Doctoral Thesis / Dissertation, 2017
134 Pages, Grade: PhD
List of tables
List of figures
List of abbreviations
1.2 TECHNOLOGICAL TRENDS
1.3 WIRELESS SENSOR NETWORKS
1.3.1 Wireless networks
1.3.2 Data-oriented wireless networks
1.3.3 Micro sensor networks
1.4 NODE ARCHITECTURE
1.5 WIRELESS SENSOR NETWORK (WSN)
1.5.1 Sensors in internet
1.5.2 Comparison of traditional networks and wireless sensor networks
1.6 WSN APPLICATIONS
1.7 CHALLENGES AND CONSTRAINTS
1.7.2 Ad-hoc deployment
1.7.3 Unattended operation
1.8 ENERGY EFFICIENT DESIGN OF WIRELESS SENSOR NODES
1.9 MOTIVATION FOR RESEARCH WORK
1.10 OBJECTIVE OF THE THESIS
1.11 THE ORGANIZATION OF THESIS
2 LITERATURE SURVEY
2.2 CHARACTERISTIC OF SENSOR NETWORK
2.4 QUALITY OF SERVICE
2.4.1 Topology management
2.4.3 Controlled mobility
2.4.4 Data aggregation
2.4.5 Network topology
2.6 ENERGY MANAGEMENT
2.7 ENERGY CONSUMPTION FACTORS
2.8 COMMON ENERGY SAVING METHODS
2.8.1 Time synchronization
2.8.2 Dynamic power management
2.8.3 Real time support
2.8.4 Transmission power control
2.8.5 Encryption schemes
2.8.6 Data management
2.8.7 Compression techniques
2.9 STUDY ON DATA COMPRESSION AND DEPLOYMENT
2.9.1 Data compression
2.9.2 Deployment and coverage
2.10.1 Challenges in data gathering in WSN
3 ENERGY EFFICIENCY IN WSN USING DATA COMPRESSION TECHNIQUES
3.1.1 Data compression
3.1.2 Information and entropy
3.1.3 Compression algorithm
3.2 PROPOSED MATRIX - RLE (M-RLE) ALGORITHM
3.2.1 RLE algorithm
3.2.2 Matrix RLE
3.2.3 Pseudo code of the proposed algorithm
3.3 COMPRESSION PERFORMANCES
3.4 SECOND PROPOSED ALGORITHM - QUINE M CLUSKEY BOOLEAN REDUCTION METHOD FOR COMPRESSION (QMBRC) ALGORITHM
3.4.1 Proposed algorithm
3.4.3 Energy estimation
3.4.4 Compression ratio
4 SECURITY ENABLED ENERGY EFFICIENCY IN WSN
4.1.1 Unreliable communication
4.2 SECURITY REQUIREMENTS
4.3 DEFENSIVE MEASURES
4.4 MIDDLEWARE SECURITY
4.5 ALGORITHM FOR WSN ENERGY EFFICIENT SECURE MIDDLEWARE
4.5.1 WSN middleware
4.6 PROPOSED ALGORITHM : SEEMd SECURITY ENABLED ENERGY EFFICIENT MIDDLEWARE
4.6.1 Algorithm 1- Second chance approach
4.6.2 Algorithm 2- Distance estimation approach
4.7 SECURITY ENHANCEMENT OF WSN DATA USING SYMMETRIC DATA ENCRYPTION THROUGH TABULATION METHOD OF BOOLEAN FUNCTION REDUCTION
4.7.2 Privacy measures using encryption
4.8 CRYPTOGRAPHIC TECHNIQUES
4.9 PROPOSED ALGORITHM
5 ENERGY AWARE DATA DEPLOYMENT IN WSN
5.2 SENSOR DEPLOYMENT METHODS
5.4 SLEEP STATE TRANSITION POLICY
5.5 PROPOSED ALGORITHM - ENERGY AWARE NODE DEPLOYMENT IN WSN WITH STRAIGHT LINE TOPOLOGY
5.6 DATA COMPRESSION
6 APPLICATION BASED ON WEB ENABLED ENERGY EFFICIENT WSN NETWORK IN AN AGRICULTURE FIELD
6.2.1 Objective of the
6.2.3 PIC micro
6.3 IMPLEMENTATION OF ALGORITHM FOR ENERGY EFFICIENT DATA COLLECTION
6.3.1 Sleep wake up approach
6.4 WEB BASED MONITORING
6.5 WEB ARCHITECTURE AND DESIGN
6.6 DATABASE SYSTEM AND WEB SEVER
6.7 GRAPHIC USER INTERFACE (GUI)
7 CONCLUSION AND FUTURE SCOPE
7.2 FUTURE SCOPE
PUBLICATIONS IN INTERNATIONAL JOURNALS
TABLE NO TITLE PAGE NO
1.1 Chronology of data-oriented wireless networks
2.1 The general features of WSN
2.2 QoS challenges in WSN
2.3 The sensor constraints details
2.4 Comparison of applications of WSN
2.5 Power consumption in various WSN modules
2.6 Comparison of various compression algorithms
2.7 Comparison of coverage algorithms
3.1 Analysis of data sets
3.2 Node energy depletion chart
5.1 Parameters used in performance analysis
6.1 Database Node details
6.2 Database NSensor details
6.3 Database Sensor data
6.4 Database Alert details
6.5 Database Alert log
6.6 Sensor output for transmission
1.1 Internal architecture of sensor node
1.2 A sensor node
1.3(a) WSN structure
1.3 (b) WSN structure
1.4 WSN and internet
1.5 Power consumption of sensor node subsystem
2.1 Parameters of QoS
3.1 Flow chart of the proposed algorithm
3.2 Compression ratio
3.3 Compression factor
3.4 Saving percentage
3.5 The architecture of proposed algorithm
3.6 Block diagram of the proposed encryption algorithm
3.7 Input bits vs Output bits (After compression)
3.8 Energy depletion comparison
3.9 Compression ratio on for each group of data sets
4.1 Middleware - WSN
4.2 Data flow diagram for SEEMd (Algorithm 1)
4.3 Data flow diagram for SEEMd (Algorithm 2)
4.4 Energy efficiency using proposed algorithm
4.5 Basic architecture of symmetric key encryption
4.6 Flow chart of the proposed algorithm
4.7 The block diagram ( Decryption )
5 .1(a) Deterministic deployment
5.1(b) Random deployment
5.2 Node deployment path in a sensing field
5.3 Active nodes in first circular sleep wake up nodes
5.4 Illustration of straight-line deployment
5.5 Energy consumed for a node n for a circular coverage
5.6 Circular node energy depletion comparison
5.7 Compression parameters with proposed algorithm
5.8 Comparison of energy with deployment and compression
6.1 XBee Module mounting to an RS232 interface board
6.2 Circuit diagram - transmitter node 1 (Temperature sensor)
6.3 Circuit diagram - receiver (Header node)
6.4 Flow chart of the algorithm
6.5 Web based monitoring- block diagram.
6.6 Architecture of web based WSN
6.7 Analysis -GUI screenshot of the prototype
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This is to certify that the thesis titled “Certain Power Management Algorithms for Wireless Sensor Networks by Energy Efficient Data Transmission, Security and Node Deployment” submitted to the Bharathiar University, in partial fulfillment of the requirements for the award of the Degree of Doctor of Philosophy in Electronics is a record of original research work done by Mr. Linoy A. Tharakan during the period 2013 to 2017 of his research in the Research & Development Centre, Bharathiar University, Coimbatore- 641 046 under my supervision and guidance and the thesis has not formed the basis for the award of any Degree / Diploma / Associateship/ Fellowship or other similar title to any candidate of any University.
Date: Signature of the G
Research and Development Centre Bharathiar U
I, Linoy A. Tharakan hereby declare that the thesis, entitled “Certain Power Management Algorithms for Wireless Sensor Networks by Energy Efficient Data Transmission, Security and Node Deployment” submitted to the Bharathiar University, in partial fulfillment of the requirements for the award of the Degree of Doctor of Philosophy in Electronics is a record of original and independent research work done by me during 2013 to 2017 under the supervision and guidance of Dr. R. Dhanasekaran, Professor and Director-Research, Syed Ammal Engineering College, Ramanathapuram and it has not formed the basis for the award of any Degree / Diploma / Associateship / Fellowship or other similar title to any candidate in any U
Date: Signature of the C
CERTIFICATE OF GENUINESS OF THE PUBLICATION
This is to certify that the Ph.D. candidate Mr. LINOY A. THARAKAN working under my supervision has published research articles in the refereed journals named International Journal of Applied Engineering Research with Volume Number 10, Number 17, Page Nos. 13358- 13362 and year of publication 2015 published by Research India Publications,
ARPN Journal of Engineering and Applied Sciences with Volume Number 10, Number 7, Page Nos. 2984-2988 and year of publication 2015 published by Asian Research Publishing Network,
International Journal of Computer Applications with Volume Number 10, Number 7 Page Nos. 1-4 and year of publication 2015 published by IJCA Journal and International Journal of Applied Engineering Research with Volume Number 10, Number 55, Page Nos. 3470-3474 and year of publication 2015 published by Research India Publications.
The contents of the publications incorporates part of the results presented in his thesis.
Signature of the Scholar Research S
I bow my head in front of the Almighty for all the blessings showered on me all through my life, particularly during the time, this Research was carried out.
It gives me immense pleasure to express my deep gratitude to all those who have been my constant support and helped me to complete this research. I extend my sincere thanks to Prof. Dr. A. Ganapathi, Vice-Chancellor, Bharathiar University, Dr. B Vanitha, Registrar, Bharathiar University and Dr. K Murugan, Director Research and Development Centre, Bharathiar University, Coimbatore, for providing me all facilities to carry out this research work.
I express my profound sense of indebtedness to my research guide Dr.R. Dhanasekaran, Professor and Director-Research, Syed Ammal Engineering College, Ramanathapuram, for his valuable ideas, appropriate suggestions, stimulated discussions and useful comments. Above all, I express my gratitude for his constant encouragement and support throughout my research work.
I also wish to express my sincere thanks to Dr. M. Muruganand, Assistant Professor and Head, Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, for providing his noble patronage.
I wish to record my sincere thanks to Prof. Dr. R Jubi, Mar Thoma Institute of Information Technology, Chadayamangalam, Kerala, for his support and help from the very beginning of my research, in many ways.
I would run out of words in expressing my gratitude to my family members who gave me all the strength and support during the critical stages of my research work.
Wireless Sensor Networks (WSNs) is fast emerging as prominent study area that attracting considerable research attention globally. The field has seen tremendous development in design and development of application related interfaces with sensor networks. Sensor network finds applications in several domains such as medical, military, home networks, space and so on. Many researchers strongly believe that WSNs can become as important as the internet in the near future. Just as the internet allows access to digital information anywhere, WSNs could easily provide remote interaction with the physical world. It is going to be the backbone of Ubiquitous Computing (UBICOMP). Key changes in the scalability of network protocols, the design of energy efficient protocols, the design of data handling, localization techniques, sensor node design and development of exciting real-time new applications that exploit the prospects of WSNs. These parameters describe how to build WSN networks, from layers of communication protocol through the design of network nodes.
The thesis mainly focuses on the energy efficiency of the WSNs and suggests five ways to overcome the energy depletion during communication, sensing and node deployment. Communication between the nodes and the central base station consumes more energy than anything in the network. So reducing the data overload during communication significantly increases the lifetime of the sensor networks. Through local collaboration among sensors, elimination of duplicate data, participation of relevant nodes in the given task etc. can produce a significant difference in energy conservation, thereby increasing the life time of the sensor network.
So, for reducing the communication overload, compression algorithms are very much helpful. Energy-aware compression algorithms such as (Run length Encoding) RLE, Run Length Encoding with a K-Precision (KRLE), Huffman encoding (HE), Lempel Zev Welch (LZW) Algorithm are commonly used for this purpose. In this thesis, propose compression algorithms such as Matrix Run length encoding (MRLE) and Quine Mc-Cluskey Boolean Reduction Compression Algorithm (QMBRCA) which give significant results in comparison than the existing algorithms.
As the number of nodes increases, data security becomes the most challenging part of the network. The intruders can hack the data any time during processing, transmission or at the receiver end. So, as a popular approach data encryption is the most commendable approach in today’s network. Asymmetric key encryption consumes more energy in processing and so not recommended for WSNs. Symmetric key encryption gives better performance with respect to asymmetric key encryption in WSN applications. It uses less computational power due to relatively effortless mathematical operations, and eventually spends less power. This thesis also proposes a symmetric data encryption through Tabulation method of Boolean function reduction for the WSNs for secure data transmission. It also suggests a new secure approach, SEEMd, Security Enabled Energy Efficient Middleware algorithm for the critical data sensing and gives a second chance to the nodes before it falls into to sleep mode for energy management.
Sensor deployment is another energy conserving area in WSNs. In most of the applications, sensors are deployed through random approach with no specific structure or topology. This approach consumes unwanted energy loss and affects the lifetime of the nodes. In deterministic deployment also sensor nodes may not be managed well with respect to energy conservation. This thesis, suggests an energy aware node deployment in WSN with straight line topology which may sense the area in a specific pattern and gives significant energy saving.
WSNs are designed for applications which range from small-size healthcare surveillance systems to large-scale agricultural monitoring or environmental monitoring. Thus, any WSN deployment, data aggregation, processing and communication have to assure minimum Quality of Service (QoS) in the network from application to application. In this circumstances, the proposed algorithms in this thesis proved to be efficient and reliable in energy saving and life time enhancement.
Wireless Sensor Network (WSN) has come into prominence in recent years as they have the potential to transform many aspects of our life. The design, implementation and operation of a sensor network requires the convergence of many areas of technology, including networking, signal processing, information management, embedded technology and distributed systems approaches and algorithms. In short, a WSN is a network that may consist a pair of nodes to several hundreds of tiny nodes with sensors attached and are capable able to exchanging information. Wireless networks are of varied form-factor and types, being increasingly used in the sensing and communication among devices. User mobility, flexibility, ease of use and affordability are few of various reasons for making them very sensible in numerous unique applications. The diversity of the use and area of applications supported by wireless ad-hoc and sensor networks explain its acceptance in technological world. These applications comprise of small as well as the large scenario of various domains including environmental monitoring, wildlife protection, emergency rescue, home monitoring, target tracking, exploration mission in hostile environments, agricultural monitoring etc.
With a variety of applications coming in, new challenges for information processing in sensor networks also arise. Thus it called for new and superior computational representations, algorithms, protocols, design methodologies and tools that should be discovered to support distributed signal processing, information storage, management of data, networking and applications development.
Embedded computing, wireless networking, automated sensing etc, are not new to the technological world. Yet, it has only been in the past few years that an integrated technology convergence of computation, communication and sensing has matured sufficiently on large scale with being economically accessible.
Microprocessors have undergone gradual and inexorable transformation in their speed and storage since their introduction. Gordon Moore, in 1965, famously predicted that there would be an exponential growth in the number of transistors per integrated circuit. While such high-end technology has taken center stage, a dramatic but much quieter revolution has also taken place at the low end. It has been seen that today’s smaller processors can show the same computational power as high-end systems from twenty to thirty years ago, but at a tiny fraction of the cost, size and power. There is not a much computational difference in its power with small microcontrollers and microprocessor based systems. Indeed, some 95% of processors manufactured are intended for so-called “embedded applications” rather than for personal computers on desks. But embedded processors have been typically designed to work in isolation, are capable of work independently for dedicated applications.
Wireless technology and networking facilitates an integration of independent processors into an interconnected collective. This in turn boosts-up the embedded systems as well as wireless technology. This has driven the technology through an optimization similar to microprocessors, with modern radios consuming less power, space, and money than their predecessors. But importantly, sensor networks differ significantly in their communication model from typical consumer wireless devices. The primary focus of communication is interaction of nodes with each other; not delivery of data to the user. In sensor networks, a user does not micro-manage the flow of information, rather, users are not aware of every datum and computation, instead, are informed only of the highest-level conclusions or results. Likewise, in sensor networks, the aim is not necessarily to provide a complete record of every sensor reading as raw data, but rather to perform analysis and processing data converting it into information.
Network infrastructure interconnects telecommunication devices to provide them with means of information interexchange. Telecommunication modules are devices allowing users to run applications that communicate with other through the information network infrastructure. Wired information network infrastructures have evolved throughout the past century to support the transmission of voice, data, and video. Until recently, networks designed for monitoring and controlling sensors or actuators on a network were limited in application and scope due to a major network design consideration; the cables were required to connect the various sensors and actuators to a centralized collection point. In addition to the costs associated with installing and maintaining communication cables (fiber optic cables or copper wires), this type of network infrastructure prevents mobility and severely limits the feasible applications of such a network. To access these wired information infrastructures, wireless networks use the service of a mobile telecommunications terminal.
In the 1970s, ALOHANET research project, (University of Hawaii) treated the origins of radio frequency based wireless networking and thereafter, it led wireless networking to one of the fastest growing technologies of the early 21st century subsequently it caused the extension of the IEEE 802.11 standard then the subsequent development of interoperability certification by the Wireless Ethernet Compatibility Alliance (WECA). From the 1970s through the early 1990s, the growing demand for wireless connectivity could only be met by expensive hardware, which offered little interoperability of equipment from different companies, having no security mechanisms and offering poor performance.
Data-oriented wireless networks have two versions, the local broadband ad- hoc networks, and the wide area wireless data. Wireless local networks maintain higher data rates and ad-hoc operation for a lower number of users. The term broadband wireless local networks are usually referred to as Wireless Local Area Networks (WLANs) and as Wireless Personal Area Networks (WPANs) for the ad- hoc local networks. The idea of WLANs was first introduced in the early 1980s. However, the first WLAN products were introduced in the market about 10 years later. Table 1.1 provides the chronology of data-oriented wireless networks.
Micro sensors belong to the family of sensor networks that use many distributed sensors to gather information on entities of interest. Sensors are the eyes and ears of the networks. The components and materials inside the sensors change their electrical characteristics when exposed to varying environmental conditions. These changes are predictable over a certain range with the sensors. Early sensor networks include the radar networks used in air traffic control and certain defense applications. The national power grid, with its many sensors, can be viewed as one
Table 1.1 Chronology of data-oriented wireless networks.
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sensor network. It is designed and managed with specialized computers and communication lines, and before the term “sensor networks” came into vogue. Modern sensors like smart disposable micro sensors that can be deployed on underground ground, in the air, under water, on bodies, in vehicles, and inside buildings and certain environmental conditions, agricultural lands.
The sensor nodes are the backbone of a WSN. The node stores and executes the communication protocols and the data-processing algorithms. It is through a node that sensing, processing, and communication take place. The quality, frequency, and size of the sensed data extracted from the network are influenced by the hardware resources existing in the node. Therefore, the design and implementation of a sensor node in a network is a vital step.
Each node integrates with subsystems of sensing, processing, communication, and power. The designer has a plethora of options in deciding how to design, construct and put together these subsystems into a unified, programmable node. The processor subsystem is the core element of the sensor node and the selection of a processor determines the tradeoff between efficiency and flexibility; in terms of both energy and performance. There are microcontrollers, application-specific integrated circuits digital signal processors and Field Programmable Gate Arrays (FPGAs) that are in row to contribute in the processor subsystem.
Sensor hardware is shown in Fig. 1.1. There are a number of ways to connect the sensing subsystem with the processor. On the one hand sensors have their own built-in Analog to Digital Converters (ADCs) which can directly connect to the processor through a standard chip-to-chip protocol. On the other hand user can connect two or more analog sensors with an integrated high speed ADC chip. Most microcontrollers have one or more internal ADCs to interface with analog devices.
The communication subsystem is the most energy demanding subsystem and its power consumption should be regulated. Almost all commercially available transceivers provide a controlling functionality to switch the transceiver between various active operation levels such as active, idle and sleep state. The subsystem provides DC power to all the other subsystems to bias their active components such as registers, amplifiers, crystal oscillators, and counters. Moreover, provides DC-DC converters so that each subsystem can obtain the right amount of bias voltage. A typical sensor node is shown in Fig 1.2.
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Fig.1.1 Internal architecture of a sensor
Like many other technologies, the origin of WSNs can be seen in military and heavy industrial applications, and later introduced into daily life and consumer applications. The Sound Surveillance System (SOSUS), the first wireless network that resembles a modern WSN developed by the United States Military in the 1950’s was to sense and track Soviet submarines. This network, distributed in the Atlantic and Pacific oceans used submerged acoustic sensors named hydrophones. This distributed sensing technology is still in service today, but for more peaceful functions of monitoring undersea wildlife and volcanic activity. The United States Defense Advanced Research Projects Agency (DARPA) also initiated a Distributed Sensor Network (DSN) program in 1980. With the birth of DSN and its progression into academia through partnering universities such as Carnegie Mellon University and the Massachusetts Institute of Technology Lincoln Labs, WSN technology soon found a home in academia and civilian scientific research.
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Fig. 1.2 A Sensor node
Sensing is a procedure used to collect information about a physical parameter or process, including the occurrence of events such as changes in state - drop in temperature, pressure, sound vibration light etc. Any object performing such a task is called a sensor. From a technical perspective, a sensor is a device that translates parameters or events in the physical world into signals that can be measured and analyzed.
The following are some of the most important characteristics of a sensor:
- Transfer Function
- Span or Dynamic Range
The transfer function shows the functional relationship between physical input signal and electrical output signal. Usually, this relationship is represented as a graph showing the relationship between the input and output signal.
The sensitivity is defined in terms of the relationship between input physical signal and output electrical signal. It is generally the ratio between a small change in electrical signal to a small change in physical signal. As such, it may be expressed as the derivative of the transfer function with respect to the physical signal.
The range of input physical signals that may be converted to electrical signals by the sensor is the dynamic range or span. Signals outside of this range are expected to cause unacceptably large inaccuracy. This span or dynamic range is usually specified by the sensor supplier as the range over which other performance characteristics described in the data sheets are expected to apply. All sensors produce some output noise in addition to the output signal. In some cases, the noise of the sensor is less than the noise of the next element in the electronics, or less than the fluctuations in the physical signal, in which case it is not important. Many other cases exist in which the noise of the sensor limits the performance of the system based on the sensor. Noise is generally distributed across the frequency spectrum.
WSN has been identified as an important technology forming the back bone of many industries and services for the 21st century. A sensor network is an infrastructure consisting of monitoring a parameter, processing the sensed data and transmitting the processed data to higher hierarchical modules elements. Researchers see WSNs as an ‘‘exciting emerging domain of deeply networked systems of low-power wireless motes with a tiny amount of processor and memory, and large federated networks for high-resolution sensing of the environment”. WSN provides an infrastructure based service that couple between traditional computing and real-time environment.
Low cost, intelligent devices with a couple of onboard sensors, with wireless links and the internet , deployed in huge numbers, provide exceptional opportunities for managing and controlling homes, cities, and the environment. In addition, networked micro-sensors provide vast technological applications in the defense arena, generating new capabilities for reconnaissance and surveillance as well as other tactical applications. These sensors and actuators with local processing power can be deployed on the ground, in the air, underwater, on human bodies, in vehicles, and inside buildings. Each sensor node will have local processing capability and will have one or more onboard sensors, operating in the acoustic, seismic, infrared, and magnetic modes.
A given computing capacity becomes exponentially smaller and cheaper with each passing year. Technology advancement in various research centers use the semiconductor techniques that lead this miniaturization to build communication devices in exceptionally tiny mechanical structures that sense fields and forces in the physical world. These low-power, inexpensive, communication nodes can be deployed throughout a physical space, providing dense sensing close to physical phenomena, processing and communicating this information, and coordinating actions with other nodes. Intelligent WSNs has the ability to self-organizing and self-healing capability. This implies, no administrative duties associated with establishing and maintaining a WSN so it's called Wireless ad-hoc network, illustrated in Fig 1.3(a) and Fig 1.3(b). A wireless network connected to the internet may have a piece of networking hardware to interface with user and or other network called the gateway through which all the routing algorithms are implemented.
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Fig. 1.3(a) WSN
In an ad-hoc network, sensor nodes attached to a wireless module can be randomly placed and moved as needed. If the network needs to scale up, additional sensor nodes are easily added. All this is made possible through the use of robust, efficient network protocols developed specifically for WSNs. Wireless sensor nodes are characterized by being tiny intelligent, low in cost, battery-driven, and deployed randomly or in a deterministic pattern. Network nodes are equipped with wireless transmitters and receivers using antennas that may be unidirectional ( radiation), highly directional (point-to-point), possibly steerable, or some combination thereof.
Fig. 1.3(b) illustrates the basic sensor network components. Field devices are directly connected to the sensing fields. Field devices can transmit measurement data, receive and forward packets from/to any device. Usually they may be line, loop or battery powered. Router devices are used for routing purposes, i.e., forward packets from one device to another device. Their use is not really necessary in some cases since field devices have internal routing capabilities. However, router devices can provide redundant paths to the gateway, and they can also minimize energy consumption in field devices. The connection between the plant automation network and the wireless network is provided by the gateway. The gateway works as a sink point for all wireless traffic. The logical communication with the wireless network occurs through access points installed in the gateway.
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Fig. 1.3 (b) WSN
Sensor network extends the applications to existing Internet to track the user and personalize the web. The resulting new network has an order of magnitude more expansive and dynamic than the current TCP/IP network and creates entirely types of traffic. Information collected by and transmitted on a sensor infrastructure describes the condition of the physical environment. It requires advanced query interfaces and search engines to effectively support user-level functions. Sensor network may inter-network with an IP core network via a number of gateways, as in Fig. 1.4
Fig. 1.4 WSN and
Dedicated gateway routes user commands and queries to appropriate nodes in a sensor network. It also routes sensory data, at times aggregated and summarized, to users who have requested it or are expected to utilize the information. A data back-up or storage service may be present at the gateway, also with data logging at each sensor. The repository may serve as an intermediary between users and sensors, providing a continual data storage. Additionally, one or more data storage devices may be attached to the Internet Protocol (IP) network to archive data from a of edge sensor networks and to support a variety of user initiated browsing and search functions. Communicating one bit over the wireless medium at short ranges consumes far more energy than processing a bit. In spite of the progress in silicon chip fabrication technologies, wireless communication still continues to dominate the energy burning-up of networked embedded systems for the likely future.
It is highly recommended to minimize the amount and range of communication as much as possible by local collaboration, duplicate data- suppression, or involve only the nodes that are relevant to a given task etc. which can significantly extend the life of sensor nodes and leave nodes free for maintaining multiuser applications. Besides, the shorter radio transmission range improves the Radio Frequency (RF) spectrum usage and increases throughput for a network. The information management and networking for this novel network will require more than just faster routers, switchers, and browsers.
To optimize for performance and resources such as energy, one has to rethink the existing Transmission Control Protocol /Internet Protocol (TCP/IP) stack and design a suitable sensor network abstraction to support application development. Networking will be closely attached with the needs of sensing and control, and hence the application semantics. For instance, in many applications, it is more appropriate to address nodes in a sensor network by physical properties, such as localization or proximity, than by IP addresses. Because of the peer-to-peer connectivity and the lack of a global infrastructure support, the sensors have to rely on discovery protocols to construct local models about the network and environment. It should be well known that how and where that data is generated by sensors and consumed by users would affect the way the data is compressed, routed, and aggregated. Volatility and mobility in wireless links preclude the use of many existing edge-network gateway protocols for internetworking IP and sensor networks.
Traditional networks have following characteristics:
General purpose Primary design consideration is performance.
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