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Smart Sensors and Sensor Networks

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1 Smart Sensors and Sensor Networks
“Politehnica” University of Timişoara Faculty of Automation and Computers Smart Sensors and Sensor Networks Master program, 1st year Lecturer: prof. dr. ing. Mircea POPA

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Lecture 1 SSSN: definitions, classifications, challenges, applications

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Content: Smart sensors and sensor networks: definitions, classifications, challenges, applications Sensor network architectures Time synchronization and communication protocols Physical layer, MAC and link layer protocols Localization and positioning Coverage and topology control Routing protocols Data gathering and fusion In network processing Energy management Security, privacy, reliability and fault tolerance

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References: M. Ilyas and I. Mahgoub (editors), Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems; CRC Press, 2005 H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks; John Wiley and Sons, 2007

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Definitions There are 2 types of computations: Based on information processing; Based on processing the physical environment: embedded systems (98% of all computing devices are used in embedded systems (2000)); A step further is the spreading of the embedded control around us → Ambient Intelligence, Ubiquitous and Pervasive Computing; Is crucially based on sensors; 2 types: Wired: wiring is expensive (up to US$200/ sensor); Wireless: large spreading in our days; Because of their limited computational power, wireless sensors are connected in wireless sensor networks; Independent nodes collaborate to fulfill their task; A node has computational, wireless communication and sensing functionalities;

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A sensor network node is made of 5 main components: Controller: processes the data, is capable of executing code; Memory: store programs and intermediate data; its organization is established by the controller; Sensors and actuators: the interface to the physical world; monitors and controls parameters of the environment; Communication: ensures the collaboration of the nodes; is wireless; Power supply: offers the energy for all the other components; batteries usual no rechargeable, sometimes with possibilities to obtain energy from the environment;

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Controllers: the core of the SN: Microcontrollers, DSPs, FPGAs, ASICs: An FPGA can be reprogrammed “in the field’ to adapt to the real time requirements; it costs time and energy; An ASIC has high energy efficiency and performance but low flexibility; The generally preferred solution: the microcontroller; Memories: RAM, EEPROM and Flash; No rules: the memory requirements are highly application dependent; Communication: Wired: serial buses: CAN, LIN, SPI etc. Wireless: RF: provides long range, high data rates, acceptable error rates and reasonable energy consume; communication frequencies between 433 MHz and 2,4 GHz are used; the conversion between a bit stream and radio waves is done by a transceiver; Optical: requires line of sight between the nodes; Ultrasound: in mediums were RF and optical waves do not penetrate (e.g. water); or as a secondary way of communication with a different speed;

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Sensors and actuators: Sensors can be divided in 3 categories: Passive, omnidirectional: they can measure a physical quantity without influencing the environment (passive); there is no direction parameter in the measurement (omnidirectional); examples: thermometer, light sensor, vibration, humidity, air pressure, smoke detectors etc. Passive, narrow – beam: the direction parameter exists in the measurement process; example: a camera; Active: they actively probe the environment; examples: sonar, radar, some seismic sensors which generate shock waves by small explosions; Trade-offs include accuracy, area of coverage, dependability, energy consumption, size, cost; Actuators: they execute physical operations: open or close a relay, controls a motor, a light bulb etc. Power supply: 2 tasks: Storing energy and providing power: with batteries; Attempting to replenish the consumed energy;

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Main features of the sensor networks are: Self – organizing capabilities; Short – range broadcast communication; Multihop routing; Dense deployment and cooperative effort of sensor nodes; Frequently changing topology due to fading, node failures and node deaths; Limitations in cost, size, energy, transmit power, memory and computing power; Sensor networks vs. traditional data networks: SNs have severe energy, computation, storage and bandwidth constraints; the major limitation is the energy because usual they have not renewable energy sources; SNs and TDNs have different positions and interactions with the users: In a TDN, the user is connected to a node and requests a service; the user interacts with another user or service at the other end; In a SN, the user is not interested in reading one or two nodes but in observing the whole physical process; the two – entity model does not suits here because there are not sources and destinations but users and the whole network; a SN does not connect different parties together but provide information to users;

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Wireless sensor networks vs. conventional wireless ad hoc networks: The majority of existing WSNs are ad hoc and multihop type; Advantages of ad hoc architecture: Can be easily tailored to specific applications; There is not predetermined infrastructure so the SNs can be rapidly and randomly deployed and scaled; new nodes can be added and existing nodes can leave without affecting the functionality of the other nodes; Is highly robust to single or even multiple node failures and offers a high level of fault tolerance because of node redundancy and its distributed nature; The bandwidth can be reused; it comes also from the multihop communication: the communication is local and within a small range; The multihop communication leads to energy efficiency; large – scale propagation follows an exponential law to the transmitting distance, with exponent 2 to 4 depending on the transmission environment; the power transmission can be reduced in orders of magnitude by using multihop routing with short hopes instead of a single – hop routing with a long distance for the same destination; Existing ad hoc network architectures and protocols are not directly suitable for SNs because of the application requirements;

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The main differences are:

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Classification of WSNs Although WSNs are application oriented and, thus, very diverse, there are some criteria for classifying them: Data dependencies: In nonaggregating WSNs data is transmitted “as is”; there is low node density and high node capacity; the total traffic load may increase rapidly leading to high latency and high energy consume; it offers high accuracy; In aggregating systems, nodes are close and the information from multiple sensors can be highly correlated leading to reduced traffic load and energy savings; the intermediate nodes may need computational and memory resources; is appropriate in large – scale systems with densely distributed sensor nodes; the end users are interested in the collective information with moderate accuracy:

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Distribution of sensors: Deterministic: sensor nodes occupy fixed or preplanned positions; simple control and easy implementation; appropriate for only a few applications with fixed and known data collecting points; Dynamic: the location of sensors is not available a priori, they have dynamic positions; is more scalable and flexible but the control is more complex; large number of applications; Control scheme: Non self – configurable: the sensor nodes rely on a central node to command them and collect data from them; appropriate only for small – scale networks, covering small areas; easy control algorithms; Self – configurable: the sensor nodes are able to autonomously establish and maintain connectivity by themselves and collaboratively work for sensing and sending data; appropriate for large – scale complex networks; complex control algorithms; A specific WSN may have characteristics of different types; for example a WSN may be self – configurable, deterministic, nonaggregating and multihop;

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Technical challenges WSNs are working in the following conditions: Massive and random deployment: many WSNs are made of hundreds or even thousands of sensor nodes spread randomly over areas or dropped densely on inaccessible terrains; self – configuration is necessary; Data redundancy: it comes from the dense deployment of the sensors; Limited resources: constraints in energy, computation, memory and bandwidth; generally the batteries are nonrechargeable or irreplaceable; Ad hoc architecture and unattended operation: the sensors must establish and maintain connections autonomously; Dynamic topologies: there may be several causes: Failure of nodes because the exhaustion of power or hardware failure; generally this takes place without any notification to the other nodes; New nodes are added without any notification to the existing nodes; The monitored environment may change and affect physically sensors or render the information they gather obsolete;

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Error – prone wireless medium: the wireless medium incurs more errors than a wired one; it can be noisy or can cause severe signal attenuation; Diverse applications: the architectural requirements for diverse applications may vary significantly; Safety and privacy: Should be essential considerations because many WSNs are used in military or surveillance applications; However, security is a very difficult problem because WSNs are resource limited while security solutions require important resources; In many cases, the security and privacy problems are passed to the application level and are not approached at the communication protocol level; QoS requirements: The quality refers to the accuracy with which the data sensed match with the processes from the environment; Accuracy in WSNs emphasizes the characteristic of the aggregated data of all sources instead of individual nodes; accuracy may be measured by the amount of data; Latency is a parameter of QoS: data with long latency may be outdated and lead to wrong decisions;

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The mentioned conditions lead to the following design objectives and directions: Small microsensor devices: Affordable and compact units are essential to massive deployment of sensors The cost of the sensor node is also important in large – scale WSNs; The smaller the sensor is, the lower interference the sensor would have on the observed objects and the easier the deployment would be; Scalable and flexible architectures and protocols: the system should be scalable and flexible to the enlargement of the network scale; Localized processing and data fusion: Localized processing is necessary to eliminate data redundancy; data must be filtered, processed and only after that it must be transmitted; Data fusion (aggregation) may be done by intermediate nodes in order to increase efficiency; Adaptability: WSNs should adapt to the dynamicity of the environment; communication noise and sensor diversity may induce nondeterministic phenomena so adaptive fidelity signal processing is welcome at the sensor level;

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Resource efficiency design: resource efficiency is very important regardless of the complexity; Energy efficiency is crucial to the lifetime of the system; Low power modes at the hardware level, power – saving modes on MAC layer, power – aware routing on network layer are necessary; Minimization of the number of circuits (e.g. avoidance of external memories) and use of low power circuits (attention to the delay penalty); The use of algorithms with low complexity will reduce the computation time and thus save power; Bandwidth efficient protocols can accelerate data delivery (clustering, broadcast and multicast trees, multihop communication); There is not a unique definition of system lifetime for all applications or cases; A system may be declared dead if: The first node fails or consumes all its energy; A number of nodes dies; All nodes die; System lifetime can also be measured using application – specific parameters, such as the time until the system can no longer provide acceptable results;

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Self – configuration: Randomly and massively deployed sensor nodes have to be self – configurable in order to set up the network connection and commence operation; WSNs are dynamic during their lifetime; Sensor nodes transit among different states: off, sleep, startup, idle, transmitting, receiving, failure; protocols should have the capability of forming connections autonomously regardless of the states the nodes are in; New links should be established in case of node failure or link congestion; Power – aware routing should be applied: for example packets could be routed through some subsets of the network in which nodes have more residual energy in order to realize an equal dissipation of energy among nodes over the entire network; Reliability and fault tolerance: In many cases, data must be delivered reliably over the noisy, error – prone and time – varying wireless channel; Data verification and correction on each layer becomes critical; It is desirable that nodes perform self – testing, self – calibrating, self – repair and self – recovery procedures;

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Security design: important for specific applications (e.g. military); QoS design with resource constraints: The two measures of QoS in WSNs are: accuracy and latency; In general the amount of data determines the level of accuracy; A trade – off between the two aspects is necessary because large amounts of data (high accuracy) need large bandwidth causing more contention during transmission and increasing the latency; A trade – off between QoS and resource consumption is also necessary: high accuracy requires high power; local computation may decrease the amount of data transmitted but it requires computation which means power consumption and higher latency; Others: Attribute – based naming and data centric routing: users are more interested in querying the property of the interested phenomenon, rather than a specific node; for ex. it is important to know the temperature in a certain room or the areas were the temperature is over 400C than to know the temperature read by a certain node; Locality of information: reported data may be meaningful when associated with the exact knowledge of the sensor’s position;

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Applications WSNs are able to monitor a wide range of physical parameters: Temperature, Humidity, Light, Pressure, Object motion, Soil composition, Noise level, Presence of a certain object, Characteristics of an object such as weight, size, speed, direction, latest position etc. Due to WSNs’ reliability, self – organization, flexibility and ease of deployment, the applications vary widely; they can be applied to almost any environment especially in those were conventional wired sensor networks are impossible or unavailable, such as in inhospitable terrains, battlefields, after natural disasters etc.

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Based on the types of interactions between a WSN and the environment, the applications of sensor networks can be divided in two classes: Querying and Tasking; Querying applications: concern how information collected by a sensor network can be retrieved based on a specific criteria; A sensor node may be able to collect temperature, humidity, light, pressure etc. Applications may need simple raw of data from specific sensors; Applications may need more complex data involving filtering and aggregation, e.g. which region of the sensed area has the highest temperature; The observer will receive only the processed data, thus system resources will be conserved; The collected information could also be used to diagnose the health of the sensors;

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Tasking applications: involve programming sensor nodes to perform specific actions upon certain events; Events can be physical environment changes, messages from nearby sensor nodes, triggers from hardware/ software modules inside a sensor node; A simple task can be to ask a sensor to send data when it senses something unusual in its surroundings; A more complex task may require distributed coordination or collaboration among sensor nodes to achieve higher accuracy and efficiency; for instance, tracking a moving object in an area by simply having every single sensor node periodically and blindly monitor its surroundings can be very energy efficient; instead, collaboration is useful among the nodes surrounding the tracked object; Tasking applications can use information obtained from sensor nodes connected to actuators, to adapt nodes’ behavior or movement pattern to achieve better sensing performance; for environmental control applications, actuators can be used to affect the physical environment;

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Based on the interaction patterns between sources (nodes that sense data) and sinks (nodes that receive the data, they can be or not part of the sensor network) the applications can be divided in: Event detection: sources report to the sinks the occurrence of a specified event; simple events can be detected locally by a single sensor node (e.g. a temperature threshold is exceeded); more complicated events may require the collaboration of several sources to decide if it occurred (e.g. a temperature gradient becomes to steep); if several different events can occur, event classification must be considered; the classification criteria are application dependent; Periodic measurements: sensors periodically report measured values; these reports may be triggered by a detected event; the reporting period is application dependent; Tracking: the source of an event can be mobile (e.g. an intruder in surveillance scenarios); the source’s position, its speed and direction are reported to the sink; collaboration is necessary between the sensors for establishing the mentioned parameters;

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Function approximation and edge detection: The way a physical value, like temperature, changes from one place to another can be regarded as a function of location; so an approximation of this unknown function can be obtained leading to a map which is delivered to the sink; the updating of this map depend on the application’s needs; A relevant problem can be to find areas of points of the same given value; an example is to find the isothermal points in a forest fire applications to detect the border of the fire; this can be generalized to find edges in such functions or to sent messages along the boundaries of patterns in both space and time; The applications are diverse also considering the deployment options: There are applications with well – planned, fixed deployment of sensors (e.g. in machinery maintenance applications); There are applications with random deployment by dropping a large number of sensors over a specified area; Sensor nodes can be mobile themselves and move, in a postdeployment phase, to positions were their tasks can be better fulfilled; Sensor nodes can be mobile because they are attached to other objects (e.g. in logistic applications); the network has to adapt itself to the location of nodes;

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Application areas: General engineering: Automotive telematics: cars are equipped with dozens of sensors and actuators for improving the safety and efficiency of traffic; Sensing and maintenance in industrial plants: complex industrial robots are equipped with many sensors; the wired connection to a main computer is expensive and subject to accidents due to the robot’s movement; the wireless connection is the preferred solution; by mounting small coins on the sensor nodes, the induction is exploited for obtaining power supply; Aircraft drag reduction: flow sensors are combined with blowing/ sucking actuators mounted on the wings of the airplane; Smart office spaces: areas are equipped with light, temperature, movement sensors, microphones for voice activation etc. Tracking of goods in retail stores: tagging facilitates the store and warehouse management; Tracking of containers and boxes: shipping companies are assisted in keeping track of their goods; Tracking fleets of cars; Social studies: connecting sensors to humans offers information about the social behavior; Commercial and residential security;

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Agricultural and environmental monitoring: Precision agriculture: precise control of irrigation and fertilizer concentrations are possible; Planetary exploration: exploration and surveillance in inhospitable environments such as remote geographic regions or toxic locations can take place; Geophysical monitoring: in order to increase the resolution in detecting seismic activities; Monitoring of freshwater quality: useful especially in remote locations or under adverse conditions; the sensors are needed because of the complex spatiotemporal variability in hydrologic, chemical and ecological parameters; in additions sensors along the coast could alert surfers, swimmers and fishermen to dangerous levels of bacteria; Zebranet: it is a project for tracking the movement of zebras in Africa; Habitat monitoring: sensors can be deployed for measuring the parameters of an habitat, such as humidity, pressure, temperature, infrared radiation, total solar radiation, photosynthetically active radiation etc. Disaster detection: forest fire and floods can be detected and localized; Contaminant transport: the assessment of exposure levels requires high spatial and temporal sampling rates, possibly to be provided by WSNs;

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Ecosystem monitoring: a class of applications with benefits for life and science study; WSNs can provide information on several environmental conditions, such as soil and air chemistry, plant and animal species population and behaviors, permitting long – term automatic identification, recording and analysis of interesting events; WSNs have advantages over the traditional methods of environment monitoring, such as: Noninvasive deployment: wireless sensors can be dropped on remote islands or dangerous places; Anytime deployment: sensors can be deployed in any interesting period, for example before the producing season of some species of animals; Minimal interference: there are animal species which are very sensitive to unexpected visits in certain periods, leading to a dramatic increase of mortality; macrosensor equipment is not desirable; High level of robustness and accuracy: by integrating data aggregation and signal processing within the neighborhood sensors, WSNs become more robust to node failure; self – configurability ensures adaptability to the dynamic physical world; Ease of networking: are capable to connect to the Internet, offering remote control, monitoring, and collection of data by one or several users; Low cost: compared to human – attended methods;

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Military applications: Asset monitoring and management: commanders can monitor the status and locations of troops, weapons and supplies; WSNs became an integral part of military command, control, communications, computing intelligence, surveillance, reconnaissance and targeting (C4ISRT) systems; Surveillance and battle-space monitoring: vibration and magnetic sensors can report vehicle and personnel movement, permitting close surveillance of the enemy; Urban warfare: sensors are deployed in buildings that have been cleared to prevent reoccupation; movements of friends and foe are displayed in PDA – like devices carried by soldiers; snipers can be localized by the collaborative effort of multiple acoustic sensors; Protection: sensitive areas such as atomic plants, bridges, retaining walls, oil or gas pipelines, communication towers, ammunition depots, headquarters can be protected by sensor fields able to discriminate between intruders; biological and chemical attacks can be detected early or even prevented by sensors acting as warning systems; Self – healing minefields: a self – healing minefield is an intelligent, dynamic obstacle that senses relative positions and responds to an enemy’s breaching attempt by physical recognition;

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Medical care: Medical sensing and monitoring: physiological data such as body temperature, blood pressure and pulse can be sensed and automatically sent to a computer or physician; they can be used for health status monitoring and medical exploration; tiny sensors in the blood stream, possibly powered by a weak electromagnetic field, can continuously analyze the blood and prevent coagulation and thrombosis; Remote virus monitoring: spreading large number of wireless sensors in regions which lack reliable communication permits to collect and transmit data such as incident of disease and characteristics of infected population and to monitor environmental conditions, such as amount of rainfall and humidity, that support the proliferation of virus – carrying insects; risk maps for different viruses can be obtained; Micro – surgery: a swarm oh micro – robots may collaborate to perform microscopic and minimally invasive surgery; Remote surveillance for old people or people with disabilities; Other applications: artificial retina, glucose level monitoring for diabetes patients, organ monitoring for organ transplant purposes, cancer detection for people working or living in high – risk conditions, drug administration and distribution;

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Civil engineering: Monitoring of structures: sensors can be placed in bridges to detect and warn of structural weakness and in water reservoirs to spot hazardous materials; sensors can be used also to study the reaction of tall buildings to wind and earthquakes and to monitor the material fatigue; Urban planning: urban planners will track groundwater patterns and how much carbon dioxide cities are expelling, enabling them to take optimum land – use decisions; Disaster recovery: sensor robots can be used to locate signs of life in buildings affected by disasters; Smart homes and working buildings: offers intelligent living environments: Remote metering: remote reading of utility meters, such as water, gas or electricity; the readings will be sent wirelessly; parking meters which send warning signals before the parking time expires; Smart space: intelligent lighting system, HVAC systems, intelligent heating system, integration of multimedia systems, the final purposes being to customize the living space to different members of the family and to optimize the energy consume; Smart security systems: allows remote visualization of the property and offer high reliable security system with possibility of remote alarm to officials; Smart working buildings: customizes the working area and offers high communication capabilities;

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Examples Smart sensors: Cricket sensors: RF sensors; Ultrasonic sensors; Tiny OS; Powered with batteries or externally; RS232 interface for being programmed or transferring data; 2 modes: Beacon, Listener; The time difference between the RF pulse and the ultrasonic pulse indicates the distance among a beacon and a listener; Applications: space identifiers and position coordinates: the beacons are deployed in a delimited space and they will indicate the position of one or several mobile listeners;

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MICAz (MICA Mote family) Developed at University of California at Berkeley; ATmega 128L microcontroller; ZigBee RF interface; MICA2DOT Developed at University of California at Berkeley; Same microcontroller; RF interface;

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Schematics and dimensions of a Mica node:

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EYES nodes: Developed by Infineon; Texas Instrument MSP 430 microcontroller, an Infineon radio modem TDA 5250 (it also reports the measured signal strength to the controller), a USB interface to a PC and possibility to add additional sensor/ actuators; BT nodes Developed at ETH Zurich; ATmega 128L microcontroller, 64 – 180 Kb RAM, 128 Kb Flash, Bluetooth interface

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ScaterWeb Developed at Freie Universitat Berlin; MSP 430 microcontroller, embedded web interface, Bluetooth, IIC, CAN interfaces;

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