible.
Error Correcting codes
The birth of Error Correction Coding also known as Forward Error correcting codes (FEC) came in the late 1940’s when Shannon mathematically showed that a limit on the channel capacity could be achieved when transmitting information through a noisy channel [12]. Channel capacity is a theoretical measure of the fastest rate at which error free transmission can be realized. Traditional modulation techniques deliver performance significantly inferior to that predicted by Shannon’s work (Binary phase shift keying by about 10dB in non-fading Gaussian channel)[13], but when incorporated with error correction coding, most digital modulation schemes can achieve performance that approaches channel capacity near to Shannon’s limit.
Figure 1.1 shows an error correcting code within a digital communications system. The channel encoder adds code bits to the transmission bit stream, based on the data bits at its input. These extra bits are used by the channel decoder at the receiver to correct errors introduced into the transmission stream by a noisy or fading channel. The disadvantages of FEC are two-fold. Firstly, the addition of extra bits into the transmission stream has the effect that, if the original rate of transmission of ‘useful’ data bits is to remain the same, the symbol rate over the channel must be increased, thus increasing the bandwidth needed to transmit the signal. Frequently, bandwidth expansion is not an option in modern wireless systems, where frequency spectrum is often highly regulated and bandwidth is costly.
MOTIVATION
One of the major challenges at the physical layer of wireless sensor networks is in usage of appropriate channel coding schemes to protect transmitted messages and achieve a desired bit error rate, while maintaining low complexity and low power consumption features of sensor nodes but the efficiency and reliability of the data transmission are always contradictory in digital communication system. Consider a practical application of Wireless Sensor Networks where it is used for monitoring remote and isolated areas, and collecting information about unexpected phenomena like volcano eruptions or enemy movement in the battle field. In these applications the channel state is expected to be continuously varying because of the dynamic changes in environmental factors. Also vehicles and rocks movements can crash some nodes which can separate parts of the network. In this context it becomes hard for the network to deliver the collected information, even when transmitting at the maximum power, without strong error correction techniques. Using ARQ to tackle erroneous packets in such environment is inefficient, because of the high number of retransmissions needed. Also ARQ techniques introduce high latency, where the repeated retransmission consumes considerable time, which leads to a high delay between detection moment of an event at the sensor nodes and informing the base station about that event. Retransmission also consumes large amount of energy from both transmitting and receiving nodes.
A wireless sensor.docx (Size: 789.62 KB / Downloads: 66)
INTRODUCTION
Wireless Communication System
A wireless sensor network (WSN) is a collection of nodes organized into a co-operative network [1]. Each node consists of processing capability (one or more micro controllers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single omnidirectional antenna), have a power source (e.g., batteries or an embedded form of energy harvesting), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. These nodes in real time environment work as spatially distributed autonomous sensors which are used to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, enabling also to control the activity of the sensors. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes"(demo video) of genuine microscopic dimensions have yet to be created it is believed these systems are as much efficient the others. The cost of sensor nodes is also similarly variable, ranging from hundreds of dollars to a few pennies, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth.
Evolution of Wireless Communication Systems
First-generation wireless systems were developed to allow a user to maintain continuous access to telephone connections within the service area of a network provider. These systems were characterized by analog transmission of voice signal, and a nonexistent inter-operability between existing standards. Examples of first-generation systems include the total access communication system (TACS) used in the United Kingdom, and the advanced mobile system (AMPS) of the United States [9]. The need for more efficient spectrum use, as well as an interest to exploit Improved transmission performance offered by digital encoding and modulation techniques, led to the migration towards second-generation systems. Examples of second-generation systems include the United States Digital Cellular (USDC) standards IS-136 and IS-54, as well as Europe’s Global System for Mobile (GSM) communication [9]. Third-generation wireless systems are developed to provide an integrated wide-band communication network capable of providing excellent performance while supporting large numbers of mobile equipment. Design efforts on third-generation standards are presently on going, and different standards organizations are working together to define universally accepted specifications.The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.
Requirements of Wireless Communication Systems
The goal of the wireless communication provider is to deliver communication links with efficiency comparable to that of wire line systems, at reasonable costs and convenience to as many subscribers as possible. A system with good performance should be able to deliver clear, uncorrupted data/voice/video connection with very little latency, and minimal power consumption. In digital systems, the ability to achieve uncorrupted data transmission is directly proportional to a system’s probability of bit error, which is in turn inversely proportional to the power of data transmission. One of the major drives in wireless communication is the effort to increase power efficiency, thus delivering reliable links at lower and lower power levels. In order for wireless system to serve efficiently, there is a need to maximize the capacity of the systems so that all the subscribers can be efficiently served as possible.
Error Correcting codes
The birth of Error Correction Coding also known as Forward Error correcting codes (FEC) came in the late 1940’s when Shannon mathematically showed that a limit on the channel capacity could be achieved when transmitting information through a noisy channel [12]. Channel capacity is a theoretical measure of the fastest rate at which error free transmission can be realized. Traditional modulation techniques deliver performance significantly inferior to that predicted by Shannon’s work (Binary phase shift keying by about 10dB in non-fading Gaussian channel)[13], but when incorporated with error correction coding, most digital modulation schemes can achieve performance that approaches channel capacity near to Shannon’s limit.
Figure 1.1 shows an error correcting code within a digital communications system. The channel encoder adds code bits to the transmission bit stream, based on the data bits at its input. These extra bits are used by the channel decoder at the receiver to correct errors introduced into the transmission stream by a noisy or fading channel. The disadvantages of FEC are two-fold. Firstly, the addition of extra bits into the transmission stream has the effect that, if the original rate of transmission of ‘useful’ data bits is to remain the same, the symbol rate over the channel must be increased, thus increasing the bandwidth needed to transmit the signal. Frequently, bandwidth expansion is not an option in modern wireless systems, where frequency spectrum is often highly regulated and bandwidth is costly.
MOTIVATION
One of the major challenges at the physical layer of wireless sensor networks is in usage of appropriate channel coding schemes to protect transmitted messages and achieve a desired bit error rate, while maintaining low complexity and low power consumption features of sensor nodes but the efficiency and reliability of the data transmission are always contradictory in digital communication system. Consider a practical application of Wireless Sensor Networks where it is used for monitoring remote and isolated areas, and collecting information about unexpected phenomena like volcano eruptions or enemy movement in the battle field. In these applications the channel state is expected to be continuously varying because of the dynamic changes in environmental factors. Also vehicles and rocks movements can crash some nodes which can separate parts of the network. In this context it becomes hard for the network to deliver the collected information, even when transmitting at the maximum power, without strong error correction techniques. Using ARQ to tackle erroneous packets in such environment is inefficient, because of the high number of retransmissions needed. Also ARQ techniques introduce high latency, where the repeated retransmission consumes considerable time, which leads to a high delay between detection moment of an event at the sensor nodes and informing the base station about that event. Retransmission also consumes large amount of energy from both transmitting and receiving nodes.