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This article is presented by:
JOEL CHRISTOPHER JORDAN
B.S., University of Illinois at Urbana-Champaign, 2003
INTRODUCTION
Wireless sensor networks have generated much research interest in recent years as advances in electronics technology have made them feasible. In general, such a network consists of many nodes scattered over an area to provide distributed sensing and data processing [1]. These networks can enable unattended monitoring of physical quantities over large areas on a scale that would be prohibitively expensive to accomplish with humans. Many uses have been suggested for wireless sensor networks, including habitat [2] and medical monitoring [3]. Many groups have designed sensor nodes. These include Berkeley’s Mica motes [4] and PicoRadio projects [5], MIT’s μAmps [6], and Rice’s GNOMES [7], as well as many others. All of these sensors have similar goals, such as small physical size, low power consumption, and rich sensing abilities. In addition, the TinyOS project [8] provides a framework for designing flexible distributed applications for data collection and processing across a sensor network. Many sensor network applications require the collection of data over long periods of time. Sensor nodes are generally powered with batteries, putting a limit on how small the node can be made for a given lifetime. Unfortunately, it is unlikely that battery capacities will increase dramatically in the near future. Historically, battery charge density has increased by a mere 2% per year over the last 50 years [9]. As an example, a CR2032 lithium coin cell, about the size of a quarter, would provide an average of only 75 μW if used completely over a year. As an alternative to batteries, sensor nodes can scavenge energy from their environment. Ambient light, mechanical vibrations, or even acoustic sources could provide power to operate a sensor. Research suggests that up to 100 μW/cm3 can be obtained from vibrational sources [10]. A thin-film solar cell may provide 5 mW/cm2 of power
in bright sunlight, but only about 15 μW/cm2 at desk level in office lighting. Unlike batteries, these ambient sources are often unreliable. A solar-powered node could no longer operate if a power outage turned off the lights in a building. Sensor nodes, then, must operate with extremely low power dissipation. However, consider that a typical commercial radio transceiver requires 10 mW of power in receive mode and 35 mW while transmitting . Recent research has produced a transceiver design which needs only 1 mW in its receive mode and 25 mW while transmitting . Even this is more power than a small sensor node can produce. A solution to this problem is low-duty-cycle operation, where sensors spend a large percentage of the time in a low-power sleep mode. Because the power source is often unreliable, the duty cycle will be unreliable, varying with the amount of power available. Others have constructed self-powered sensor nodes with low-duty-cycle operation . However, existing routing algorithms have problems when operating on such hardware. Some, such as GEAR, include power reserves in the route selection heuristic so that routes prefer nodes with more power available. Unfortunately, it requires nodes to constantly listen for transmissions from neighbors, so low-duty-cycle operation is not possible. Other algorithms, such as LEACH , rely on time division multiple access (TDMA) schemes to acheive low duty cycle operation. In this type of algorithm, a master node assigns communication time slots to slave nodes, which only turn on their radios during these time slots. Because self-powered nodes may have unreliable power sources, however, they cannot be guaranteed to wake up as scheduled. To deal with these problems, stochastic sensor networks have been proposed . In such a network, nodes store power while in an inactive mode, then become active until the stored energy is depleted, at which point they return to the inactive state. This process is unsynchronized between the sensor nodes, thus forming a stochastic sensor network. Also, no routing is used. Instead, data is propagated to its destination using much simpler stochastic flooding. Such a network can be made reliable under certain assumptions about the active node density . Furthermore, high-level protocols can be layered onto the network to enable rich applications . While simulations have verified that these networks should work, no real-world testing has taken place. If this theory can be demonstrated in real nodes, it would have great advantages for enabling simple, robust networks of self-powered sensors. Such testing requires a wireless sensor node with rich power management features that no existing architectures offer. Therefore, a new sensor architecture has been designed with extremely low power consumption in mind. This sensor uses solar cells to collect energy and store it in a large reservoir capacitor. While in the inactive state, the sensor can check its stored power levels to determine whether to enter the active mode or to continue storing energy.
This article is presented by:
JOEL CHRISTOPHER JORDAN
B.S., University of Illinois at Urbana-Champaign, 2003
DESIGN AND IMPLEMENTATION OF A STOCHASTIC
WIRELESS SENSOR NETWORK
WIRELESS SENSOR NETWORK
INTRODUCTION
Wireless sensor networks have generated much research interest in recent years as advances in electronics technology have made them feasible. In general, such a network consists of many nodes scattered over an area to provide distributed sensing and data processing [1]. These networks can enable unattended monitoring of physical quantities over large areas on a scale that would be prohibitively expensive to accomplish with humans. Many uses have been suggested for wireless sensor networks, including habitat [2] and medical monitoring [3]. Many groups have designed sensor nodes. These include Berkeley’s Mica motes [4] and PicoRadio projects [5], MIT’s μAmps [6], and Rice’s GNOMES [7], as well as many others. All of these sensors have similar goals, such as small physical size, low power consumption, and rich sensing abilities. In addition, the TinyOS project [8] provides a framework for designing flexible distributed applications for data collection and processing across a sensor network. Many sensor network applications require the collection of data over long periods of time. Sensor nodes are generally powered with batteries, putting a limit on how small the node can be made for a given lifetime. Unfortunately, it is unlikely that battery capacities will increase dramatically in the near future. Historically, battery charge density has increased by a mere 2% per year over the last 50 years [9]. As an example, a CR2032 lithium coin cell, about the size of a quarter, would provide an average of only 75 μW if used completely over a year. As an alternative to batteries, sensor nodes can scavenge energy from their environment. Ambient light, mechanical vibrations, or even acoustic sources could provide power to operate a sensor. Research suggests that up to 100 μW/cm3 can be obtained from vibrational sources [10]. A thin-film solar cell may provide 5 mW/cm2 of power
in bright sunlight, but only about 15 μW/cm2 at desk level in office lighting. Unlike batteries, these ambient sources are often unreliable. A solar-powered node could no longer operate if a power outage turned off the lights in a building. Sensor nodes, then, must operate with extremely low power dissipation. However, consider that a typical commercial radio transceiver requires 10 mW of power in receive mode and 35 mW while transmitting . Recent research has produced a transceiver design which needs only 1 mW in its receive mode and 25 mW while transmitting . Even this is more power than a small sensor node can produce. A solution to this problem is low-duty-cycle operation, where sensors spend a large percentage of the time in a low-power sleep mode. Because the power source is often unreliable, the duty cycle will be unreliable, varying with the amount of power available. Others have constructed self-powered sensor nodes with low-duty-cycle operation . However, existing routing algorithms have problems when operating on such hardware. Some, such as GEAR, include power reserves in the route selection heuristic so that routes prefer nodes with more power available. Unfortunately, it requires nodes to constantly listen for transmissions from neighbors, so low-duty-cycle operation is not possible. Other algorithms, such as LEACH , rely on time division multiple access (TDMA) schemes to acheive low duty cycle operation. In this type of algorithm, a master node assigns communication time slots to slave nodes, which only turn on their radios during these time slots. Because self-powered nodes may have unreliable power sources, however, they cannot be guaranteed to wake up as scheduled. To deal with these problems, stochastic sensor networks have been proposed . In such a network, nodes store power while in an inactive mode, then become active until the stored energy is depleted, at which point they return to the inactive state. This process is unsynchronized between the sensor nodes, thus forming a stochastic sensor network. Also, no routing is used. Instead, data is propagated to its destination using much simpler stochastic flooding. Such a network can be made reliable under certain assumptions about the active node density . Furthermore, high-level protocols can be layered onto the network to enable rich applications . While simulations have verified that these networks should work, no real-world testing has taken place. If this theory can be demonstrated in real nodes, it would have great advantages for enabling simple, robust networks of self-powered sensors. Such testing requires a wireless sensor node with rich power management features that no existing architectures offer. Therefore, a new sensor architecture has been designed with extremely low power consumption in mind. This sensor uses solar cells to collect energy and store it in a large reservoir capacitor. While in the inactive state, the sensor can check its stored power levels to determine whether to enter the active mode or to continue storing energy.
