Efficient Energy Management for Smart Sensor Nodes

1. Smart Sensors and Sensor Networks Lecture10 Energy management 1 (node level)

2. Smart Sensors and Sensor Networks Power supply of sensor nodes • 2 aspects: • Storing energy and providing power in the required form; • Replenishing consumed energy by scavenging it from some external power source; • Storing energy: • Conventional done using batteries (a normal AA battery stores about 2.2 – 2.5 Ah at 1.5 V); • Batteries: nonrechargeable (primary batteries) or rechargeable (secondary batteries); • Batteries are electrochemical stores for energy; chemicals are the main determining factor of battery technology; • Requirements: • Capacity: high capacity at a small weight, small volume and low price; the main metric is energy per volume, J/cm3; • Capacity under load: the larger the battery, the more power can be delivered instantaneously; the rated battery capacity specified by the manufacturer is only valid as long as maximum discharge currents are not exceeded;

3. Smart Sensors and Sensor Networks • Self-discharge: it should be low; they have to last for a long time irrespective of whether power is drawn from them or not; • Efficient recharging: should be efficient even at low and intermittently available recharge power; consequently, the battery should not exhibit any memory effect; • Relaxation: the relaxation effect means the seeming self-recharging of an empty or almost empty battery when no current is drawn from it, based on chemical diffusion processes within the cell; battery lifetime and usable capacity is considerably extended if this effect is leveraged; an example: the use of multiple batteries in parallel and schedule the discharge from one battery to another, depending on the relaxation properties and power requirements of the operations to be supported;

4. Smart Sensors and Sensor Networks • Unconventional energy stores: • Fuel cells: also an electro-chemical storage of energy, directly producing energy by oxidizing hydrogen or hydrocarbon fuels; high energy densities: e.g. methanol as a fuel stores 17.6 KJ/cm3; available systems still require a no negligible minimum size for pumps and valves; • Mini heat engines with hydrocarbons: obtaining the desired sizes still requires a considerable research effort in MEMSs; predictions regarding power vary between 0.1 – 10 W/ 1cc; • Radioactive substances; • “Gold caps”: high – quality and high – capacity capacitors: store large amounts of energy, can be easily and quickly recharged and do not wear out over time; • DC – DC conversion: • Problem: the reduction of the battery’s voltage as its capacity drops; consequently, less power is delivered to the senor node’s circuits influencing the oscillator frequency and transmission power; • Solution: a DC – DC converter; it draws increasingly higher current from the battery when this becomes weak; advantage: predictable operation during the entire life cycle; disadvantage: reduces the overall efficiency and speeds up battery death;

5. Smart Sensors and Sensor Networks • Energy scavenging: approaches: • Photovoltaics: solar cells: • The available power depends on whether nodes are used outdoors or indoors and on the time of day and whether for outdoor usage; • The resulting power is about 10 µW/cm2 indoors and 15 mW/cm2 outdoors; • Single cells achieve a stable output voltage of about 0.6 V as long as the drawn current does not exceed a critical threshold; • Solar cells are usually used to recharge secondary batteries; • Temperature gradients: • Differences in temperature can be directly converted to electrical energy; • Theoretically, even small difference, for example 5 K, can produce considerable power; • Practical devices are influenced by the Carnot efficiency; an example: an generator that achieves about 80 µW/cm2 at about 1 V from a 5 K temperature difference; • Flow of air/ liquid: the power source is the flow of air or liquid in wind mills or turbines; the challenge is the miniaturization, there are results only for millimeter-scale MEMS gas turbines;

6. Smart Sensors and Sensor Networks • Vibrations: • A pervasive form of mechanical energy is vibrations: walls or windows in buildings are resonating with cars or trucks passing in the streets, machinery have low-frequency vibrations and so on; • The available energy depends on both amplitude and frequency of the vibrations and range from about 0.1 µW/cm3 up to 10000 µW/cm3; • Converting vibrations to electrical energy can be done through electromagnetic, electrostatic or piezoelectric principles; practical devices of 1 cm3 can produce about 200 µW/cm3 from 2.25 m/s2, 120 Hz vibration sources, sufficient to power simple wireless transmitters; • Pressure variations: • A variation of pressure can also be used as a power source; piezoelectric generators; • An example: the inclusion of such a generator in the heel of a shoe, generating power when a human walks; it generates about 330 µW/cm2; • Overview of typical values of power and energy densities for different energy sources: • The values from the table are for orientation; they are strongly influenced by the environments and technologies;

7. Smart Sensors and Sensor Networks • Energy scavenging has to be combined with secondary batteries as the actual power sources are not able to provide power consistently, uninterruptedly at a required level; worse, they tend to fluctuate in time; this requires additional circuitry and a battery technology that can be recharged at low currents; • The alternative: to align the task execution pattern of the sensor network (that is which sensor is active and when) with the characteristics of energy scavenging.

8. Smart Sensors and Sensor Networks Energy consumption of sensor nodes • Energy consumption of a sensor node must be tightly controlled; the consumption have 3 components: sensing, computing and communicating; • Proper operation reduces energy consumption: • The crucial observation is that most of the time a wireless sensor node has nothing to do; • Turning it off is not a solution because the sensor node must be active at an external stimuli or at a certain time; • The solution: several operational states with different levels of consumption; • The graded sleep state model is complicated by the fact that transitions between states take both time and energy: • The deeper the sleep state, the more time and energy is needed; • Sometimes it may be worthwhile to remain in an idle state instead of going to deeper sleep states even from an energy consumption point of view;

9. Smart Sensors and Sensor Networks • The energy saving is: • Once the event to be processed occurs, an additional overhead of: is incurred to come back to the operational state before the event can be processed; this energy is an overhead since no useful activity can be done during this time; • Switching to a sleep mode is only beneficial if Eoverhead< Esaved or, equivalently, if the time to the next event is sufficiently large:

10. Smart Sensors and Sensor Networks • Sensing energy: • The sensing unit is made of a sensor and/ or an actuator and the analog – digital converter; • The energy consumption is due to: physical signal sampling and conversion to electrical signal, signal conditioning and analog to digital conversion; • It varies with the hardware, the solution and the application: • Light or temperature sensors consume less than sonars; • The sampling rate is important; • Interval sensing consumes less than continuous monitoring; interval sensing can be used as a power – saving approach to reduce unnecessary sensing; however, one must take into account the overhead at transitions; • Computing energy: • Is achieved by the processor (mainly) and the memory; • Consists in 2 parts: the switching energy and the leakage energy; • The switching energy is determined by the supply voltage and the total capacitance switched by executing software; • The leakage energy refers to the energy consumption when no computation takes place; it can reach 50% of the total computing energy;

11. Smart Sensors and Sensor Networks • The concept of system partitioning can be used for reducing computing energy; approaches: • To remove the intensive computation to a remote processing center with static power supply; • To spread complex computation among more sensors instead of overloading several of them; • The processor: • Low – power modes: example: Intel StrongARM: • Normal mode: 400 mW; • Idle mode: 100 mW; • Sleep mode: 50 µW; • Dynamic voltage scaling (DVS): states behind the following formula: Ed = CSWfVdd2 (only for CMOS chips); • The idea is to compute the task only at the speed that is required to finish it before the deadline; a controller running at lower speed consumes less power and, additionally, the supply voltage can be reduced; • The Transmeta Crusoe processor can be scaled from 700 MHz at 1.65 V down to 200 MHz at 1.1 V; the energy/ instruction is reduced to 44%; • Care has to be taken to operate the controller within its specifications; there are minimum and maximum clock rates and power supply levels for each device; • DVS must be controlled from the operating system or from the application;

12. Smart Sensors and Sensor Networks • The memory: • Relevant kinds of memory are: on-chip memory of a microcontroller and Flash memory (of-chip RAM is rarely if ever used); • The construction and usage of Flash memory can heavily influence node lifetime; the relevant metrics are read and write times and energy consumption; • Writing requires more energy than reading: for the Flash memory on the Mica motes, reading requires 1.111 nAh while writing requires 83.333 nAh; • Communicating energy: • Is the major contributor to the total energy expenditure and is determined by the total amount of communication and the transmission distance; • Signal propagation follows an exponential law of transmitting distance (usually with exponent 2 to 4 depending on the transmission environment); • The equations that model the energy consumption for transmitting and receiving a bit take into account the power of the amplifier, the power of the circuits found in the transmitter and in the receiver, the data rate and the startup energy;

13. Smart Sensors and Sensor Networks • Observations: • Transmitting and receiving have comparable power consumption; • At reception, idle and active states have comparable power consumption; • Startup energy is considerable; • Solutions for reducing the communicating energy: • Data aggregation to eliminate redundancy in neighboring nodes; • Collaborative signal and information processing to do local processing; • Negotiation – based protocols to reduce unnecessary replicated data; • Multihop communication; • Clustering – based hierarchies;

14. Smart Sensors and Sensor Networks • Communicating vs. computing energy: • The ratio of communicating 1 bit over the wireless medium to that of processing the same bit is in the order of 200 – 10000; • Communicating 1 Kb of data over 100 m consumes roughly the same amount of energy as computing 3000000 instructions; • The energy required for computation is smaller than for communicating but cannot be ignored; • Energy conserving directions in WSNs: • A certain degree of dependency exists between several solutions: eliminating unnecessary sensing reduces data communication, reducing communication energy, but requires more complex control, increasing computation energy;

15. Smart Sensors and Sensor Networks Dynamic Power Management • DPM is a technique to reduce power consumption without degrading performance; • The management of the power consumption is done by the OS or, in simple systems, by the application; • In many cases, WSNs have real – time requirements; power reduction must be carefully balanced against the need for real – time responsiveness; • The basic idea behind DPM is to shut down devices when they are not needed and wake up them when necessary; an optimum shutdown policy is a difficult task; • Transitioning in sleep states and back has time and energy overhead; implementing the right policy for transitioning is critical; • Additional savings can be done in the active state by reducing the clock frequency and the level of the power supply in periods of reduced activity; peak performance may be compromised but peak performance is not continuously needed;

16. Smart Sensors and Sensor Networks • DPM has two forms: • Idle Power Management; • Active Power Management; • Multiple shutdown states • Many devices have multiple power modes; i.e. the StrongARM SA – 1100 has 3 power modes: run, idle and sleep; • Each of these modes is associated with a progressively lower level of power consumption; • An open interface specification, called Advanced Configuration and Power Management Interface, ACPI, standardizes how the OS can interface with devices characterized by multiple power states to provide DPM; it is intended for mobile systems (laptops, notebooks etc) and not for sensors; • ACPI supports a finite state model for system resources and specifies the hardware/ software interface that should be used to control them; • ACPI controls the power consumption of the whole system as well as the power state of each device;

17. Smart Sensors and Sensor Networks • An ACPI compliant system has 5 global states, SystemStateS0 (working state) and SystemStateS1 – S4 (4 different levels of sleep states); • An ACPI compliant device has 4 states: PowerDeviceD0 and D1 – D3; • Sleep states for a sensor node based on a StrongARM processor: • s0 is the completely active state of the node; • In s1, the node senses and receives data but the processor is in standby; • s2 is similar to state s1, except that the processor is powered down and is waked up when the sensor or the radio receives data; • In s3 only the sensing front – end is off; • s4 is the completely off state of the device;

18. Smart Sensors and Sensor Networks • Sleep state transition policy • Assume an event is detected by a sensor at time t0; it finishes processing it at time t1 and the next event occurs at time t2 = t1 + ti; at time t1, the node decides to transition to a sleep state sk from the active state s0: • Each state sk has a power consumption Pk and the transition time to it from the active state and back is given by тd,k and тu,k, respectively; by the definition of sleep states, Pj > Pi, тd,I > тd,j and тu,I > тu,j for any i > j; • The power consumption between the sleep modes is modeled as a linear ramp between the states, although the variation is in steps;

19. Smart Sensors and Sensor Networks • A set of sleep time thresholds, {Tth,k}, corresponding to the states {sk} will be derived such that transitioning to a sleep state sk from state s0 will result in a energy loss if the idle time ti< Tth,k because of the transition energy overhead; no productivity work can be done in the transition period; • The energy savings are given by the area under the graphs and is expressed with the following relation: • Such a transition is only justified when Esave,k> 0; this leads to the following energy gain threshold: • The longer the delay overhead of the transition is, the higher the energy gain threshold, and the more the difference between the P0 and Pk is, the smaller the threshold;

20. Smart Sensors and Sensor Networks • Active power management • Reducing the operating frequency during periods of reduced activity results in a linear decrease in power consumption but does not reduces the total energy consumed per task; • Reducing the frequency and the voltage, energy reduction can be obtained: • Significant energy savings can be realized by recognizing that peak performance is not always required and therefore the operating frequency and voltage can be dynamically adapted to the instantaneous processing requirements;

21. Smart Sensors and Sensor Networks • System implementation: • The first generation µAMPS sensor node: • StrongARM SA-1100 processor with 1 MB on board SRAM and Flash memory; • A baterry of 4.0 V from which a 3.3 V power supply is obtained for all the digital circuits; a separate analog power supply is also generated to isolate the digital power supply noise from the analog circuits; • The core power supply is generated by a DVS circuit, regulating the power supply from 0.925 V to a maximum of 2.0 V with a conversion efficiency of about 85 %; • The board contains an acoustic sensor and RS 232 and USB connectors for remote debugging and connecting to a debug PC; there is another 16 b bus interface connector allowing other sensors to be connected (e.g., a seismic sensor) and the RF module; this consists of a dual power 2.4 GHz radio for 10 and 100 m ranges; • An envelop detect mechanism has also been incorporated into the sensor circuit, which bypasses the A/D circuit and wakes the processor when the signal energy crosses a certain programmable threshold; this feature can significantly reduce power consumption in the sense mode and facilitates event – driven computation;

22. Smart Sensors and Sensor Networks • The DVS circuit: • For example for code 00000 the Vcore is 1.000 V, for code 00110 the Vcore is 1.250; and for code 01011 the Vcore is 1.750 V;

23. Smart Sensors and Sensor Networks • System – level power savings: • When running in the active mode, the power consumption is about 1 W; active power management using DVS results in about 53 % maximum power savings; the actual savings depend on the workload implemented;

24. Smart Sensors and Sensor Networks • The table shows the measured power consumption of the sensor node in various modes of operation: • The figure shows the overall power savings attributed to various power management hooks:

25. Smart Sensors and Sensor Networks • The figure shows the impact of power management techniques as a function of the workload and duty cycle requirements: • At an average workload requirement of 50 %, with slow variation, the estimated energy savings is about 30 %; • Idle mode energy savings is significant; at a 1 % duty cycle, the battery life can be improved by a factor of over 27 and at a 10 % duty cycle, the battery life improvement is by a factor of about 10;

26. Smart Sensors and Sensor Networks More about Dynamic voltage scaling • DVS is a technique that varies the supply voltage and the clock frequency, based on the computational load, to provide desired performance with the minimal amount of energy consumption; it is based on the DVS capability of the processor; • The multiple voltage design methodology is based on 2 obs.: • Three stage processing in sensor nodes: message processing in sensor nodes can be modeled as 3 sequential and dependent stages: preprocessing, data processing and postprocessing; in the preprocessing stage, raw data received from other sensors are decrypted and filtered; on the postprocessing stage data are compressed, if data are stored locally, or encrypted, if data must be sent out under security requirements; • Large variety of data processing requirements: the computational load can be largely unbalanced; in some information – intensive areas, a sensor node may receive a large amount of data needing intensive computation for extracting useful information; a sensor on a communication path may act as a messenger that only needs to forward messages without performing computation on the data;

27. Smart Sensors and Sensor Networks • An example: a secure sensor network: • Each sensor receives an encrypted message from other nodes every 5 s • The sensor must decrypt the message, process the data and send out the result before the arrival of the next message; • A message contains a certain number of packets of fixed size; • Suppose the RSA algorithm is used as the encryption function, which requires 110 ms and 5 ms to decrypt and encrypt a single packet, respectively; • Let consider 2 messages, m1 and m2, both with 10 packets; assume that m1 requires 2 s for data processing and needs 20 packets for the encrypted processing result and that m2 demands a forward; therefore no data processing is needed and the encryption results in a 10 – packet message; • Case 1 (no energy – driven approach, fixed 3.3 V power supply): • The processor will be on for data decryption/ encryption and processing with a power consumption of 230 mW at the 3.3 V voltage; it stays in the idle state from the completion of encryption to the arrival of the next message;

28. Smart Sensors and Sensor Networks • For message m1: 110 ms x 10 = 1.1 s is necessary for decryption, 2 s for data processing and 5 ms x 20 = 0.1 s for result encryption; • For message m2: 1.1 s, 0 and 0.05 s; • This gives a total execution time of 4.35 s; if power consumption when the system is idle is ignored, the energy consumption will be 230 mWx4.35 s=1 J; • Case 2 (with energy – driven approach): • Comparison in case of computing 128 b multiplication (the basic function for the public key algorithm) under 3 supply voltages:

29. Smart Sensors and Sensor Networks • A message header is added to the first packet of every message; it gives the receiver sensor information about the current message such as the length of the message, expected processing time and length of the result; • After encrypting the first packet, the sensor will be able to get the approximate computation load and to select the lowest voltage level accordingly, so that the required data processing and result encryption can be completed with the least amount of energy; • The sensor decrypts the first packet of a message in the first 110 ms then selects the proper voltages for processing and encryption and stays idle waiting for the next message; • It takes 230 mW x 0.11 s + 82 mW x 4.485s + 7.5 mW x 0.4 s = 396 mJ for m1 and 230 mW x 110 ms + 7.5 mW x 4.16 s = 56.5 mJ for m2, giving a total energy consumption of 452.5 mJ meaning a saving of 54 % over case 1;

30. Smart Sensors and Sensor Networks • Processor with multiple supply voltages • Dynamic power is proportional to vdd2; roughly speaking, a system’s power dissipation is halved if its supply voltage is reduced with 30 %; • However, the power/ energy savings have a cost: reduced throughput, slower system clock frequency and longer gate delay; the gate delay is proportional with vdd/ (vdd – vt)β, vt is the threshold voltage and βЄ (1, 2) is a technology – dependent constant; • A trade off must be found among to scale voltage as low as possible to reduce energy and caution to avoid missing required computations and deadlines; • A DVS system means that there is a set of discrete voltages and the system can operate at any voltage level, with different speeds and power consumptions, to accomplish the same task (or same amount of computation); • Let v1 < v2 < … < vn be different voltages; • Suppose that the processor finishes a task in time Tref with power dissipation Pref at the reference voltage vref and threshold voltage vt;

31. Smart Sensors and Sensor Networks • At supply voltage vi, to finish the same task, the processing time T(vi), the power dissipation P(vi) and the energy dissipation to complete this task E(vi) are given as follows:

32. Smart Sensors and Sensor Networks • Dynamic voltage scaling on sensor nodes: • The encrypted data packets received by the radio transceivers are passed to the processor which decrypts and authenticates the data at the current voltage; • The processor checks whether the packet contains a message header; if not, it continues message decryption for the following packets; if yes, it obtains information about the size of the message, estimated processing load and size of the result;

33. Smart Sensors and Sensor Networks • The computation workload is made of components for decrypting k packets, encrypting k packet (if necessary), processing the current message and other jobs currently running on the receiver sensor node; • The goal of preprocessing is to decrypt the message using the most energy – efficient voltage and determine the voltage for data processing; • The decryption voltage is decided based on the information provided by the message header; the processor will decrypt the remaining packets of the message at this voltage; • Once data decryption is done, the processor gains complete knowledge of the data and can update the voltage for data processing in a similar fashion; this completes the preprocessing and the processor will start processing the data with the selected voltage; • After data processing, the processor will halt (go to idle state) if it is not necessary to forward the result to other sensor nodes; • Otherwise, it will construct the message header, reevaluate the size of the encrypted result and select a proper voltage for data encryption; • The encrypted data goes to the radio transceiver and will be sent out (the postprocessing);

34. Smart Sensors and Sensor Networks • Simulations on different system configurations: • Energy savings: 58 % in the M – core system to 73 % in the StrongARM core, with an average of 64 %; it comes from processor computation, encryption and decryption;

35. Smart Sensors and Sensor Networks • Computation time: • Average time and energy consumptions in 10 simulations: