Smart Sensors and Sensor Networks

Smart Sensors and Sensor Networks Lecture12 Energy constraint (overview)

Smart Sensors and Sensor Networks Problem: lifetime of WSNs • The required lifetime of a WSN depends on the application type and can reach several years; • Usually the sensors cannot be accessed by people for economic or geographical reasons; • As a consequence the sensors have to be autonomous, they have to be powered by batteries; • In some applications one may find a small number of sensors with fixed power supplies but they have predetermined, specific roles, such as gateway to Internet; • The majority of sensors must have mobile power supplies; • In most cases the replacement or replenishment of the batteries is difficult or impossible, so the energy is the main constraint in designing and maintaining WSNs.

Smart Sensors and Sensor Networks Energy consumption sources in WSNs • Sources at sensor level and sources at network level; • Sources at sensor level are in accordance with its main blocks: sensing, processing, communicating and power supply; • The energy spent by the sensing block depends on the phenomenon to be sensed, duty cycle and sampling rate; • The phenomenon: from 0.4 mW (STCN temperature sensor) – 1250 mW (FCS-GL1/2A4-AP8X-H1141 flow control sensor); • The duty cycle is the ratio between the working time and the operational period; • High sampling rate and high duty cycle give high accuracy but also high consumption; • A trade off must be established between the sampling rate, duty cycle and accuracy; It depends on the application type being different for environment monitoring, tracking targets and event triggered applications;

Smart Sensors and Sensor Networks • The energy consumption of the processing block: • The energy consumption of the processor; • The energy consumption of the extra circuits (external memories + extra logic); • The energy consumption of the processor: the switching energy and the leakage energy; • Both are highly dependent on the supply voltage; • The switching energy is determined also by the total capacitance switched by the running software; • This can be reduced by shortening the length of the wires between the circuits and by decreasing the number of circuit inputs connected to the same output; • The leakage energy is consumed when no operation takes place;

Smart Sensors and Sensor Networks • The energy consumed by the communicating block is the most consistent from the overall energy consumption of a sensor; • Researches have shown high differences between the communicating and processing energy; examples: • At the Berkeley Mote sending and receiving 1 b costs about 1 mJ, respectively 0.5 mJ, while 1 mJ is enough for executing about 120 instructions; • For other processors the ratio is even higher, for example 1/220-2900 for the MEDUSA II nodes and 1/1500-2700 for the WIN nodes; • The communicating energy depends on several parameters: • modulation scheme, • data rate, • duty cycle and • the state in which the radio module is;

Smart Sensors and Sensor Networks • The state can be: • transmitting, • receiving, • idle or • sleep; • Transmitting and receiving energies may have close values, e.g. 35 and 38 mW for CC240 radio module, 111 and 111 mW for JN-DSJN513x, or different values but in the same range, e.g. 42 and 29 mW for CC1000 and 36 and 9 mW for TR1000; • The same for receiving and idling energies; • The least energy is consumed in sleeping mode, dropping to units or tens of μW;

Smart Sensors and Sensor Networks • The energy consumption sources at network level are due to: • MAC protocols, • routing, • network topology and • node deployment; • Energy is wasted in MAC protocols because of: • idle listening, • collision and congestion, • overhearing, • overemitting and • overheads; • Idle listening appears when a node listens an idle channel waiting possible packets, collision and congestion require retransmission and are favored by the high density of the network, overemitting happens when a sender sends a packet to the receiver but this one is not ready and the packet must be sent again and overheads means extra control packets, such as beacons, RTS-CTS packets and ACK packets;

Smart Sensors and Sensor Networks • Routing protocols can be divided in 3 categories: • Direct approach, • Location based and • Attribute based or Data centric; • An example for the first category is the flooding type protocol; it is very simple but not energy efficient; • In Location based routing sensors communicate based on their location identity; this requires that all the nodes are aware of their location and this can be achieved by adding GPS receiver to all the nodes or to part of them; in the second case new protocols must be used for grouping the nodes without GPS receivers around a node with GPS receiver for establishing their positions; the GPS receivers increase the cost and energy consumption; • In Data centric routing decisions are taken based on the data held by the sensors rather than their location; energy efficiency can be obtained by reducing the number of packets to be routed and, for that, data aggregation and in-network processing should be used;

Smart Sensors and Sensor Networks • WSNs can be organized in: flat, hierarchical (or tiered) and cluster-based topologies; • In flat topologies, packets travel form node to node till the destination; • In hierarchical topology, the nodes are organized in groups controlled by one of them, in several tiers; a node communicates only with its group head; • In cluster-based topology, the nodes are organized in clusters, with a cluster head and on the top of the cluster heads there is the so called sink or central node; • The transmitting energy is determined by the formula Rα where R is the transmitting range and α is an exponent with values between 2 and 4 depending on the transmission environment (free space or space with multiple-path interferences or local noise); this shows that energy can be saved by using multi hop communication instead of single hop communication; it is of high importance to establish the optimum number of hops;

Smart Sensors and Sensor Networks • There are 3 main approaches in node deployment: deterministic, random and pattern-based or grid; • In deterministic deployment, the sensors are placed in predetermined positions; this solution requires a good knowledge of the area to be monitored and is suitable only for small-scale applications; • Random deployment is suitable for hostile environments and is scalable to large-scale applications; the sensor nodes are thrown randomly in one or several steps; because the environment is not known, generally many sensors are necessary this increasing the cost of the network; • Grid-based deployment is simple and scalable; the nodes are placed in a regular form, row-by-row, using a moving carrier; in reality, the placement may be less regular due to terrain accidents and placement errors.

Smart Sensors and Sensor Networks Classification of solutions: • Solutions for: • Energy conservation: • Node level; • Network level; • Energy replenishment • Node level; • Network level; • The solutions for energy conservation at node level are oriented on: sensing, processing and communicating; • The sensing energy can be minimized by: adaptive sampling, hierarchical sensing, model-based active sampling and triggered sensing; • The processing energy can be reduced by: dynamic voltage scaling, dynamic frequency scaling and low power modes; • The communicating energy can be reduced by best modulation strategy, reducing the number of bits and intelligent radio hardware;

Smart Sensors and Sensor Networks • The solutions for energy conservation at network level solutions are grouped in: • MAC protocols, • Routing protocols, • In-network processing, • Data aggregation and • System partitioning; • The solutions for energy replenishment at node level are divided in: • Energy recovery and • Energy harvesting; energy harvesting solutions are based on converting: • solar energy, • mechanical energy, • wind energy, • thermal energy. • At network level, only the network replenishment solution was identified.

Smart Sensors and Sensor Networks

Smart Sensors and Sensor Networks • Energy conservation solutions • Adaptive sampling: • Adaptive sampling means to adapt dynamically the sampling rate depending on the application requirements and the available energy; it reduces the number of samples by exploiting spatio-temporal correlations between data; • Temporal correlation may appear when the monitored phenomenon varies slowly; spatial correlation may appear when the monitored phenomenon does not change sharply between areas covered by neighboring nodes; • The number of samples can be reduced while maintaining a certain level of accuracy; • Hierarchical sensing • Hierarchical sensing assumes that the same phenomenon is sensed by sensors with different parameters: some sensors provide high accuracy but they are energy hungry while other sensors are energy efficient but provide a limited accuracy; • The final measurement is obtained by inferring the readings of all sensors; a trade off must be established between the accuracy and the energy consumption;

Smart Sensors and Sensor Networks • Model-based active sampling: • The idea is to predict the data that should be acquired instead of acquiring it, hence saving the energy needed for sensing; • This can be done by using a model of the phenomenon which is build on top of an initial set of sampled data; if the model is no more satisfactory, it must be updated by using a new set of acquired data; • Triggered sensing: • It consists in using two types of sensors for the same phenomenon: sensors with high accuracy and high energy consumption and sensors with low accuracy and low energy consumption; • Unlike the hierarchical sensing, where all the sensors work at the same time, here, the low energy sensors are firstly activated and when they sense some activity in the monitored field the sensors with high accuracy are activated for providing fine grained data;

Smart Sensors and Sensor Networks • Dynamic Voltage Scaling (DVS) and Dynamic Frequency Scaling (DFS): • The mathematical support consists in: • The formula gives the commutation power which is the main component of the power consumption in CMOS circuits; • By reducing the level of Vdd the power decreases drastically since the impact of the supply voltage is high; • As it is shown in literature, there is an optimal point for Vdd, where the energy is minimum, but which is less than the threshold voltage (Vt) for most circuits; the problem is that some circuits, e.g. SRAMs, do not operate reliably below Vt; • The best idea is to use a multiple DVS: to maintain high values for the supply voltage on critical paths and for critical tasks and to reduce the values when the requirements are more relaxed; • The commutation power decreases linearly with the clock frequency; the first disadvantage is the decrease of the throughput; the second disadvantage is the increase of the latency which can lead to fail some time requirements; • DVS and DFS are solutions for decreasing the power consumption not necessarily the energy consumption;

Smart Sensors and Sensor Networks • Low power modes: • Processors and radio modules from sensors may enter low power modes. Such a circuit may have several low power modes; • For example the 80C51 microcontroller has Active mode, Idle mode and Power-down mode; • In Idle mode the CPU is halted but the internal periphery is active this allowing to sense the environment and to act on it; • In Power-down or Sleep mode the entire processor is halted this leading to important energy savings; • The microcontroller needs 16 mA in Active mode, 3.7 mA in Idle mode and 50 μA in Power-down mode; • However, the energy saving is affected by the startup energy and startup time a circuit needs for going from the low power mode in the active mode; • A processor needs time for ramping up phase-locked loops or voltage controlled oscillators and for restoring the processor context; • During this transition time no operation is possible; • For example, the μAMPS-1 transceiver needs a startup time of 466 μs and startup energy of 58 mW, [8]

Smart Sensors and Sensor Networks • Modulation strategy: • The communication can became efficient by reducing the transmission time;this can be obtained by codifying more than one bit per symbol, that is M-ary modulation should be used; • The disadvantages are: M-ary modulation requires more complex digital and analog circuitry than 2-ary modulation, M-ary modulation schemes require an increased radiated power, for increasing M, to achieve the same bit error target and choosing M-ary modulation schemes may be irrelevant in WSNs where it is expected that most packets are short and for such packets the startup energy dominates overall energy consumption; • Reducing the number of bits: • The solution is based on compression and aggregation of data; • Intelligent radio hardware: • Generally, a sensor node may have 2 roles: to sense or to route packets received from other sensors; it is shown that in a typical sensor network, around 65% of all the received packets require a node to act as a router; in most nodes, protocol functionality is implemented in the CPU, meaning every packet, regardless its destination, is processed by CPU; this is a energy waste for the CPU; • Intelligent radio hardware should be able to detect the packets that must only be redirected and avoid their processing by the CPU;

Smart Sensors and Sensor Networks • MAC protocols: • When designing energy efficient MAC protocols, the following parameters should be considered: network topology, deployment strategy, antenna mode, controlling mechanisms, delay, throughput, QoS requirements and number of channels to be used in communication; • For example, directional antennae in WSNs became an interesting alternative to omnidirectional antennae due to their potential for high throughput and reduced delay, interference and power transmission; by rotating the orientation of directional antennae, the signal level at the receiver can be increased; • Routing protocols: • Research on energy-efficient routing has two main targets: • minimizing energy cost per packet and • balancing energy consumption in the network; • If only the first target will be considered, some nodes will be overloaded, they will deplete their energy faster and the network will became disconnected or not operational;

Smart Sensors and Sensor Networks • In-network processing: • In-network processing or cooperative computing means that several sensors jointly work to take a decision; sometimes a single sensor can not decide if an event occurred or not and it has to collaborate with other sensors to take a decision and to send it to a remote location; in-network processing saves energy by increasing the weight of the local computation to communication’s detriment; although some communication still exists this is between neighboring nodes so the communication energy is low; • Data aggregation: • In order to reduce the size of the sent data, thus saving energy, a solution is to aggregate data and to send once the relevant data; examples are operations such as: sum, average, max, min or data fusion which consists in combination of unreliable data measurements to produce a more accurate signal; data fusion may be achieved by reducing the uncorrelated noise and enhancing the common signal; the data aggregation problem can be approached as a bicriteria optimization problem: to minimize energy consumption of a sensor and the latency cost of a message; • System partitioning: • System partinioning means to allocate the intensive computation tasks to certain nodes which have more powerfull processing and energy ressources; another approach is to spread complex computation tasks among more sensors to avoid overloading certain nodes;

Smart Sensors and Sensor Networks • Energy replenishment solutions: • Battery recovery: • This solution is based on the battery recovery or battery relaxing effect which means that an empty or almost empty battery self-recharges when no current is drawn from it; • This is based on chemical diffusion process within the cell; • For maximising the benefits a scheme must be developped for chousing and adjusting idle periods of batteries before reaching the saturation threshold and avoiding too much idling time; • Such a scheme should take into account the duty cycle parameter and buffering strategies. • Energy harvesting: • Energy harvesting consists in extracting energy from the environment; • The final goal is the so called energy neutral operation, when the sensor node operates without batteries having the energy harvested the only energy source;

Smart Sensors and Sensor Networks • Solar energy: • Solar energy is converted to electrical energy by solar panels; • Generally, sensor nodes are small and light weight, so the size of the solar panels must be limited; • Thus, an important parameter is the power density which can be obtained (in W/m2 or W/kg); for example: Heliomote, a solar energy harvesting system which uses a solar panel of area 3.75 inches x 2.5 inches; his panel outputs 60mA at a voltage of 3.3V;this power can recharge two AA-sized Ni-MH battery of capacity 1800mAh each. • Another solution is based on the maximum power point tracking; • The energy converted from solar source depends also on the particular location on the earth’s surface, time of day, latitude, atmospheric conditions and incidence angle of the sun’s beams; • The average values for the solar energy received are from 300 W/m2 near the ecuador to 100 W/m2 near the poles; • The solar energy varies also with the season being 5-25 times less in winter than in summer for temperate regions, depending on atmospheric conditions too; • Storage elements for the energy become necesary for ensuring the proper functioning of the WSN during all seasons, day and night;

Smart Sensors and Sensor Networks • Mechanical energy: • Mechanical energy from pressure, vibrations or force can be converted in electrical energy by using piezoelectric materials; • Walking can generate voltage if a piezo-electric element is mounted in a shoe; • Pushing buttons/keys can also generate electricity; • Vibrations are another source for electrical energy obtained by converting mechanical energy: • The generated electrical energy depends on the amplitude of the vibration, on its frequency and on the extend to which the presence of the harvesting device affects the vibration; • This, in turn, is affected by the difference between the masses of the harvesting device and the vibrating mass; • Values for the available energy ranges from 0.1 μW/cm3 up to 10000 μW/cm3; • Vibrations are present in cars and in build environments; measurements for a number of vibration sources have shown that the amplitude and frequency varies from 12 m/s2 at 200 Hz in a car engine compartment to 0.2 m/s2 at 100 Hz for the floor in an office building with the majority of measured sources having the fundamental frequency in the range 60-200 Hz;

Smart Sensors and Sensor Networks • Wind energy: • Wind energy is converted through rotors and turbines that convert circular motion into frequency and than in voltage;the principle of electromagnetic induction is used;the main disadvantage consists in the size of the turbine which is too big compared with the requirements for most sensor nodes; • Thermal energy: • Thermoelectric generators generates electrical potential from thermal difference between two points;high efficiency requires high temperature difference; the literature describes solutions which used the thermal difference between the air and soil, the ambient temperature difference and solid-states thermoelectric generators; a commercial device provides 100 μW from a 10 K temperature difference in a 9.3 mm diameter device 1.4 mm thick; • Electrostatic energy: • It is a form of mechanical – electrical energy conversion;it is based on changing capacity of vibration-dependent varaiable capacitors; vibrations separates the plates of an initially charged variable capacitor; the main advantages are the possibility to integrate with microelectronics and the fact that they do nod need any smart material and the main disadvantage is the need of initially charging the capacitor;

Smart Sensors and Sensor Networks • Radio frequency energy: • Harvesting radio frequency energy is done in RFID (Radio Frequency Identification) systems; • RFID tags are used to identify, locate and track people, animals and assets; • There are active and passive RFID tags; passive RFID tags power themselfs by using the radio frequency energy emited by active RFID tags; passive RFID tags report to active RFID tags their ID and location specific data; for harvesting the received energy, passive RFID tags must be tuned to the frequency of the radio source and the distance between an active and a passive RFID tag shoud be in the range of few meters; contrarly, the harvesting process will have very low efficiency because the ambient level of RF frequency is low and spread over a wide spectrum and converting it would require large broadband antennas; • Network replenishment: • Network replenishment extends network lifetime by adding new nodes, on the fly, after the initial deployment of nodes; • This ensures energy saving because a minimum number of nodes have to be deployed initially; additionally, this increases network lifetime by replacing died nodes with new ones;

Smart Sensors and Sensor Networks Conclusions • High independency is obtained by harvesting the environment but the disadvantages are low efficiency and the fluctuation in time of the generated level of electrical energy; • The solutions consist in developing energy usage profiles which match the energy generated or to use energy storage elements.

Smart Sensors and Sensor Networks