Fundamentals Of Wireless Sensor Networks Theory And Practice Pdf

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Zhou, Zhongliang, and Lihong Zhang.

Wireless sensor network WSN refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on.

The Art of Wireless Sensor Networks

Wireless sensor network WSN refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on.

These are similar to wireless ad hoc networks in the sense that they rely on wireless connectivity and spontaneous formation of networks so that sensor data can be transported wirelessly. WSNs are spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature , sound , pressure , etc. The more modern networks are bi-directional, both collecting data from distributed sensors [2] and enabling control of sensor activity. The WSN is built of "nodes" — from a few to several hundreds or even thousands, where each node is connected to one or sometimes several sensors.

Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller , an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting.

A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning " motes " of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth.

The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding. Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. A military example is the use of sensors to detect enemy intrusion; a civilian example is the geo-fencing of gas or oil pipelines.

There are several types of sensor networks for medical applications: implanted, wearable, and environment-embedded. Implantable medical devices are those that are inserted inside the human body.

Wearable devices are used on the body surface of a human or just at close proximity of the user. Environment-embedded systems employ sensors contained in the environment. Possible applications include body position measurement, location of persons, overall monitoring of ill patients in hospitals and at home. Devices embedded in the environment track the physical state of a person for continuous health diagnosis, using as input the data from a network of depth cameras, a sensing floor , or other similar devices.

Body-area networks can collect information about an individual's health, fitness, and energy expenditure. Especially due to the integration of sensor networks, with IoT, the user authentication becomes more challenging; however, a solution is presented in recent work.

There are many applications in monitoring environmental parameters, [11] examples of which are given below. They share the extra challenges of harsh environments and reduced power supply.

Wireless sensor networks have been deployed in several cities Stockholm , London , and Brisbane to monitor the concentration of dangerous gases for citizens. These can take advantage of the ad hoc wireless links rather than wired installations, which also make them more mobile for testing readings in different areas.

A network of Sensor Nodes can be installed in a forest to detect when a fire has started. The nodes can be equipped with sensors to measure temperature, humidity and gases which are produced by fire in the trees or vegetation. The early detection is crucial for a successful action of the firefighters; thanks to Wireless Sensor Networks, the fire brigade will be able to know when a fire is started and how it is spreading.

A landslide detection system makes use of a wireless sensor network to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. Through the data gathered it may be possible to know the impending occurrence of landslides long before it actually happens. Water quality monitoring involves analyzing water properties in dams, rivers, lakes and oceans, as well as underground water reserves. The use of many wireless distributed sensors enables the creation of a more accurate map of the water status, and allows the permanent deployment of monitoring stations in locations of difficult access, without the need of manual data retrieval.

Wireless sensor networks can be effective in preventing adverse consequences of natural disasters , like floods. Wireless nodes have been deployed successfully in rivers, where changes in water levels must be monitored in real time. Wireless sensor networks have been developed for machinery condition-based maintenance CBM as they offer significant cost savings and enable new functionality. Wireless sensors can be placed in locations difficult or impossible to reach with a wired system, such as rotating machinery and untethered vehicles.

Wireless sensor networks also are used for the collection of data for monitoring of environmental information. The statistical information can then be used to show how systems have been working. The advantage of WSNs over conventional loggers is the "live" data feed that is possible. It may be used to protect the wastage of water. Wireless sensor networks can be used to monitor the condition of civil infrastructure and related geo-physical processes close to real time, and over long periods through data logging, using appropriately interfaced sensors.

Wireless sensor networks are used to monitor wine production, both in the field and the cellar. WATS would be made up of wireless gamma and neutron sensors connected through a communications network. Data picked up by the sensors undergoes "data fusion" , which converts the information into easily interpreted forms; this data fusion is the most important aspect of the system.

The data fusion process occurs within the sensor network rather than at a centralized computer and is performed by a specially developed algorithm based on Bayesian statistics. Data processed in the field by the network itself by transferring small amounts of data between neighboring sensors is faster and makes the network more scalable. An important factor in WATS development is ease of deployment , since more sensors both improves the detection rate and reduces false alarms.

One barrier to the implementation of WATS is the size, weight, energy requirements and cost of currently available wireless sensors. WATS was profiled to the U. House of Representatives' Military Research and Development Subcommittee on October 1, during a hearing on nuclear terrorism and countermeasures.

Cross-layer is becoming an important studying area for wireless communications. So the cross-layer can be used to make the optimal modulation to improve the transmission performance, such as data rate , energy efficiency, QoS Quality of Service , etc.

They usually consist of a processing unit with limited computational power and limited memory, sensors or MEMS including specific conditioning circuitry , a communication device usually radio transceivers or alternatively optical , and a power source usually in the form of a battery. Other possible inclusions are energy harvesting modules, [25] secondary ASICs , and possibly secondary communication interface e.

RS or USB. The base stations are one or more components of the WSN with much more computational, energy and communication resources. They act as a gateway between sensor nodes and the end user as they typically forward data from the WSN on to a server.

Other special components in routing based networks are routers, designed to compute, calculate and distribute the routing tables. One major challenge in a WSN is to produce low cost and tiny sensor nodes. There are an increasing number of small companies producing WSN hardware and the commercial situation can be compared to home computing in the s. Many of the nodes are still in the research and development stage, particularly their software.

Also inherent to sensor network adoption is the use of very low power methods for radio communication and data acquisition. The Gateway acts as a bridge between the WSN and the other network. This enables data to be stored and processed by devices with more resources, for example, in a remotely located server.

There are several wireless standards and solutions for sensor node connectivity. Thread and ZigBee can connect sensors operating at 2. The IEEE With the emergence of Internet of Things , many other proposals have been made to provide sensor connectivity. LORA [27] is a form of LPWAN which provides long range low power wireless connectivity for devices, which has been used in smart meters and other long range sensor applications. Wi-SUN [28] connects devices at home.

WSNs may be deployed in large numbers in various environments, including remote and hostile regions, where ad hoc communications are a key component. For this reason, algorithms and protocols need to address the following issues:. To conserve power, wireless sensor nodes normally power off both the radio transmitter and the radio receiver when not in use.

Wireless sensor networks are composed of low-energy, small-size, and low-range unattended sensor nodes. Recently, it has been observed that by periodically turning on and off the sensing and communication capabilities of sensor nodes, we can significantly reduce the active time and thus prolong network lifetime.

These limitations call for a countermeasure for duty-cycled wireless sensor networks which should minimize routing information, routing traffic load, and energy consumption. Researchers from Sungkyunkwan University have proposed a lightweight non-increasing delivery-latency interval routing referred as LNDIR. This scheme can discover minimum latency routes at each non-increasing delivery-latency interval instead of each time slot. Furthermore, this novel routing can also guarantee the minimum delivery latency from each source to the sink.

Performance improvements of up to fold and fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes. Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems.

They more strongly resemble embedded systems , for two reasons. First, wireless sensor networks are typically deployed with a particular application in mind, rather than as a general platform.

Second, a need for low costs and low power leads most wireless sensor nodes to have low-power microcontrollers ensuring that mechanisms such as virtual memory are either unnecessary or too expensive to implement.

However, such operating systems are often designed with real-time properties. TinyOS is perhaps the first [34] operating system specifically designed for wireless sensor networks. TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed of event handlers and tasks with run-to-completion semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS signals the appropriate event handler to handle the event.

Event handlers can post tasks that are scheduled by the TinyOS kernel some time later. Online collaborative sensor data management platforms are on-line database services that allow sensor owners to register and connect their devices to feed data into an online database for storage and also allow developers to connect to the database and build their own applications based on that data.

Examples include Xively and the Wikisensing platform. Such platforms simplify online collaboration between users over diverse data sets ranging from energy and environment data to that collected from transport services. The architecture of the Wikisensing system [36] describes the key components of such systems to include APIs and interfaces for online collaborators, a middleware containing the business logic needed for the sensor data management and processing and a storage model suitable for the efficient storage and retrieval of large volumes of data.

At present, agent-based modeling and simulation is the only paradigm which allows the simulation of complex behavior in the environments of wireless sensors such as flocking. Agent-based modelling was originally based on social simulation. Infrastructure-less architecture i.

Fundamentals of Wireless Sensor Networks (eBook, PDF)

WSN is one of active and operational subjects, although the extended care to apply it [ 1, 2 ], many levels of studies are considered like infrastructure technology, electronics, microprocessors, digital signal processors DSP, communications, battery production technology and others of power sources [ 3—6 ]. Additional subjects are involved as network management, routing, types of protocols applied. Estimating capacity of transmitted data, network lifetime, maintenance, and fault tolerance are topics of important role in studding and implementing WSN [ 7—9 ]. Infrastructure of network includes methods of how the node distributed in the range of interest RoI and sometimes accesses the exact coordinates of each node two or three dimensions. Knowing exact coordinates of nodes plays an important role especially in applications like indicating fire in a forest or a leakage in a petroleum pipe. The mechanism of allocated node position called localization while applying statistical information about node position, the number of connections between nodes may give approximate information about the efficiency and capacity of the network.

Du kanske gillar. Spara som favorit. In this book, the authors describe the fundamental concepts and practical aspects of wireless sensor networks. The book provides a comprehensive view to this rapidly evolving field, including its many novel applications, ranging from protecting civil infrastructure to pervasive health monitoring. Using detailed examples and illustrations, this book provides an inside track on the current state of the technology.

Fundamentals of Wireless Sensor Networks Theory and Practice

Download Fundamentals of Wireless Sensor Networks — In this book Fundamentals of Wireless Sensor Networks , the authors describe the fundamental concepts and practical aspects of wireless sensor networks. The book provides a comprehensive view to this rapidly evolving field, including its many novel applications, ranging from protecting civil infrastructure to pervasive health monitoring. Using detailed examples and illustrations, this book provides an inside track on the current state of the technology.

Rapid advances in the areas of sensor design, information technologies, and wireless networks have paved the way for the proliferation of wireless sensor networks.

Wireless sensor networks WSNs enable many applications such as intelligent control, prediction, tracking, and other communication network services, which are integrated into many technologies of the Internet-of-Things. The conventional localization frameworks may not function well in practical environments since they were designed either for two-dimensional space only, or have high computational costs, or are sensitive to measurement errors. In order to build an accurate and efficient localization scheme, we consider in this paper a hybrid received signal strength and angle-of-arrival localization in three-dimensional WSNs, where sensors are randomly deployed with the transmit power and the path loss exponent unknown. Moreover, in order to avoid the difficulty of solving the conventional maximum-likelihood estimator due to its non-convex and highly complex natures, we derive a weighted least squares estimate to estimate jointly the location of the unknown node and the two aforementioned channel components through some suitable approximations. Simulation results confirm the effectiveness of the proposed method.

Jetzt bewerten Jetzt bewerten. In this book, the authors describe the fundamental concepts andpractical aspects of wireless sensor networks. The book provides acomprehensive view to this rapidly evolving field, including itsmany novel applications, ranging from protecting civilinfrastructure to pervasive health monitoring. Using detailedexamples and illustrations, this book provides an inside track onthe current state of the technology.

This book serves as an introductory text to the field of wireless sensor networks at both graduate and advanced undergraduate level, but it will also appeal to researchers and practitioners wishing to learn about sensor network technologies and their application areas, including environmental monitoring, protection of civil infrastructure, health care, precision agriculture, traffic control, and homeland security. This book serves as an introductory text to the field ofwireless sensor networks at both graduate and advancedundergraduate level, but it will also appeal to researchers andpractitioners wishing to learn about sensor network technologiesand their application areas, including environmental monitoring,protection of civil infrastructure, health care, precisionagriculture, traffic control, and homeland security. Enter your mobile number or email address below and we'll send you a link to download the free Kindle App.

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