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Chapter 1. Introduction of Wireless Sensor Network
1.1 Wireless Sensor Networks
1.2 Sensor network application classes
1.3 Environmental Data Collection
1.4 Security Monitoring
1.5 Node Tracking Scenarios
1.6 Hybrid Networks
1.7 System Evaluation Metrics
1.10 Cost and Ease of Deployment
1.11 Response Time
1.12 Temporal Accuracy
1.14 Effective Sample Rate
Chapter 2. Applications of Wireless Sensor Network
2.1 Ecological habitat monitoring
2.2. Military surveillance and target Tracking
2.3. Structural and Seismic Monitoring
2.4. Industrial and Commercial networked sensing
2.5. Environmental monitoring
2.6 Creative industries
2.7 Precision agriculture
2.8 Disasters response
2.10 Health (Body Area Networks)
2.11. Monitoring health of structures (like bridges)
2.12. Supply chain management:
2.13. Underwater Sensor Networks:
2.14. Future markets:
Chapter 3. Routing in Cluster based WSN
3.2 Clustered Based Sensor System Architecture
3.3 Objectives of Clustering in Wireless Sensor Network
3.4 Cluster Based Sensor Network System Model
3.5 Clustered Based Sensor Network State
3.6 Overview of Clustering Algorithms
3.7 Battery Characteristics and Models
3.8. Classifying Cluster Techniques
3.9. Taxonomy of Clustering Attributes
3.10 Performance Metrics
Chapter 4. Cluster Based Distribution Routing Protocol
4.3 Simulation results and performance Analysis
Chapter 5. MAC Protocol for WSN & MANET
5.1 Wireless Local Area Network
5.2 Wireless LAN Standards
5.3 Wireless Mesh Network (WMN)
5.4 Mobile Ad-Hoc Networks
5.5 Applications, Limitations and Effect of Mobility on Protocol Stack
Due to Mobile Ad-Hoc Network
5.6 Security of Nodes on Mobile Ad-Hoc Networks
5.7 Overview of IEEE
5.8 IEEE 802.11 MAC Standards
5.9 Ad Hoc Networking
Chapter 6. Routing Protocols in Wireless Sensor Network
6.2 Classifications of Routing Protocols
6.3 Protocol by Operation
6.4 Protocol by Network Structure
Chapter 7. Transport Control Protocols for WSN
7. 1 Internet Protocol
7.2 Transmission Control Protocol
7.3 Issues for Adopting TCP/IP in a WSN
7.4 The Existing Transport Control Protocols for WSNs
Chapter 8. Middleware for WSN
8.1 Data Driven Approach
8.2 Event Based Approach
8.3 QoS Oriented Approach
8.4 Internet Oriented Approach
8.5 Agent Based Approach
8.6 Centralized Approach
Chapter 9. Basic Concept of Cloud Computing with WSN
9.1. Definition of Cloud Computing
9.2. Characteristics of Cloud
9.3. Need for Cloud
9.4. Types of cloud
9.5. Cloud Services
9.6 Cloud Computing Based WSN Architecture
9.7 Execution of Real Time Alert System over Cloud Environment
9.8 Integrating Sensors with the Cloud Environment Using Dynamic
9.9 Integrating WSNs and Cloud computing using Distributed Shared
9.10 Software as a Service Approach with WSN
9.11 Platform as a Service with WSN
9.12 Application of Cloud Computing with WSN
Chapter 10. Data Transmission over the WSN
10.1 Single Packet Delivery
10.2 Single Path Delivery with MAC Layer Retransmissions
10.3 The Hidden Node problem
10.4 S-MAC Protocol
10.5 Single Path Delivery with End-to-End Retransmissions
10.6 Using Multiple Paths
10.7 Packet Block Delivery
10.8 Packet Stream Delivery
10.9 Reliable Multi-hop Routing
This book, Introductory Concepts of Wireless Sensor Network provides the details study of Wireless Sensor Network Introduction, Application, Middleware and basic concept of cloud computing with WSN. It caters to the needs of the students by making them understand the practical utility of WSN in the field of engineering, science and technology. An essentially elementary approach including solved and unsolved problem, diagram and figures have been included to appeal to a wider readership. The book introduces the science and engineering of WSN and provides in depth coverage of different protocols and application of WSN. It emphasizes the fundamental concepts and analytical tools required for a topic, develops the relevant theory, algorithms and protocols. This book also uses the Data Transmission concepts for secure data transmission over the wireless sensor network in distributed environment. This book is useful for undergraduates, postgraduates and research scholar students for their course work and research projects in the field of engineering, science and technology.
The text is organized into ten chapters, Chapter 1 and Chapter 2 provides Basic concept of wireless sensor network and their application in real life. Chapter 3 and Chapter 4 Routing in Cluster Based Wireless Sensor Networks and Cluster Based Distribution Routing Protocol for wireless sensor network. Chapter 5 and Chapter 6, includes MAC Protocol for WSN & MANET and Routing Protocols in Wireless Sensor Network. Chapter 7 and Chapter 8, give the brief introduction of Transport Control Protocols for WSN and different Middleware’s for WSN. Chapter 9 and Chapter 10 give brief Concept of Cloud Computing with WSN and Data Transmission over the WSN. Finally, this book includes un-solved problems, exercise and list of projects that are useful for both graduate and post-graduate students.
There have been several influences from our family and friends who have sacrificed lot of their time and attention to ensure that we are kept motivated to complete this crucial project.
1.Aimed of basic and fundamental issue of WSN
2.Coverage wide application areas and problems
3.Includes number of solved, unsolved and multiple-choice problems.
4.Provides an entire chapter on cloud computing with WSN.
5.Devotes whole chapters on the transmission control protocol for WSN and Data Transmission over the wireless network.
6.Incorporates a lucid style of writing with easy to understand mathematics.
7.Students friendly and Concise
8.Useful for UG, PG and research scholar
The emerging field of wireless sensor networks combines sensing, computation, and communication into a single tiny device. Through advanced mesh networking protocols, these devices form a sea of connectivity that extends the reach of cyberspace out into the physical world. As water flows to fill every room of a submerged ship, the mesh networking connectivity will seek out and exploit any possible communication path by hopping data from node to node in search of its destination. While the capabilities of any single device are minimal, the composition of hundreds of devices offers radical new technological possibilities. The power of wireless sensor networks lies in the ability to deploy large numbers of tiny nodes that assemble and configure themselves. Usage scenarios for these devices range from real-time tracking, to monitoring of environmental conditions, to ubiquitous computing environments, to in situ monitoring of the health of structures or equipment. While often referred to as wireless sensor networks, they can also control actuators that extend control from cyberspace into the physical world. The most straightforward application of wireless sensor network technology is to monitor remote environments for low frequency data trends. For example, a chemical plant could be easily monitored for leaks by hundreds of sensors that automatically form a wireless interconnection network and immediately report the detection of any chemical leaks. Unlike traditional wired systems, deployment costs would be minimal. Instead of having to deploy thousands of feet of wire routed through protective conduit, installers simply have to place quarter-sized device, at each sensing point. The network could be incrementally extended by simply adding more devices no rework or complex configuration. With the devices presented in this thesis, the system would be capable of monitoring for anomalies for several years on a single set of batteries. In addition to drastically reducing the installation costs, wireless sensor networks have the ability to dynamically adapt to changing environments. Adaptation mechanisms can respond to changes in network topologies or can cause the network to shift between drastically different modes of operation. For example, the same embedded network performing leak monitoring in a chemical factory might be reconfigured into a network designed to localize the source of a leak and track the diffusion of poisonous gases. The network could then direct workers to the safest path for emergency evacuation. Current wireless systems only scratch the surface of possibilities emerging from the integration of low-power communication, sensing, energy storage, and computation. Generally, when people consider wireless devices they think of items such as cell phones, personal digital assistants, or laptops with 802.11. These items costs hundreds of dollars, target specialized applications, and rely on the pre-deployment of extensive infrastructure support. In contrast, wireless sensor networks use small, low-cost embedded devices for a wide range of applications and do not rely on any pre-existing infrastructure. The vision is that these devise will cost less that $1 by 2005. Unlike traditional wireless devices, wireless sensor nodes do not need to communicate directly with the nearest high-power control tower or base station, but only with their local peers. Instead, of relying on a pre- deployed infrastructure, each individual sensor or actuator becomes part of the overall infrastructure. Peer-to-peer networking protocols provide a mesh-like interconnect to shuttle data between the thousands of tiny embedded devices in a multi-hop fashion. The flexible mesh architectures envisioned dynamically adapt to support introduction of new nodes or expand to cover a larger geographic region. Additionally, the system can automatically adapt to compensate for node failures.
The vision of mesh networking is based on strength in numbers. Unlike cell phone systems that deny service when too many phones are active in a small area, the interconnection of a wireless sensor network only grows stronger as nodes are added. As long as there is sufficient density, a single network of nodes can grow to cover limitless area. With each node having a communication range of 50 meters and costing less that $1 a sensor network that encircled the equator of the earth will cost less than $1M. It depicts a precision agriculture deployment an active area of application research. Hundreds of nodes scattered throughout a field assemble together, establish a routing topology, and transmit data back to a collection point. The application demands for robust, scalable, low-cost and easy to deploy networks are perfectly met by a wireless sensor network. If one of the nodes should fail, a new topology would be selected and the overall network would continue to deliver data. If more nodes are placed in the field, they only create more potential routing opportunities.
The concept of wireless sensor networks is based on a simple equation:
Sensing + CPU + Radio = Thousands of potential applications
As soon as people understand the capabilities of a wireless sensor network, hundreds of applications springs to mind. It seems like a straightforward combination of modern technology. However, actually combining sensors, radios, and CPU’s into an effective wireless sensor network requires a detailed understanding of the both capabilities and limitations of each of the underlying hardware components, as well as a detailed understanding of modern networking technologies and distributed systems theory. Each individual node must be designed to provide the set of primitive’s necessaries to synthesize the interconnected web that will emerge as they are deployed, while meeting strict requirements of size, cost and power consumption. A core challenge is to map the overall system requirements down to individual device capabilities, requirements and actions. To make the wireless sensor network vision a reality, architecture must be developed that synthesizes the envisioned applications out of the underlying hardware capabilities. To develop this system architecture, we work from the high-level application requirements down through the low-level hardware requirements. In this process, we first attempt to understand the set of target applications. To limit the number of applications that we must consider, we focus on a set of application classes that we believe are representative of a large fraction of the potential usage scenarios. We use this set of application classes to explore the system-level requirements that are placed on the overall architecture. From these system-level requirements we can then drill down into the individual node-level requirements. Additionally, we must provide a detailed background into the capabilities of modern hardware. After we present the raw hardware capabilities, we present a basic wireless sensor node.
The three application classes we have selected are: environmental data collection, security monitoring, and sensor node tracking. We believe that the majority of wireless sensor network deployments will fall into one of these class templates.
A canonical environmental data collection application is one where a research scientist wants to collect several sensor readings from a set of points in an environment over a period of time in order to detect trends and interdependencies. This scientist would want to collect data from hundreds of points spread throughout the area and then analyse the data offline. The scientist would be interested in collecting data over several months or years in order to look for long-term and seasonal trends. For the data to be meaningful it would have to be collected at regular intervals and the nodes would remain at known locations. At the network level, the environmental data collection application is characterized by having a large number of nodes continually sensing and transmitting data back to a set of base stations that store the data using traditional methods. These networks generally require very low data rates and extremely long lifetimes. In typical usage scenario, the nodes will be evenly distributed over an outdoor environment. This distance between adjacent nodes will be minimal yet the distance across the entire network will be significant. After deployment, the nodes must first discover the topology of the network and estimate optimal routing strategies. The routing strategy can then be used to route data to a central collection points. In environmental monitoring applications, it is not essential that the nodes develop the optimal routing strategies on their own. Instead, it may be possible to calculate the optimal routing topology outside of the network and then communicate the necessary information to the nodes as required. This is possible because the physical topology of the network is relatively constant. While the time variant nature of RF communication may cause connectivity between two nodes to be intermittent, the overall topology of the network will be relatively stable. Environmental data collection applications typically use tree-based routing topologies where each routing tree is rooted at high-capability nodes that sink data. Data is periodically transmitted from child node to parent node up the tree-structure until it reaches the sink. With tree-based data collection each node is responsible for forwarding the data of all its descendants. Nodes with a large number of descendants transmit significantly more data than leaf nodes. These nodes can quickly become energy bottlenecks. Once the network is configured, each node periodically samples its sensors and transmits its data up the routing tree and back to the base station. For many scenarios, the interval between these transmissions can be on the order of minutes. Typical reporting periods are expected to be between 1 and 15 minutes; while it is possible for networks to have significantly higher reporting rates. The typical environment parameters being monitored, such as temperature, light intensity, and humidity, does not change quickly enough to require higher reporting rates. In addition to large sample intervals, environmental monitoring applications do not have strict latency requirements. Data samples can be delayed inside the network for moderate periods of time without significantly affecting application performance. In general, the data is collected for future analysis, not for real-time operation. In order to meet lifetime requirements, each communication event must be precisely scheduled. The senor nodes will remain dormant a majority of the time; they will only wake to transmit or receive data. If the precise schedule is not met, the communication events will fail. As the network ages, it is expected that nodes will fail over time. Periodically the network will have to reconfigure to handle node or link failure or to redistribute network load. Additionally, as the researchers learn more about the environment they study, they may want to go in and insert additional sensing points. In both cases, the reconfigurations are relatively infrequent and will not represent a significant amount of the overall system energy usage. The most important characteristics of the environmental monitoring requirements are long lifetime, precise synchronization, low data rates and relatively static topologies. Additionally, it is not essential that the data be transmitted in real-time back to the central collection point. The data transmissions can be delayed inside the network as necessary in order to improve network efficiency.
Our second class of sensor network application is security monitoring. Security monitoring networks are composed of nodes that are placed at fixed locations throughout an environment that continually monitor one or more sensors to detect an anomaly. A key difference between security monitoring and environmental monitoring is that security networks are not actually collecting any data. This has a significant impact on the optimal network architecture. Each node has to frequently check the status of its sensors but it only has to transmit a data report when there is a security violation. The immediate and reliable communication of alarm messages is the primary system requirement. These are “report by exception” networks. dditionally, it is essential that it is confirmed that each node is still present and functioning. If a node were to be disabled or fail, it would represent a security violation that should be reported. For security monitoring applications, the network must be configured so that nodes are responsible for confirming the status of each other. One approach is to have each node be assigned to peer that will report if a node is not functioning. The optimal topology of a security monitoring network will look quite different from that of a data collection network. In a collection tree, each node must transmit the data of all of its decedents. Because of this, it is optimal to have a short, wide tree. In contrast, with a security network the optimal configuration would be to have a linear topology that forms a Hamiltonian cycle of the network. The power consumption of each node is only proportional to the number of children it has. In a linear network, each node would have only one child. This would evenly distribute the energy consumption of the network. The accepted norm for security systems today is that each sensor should be checked approximately once per hour. Combined with the ability to evenly distribute the load of checking nodes, the energy cost of performing this check becomes minimal. A majority of the energy consumption in a security network is spent on meeting the strict latency requirements associated with the signalling the alarm when a security violation occurs. Once detected, a security violation must be communicated to the base station immediately. The latency of the data communication across the network to the base station has a critical impact on application performance. Users demand that alarm situations be reported within seconds of detection. This means that network nodes must be able to respond quickly to requests from their neighbors to forward data. In security networks reducing the latency of an alarm transmission is significantly more important than reducing the energy cost of the transmissions. This is because alarm events are expected to be rare. In a fire security system alarms would almost never be signalled. In the event that one does occur a significant amount of energy could be dedicated to the transmission. Reducing the transmission latency leads to higher energy consumption because routing nodes must monitor the radio channel more frequently.
In security networks, a vast majority of the energy will be spending on confirming the functionality of neighboring nodes and in being prepared to instantly forward alarm announcements. Actual data transmission will consume a small fraction of the network energy.
A third usage scenario commonly discussed for sensor networks is the tracking of a tagged object through a region of space monitored by a sensor network. There are many situations where one would like to track the location of valuable assets or personnel. Current inventory control systems attempt to track objects by recording the last checkpoint that an object passed through. However, with these systems it is not possible to determine the current location of an object. For example, UPS tracks every shipment by scanning it with a barcode whenever it passes through a routing center. The system breaks down when objects do not flow from checkpoint to checkpoint. In typical work environments, it is impractical to expect objects to be continually passed through checkpoints. With wireless sensor networks, objects can be tracked by simply tagging them with a small sensor node. The sensor node will be tracked as it moves through a field of sensor nodes that are deployed in the environment at known locations. Instead of sensing environmental data, these nodes will be deployed to sense the RF messages of the nodes attached to various objects. The nodes can be used as active tags that announce the presence of a device. A database can be used to record the location of tracked objects relative to the set of nodes at known locations. With this system, it becomes possible to ask where an object is currently, not simply where it was last scanned. Unlike sensing or security networks, node tracking applications will continually have topology changes as nodes move through the network. While the connectivity between the nodes at fixed locations will remain relatively stable, the connectivity to mobile nodes will be continually changing. Additionally, the set of nodes being tracked will continually change as objects enter and leave the system. It is essential that the network be able to efficiently detect the presence of new nodes that enter the network.
In general, complete application scenarios contain aspects of all three categories. For example, in a network designed to track vehicles that pass through it, the network may switch between being an alarm monitoring network and a data collection network. During the long periods of inactivity when no vehicles are present, the network will simply perform an alarm monitoring function. Each node will monitor its sensors waiting to detect a vehicle. Once an alarm event is detected, all or part of the network, will switch into a data collection network and periodically report sensor readings up to a base station that track the vehicles progress. Because of this multi-modal network behaviour, it is important to develop a single architecture that and handle all three of these application scenarios.
Now that we have established the set of application scenarios that we are addressing, we explore the evaluation metrics that will be used to evaluate a wireless sensor network. To do this we keep in mind the high-level objectives of the network deployment, the intended usage of the network, and the key advantages of wireless sensor networks over existing technologies. The key evaluation metrics for wireless sensor networks are lifetime, coverage, cost and ease of deployment, response time, temporal accuracy, security, and effective sample rate. Their importance is discussed below. One result is that many of these evaluation metrics are interrelated. Often it may be necessary to decrease performance in one metric, such as sample rate, in order to increase another, such as lifetime. Taken together, this set of metrics form a Multidimensional space that can be used to describe the capabilities of a wireless sensor network. The capabilities of a platform are represented by a volume in this multidimensional space that contains all of the valid operating points. In turn, a specific application deployment is represented by a single point. A system platform can successfully perform the application if and only if the application requirements point lies inside the capability hyperspace. One goal of this chapter is to present an understanding of the trade-offs that link each axis of this space and an understanding of current capabilities. The architectural improvements and optimizations we present in later chapters are then motivated by increasing the ability to deliver these capabilities and increasing the volume of the capability hypercube.
Critical to any wireless sensor network deployment is the expected lifetime. The goal of both the environmental monitoring and security application scenarios is to have nodes placed out in the field, unattended, for months or years. The primary limiting factor for the lifetime of a sensor network is the energy supply. Each node must be designed to manage its local supply of energy in order to maximize total network lifetime. In many deployments, it is not the average node lifetime that is important, but rather the minimum node lifetime. In the case of wireless security systems, every node must last for multiple years. A single node failure would create vulnerability in the security systems. In some situations, it may be possible to exploit external power, perhaps by tapping into building power with some or all nodes. However, one of the major benefits to wireless systems is the ease of installation. Requiring power to be supplied externally to all nodes largely negates this advantage. A compromise is to have a handful of special nodes that are wired into the building’s power infrastructure. In most application scenarios, a majority of the nodes will have to be self powered. They will either have to contain enough stored energy to last for years, or they will have to be able to scavenge energy from the environment through devices, such as solar cells or piezoelectric generators. Both of these options demand that that the average energy consumption of the nodes be as low as possible. The most significant factor in determining lifetime of a given energy supply is radio power consumption. In a wireless sensor node, the radio consumes a vast majority of the system energy. This power consumption can be reduced through decreasing the Transmission output power or through decreasing the radio duty cycle. Both of these alternatives involve sacrificing other system metrics.
Next to lifetime, coverage is the primary evaluation metric for a wireless network. It is always advantageous to have the ability to deploy a network over a larger physical area. This can significantly increase a system’s value to the end user. It is important to keep in mind that the coverage of the network is not equal to the range of the wireless communication links being used. Multi-hop communication techniques can extend the coverage of the network well beyond the range of the radio technology alone. In theory, they have the ability to extend network range indefinitely. However, for a given transmission range, multi-hop networking protocols increase the power consumption of the nodes, which may decrease the network lifetime. Additionally, they require a minimal node density, which may increase the deployment cost.
Tied to range is a network’s ability to scale to a large number of nodes. Scalability is a key component of the wireless sensor network value proposition. A user can deploy a small trial network at first and then can continually add sense points to collect more and different information. A user must be confident that the network technology being used is capable of scaling to meet his eventual need. Increasing the number of nodes in the system will impact either the lifetime or effective sample rate. More sensing points will cause more data to be transmitted which will increase the power consumption of the network. This can be offset by sampling less often.
A key advantage of wireless sensor networks is their ease of deployment. Biologists and construction workers installing networks cannot be expected to understand the underlying networking and communication mechanisms at work inside the wireless network. For system deployments to be successful, the wireless sensor network must configure itself. It must be possible for nodes to be placed throughout the environment by an untrained person and have the system simply work. Ideally, the system would automatically configure itself for any possible physical node placement. However, real systems must place constraints on actual node placements. It is not possible to have nodes with infinite range. The wireless sensor network must be capable of providing feedback as to when these constraints are violated. The network should be able to assess quality of the network deployment and indicate any potential problems. This translates to requiring that each device be capable of performing link discovery and determining link quality. In addition to an initial configuration phase, the system must also adapt to changing environmental conditions. Throughout the lifetime of a deployment, nodes may be relocated or large physical objects may be placed so that they interfere with the communication between two nodes. The network should be able to automatically reconfigure on demand in order to tolerate these occurrences. The initial deployment and configuration is only the first step in the network lifecycle. In the long term, the total cost of ownership for a system may have more to do with the maintenance cost than the initial deployment cost. The security application scenario in particular requires that the system be extremely robust. In addition to extensive hardware and software testing prior to deployment, the sensor system must be constructed so that it is capable of performing continual self- maintenance. When necessary, it should also be able to generate requests when external maintenance is required. In a real deployment, a fraction of the total energy budget must be dedicated to system maintenance and verification. The generation of diagnostic and reconfiguration traffic reduces the network lifetime. It can also decrease the effective sample rate.
Particularly in our alarm application scenario, system response time is a critical performance metric. An alarm must be signalled immediately when an intrusion is detected. Despite low power operation, nodes must be capable of having immediate, high-priority messages communicated across the network as quickly as possible. While these events will be infrequent, they may occur at any time without notice. Response time is also critical when environmental monitoring is used to control factory machines and equipment. Many users envision wireless sensor networks as useful tools for industrial process control. These systems would only be practical if response time guarantees could be met. The ability to have low response time conflicts with many of the techniques used to increase network lifetime. Network lifetime can be increased by having nodes only operate their radios for brief periods of time. If a node only turns on its radio once per minute to transmit and receive data, it would be impossible to meet the application requirements for response time of a security system. Response time can be improved by including nodes that are powered all the time. These nodes can listen for the alarm messages and forward them down a routing backbone when necessary. This, however, reduces the ease of deployment for the system.
In environmental and tracking applications, samples from multiple nodes must be cross-correlated in time in order to determine the nature of phenomenon being measured. The necessary accuracy of this correlation mechanism will depend on the rate of propagation of the phenomenon being measured. In the case of determining the average temperature of a building, samples must only be correlated to within seconds. However, to determine how a building reacts to a seismic event, millisecond accuracy is required. To achieve temporal accuracy, a network must be capable of constructing and maintaining a global time base that can be used to chronologically order samples and events. In a distributed system, energy must be expended to maintain this distributed clock. Time synchronization information must be continually communicated between nodes. The frequency of the synchronization messages is dependent on the desired accuracy of the time clock. The bottom line is maintenance of a distributed time base requires both power and bandwidth.
Despite the seemingly harmless nature of simple temperature and light information from an environmental monitoring application, keeping this information secure can be extremely important. Significant patterns of building use and activity can be easily extracted from a trace of temperature and light activity in an office building. In the wrong hands, this information can be exploited to plan a strategic or physical attack on a company. Wireless sensor networks must be capable of keeping the information they are collecting private from eavesdropping. As we consider security oriented applications, data security becomes even more significant. Not only must the system maintain privacy, it must also be able to authenticate data communication. It should not be possible to introduce a false alarm message or to replay an old alarm message as a current one. A combination of privacy and authentication is required to address the needs of all three scenarios. Additionally, it should not be possible to prevent proper operation by interfering with transmitted signals. Use of encryption and cryptographic authentication costs both power and network bandwidth. Extra computation must be performed to encrypt and decrypt data and extra authentication bits must be transmitted with each packet. This impacts application performance by decreasing the number of samples than can be extracted from a given network and the expected network lifetime.
In a data collection network, effective sample rate is a primary application performance metric. We define the effective sample rate as the sample rate that sensor data can be taken at each individual sensor and communicated to a collection point in a data collection network. Fortunately, environmental data collection applications typically only demand sampling rates of 1-2 samples per minute. However, in addition to the sample rate of a single sensor, we must also consider the impact of the multi-hop networking architectures on a nodes ability to effectively relay the data of surrounding nodes. In a data collection tree, a node must handle the data of all of its descendents. If each child transmits a single sensor reading and a node has a total of 60 descendants, then it will be forced to transmit 60 times as much data. Additionally, it must be capable of receiving those 60 readings in a single sample period. This multiplicative increase in data communication has a significant effect on system requirements. Network bit rates combined with maximum network size end up impacting the effective per-node sample rate of the complete system. One mechanism for increasing the effective sample rate beyond the raw communication capabilities of the network is to exploit in-network processing. Various forms of spatial and temporal compression can be used to reduce the communication bandwidth required while maintaining the same effective sampling rate. Additionally, local storage can be used to collect and store data at a high sample rate for short periods of time. In- network data processing can be used to determine when an “interesting” event has occurred and automatically trigger data storage. The data can then be downloaded over the multi-hop network as bandwidth allows.
Triggering is the simplest form of in-network processing. It is commonly used in security systems. Effectively, each individual sensor is sampled continuously, processed, and only when a security breach has occurred is data transmitted to the base station. If there were no local computation, a continuous stream of redundant sensor readings would have to be transmitted. We show how this same process can be extended to complex detection events.
1.14.1 Individual Node Evaluation Metrics
Now that we have established the set of metrics that will be used to evaluate the performance of the sensor network as a whole, we can attempt to link the system performance metrics down to the individual node characteristics that support them. The end goal is to understand how changes to the low-level system architecture impact application performance. Just as application metrics are often interrelated, we will see that an improvement in one node-level evaluation often comes at the expense of another.
To meet the multi-year application requirements individual sensor nodes must be incredibly low-power. Unlike cell phones, with average power consumption measured in hundreds of milliamps and multi-day lifetimes, the average power consumption of wireless sensor network nodes must be measured in micro amps. This ultra-low-power operation can only be achieved by combining both low-power hardware components and low duty-cycle operation techniques. During active operation, radio communication will constitute a significant fraction of the node’s total energy budget. lgorithms and protocols must be developed to reduce radio activity whenever possible. This can be achieved by using localized computation to reduce the streams of data being generated by sensors and through application specific protocols. For example, events from multiple sensor nodes can be combined together by a local group of nodes before transmitting a single result across the sensor network.
The wide range of usage scenarios being considered means that the node architecture must be flexible and adaptive. Each application scenario will demand a slightly different mix of lifetime, sample rate, response time and in-network processing. Wireless sensor network architecture must be flexible enough to accommodate a wide range of application behaviors. Additionally, for cost reasons each device will have only the hardware and software it actually needs for a given the application. The architecture must make it easy to assemble just the right set of software and hardware components. Thus, these devices require an unusual degree of hardware and software modularity while simultaneously maintaining efficiency.
In order to support the lifetime requirements demanded, each node must be constructed to be as robust as possible. In a typical deployment, hundreds of nodes will have to work in harmony for years. To achieve this, the system must be constructed so that it can tolerate and adapt to individual node failure. Additionally, each node must be designed to be as robust as possible.
System modularity is a powerful tool that can be used to develop a robust system. By dividing system functionality into isolated sub-pieces, each function can be fully tested in isolation prior to combining them into a complete application. To facilitate this, system components should be as independent as possible and have interfaces that are narrow, in order to prevent unexpected interactions. In addition to increasing the system’s robustness to node failure, a wireless sensor network must also be robust to external interference. As these networks will often coexist with other wireless systems, they need the ability to adapt their behavior accordingly. The robustness of wireless links to external interference can be greatly increased through the use of multi-channel and spread spectrum radios. It is common for facilities to have existing wireless devices that operate on one or more frequencies. The ability to avoid congested frequencies is essential in order to guarantee a successful deployment.
In order to meet the application level security requirements, the individual nodes must be capable of performing complex encrypting and authentication algorithms. Wireless data communication is easily susceptible to interception. The only way to keep data carried by these networks private and authentic is to encrypt all data transmissions. The CPU must be capable of performing the required cryptographic operations itself or with the help of included cryptographic accelerators. In addition to securing all data transmission, the nodes themselves must secure the data that they contain. While they will not have large amounts of application data stored internally, they will have to store secret encryption keys used in the network. If these keys are revealed, the security of the network could crumble. To provide true security, it must be difficult to extract the encryption keys of from any node.
A key evaluation metric for any wireless sensor network is its communication rate, power consumption, and range. While we have made the argument that the coverage of the network is not limited by the transmission range of the individual nodes, the transmission range does have a significant impact on the minimal acceptable node density. If nodes are placed too far apart it may not be possible to create an interconnected network or one with enough redundancy to maintain a high level of reliability. Most application scenarios have natural node densities that correspond to the granularity of sensing that is desired. If the radio communications range demands a higher node density, additional nodes must be added to the system in to increase node density to a tolerable level. The communication rate also has a significant impact on node performance. Higher communication rates translate into the ability to achieve higher effective sampling rates and lower network power consumption. As bit rates increase, transmissions take less time and therefore potentially require less energy. However, an increase in radio bit rate is often accompanied by an increase in radio power consumption. All things being equal, a higher transmission bit rate will result in higher system performance. However, we show later that an increase in the communication bit rate has a significant impact on the power consumption and computational requirement of the node. In total, the benefits of an increase in bit rate can be offset by several other factors.
The two most computationally intensive operations for a wireless sensor node are the in-network data processing and the management of the low-level wireless communication protocols. As we discuss later, there are strict real-time requirements associated with both communication and sensing. As data is arriving over the network, the CPU must simultaneously control the radio and record/decode the incoming data. Higher communication rates required faster computation.
The same is true for processing being performed on sensor data. Analog sensors can generate thousands of samples per second. Common sensor processing operations include digital filtering, averaging, threshold detection, correlation and spectral analysis. It may even be necessary to perform a real-time FFT on incoming data in order to detect a high-level event.
In addition to being able to locally process, refine and discard sensor readings, it can be beneficial to combine data with neighboring sensors before transmission across a network. Just as complex sensor waveforms can be reduced to key events, the results from multiple nodes can be synthesized together. This in-network processing requires additional computational resources.
In our experience, 2-4 MIPS of processing are required to implement the radio communication protocols used in wireless sensor networks. Beyond that, the application data processing can consume an arbitrary amount of computation depending on the calculations being performed.
18.104.22.168 Time Synchronization
In order to support time correlated sensor readings and low-duty cycle operation of our data collection application scenario, nodes must be able to maintain precise time synchronization with other members of the network. Nodes need to sleep and awake together so that they can periodically communicate. Errors in the timing mechanism will create inefficiencies that result in increased duty cycles. In distributed systems, clocks drift apart over time due to inaccuracies in Time keeping mechanisms. Depending on temperature, voltage, humidity, time keeping oscillators operate at slightly different frequencies. High- precision synchronization mechanisms must be provided to continually compensate for these inaccuracies.
22.214.171.124 Size and Cost
The physical size and cost of each individual sensor node has a significant and direct impact on the ease and cost of deployment. Total cost of ownership and initial deployment cost are two key factors that will drive the adoption of wireless sensor network technologies. In data collection networks, researchers will often be operating off of a fixed budget. Their primary goal will be to collect data from as many locations as possible without exceeding their fixed budget. A reduction in per- node cost will result in the ability to purchase more nodes, deploy a collection network with higher density, and collect more data. Physical size also impacts the ease of network deployment. Smaller nodes can be placed in more locations and used in more scenarios. In the node tracking scenario, smaller, lower cost nodes will result in the ability to track more objects.
126.96.36.199 Hardware Capabilities
Now that we have identified the key characteristics of a wireless sensor node we can look at the capabilities of modern hardware. This allows us to understand what bit rate, power consumption, memory and cost we can expect to achieve. A balance must be maintained between capability, power consumption and size in order to best address application needs. This section gives a quick overview of modern technology and the tradeoffs between different technologies. We start with a background of energy storage technologies and continue through the radio, CPU, and sensors.
Just as power consumption of system components are often expressed in milliamps, batteries are generally rated in milliamp-hours (mah). In theory, a 1000 mah battery could support a processor consuming 10 ma for 100 hours. In practice, this in not always true. Due to battery chemistry, voltage and current levels vary depending on how the energy is extracted from a battery. Additionally, as batteries discharge their voltage drops. If the system is not tolerant to a decrease in voltage it may not be possible to use the full rated capacity of a battery. For example, a 1.5 V alkaline battery is not considered empty by the manufacturer until it is outputting only.
188.8.131.52 Battery Technologies
There are three common battery technologies that are applicable for wireless sensor networks Alkaline, Lithium, and Nickel Metal Hydride. An AA Alkaline battery is rated at 1.5 V, but during operation it ranges from 1.65 to .8 V and is rated at 2850 mah. With a volume of just 8.5 cm3, it has an energy density of approx 1500 Joules/cm3. While providing a cheap, high capacity, energy source, the major drawbacks of alkaline batteries are the wide voltage range that must be tolerated and their large physical size. Additionally, lifetimes beyond 5 years cannot be achieved because of battery self-discharge. The shelf-life of an alkaline battery is approximately 5 years. Lithium batteries provide an incredibly compact power source. The smallest versions are just a few millimeters across. Additionally, they provide a constant voltage supply that decays little as the battery is drained. Devices that operate off of lithium batteries do not have to be as tolerant to voltage changes as devices that operate off of alkaline batteries. Additionally, unlike alkaline batteries, lithium batteries are able to operate at temperatures down to -40 C. The most common lithium battery is the CR2032. It is rated at 3V, 255 mah and sells for just 16 cents. With a volume of 1 cm3, it has and energy density of 2400 J/cm3. In addition to traditional lithium batteries, there are also specialized Tadiran lithium batteries that have densities as high as 4000 J/cm3 and tolerate a wide temperature range. One of the drawbacks of lithium batteries is that they often have very low nominal discharge currents.
In this chapter has introduced the tracking problem as a representative problem for studying a number of information processing issues for sensor networks. While we have focused on the different issues in the wireless sensor networks, and also summarize the brief introduction of the different problems comes in the sensor networks.
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The applications for WSNs are many and varied. They are used in commercial and industrial applications to monitor data that would be difficult or expensive to monitor using wired sensors. They could be deployed in wilderness areas, where they would remain for many years (monitoring some environmental variable) without the need to recharge/replace their power supplies. They could form a perimeter about a property and monitor the progression of intruders (passing information from one node to the next). There are a many uses for WSNs.
Typical applications of WSNs include monitoring, tracking, and controlling. Some of the specific applications are habitat monitoring, object tracking, nuclear reactor controlling, fire detection, traffic monitoring, etc. In a typical application, a WSN is scattered in a region where it is meant to collect data through its sensor nodes.
Unattended wireless sensor networks offer us a cleaner, remoteobserver come within reach of to habitat monitoring. One of the experimental deployments of wireless sensor networks is habitat monitoring, on Great Duck Island, Maine. The sensor network - transmitted data were made obtainable over the web.
Wireless sensor networks can be swiftly deployed for surveillance and used to provide battlefield intelligence regarding the location, numbers, movement and identity of troops and vehicles and for detection of chemical, biological, and nuclear weapons. US Defense Advanced Research Projects Agency (DARPA),most notably through a program Sensor Information Technology (SensIT) from 1992 to 2002 make research on Sensor network’s military implementation.
Wireless Sensor Network is used to monitor the condition of civil structure. The structure could be buildings, bridges and roads even aircraft. Sensors are placed densely on the structure.
In industrial manufacturing services, sensors and` actuators are used for process monitoring and control. The key advantages of it is that it can improve both the cost and flexibility associated with installing, maintaining, and upgrading wired systems. Commercial networked sensing standards includes IEEE 802.15.4 standard and collaborative industry efforts such as: Zigbee Alliance.
Following are some of the projects and research plans sought in the environment monitoring application of wireless sensor networks.
1. Watershed: Correctly managing our watersheds is essential to ensure water supply to the increasing human population in the world. Collecting data for understanding the water systems of rivers and lakes including the impact of environmental factors and human activity.
2. Scientific investigation: Sensor networks are being used for various scientific explorations including ecological and environmental ones.
3. Pollution monitoring: Growing urban and industrial regions need efficient pollution monitoring technology.
4. Weather study1 (Singapore example): Detailed measurements of weather phenomenon at fine granularity help manage weather dependent industries such as agriculture and also help understand other effects such as spread of epidemics.
5. Threat-Identification: Sensors can be used to identify potential threats such as chemical contamination of water distribution system at various locations, pathogens in the environments, and other subtle changes in critical infrastructure.
6. Coal mine monitoring for poisonous gases.
Wireless Sensor Networks has made a significant impact in the automation and control of the industrial processes. The benefits of WSN in industrial applications are to increase production efficiencies, to reduce environmental impact, to form a close loop by both sensing and controlling various equipment at disjoint locations. The sensor nodes can be placed at remote and manually inaccessible locations because of their small size and capability to communicate wirelessly. The WSNs are hence found useful for steel, chemical, oil and gas, pulp and paper, and petroleum industries. Further the sensor nodes capability to sense and control the atmospheric parameters makes them useful for pharmaceutical, fabrication and cultivation industries.
Many initial deployments of wireless sensor networks have shown promise to address various issues faced by rural community. With the help of WSNs, many of the farming activities can be precisely done resulting in yield optimization and minimization of the cost incurred in farming. The sensor nodes may be deployed on the field to measure various atmospheric and soil parameters. These can help in making decision on irrigation, fertilizer and pesticide applications. The WSNs may also serve for the applications such as intruder detection, pest detection, plant disease prediction, fire detection, automating irrigation etc.
2.8 Disasters response
Wireless sensor networks are also found useful for detection of various disasters such as Landslide [amrita], Volcanoes and forest fire. When sensor nodes detect occurrence of any such events they communicate that information to their neighboring nodes for in-network data aggregation. A cluster head or sink node makes the decision on the disaster occurrence considering the information received from various sensor nodes. Such collaborative decision making improves the reliability of the decision made by the entire network.
The usage of sensor nodes in the vehicles has led to envision of various automotive applications of the wireless sensor networks. The cabling required to be done to connect various sensors in any automobile can be redundant by using wireless sensor nodes. This simplifies placement of the sensors resulting in more accurate measurements. The vehicle to vehicle communication and vehicle to roadside static node communication gives rise to enormous applications such as smart parking, collision avoidance, multimedia data transfer, disaster detection, traffic information communication. Vehicular WSN are also useful to prevent road accidents and prevent vehicles from crashing into each other, prevent speeding streamline traffic management. Some of these applications face the challenges of high speed multi hop transmissions, considering high mobility of vehicles. The power constrain of a WSNs may or may not be relevant, depending on the placement of the nodes, in the automotive application.
2.10 Health (Body Area Networks)
Body area networks are formed by either wearing sensor nodes or implanting them into the human body to measure various parameters. These are capable of communicating wirelessly with each other and with a base station situated at hospital and home in real-time. The sensor nodes are capable of observing critical parameters of a patient to detect the occurrences of disease attacks such as heart attack, diabetes and asthma and communicate the same to the mobile device carried by a family member and a doctor. Physiological sensors worn by patients in their own homes can help doctors deliver healthcare for regions where local healthcare staff is in shortage or hospital beds are scarce. Such systems are also very useful for elder-care.
A vast literature presents the advantages of deploying wireless sensor networks for monitoring health of the bridges as compared to traditional methods. Further, protecting historical monuments by having sensors monitor structural integrity, environmental factors, and usage loads are other applications of wireless sensor networks. The major issue of power requirement of the sensor nodes has been given due consideration by proposing techniques like energy harvesting and use of solar panels. Park et al. has compared performance of MEMS accelerometer that come with wireless sensor nodes with the traditional sensing devices. Accelerometer is suggested to be used to measure vibrations when the trains pass over bridges. They address issues related to time synchronization, routing, data collection, aggregation from sensors and communication to a central device. More research is required to detect health of bridges using other sensors such as temperature, humidity and corrosion etc. Multimodal data fusion (from more than one type of sensors) techniques are hence required to be developed to determine health of bridges.
Wireless sensor networks along with RFID are found useful for managing the temperature of food products as they traverse roads, sea and storage. The integrated WSN with RFID are also envisaged for applications of preserving medicines requiring stringent storage and transportation requirements.
Underwater Sensor Networks use acoustic communication due to the low attenuation of acoustic waves in water, especially in thermally stable, deep water settings. In shallow water, acoustic communication is confronted with temperature gradients, surface ambient noise and multipath propagation due to reflection and refraction. Sound has a low propagation velocity in water. The much slower speed of acoustic propagation in water, about 1500 m/s, compared with that of EM and optical waves, lead to large propagation delays which prevents efficient communication and networking. Nevertheless, since the communication technologies like EM waves and optical communication fail under water over large distances, currently, acoustic communication is the preferred technology for underwater communication. In a specific scenario has been considered where a set of underwater sensor nodes report events to the sink node. A Path Unaware Layered Routing Protocol (PULRP) for dense underwater 3D sensor networks is proposed for an uplink transmission. The proposed algorithm must combat frequent losses of connectivity due to node mobility, energy depletion and must not result in large end-to-end delay. The proposed PURLP algorithm consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric shells, around a sink node. The radius of the concentric shells is chosen based on probability of successful packet forwarding as well as packet delivery latency. In the second phase (communication phase), we propose a method to choose the intermediate relay nodes and an on the fly routing algorithm for packet delivery from source node to sink node across the chosen relay nodes. The proposed algorithm, PULRP finds the routing path on the fly and hence it does not require any fixed routing table, localization or time synchronization processes. The findings show that the proposed algorithm has a considerably better successful packet delivery rate compared to the under-water diffusion (UWD) algorithm proposed in the paper by Lee et al. (2007). In addition, the delay involved in PULRP is comparable with that of UWD. In the PULRP has been further extended for a 2D UWSN with mobile nodes having a Random waypoint steady state distribution, which is non-uniform. The findings show that for this case of 2D UWSN with non-uniform node distribution, the proposed algorithm, PULRP- 2D, has considerably better throughput (successful packet delivery rate) compared to the underwater diffusion (UWD) algorithm for various node densities as well as node velocities. Also, the delay performance of PULRP-2D is better than that of UWD.
Urban (shopping malls, metro train stations, bus stops) and residential security is a great opportunity where monitoring services can generate widespread employment. In essence, each system has a central controller and several wireless motes (sensors to detect motion, door opening etc). India’s leadership in providing services globally can be leveraged to great advantage if we can develop a technology leadership in security surveillance system design. Remote monitoring service for a single home in the US typically costs $10-$100 per month and involves mainly responding to alarm phone calls - Indian industry can be more competitive in providing such a service.
In this chapter, we gave a brief description of different application of wireless sensor network.
. Prekshep Mehta, Deepthi Chander, M. Shahim, Kalyana Tejaswi, S. N. Merchant and U. B. Desai, “Distributed Detection for Landslide Prediction using Wireless Sensor Network”, First International Global Information Infrastructure Symposium, 2007. GIIS 2007, 2- 6 July 2007 pp 195 - 198.
. Geoffrey Werner-Allen, Jeff Johnson, Mario Ruiz, Jonathan Lees, and Mett Welsh. Monitoring volcanic eruptions with a wireless sensor network. in Proc. 2nd European Workshop on Wireless Sensor Networks (EWSN 05), January- Febuary 2005.
. Geoffrey Werner-Allen, Konrad Lorincz, Mett Welsh, Omar Marcillo, Jeff Johnson, Mario Ruiz, and Jonathan Lees. “Deploying a wireless sensor network on an active volcano”, IEEE Internet Computing Online, 10, March-April 2006.
. Carl Hartung, Richard Han, Carl Seielstad, and Saxon Holbrook. Firewxnet: A multitiered portable wireless system for monitoring weather conditions in wildland fire environments. In Proc. 4th international conference on Mobile systems, applications and services MobiSys 2006, 10, June 2006.
. Active sensing platform for wireless structural health monitoring. Musiani, D., Lin, K., and Rosing, T. S. 2007. In Proceedings of the 6th international Conference on information Processing in Sensor Networks (Cambridge, Massachusetts, USA, April 25 - 27, 2007). IPSN'07. ACM, New York, NY, 390-399. DOI= http://doi.acm.org/10.1145/1236360.1236409
. DuraNode: Wi-Fi-based Sensor Node for Real-Time Structural Safety Monitoring, Chulsung Park, Qiang Xie, and Pai H. Chou, IPSN 2005, USA
. BriMon: A Sensor Network System for Railway Bridge Monitoring. Kameswari Chebrolu, Bhaskaran Raman, Nilesh Mishra, Phani Kumar Valiveti, Raj Kumar, BriMon: A Sensor Network System for Railway Bridge Monitoring, In Proceedings of the Sixth International Conference on Mobile Systems, Applications, and Services (ACM MobiSys'08), June 2008.
. Design and Implementation of Scalable Wireless Sensor Network for Structural Monitoring, Shamim N. Pakzad, Gregory L. Fenves, Sukun Kim, and David E. Culler, In ASCE Journal of Infrastructure Engineering, March 2008, Volume 14, Issue 1, pp. 89-101.
. Multi-Purpose Wireless Accelerometers for Civil Infrastructure Monitoring, Shamim N. Pakzad, Sukun Kim, Gregory L Fenves, Steven D. Glaser, David E. Culler, and James W. Demmel, In the Proceedings of the 5th International Workshop on Structural Health Monitoring (IWSHM '05), Stanford, CA, September 2005, ed. F-K Chang, pp. 125-132.
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Grouping sensor nodes into clusters has been widely pursued by the research population in order to achieve the network scalability objective. Every cluster would have a leader, often referred to as the cluster-head. Although many clustering algorithms have been proposed in the literature for ad-hoc networks. The objective was mainly to generate stable clusters in environments with mobile nodes. Many of such techniques care mostly about node reach ability and route stability without much concern about critical design goals of Wireless Sensor Networks such as network longevity and coverage. Recently, a number of clustering algorithms have been specifically designed for Wireless Sensor Networks. These proposed clustering techniques widely vary depending on the node deployment and bootstrapping schemes, the pursued network architecture. The characteristics of the Cluster head nodes and the network operation model. A Cluster head may be elected by the sensors in a cluster or pre-assigned by the network designer. A Cluster head may also be just one or the sensors or a node that is richer in resources. The cluster membership may be fixed or variable. Cluster heads may form a second-tier network or may just ship the data to interested parties. e.g. a base-station or a command center. In addition to supporting network scalability, clustering has numerous advantages. It can localize the route set up within the cluster and thus reduce the size of the routing table stored at the individual node. Clustering can also conserve communication bandwidth since it limits the scope of inter-cluster interactions to Cluster Heads and avoids redundant exchange of messages among sensor nodes. Moreover, clustering can stabilize the network topology at the level of sensors and thus cuts on topology maintenance overhead. Sensors would care only for connecting with their Cluster Heads and, would not be affected by changes at the level of inter-Cluster Head tier. The Cluster Head can also implement optimized management strategies to further enhance the network operation and prolong the battery life of the individual sensors and the network lifetime. A Cluster Head can schedule activities in the cluster so that nodes can switch to the low-power sleep mode most of the time and reduce the rate of energy consumption. Sensors can be engaged in a round robin order and the time for transmission and reception can be determined so that the sensors reties are avoided, redundancy in coverage can be limited and medium access collision is prevented. Furthermore, a Cluster Head can aggregate the data collected by the sensors in its cluster and thus decrease the number of relayed packets.
Depending on the application, different architectures and design goals/ constraints have been considered for sensor networks. Since the performance of a routing protocol is closely related to the architectural model, in this section we strive to capture architectural issues and highlight their implications,
3.2.1 Network. Dynamics
There are three main components in a sensor network. These are the sensor nodes, sink and monitored events. Aside from the very few setups that utilize mobile sensors; most of the network architectures assume that sensor nodes are stationary. On the other hand, supporting the mobility of sinks or cluster-heads (gateways) is sometimes deemed necessary. Routing messages from or to moving nodes is more challenging since route stability becomes an important optimization factor, in addition to energy, bandwidth etc. The sensed event can be either dynamic or static depending on the application.
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