Smart Sensors and Sensor Networks

1. Smart Sensors and Sensor Networks Lecture2 Sensor network architectures

2. Smart Sensors and Sensor Networks Characteristics of WSNs • A wireless sensor network is a self – organized system composed of a large number of low – cost sensor nodes; • Self – organization means that the system can achieve the necessary organizational structures without requiring human intervention; individual nodes cooperate in achieving the required operations; • Classes of applications: system and space monitoring, habitat monitoring, target detection and tracking and biomedical applications; • Sensor nodes are usually battery based with limited resources; in most cases it is difficult or impossible to recharge each node; • Sensor nodes are smart:

3. Smart Sensors and Sensor Networks • Most common features involved in designing network architecture: • Extended coverage and easy deployment: • The number of sensor nodes may be very high and the coverage will be large; • The coverage is flexible and can be adjusted by adding/ removing/ moving sensors; unfriendly terrains can also be covered; • Reliability and flexibility: • Although the capability and reliability of a single sensor node is limited, multiple sensors offer fault tolerance, making the system more robust; • The neighbor nodes of a dead one can provide similar information; • Communication link failures are, generally, not catastrophic because multiple routing alternatives are available; • Improved monitoring capabilities and information quality: • Better monitoring capabilities about parameters with spatial and temporal variances is obtained through aggregation of data; • It was shown that the gain offered by having more sensors exceeds the benefits of getting more detailed information from each sensor; a network with many low – cost simple sensors provide more accurate information than a network with a few high – cost complex sensors;

4. Smart Sensors and Sensor Networks • Mobility: • Currently most sensors are static; • Mobility of sensors is desirable because monitoring and tracking capabilities and communication can be improved; • In many cases, sensors are deployed randomly rather than located precisely; if the desired object or target area cannot be observed based from the current location of the sensors, they may adjust their positions; • In order to improve its communication quality, the sensor node may move and rearrange its connectivity with other nodes and also reduce the transmission power for communication; Challenges in designing SNs • Identifying requirements for typical SN applications: • Qualitative analysis of representative applications can facilitate identification of more accurate design goals; • It is important to create benchmarks for sensor networks as in the case of designing new computer architectures when benchmarks suites were created for typical applications;

5. Smart Sensors and Sensor Networks • Identifying relevant technology trends: • Technologies evolve with different rates, for example the memory and processor technologies grow according to the Moore’s law, the wireless bandwidth has increased by a factor of 100 in 7 years, while the capacity of batteries grows with a rate of only 3% per year; • It is important to know were will be bottlenecks; • Different types of algorithms will be developed depending on future ratios of computation, communication and storage cost; • Balanced design: • Optimization of each and every component to the maximum extend does not mean necessarily optimization of the entire network; • Ex.: routing based only on the length of the trajectories, although attractive, will lead to the rapid energy consumption of the nodes found on that routes; • A balanced routing technique will be necessary taking into account more parameters not only the length of the trajectories;

6. Smart Sensors and Sensor Networks • Techniques for design and use of the design components: • The components of the SN node can be grouped in 2 categories according to their maturity: storage and power supplies are mature technologies and ultralow power wireless communication, sensors and actuators are younger technologies; • It is important to identify which techniques, architectures and tools can be reused and where is needed new design effort; • Overall node architecture and trade-offs: • Possible trade-offs can be envisioned; for example: • The TinyOs approach advocates aggressive communication strategy in order to reduce computation and storage at local sensor nodes; • The sensor – centered approach advocates aggressive data processing, filtering and compression in order to reduce communication; • Survey of state-of-the-art technology, components and sensor network nodes: • Several state-of-the-art sensor nodes are surveyed and decisions that influence their structure are critically evaluated;

7. Smart Sensors and Sensor Networks Functional architectures • Starting from the features of the sensor networks and the information types expected by the user, a generic functional architecture for sensor networks consists of the following components: • Hierarchical clustering: • Sensor nodes can be aggregated to form clusters, based on their energy levels and proximity, in order to support scalable operations; • The aggregation process can be recursively applied to form a hierarchy of clusters:

8. Smart Sensors and Sensor Networks • Within a cluster, a cluster head has to be elected to perform information filtering and fusion, such as periodic calculation of the average temperature of the cluster coverage area; • The clustering process should be reinitiated in case the cluster head fails or consumes its energy; • Location awareness: • Because sensor nodes are operating in physical environment, knowledge about their physical location becomes mandatory; • Location information can be obtained via several methods; • Global positioning system (GPS) provides absolute location information; • For economical reasons, however, only a subset of sensor nodes may be equipped with GPS receivers and function as location references by periodically emitting a beacon signal showing their own location so that other sensor nodes, without GPS receivers, can roughly determine their position; • Attribute – based naming: • Users are more interested in finding out which area has temperature higher than 1000 or what the average temperature in a specific area is, rather than the temperature sensed by a specific sensor; • The attribute – based naming facilitates the data – centric characteristics of sensor queries; • Logical attributes, such as IDs, can also be part of the names so traditional addressing scheme using node IDs becomes a special case of attribute – based naming;

9. Smart Sensors and Sensor Networks System architecture, protocols and algorithms • Sensor deployment strategies: • Is a fundamental issue for WSNs; • The objective of a sensor deployment plan is to achieve desirable coverage with a minimum number of sensor nodes while complying with constraints of QoS, cost, reliability and scalability; • Coverage has a twofold meaning: range and spatial localization; range refers to the geometric area and spatial localization emphasizes the relative positions of sensor nodes and targets so as to extract accurate measurements; • There are 4 methods for sensor deployment: predetermined, self – regulated, randomly undetermined and biased distribution; • Predetermined strategy is useful in 2 situations: • Knowledge of the environment or the possible target is sufficient; • When the sensing site is modeled as a grid, i.e. the two or three – dimensional space is represented by points coordinates; the granularity of the grid is determined by the desired accuracy;

10. Smart Sensors and Sensor Networks • Self – regulated strategy: • Is scalable but the computational expense may become prohibitive: • Sensor nodes may be deployed sequentially in steps by introducing path exposure as a metric of goodness; the number of sensors is chosen in each step and the cost of deployment can be minimized; • Randomly undetermined strategy: • Is more realistic for large – scale WSN application, such as unknown battlefields or hostile terrains; • Sensor nodes are generally spread uniformly in a given area; • The nodes are easily placed and therefore the cost is low; • Although the sensors can be randomly deployed, the coverage might not be uniform due to obstacles or other sources of noise in an environment; • Biased strategy: • Is useful when the sensors must be deployed non uniformly in a specified area; • Example: the deployment of sensors in a large – scale office building in which the density of sensor nodes close to the windows must be much higher that that in the middle of the room;

11. Smart Sensors and Sensor Networks • Dynamic power optimization at the nodal level: • Energy is consumed for sensing, computation and communications; power conservation can be achieved in any of these functions; • Workload in WSNs typically has the characteristics of burstiness; • Some nodes or certain components of nodes should switch to power – saving states between consecutive bursts while the functionality and QoS are still maintained; • Switching back needs overhead (time and energy consumption); • Dynamic power management offers a trade – off between power saving and latency, power consumption and state transitions; • Nodes may be in different states, with different power consumptions: off, sleep, standby, ready, receiving, transmitting; • Well – designed control algorithms for transiting the states are necessary, taking into account the fact that a transition costs latency and power consumption; • For example a transition from “off” to “sleep” might cost more energy than a transition from “sleep” to “standby”;

12. Smart Sensors and Sensor Networks • Dynamic voltage scaling and frequency scaling save energy on computation without degrading performance; • The justification is that the computational workload of a processor in WSNs is time varying and peak system performance is not always needed; • This solution needs to predict the processor’s workload so as to adjust the power supply and operating frequency; more accurate prediction will lead to higher power efficiency with less degradation to system’s performance; • Nevertheless, workloads in WSNs are mostly nondeterministic so accurate modeling the workload is an open issue; • Another approach is to optimize the transmission power of sensors: • It influences many aspects of WSN communication: one – hop communication radius, network topology and hierarchy, retransmission rate, routing path selection etc. • It can be saved through dynamically adjusting the transmission power based on the estimation of transmitting distance of each transmission; • It can be saved also taking at the whole WSN level; the optimal value is affected by propagation environment and device parameters; small transmission power might cause excessive power consumption (contrary to intuition) due to a combined effect of great number of hopes and large retransmission probability;

13. Smart Sensors and Sensor Networks • Optimal schemes at system level: • Topology management: • During the operation, some node may operate in low duty cycles by passing in “sleep” or “off” states to conserve energy; in these states a node is unable to communicate or forward packets; it would then need to be awakened in certain situations, such as when it is time to collect data; therefore, the active topology of the network changes over time; the critical issue is how to arrange sleep state transitions while ensuring robust, undergraded information collection; • A solution is to rotate the node functionality periodically to achieve balanced energy consumption among nodes; • Clustering and hierarchical architectures: • It was shown that the energy consumed for communication is the largest component in the total energy consumption of a WSN; • The ratio of communicating 1 bit over the wireless medium to that of processing the same bit is in the range of 1000 to 10000; • The power for transmission is higher than the power for reception and it grows exponentially with the distance; • Reducing the amount of traffic and distance of communication highly prolongs the system’s lifetime;

14. Smart Sensors and Sensor Networks • The solution consists in dividing the entire system in clusters, thus dividing the one – hop long – distance communication by multihop short – distance communication; • Advantages: the energy for transmission is reduced, load balancing and scalability for the growth of the network size; • Problems: how to select the cluster heads, how to organize the clusters; • The clustering strategy can be single – hop cluster or multihop cluster, based on the distance between the cluster head and its members:

15. Smart Sensors and Sensor Networks • Clusters can be also single or multilevel:

16. Smart Sensors and Sensor Networks • A distributed energy adaptive clustering strategy is LEACH; • At the beginning, each node self – selects itself as a cluster head with a predetermined probability; • The cluster head then advertises its decision to the other nodes; those decide to join a specific cluster that requires minimum communication energy; • In order to ensure the balanced energy dissipation among all nodes, LEACH rotates the cluster head by calling the self – selection and cluster formation procedure periodically; • The analytical and simulation results show that there is an optimal number of cluster heads that minimize the energy consumption; • An other solution consists in partitioning the nodes into groups so that each cluster has a similar number of nodes; • It achieves minimum energy consumption by optimizing the total spatial distance between the cluster head and its members; • An other approach consists in using the energy as a parameter: the nodes with higher energy will be the cluster heads; they will be able to manage the other nodes and forward the data collected from the cluster members to a faraway base station; • The mentioned solutions are for single – hop clusters being suitable only for networks with a small or medium number of nodes;

17. Smart Sensors and Sensor Networks • Traffic distribution and system partitioning: • One key element of traffic forwarding is the selection of an energy – efficient path from the source to the destination; • A solution consists in the route that minimizes energy consumption; • This is not always the case in order to maximize the overall system lifetime; because the nodes on such route are overused, their batteries are more likely to be exhausted, resulting in discontinuity of the network, as well as unavailability of sensing in the corresponding regions; • An other approach consists in using, in communication, those nodes which have more energy; the result will be the same, the mentioned nodes will loose rapidly their energy; • Taking the point of view of the system’s overall availability and longevity, it is desirable to balance the communication over several routes even if only one of them has minimum energy consumption; • System partitioning reduces power dissipation in the sensor nodes by removing some intensive computation to remote base stations that are not energy constrained or spreading some of the complex energy – consuming computation among more sensor nodes instead of overloading several centralized processing elements;

18. Smart Sensors and Sensor Networks • Collaborative signal and information processing (CSIP) and data aggregation: • Local processing of raw data before direct forwarding will effectively reduce the amount of communication and improve the efficiency (information/ bit transmitted); • CSIP and data aggregation are 2 typical localized paradigms for the purpose of data processing in WSNs; • CSIP combines interdisciplinary techniques, such as low power communication and computation, space – time signal processing, distributed and fault – tolerant algorithms, adaptive systems, sensor fusion and decision theory; • CSIP provides solutions to: dense spatial sampling of events, distributed asynchronous processing, progressive accuracy, optimized processing and communication, data aggregation, data fusion and querying and routing tasks; • Data aggregation is another efficient data processing approach in WSNs: it minimizes traffic load (in terms of number and/ or length of packets) through eliminating redundancy; • The node which does aggregation, applies a data – centric approach to replace the traditional address – centric approach in data forwarding;

19. Smart Sensors and Sensor Networks • Several data aggregation algorithms have been reported in the literature; there are based on duplicate suppression, maximum or minimum functions etc. • Cross – layer design • Traditional design of wireless ad hoc networks is mainly based on the layered stack:

20. Smart Sensors and Sensor Networks • This layered model simplifies the network design and leads to robust and scalable protocols; • However, the design and operation of each layer are isolated and the interface between layers is static and independent of the individual network constraints and applications; • This model is inappropriate in designing WSNs because of the constraints, especially: energy, bandwidth, memory size and CPU speed; • New approaches can be necessary to break the separation between layers: • Open issues: how to deal with information exchange across stack layers, how to realize a specific application requirement with global system constraints, how to understand and apply this design principle;

21. Smart Sensors and Sensor Networks Architecture of sensor networks • The architecture of SNs comprises: • The architecture of sensor nodes; • The general structure of sensor networks; • The communication organization; • The corresponding data architectures; • Functional layers of WSNs: • The sensor network is more application specific than traditional networks • The layers of functions of a WSN:

22. Smart Sensors and Sensor Networks • The sensing layer: performs the work of data acquisition from the detected objects; • The communication layer: performs the tasks of data correlation, data compression, data dissemination and routing: • Due to energy constraints of the WSNs and terrain characteristics, the MAC protocols and network protocols should be energy aware; • The communication layer may also contain a security sublayer dealing with security and authentication problems for some applications; • The data aggregation and fusion layer: processes data received from the communication layer and combines them; the final detection results of the network are transmitted to the upper layer; • The user layer: provides a man – machine interface with displaying and interaction functions and presents the final results to human and/or computer systems in the required forms; • Additional functional blocks: • The resource management module monitors the available resources (energy, memory, storage units) and balances the energy consumption between nodes; • The topology/coverage control module monitors the coverage and the topology;

23. Smart Sensors and Sensor Networks • Homogeneous vs. heterogeneous architectures: • The classification is done according to the component nodes criteria; • In a homogeneous SN, the sensor nodes have identical capabilities and functionality with respect to the various aspects of sensing, communication and resource constraints; • In a heterogeneous SN, each node may have different capabilities and execute different functions; for example: • Some nodes may have larger battery capacity and more powerful processing capabilities; they will aggregate and relay data; • Other nodes are smaller and will be used only for sensing and sending the data to the most closest sensor with aggregate capabilities; • A homogeneous SN is simpler and easier to deploy because all the nodes are similar; • A heterogeneous SN is more complex and its deployment more complicated because different types of nodes must be dispensed in specified areas; • Heterogeneous SNs may be more efficient but more costly also;

24. Smart Sensors and Sensor Networks • Communication mode – based sensor network classification: • Criteria: the communication mechanism; • 4 basic architectures: direct connected, flat ad hoc, peer – to – peer multihop and cluster – based multihop:

25. Smart Sensors and Sensor Networks • Direct connected: • Is not suitable for large – scale deployed sensor networks; • It is cost inefficient and, in many cases, impossible for each small sensor to communicate directly with the collector; • Multihop mode uses less power than single – hop mode (in general, the average received power is inversely proportional to the nth power of the distance, usually 2 < n ≤ 4); • Flat ad hoc: • Some sensors have routing capabilities in addition with their sensing function; • The mode is flexible and energy efficient but scalability is a problem; • The nodes closer to the collection and processing center will be primarily used to route data from other nodes, they will relay a large amount of data and their energy will be exhausted fastly, disconnecting the network; • Cluster – based multihop: • Sensor nodes form clusters; a cluster head is established according to some rule; sensor nodes transmit their data to the cluster; • Clusters may be with one level or hierarchical; • Local data aggregation and fusion is used to reduce the amount of information to be transmitted, reducing the overall energy for communication; • The cluster heads are overstressed and their energy depletion is faster;

26. Smart Sensors and Sensor Networks • Data aggregation and fusion architectures: • 3 categories: centralized, localized and hybrid: • Centralized data aggregation and fusion: • All results are received and processed by the processing center at discrete instances of time; • It could take into account all the relevant information in order to provide an optimal output; • Realization will face difficulties because of the limited capacity of the data links and synchronization problems; data transmission is an additional cost for a sensor;

27. Smart Sensors and Sensor Networks • Localized data aggregation and fusion: • The data in the neighboring nodes are highly correlated because observed objects in the physical world are highly correlated; • Localized data processing and aggregation might dramatically decrease the amount of information to be transmitted; • Determining the appropriate architecture involves trading the cost of data transport vs. localized processing; • Data that must be transmitted have a cost per byte and processor power used to reduce the raw of data to a feature set and/or a aggregated/fused result has a cost per millions of instructions per second (MIPS); • The processing power needed to generate feature vectors usually consumes less energy than transmitting the sensor raw data sets; • Hybrid data aggregation and fusion: • In order to maximize the sensor network lifetime, the localized approach is more likely; • For applications such as optimum target detection and tracking, the centralized solution suits better; • The hybrid approach balances the corresponding trade – offs, depending of the overall objective of the SN.

28. Smart Sensors and Sensor Networks Modeling WSNs • Modeling of WSNs is addressed from various aspects, such as sensing coverage, node placement, connectivity, energy consumption etc. • Performance metrics of WSNs: • Parameters which influence the behavior and evolution of a SN: • Total numbers of sensors, indicating the size of a system; • Density, which is related to the deployment pattern; • Connectivity, which describes the communication links and related reliability; • Sensing coverage range and transmit range (radius) of sensor nodes; • Power consumption of each unit and energy availability; • Movement pattern, such as speed and direction; • Performance metrics used for evaluating the design of a SN: • Lifetime/ energy efficiency: • Energy efficient protocols and algorithms must be designed to prolong the network lifetime; • The definition for lifetime may vary for different types of applications;

29. Smart Sensors and Sensor Networks • Lifetime can be defined as the time interval from the point that the sensor network starts its operation until the point that some attributes (number of active nodes, the sensing coverage, information accuracy, connectivity to the collector sensor etc.) fall off some certain threshold; • Quality: • Includes 2 aspects: accuracy and latency; • The sensor network must provide sufficient accurate information while at the same time satisfying the delay requirements; • There is a trade – off between quality and energy efficiency: the higher the accuracy and the smaller the delay are, the larger the power consumption is; • Both definitions of latency and accuracy are application dependent; for example, in target detection applications, accuracy may include the missing detection probability and false alarm probability while the latency may be defined as the interval from the time that a query is sent out to the time that the proper information is received by an end user; • Robustness: • Sensors and links are prone to fail; • Fault tolerance is needed when sensors and links fail; • Redundant deployment of sensors and replication of information between sensors are solutions; • The trade – off must be done with the cost;

30. Smart Sensors and Sensor Networks • Flexibility: • Flexibility is required in 2 directions: node configuration and network organization; • Node configuration is done through programmable circuits (e.g. microcontrollers, DSPs) and network organization is accomplished through the self – organization capability; • Scalability: • Scalability is a support for the growth of a system to an arbitrary large size; • SNs must be scalable because the number of sensors deployed may be on the order of hundreds, thousands or greater; sensor nodes may leave the network (or die) and other sensors may join the network; • A scalable design requires scalable routing protocols, naming/addressing strategies, data fusion methods independent of the number of sensors etc. • Hierarchical cluster – based architectures may achieve better scalability; • Throughput: • The available communication bandwidth is limited and the high density of nodes may generate large amounts of data; • The end – to – end transmission throughput needs to be maximized in addition to power efficiency and low cost of implementation; • In applications such as forest fire or nuclear power plant monitoring, the information disseminated may increase abruptly when an emergency occurs, requiring high bandwidth; peak throughput should satisfy the application requirements; • Low cost and low complexity of implementation: • The cost includes hardware cost, deployment cost, maintenance cost etc.

31. Smart Sensors and Sensor Networks • Modeling WSNs: • Traffic models: • Traffic characteristics of WSNs depend on operational modes; • 3 categories of operational modes: steady mode, ad hoc request/ respond mode, ad hoc threshold – based mode; • Steady mode assumes a steady flow of data from sensors to the collector; the goal is an accurate and correct estimate of the field measured at the collector site; a field with high temporal resolution requires more frequent measurements and transmissions while a field with low resolution requires less retransmissions to obtain the same degree of accuracy; the traffic is deterministic and periodic; • Ad hoc request/respond mode: the sensors respond to requests generated by the collector site; it may be targeted to a specific set of sensors for a specific time interval; • Ad hoc threshold – based mode: transmission of information is triggered by an event during which a monitored/measured field exceeds some threshold; for example: object detection, environment monitoring (forest fire monitoring); the measurements and transmission frequencies may be very different; • One or more modes can be used; they will generate different traffic loads;

32. Smart Sensors and Sensor Networks • Energy and battery models: • They are necessary to predict the lifetime of a sensor network; • Energy dissipation comprises the energy consumed for sensing, processing and communicating; the first 2 components are assumed to be constant; • The energy dissipation for a radio unit: the energy for receiving a bit (the power of the receiver electronics) and to transmit a bit (transmitter electronics energy dissipation and the radio frequency transmit power); • The radio frequency transmit power is related to the transmission distance and the path loss exponential function; • Battery models vary with the constituent material; • Battery models for sensor networks: linear, discharge rate – dependent and relaxation; • Linear model: the battery is treated as a linear storage of current; the maximum capacity of the battery is achieved regardless of the discharge rate; • Discharge rate – dependent model: battery capacity is reduced as the discharge rate increases; an efficiency rate is introduced that varies with the current and is close to 1 when the discharge rate is low; • Relaxation model: takes into account the relaxation phenomenon of real – life batteries;

33. Smart Sensors and Sensor Networks • Connectivity modeling and topology optimization: • The connectivity relies on the actual physical conditions, such as transmit power range, network density, node positions, single hop or multihop connections etc; • It provides a good indication on the network status; • Modeling the connectivity helps the development of guidelines of processes involved in design and operations of sensor networks, such as: deployment pattern and density of sensors, communication strategies among individual sensors, distributed information processing algorithms, routing and information dissemination strategies; • The connectivity is influenced by the following possibilities: • New links are added: it could happen when a node begins to contact other nodes and build new links or when a node moves to the coverage of another node and wants to establish a new link; • Old links are rewired: this happens when a node finds that one or more links are better than the existing ones for routing or data gathering; • Existing links are deleted: when a node finds that it has a large number of links or its energy is being depleted faster than its schedule; • The mean connectivity value depends on the ratio between adding a new link and deleting an existing one and the parameter implying that the probability of removing links is relative to the connectivity conditions of the system;

34. Smart Sensors and Sensor Networks • Deployment and sensing coverage models: • The deployment pattern and the sensing coverage depend on: desired accuracy, temporal and spatial resolution, evolution of the information to be gathered and disseminated, mobility of the sensors, efficiency, fault tolerance, restrictions on the locations where the sensors should be placed etc. • In order to optimize the deployment of sensors nodes and endure that the mandated requirement of sensing coverage is met, the sensing models of the sensors must be known; • Sensing models vary with sensing devices; sensors are characterized by specifications such as range resolution, range accuracy, bearing resolution and accuracy; • 2 types of coverage exist: deterministic and stochastic coverage; • Deterministic coverage: the placement is well controlled, each node is deployed in a specific position; the predefined deployment patterns could be uniform or weighted to compensate for critical monitored areas; for ex.: grid based sensor deployment; generally, the suboptimal solution can be obtained by heuristics methods; • Stochastic coverage: the sensors are randomly distributed in the environment; the coverage scheme can be uniform, Gaussian or Poisson, or may follow other distributions depending on the application.

35. Smart Sensors and Sensor Networks Industrial sensor networks • Industrial communication networks can be classified in: industrial LANs, fieldbuses, device buses and sensor/ actuator buses; • IEEE 1451 Family of Smart Transducer Interface Standards: • Describes an open and network independent communication architecture for smart transducers; • WSNs in industry: • Smart sensor networks for measuring and data transmission from mobile robots and platforms, as well as from not easily accessible parts of processes or machines; • The automotive industry: may be the most important market for WSs; • Wireless on – body sensors for monitoring the health status of old people or with debilitating conditions; • Challenges: problem definition, topology, network traffic (high bandwidth for video data in monitoring applications or low bandwidth for periodic update messages), communication maintenance, network routing, network structuring protocol (must cover: initial network formation, network maintenance and optimization of the network).