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Ari Y. Benbasat MAS.965 Spring 2003 Ari Y. Benbasat MAS.965 Spring 2003 A Broad Vague Overview of Power Saving Techniques for Ad-Hoc Wireless Sensing.

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Presentation on theme: "Ari Y. Benbasat MAS.965 Spring 2003 Ari Y. Benbasat MAS.965 Spring 2003 A Broad Vague Overview of Power Saving Techniques for Ad-Hoc Wireless Sensing."— Presentation transcript:

1 Ari Y. Benbasat MAS.965 Spring 2003 Ari Y. Benbasat MAS.965 Spring 2003 A Broad Vague Overview of Power Saving Techniques for Ad-Hoc Wireless Sensing

2 Problem Assumptions We consider a network made up of nodes containing:We consider a network made up of nodes containing: –Power source –Sensing –Processing –RF Link / Storage Nodes are inaccessibleNodes are inaccessible Nodes are randomly distributed with no a priori knowledge of othersNodes are randomly distributed with no a priori knowledge of others Nodes must be able to act aloneNodes must be able to act alone We consider a network made up of nodes containing:We consider a network made up of nodes containing: –Power source –Sensing –Processing –RF Link / Storage Nodes are inaccessibleNodes are inaccessible Nodes are randomly distributed with no a priori knowledge of othersNodes are randomly distributed with no a priori knowledge of others Nodes must be able to act aloneNodes must be able to act alone

3 The Need for Low-Power Nodes Node utility increases as lifetime increasesNode utility increases as lifetime increases –In some cases, value is nil below a certain threshold –Longer node life allows for greater market penetration Since nodes are inaccessible, they can only last as long as their initial power sourceSince nodes are inaccessible, they can only last as long as their initial power source –Node lifespan must approach lifespan of locale –Very low power nodes can run on scavenged power (not considered here. Plus the usual cost and environmental issuesPlus the usual cost and environmental issues Node utility increases as lifetime increasesNode utility increases as lifetime increases –In some cases, value is nil below a certain threshold –Longer node life allows for greater market penetration Since nodes are inaccessible, they can only last as long as their initial power sourceSince nodes are inaccessible, they can only last as long as their initial power source –Node lifespan must approach lifespan of locale –Very low power nodes can run on scavenged power (not considered here. Plus the usual cost and environmental issuesPlus the usual cost and environmental issues

4 An Examination of Power Usage (1) From M. Srivastava Mobicom02 Tutorial Power usage of WINS nodes (heavy)Power usage of WINS nodes (heavy) SA1100 processor draws 360 mWSA1100 processor draws 360 mW –Tx draws double that –Rx similar Sensor power usage negligible (23 mW)Sensor power usage negligible (23 mW) –But sensor considerations still important Power usage of WINS nodes (heavy)Power usage of WINS nodes (heavy) SA1100 processor draws 360 mWSA1100 processor draws 360 mW –Tx draws double that –Rx similar Sensor power usage negligible (23 mW)Sensor power usage negligible (23 mW) –But sensor considerations still important

5 An Examination of Power Usage (2) Power usage of Medusa II nodes (light-weight)Power usage of Medusa II nodes (light-weight) –Similar to motes Large net power change for small output gainLarge net power change for small output gain Note that Rx and Idle transceiver power similarNote that Rx and Idle transceiver power similar Sensor power now comparable to processorSensor power now comparable to processor Power usage of Medusa II nodes (light-weight)Power usage of Medusa II nodes (light-weight) –Similar to motes Large net power change for small output gainLarge net power change for small output gain Note that Rx and Idle transceiver power similarNote that Rx and Idle transceiver power similar Sensor power now comparable to processorSensor power now comparable to processor From M. Srivastava Mobicom02 Tutorial

6 Overview of Techniques Hardware TechniquesHardware Techniques –Issues concerning specific choices in the design and construction of the nodes themselves Software TechniquesSoftware Techniques –Real-time operational issues of the individual nodes RF TechniquesRF Techniques –Low-level node to node communication issues Coordinated TechniquesCoordinated Techniques –Higher level benefits of inter-node cooperation Hardware TechniquesHardware Techniques –Issues concerning specific choices in the design and construction of the nodes themselves Software TechniquesSoftware Techniques –Real-time operational issues of the individual nodes RF TechniquesRF Techniques –Low-level node to node communication issues Coordinated TechniquesCoordinated Techniques –Higher level benefits of inter-node cooperation

7 Hardware Techniques Bordering on the tautological, lower power circuitry choices lowers the draw of the nodeBordering on the tautological, lower power circuitry choices lowers the draw of the node Microcontroller ChoiceMicrocontroller Choice –Operational modes, speed, ADC and memory key –Some work being done in special purpose cores is not considered here (see Anantha at EECS MTL) Sensor ChoiceSensor Choice –Accuracy, signal processing needs and modality Battery IssuesBattery Issues –Size, lifespan, peak power draws and regulation Bordering on the tautological, lower power circuitry choices lowers the draw of the nodeBordering on the tautological, lower power circuitry choices lowers the draw of the node Microcontroller ChoiceMicrocontroller Choice –Operational modes, speed, ADC and memory key –Some work being done in special purpose cores is not considered here (see Anantha at EECS MTL) Sensor ChoiceSensor Choice –Accuracy, signal processing needs and modality Battery IssuesBattery Issues –Size, lifespan, peak power draws and regulation

8 Hardware Techniques: Microcontroller Choice Power usage in sleep and idle modes important due to low-duty cycle nature of many applicationsPower usage in sleep and idle modes important due to low-duty cycle nature of many applications –Do not ignore wake-up latencies Must select most appropriate clock speed:Must select most appropriate clock speed: –High clock speeds allow for greater processing, which can save power in other areas –Low clock speeds save switching power, but leakage current can start to dominate Ability to run ADC at variable accuracy beneficialAbility to run ADC at variable accuracy beneficial Ability to cache data locally beneficial to other categories of techniques discussed belowAbility to cache data locally beneficial to other categories of techniques discussed below Power usage in sleep and idle modes important due to low-duty cycle nature of many applicationsPower usage in sleep and idle modes important due to low-duty cycle nature of many applications –Do not ignore wake-up latencies Must select most appropriate clock speed:Must select most appropriate clock speed: –High clock speeds allow for greater processing, which can save power in other areas –Low clock speeds save switching power, but leakage current can start to dominate Ability to run ADC at variable accuracy beneficialAbility to run ADC at variable accuracy beneficial Ability to cache data locally beneficial to other categories of techniques discussed belowAbility to cache data locally beneficial to other categories of techniques discussed below

9 Hardware Techniques: Sensor Choices Some sensors allow for user control of bandwidth and accuracySome sensors allow for user control of bandwidth and accuracy Consider power necessary to get good signal to ADCConsider power necessary to get good signal to ADC –Internal signal processing usually more energy efficient, as are digital output sensors Same physical phenomenal can be measured with various modalities, e.g. motion:Same physical phenomenal can be measured with various modalities, e.g. motion: –Low power/accuracy: Tilt switch –Medium: Accelerometer –High: Stereo video Some sensors allow for user control of bandwidth and accuracySome sensors allow for user control of bandwidth and accuracy Consider power necessary to get good signal to ADCConsider power necessary to get good signal to ADC –Internal signal processing usually more energy efficient, as are digital output sensors Same physical phenomenal can be measured with various modalities, e.g. motion:Same physical phenomenal can be measured with various modalities, e.g. motion: –Low power/accuracy: Tilt switch –Medium: Accelerometer –High: Stereo video

10 Hardware Techniques: Battery Issues Of all hardware components, battery lifespan/size is improving most slowlyOf all hardware components, battery lifespan/size is improving most slowly Battery size will often match or exceed that of the sensor, tying applications to power needsBattery size will often match or exceed that of the sensor, tying applications to power needs Peak power drain (as opposed to average) can greatly reduce battery lifePeak power drain (as opposed to average) can greatly reduce battery life –Low-duty cycle can counteract this somewhat Careful choice of regulator can increase lifeCareful choice of regulator can increase life –Beware of dropout in DC-DC regulators –Beware of RF interference in switching regulators Of all hardware components, battery lifespan/size is improving most slowlyOf all hardware components, battery lifespan/size is improving most slowly Battery size will often match or exceed that of the sensor, tying applications to power needsBattery size will often match or exceed that of the sensor, tying applications to power needs Peak power drain (as opposed to average) can greatly reduce battery lifePeak power drain (as opposed to average) can greatly reduce battery life –Low-duty cycle can counteract this somewhat Careful choice of regulator can increase lifeCareful choice of regulator can increase life –Beware of dropout in DC-DC regulators –Beware of RF interference in switching regulators

11 Software Techniques Several real-time techniques are commonly used to reduce the operation power of the nodesSeveral real-time techniques are commonly used to reduce the operation power of the nodes While the hardware must be in place to allow these, they are software/OS controlledWhile the hardware must be in place to allow these, they are software/OS controlled Other ideas are being explored, such as real- time state based changes in sensor selection and collectionOther ideas are being explored, such as real- time state based changes in sensor selection and collection Several real-time techniques are commonly used to reduce the operation power of the nodesSeveral real-time techniques are commonly used to reduce the operation power of the nodes While the hardware must be in place to allow these, they are software/OS controlledWhile the hardware must be in place to allow these, they are software/OS controlled Other ideas are being explored, such as real- time state based changes in sensor selection and collectionOther ideas are being explored, such as real- time state based changes in sensor selection and collection

12 Software Techniques: Dynamic Voltage Scaling Some processors allow for real-time alteration of voltage and clock speed (such as SA1100) and can therefore be tailored to the current (expected) needsSome processors allow for real-time alteration of voltage and clock speed (such as SA1100) and can therefore be tailored to the current (expected) needs Note that at low frequency, leakage current is on par with switching currentNote that at low frequency, leakage current is on par with switching current These issues are fundamental to the issues of task scheduling, which is considered nextThese issues are fundamental to the issues of task scheduling, which is considered next Some processors allow for real-time alteration of voltage and clock speed (such as SA1100) and can therefore be tailored to the current (expected) needsSome processors allow for real-time alteration of voltage and clock speed (such as SA1100) and can therefore be tailored to the current (expected) needs Note that at low frequency, leakage current is on par with switching currentNote that at low frequency, leakage current is on par with switching current These issues are fundamental to the issues of task scheduling, which is considered nextThese issues are fundamental to the issues of task scheduling, which is considered next Figure from Chandrakasan Lab Blue line shows minimum power gradient for Intel StrongArm 1100 processor.

13 Software Techniques: Scheduling Given information about past processor usage, it is possible to predict the processor’s needs in the time sliceGiven information about past processor usage, it is possible to predict the processor’s needs in the time slice For a given task with a desired latency, there will be an optimal voltage/frequency choice which minimizes powerFor a given task with a desired latency, there will be an optimal voltage/frequency choice which minimizes power –Due to leakage current and other effects, this is not necessarily the choice for which the operation exactly meets the latency Unpredictable data suggests cautionUnpredictable data suggests caution Given information about past processor usage, it is possible to predict the processor’s needs in the time sliceGiven information about past processor usage, it is possible to predict the processor’s needs in the time slice For a given task with a desired latency, there will be an optimal voltage/frequency choice which minimizes powerFor a given task with a desired latency, there will be an optimal voltage/frequency choice which minimizes power –Due to leakage current and other effects, this is not necessarily the choice for which the operation exactly meets the latency Unpredictable data suggests cautionUnpredictable data suggests caution

14 Software Techniques: Algorithm/Accuracy Trade-offs Given a fixed algorithm, it is possible to order the coefficients such that the operations (energy) vs. accuracy curve approaches monotonicityGiven a fixed algorithm, it is possible to order the coefficients such that the operations (energy) vs. accuracy curve approaches monotonicity Done on a case by case basis. Example below shows the effects of using only the five most significant taps of an FIR filter.Done on a case by case basis. Example below shows the effects of using only the five most significant taps of an FIR filter. Given a fixed algorithm, it is possible to order the coefficients such that the operations (energy) vs. accuracy curve approaches monotonicityGiven a fixed algorithm, it is possible to order the coefficients such that the operations (energy) vs. accuracy curve approaches monotonicity Done on a case by case basis. Example below shows the effects of using only the five most significant taps of an FIR filter.Done on a case by case basis. Example below shows the effects of using only the five most significant taps of an FIR filter. Figure from Chandrakasan Lab

15 RF Techniques Most work in reducing the power in ad-hoc networks focuses on RF issues, specifically routing and scheduling (to allow idling).Most work in reducing the power in ad-hoc networks focuses on RF issues, specifically routing and scheduling (to allow idling). While the early slides did show power dominance in this area, this analysis suggests that the nodes are network elements foremost, when their principle goal is data collection.While the early slides did show power dominance in this area, this analysis suggests that the nodes are network elements foremost, when their principle goal is data collection. Therefore, we will consider only a few issues in this area which directly relate to sensor data.Therefore, we will consider only a few issues in this area which directly relate to sensor data. Most work in reducing the power in ad-hoc networks focuses on RF issues, specifically routing and scheduling (to allow idling).Most work in reducing the power in ad-hoc networks focuses on RF issues, specifically routing and scheduling (to allow idling). While the early slides did show power dominance in this area, this analysis suggests that the nodes are network elements foremost, when their principle goal is data collection.While the early slides did show power dominance in this area, this analysis suggests that the nodes are network elements foremost, when their principle goal is data collection. Therefore, we will consider only a few issues in this area which directly relate to sensor data.Therefore, we will consider only a few issues in this area which directly relate to sensor data.

16 RF Techniques: Packet Size While radiated power is of most interest, the circuitry within a transmitter is the main source of drain.While radiated power is of most interest, the circuitry within a transmitter is the main source of drain. While most costs are per bit, there is a startup power and latency that is constant regardless of packet sizeWhile most costs are per bit, there is a startup power and latency that is constant regardless of packet size Therefore, latency versus power tradeoffs must be considered in any node design.Therefore, latency versus power tradeoffs must be considered in any node design. While radiated power is of most interest, the circuitry within a transmitter is the main source of drain.While radiated power is of most interest, the circuitry within a transmitter is the main source of drain. While most costs are per bit, there is a startup power and latency that is constant regardless of packet sizeWhile most costs are per bit, there is a startup power and latency that is constant regardless of packet size Therefore, latency versus power tradeoffs must be considered in any node design.Therefore, latency versus power tradeoffs must be considered in any node design. Figure from Chandrakasan Lab As packet size increases, the startup energy of the transmitter is amortized and the cost per bit drop greatly.

17 RF Techniques: Routing Choices Routing is vital to how data is extracted from the network for usage.Routing is vital to how data is extracted from the network for usage. Each node is both a source and a relay, and power use must be balanced.Each node is both a source and a relay, and power use must be balanced. While the fourth figure uses the least Tx power (due to r 2 losses), it requires the most Rx power.While the fourth figure uses the least Tx power (due to r 2 losses), it requires the most Rx power. The optimal solution is the one-hop paths half the time, and the two-hop path the rest.The optimal solution is the one-hop paths half the time, and the two-hop path the rest. Routing is vital to how data is extracted from the network for usage.Routing is vital to how data is extracted from the network for usage. Each node is both a source and a relay, and power use must be balanced.Each node is both a source and a relay, and power use must be balanced. While the fourth figure uses the least Tx power (due to r 2 losses), it requires the most Rx power.While the fourth figure uses the least Tx power (due to r 2 losses), it requires the most Rx power. The optimal solution is the one-hop paths half the time, and the two-hop path the rest.The optimal solution is the one-hop paths half the time, and the two-hop path the rest. Four possible routings of data from the leftmost node to the base station. Figure from Chandrakasan Lab

18 Coordinated Techniques As seen in last week’s reading, it is necessary to consider data compression and routing as a single task.As seen in last week’s reading, it is necessary to consider data compression and routing as a single task. This is a very active area of research, so there are many more partial results than can be listed here. A few areas of personal interest are covered.This is a very active area of research, so there are many more partial results than can be listed here. A few areas of personal interest are covered. In general, the goal of these techniques is to exploit the regularity of data between nodes to save processing, bandwidth and ultimately power.In general, the goal of these techniques is to exploit the regularity of data between nodes to save processing, bandwidth and ultimately power. As seen in last week’s reading, it is necessary to consider data compression and routing as a single task.As seen in last week’s reading, it is necessary to consider data compression and routing as a single task. This is a very active area of research, so there are many more partial results than can be listed here. A few areas of personal interest are covered.This is a very active area of research, so there are many more partial results than can be listed here. A few areas of personal interest are covered. In general, the goal of these techniques is to exploit the regularity of data between nodes to save processing, bandwidth and ultimately power.In general, the goal of these techniques is to exploit the regularity of data between nodes to save processing, bandwidth and ultimately power.

19 Coordinated Techniques: Distributed Compression In most sensor networks, we can assume that data is spatially correlated and this correlation is spatially stationaryIn most sensor networks, we can assume that data is spatially correlated and this correlation is spatially stationary According to the Slepian-Wolf coding theorem, given a measurement of X and P(X|Y), it is possible to encode X optimally without knowing the value of instantiation of Y.According to the Slepian-Wolf coding theorem, given a measurement of X and P(X|Y), it is possible to encode X optimally without knowing the value of instantiation of Y. Therefore, X can transmit its data to Y in a more efficient fashion, given only simple assumptions and knowledge of the physical process being sampled.Therefore, X can transmit its data to Y in a more efficient fashion, given only simple assumptions and knowledge of the physical process being sampled. In most sensor networks, we can assume that data is spatially correlated and this correlation is spatially stationaryIn most sensor networks, we can assume that data is spatially correlated and this correlation is spatially stationary According to the Slepian-Wolf coding theorem, given a measurement of X and P(X|Y), it is possible to encode X optimally without knowing the value of instantiation of Y.According to the Slepian-Wolf coding theorem, given a measurement of X and P(X|Y), it is possible to encode X optimally without knowing the value of instantiation of Y. Therefore, X can transmit its data to Y in a more efficient fashion, given only simple assumptions and knowledge of the physical process being sampled.Therefore, X can transmit its data to Y in a more efficient fashion, given only simple assumptions and knowledge of the physical process being sampled.

20 Coordinated Techniques: Aggregated Collection The prior example assumed no routing structure. In this case, we assume a fixed tree.The prior example assumed no routing structure. In this case, we assume a fixed tree. If the tree node wishes to collect a specific aggregate of the data of the nodes, it is possible to process this computation as the data is transmitted up the tree, rather than solely at the root.If the tree node wishes to collect a specific aggregate of the data of the nodes, it is possible to process this computation as the data is transmitted up the tree, rather than solely at the root. Example functions for which this is beneficial are maximum and average, i.e. those with minimal state. Median, on the other hand, does not benefit.Example functions for which this is beneficial are maximum and average, i.e. those with minimal state. Median, on the other hand, does not benefit. The prior example assumed no routing structure. In this case, we assume a fixed tree.The prior example assumed no routing structure. In this case, we assume a fixed tree. If the tree node wishes to collect a specific aggregate of the data of the nodes, it is possible to process this computation as the data is transmitted up the tree, rather than solely at the root.If the tree node wishes to collect a specific aggregate of the data of the nodes, it is possible to process this computation as the data is transmitted up the tree, rather than solely at the root. Example functions for which this is beneficial are maximum and average, i.e. those with minimal state. Median, on the other hand, does not benefit.Example functions for which this is beneficial are maximum and average, i.e. those with minimal state. Median, on the other hand, does not benefit.

21 Coordinated Techniques: Localized Sensing In tracking networks, the goal is for the nodes to collaborate to find one (or more) objects moving within their sensing field.In tracking networks, the goal is for the nodes to collaborate to find one (or more) objects moving within their sensing field. Often, most nodes will sense nothing of interest, while a few will collect the data about the object in question.Often, most nodes will sense nothing of interest, while a few will collect the data about the object in question. It is possible to design routing algorithms such that data flows to/from/between this area more readily then the rest of the network through the creation of a gradient.It is possible to design routing algorithms such that data flows to/from/between this area more readily then the rest of the network through the creation of a gradient. Furthermore, this gradient allows nodes to judge their own importance in the sensing task and set their operation accordingly.Furthermore, this gradient allows nodes to judge their own importance in the sensing task and set their operation accordingly. In tracking networks, the goal is for the nodes to collaborate to find one (or more) objects moving within their sensing field.In tracking networks, the goal is for the nodes to collaborate to find one (or more) objects moving within their sensing field. Often, most nodes will sense nothing of interest, while a few will collect the data about the object in question.Often, most nodes will sense nothing of interest, while a few will collect the data about the object in question. It is possible to design routing algorithms such that data flows to/from/between this area more readily then the rest of the network through the creation of a gradient.It is possible to design routing algorithms such that data flows to/from/between this area more readily then the rest of the network through the creation of a gradient. Furthermore, this gradient allows nodes to judge their own importance in the sensing task and set their operation accordingly.Furthermore, this gradient allows nodes to judge their own importance in the sensing task and set their operation accordingly.


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