Wireless sensor network designs Hac A.

Hac A.
ISBN: 0-470-86736-1
Год: 2003
Издательство: John Wiley & Sons, Ltd.
Количество страниц: 410
Формат: PDF / RAR

Формат файла: RAR

The emergence of compact, low-power, wireless communication sensors and actuators in the technology supporting the ongoing miniaturization of processing and storage, allows for entirely new kinds of embedded system. These systems are distributed and deployed in environments where they may not have been designed into a particular control path, and are often very dynamic. Collections of devices can communicate to achieve a higher level of coordinated behavior.
Wireless sensor nodes deposited in various places provide light, temperature, and activity measurements. Wireless nodes attached to circuits or appliances sense the current or control the usage. Together they form a dynamic, multi-hop, routing network connecting each node to more powerful networks and processing resources.
Wireless sensor networks are application-specific, and therefore they have to involve both software and hardware. They also use protocols that relate to both the application and to the wireless network.
Wireless sensor networks are consumer devices supporting multimedia applications, for example personal digital assistants, network computers, and mobile communication devices. Emerging embedded systems run multiple applications, such as web browsers, and audio and video communication applications. These include capturing video data, processing audio streams, and browsing the World Wide Web (WWW). There is a wide range of data gathering applications, energy-agile applications, including remote climate monitoring, battlefield surveillance, and intra-machine monitoring. Example applications are microclimate control in buildings, environmental monitoring, home automation, distributed monitoring of factory plants or chemical processes, interactive museums, etc. An application of collective awareness is a credit card anti-theft mode. There is also a target tracking application, and applications ranging from medical monitoring and diagnosis to target detection, hazard detection, and automotive and industrial control. In short, there are applications in military (e.g. battlefields), commercial (e.g. distributed mobile computing, disaster discovery systems, etc.), and educational environments (e.g. conferences, conventions, etc.) alike.
This book introduces networked embedded systems, smart sensors, and wireless sensor networks. The focus of the book is on the architecture, applications, protocols, and distributed systems support for these networks.
Wireless sensor networks use new technology and standards. They involve small, energy-efficient devices, hardware/software co-design, and networking support. Wireless sensor networks are becoming an important part of everyday life, industrial and military applications. It is a rapidly growing area as new technologies are emerging, and new applications are being developed.
The characteristics of modern embedded systems are the capability to communicate over the networks and to adapt to different operating environments.
Designing an embedded system's digital hardware has become increasingly similar to software design. The wide spread use of hardware description languages and synthesis tools makes circuit design more abstract. A cosyn-thesis method and prototyping platform can be developed specifically for embedded devices, combining tightly integrated hardware and software components.
Users are demanding devices, appliances, and systems with better capabilities and higher levels of functionality. In these devices and systems, sensors are used to provide information about the measured parameters or to identify control states. These sensors are candidates for increased built-in intelligence. Microprocessors are used in smart sensors and devices, and a smart sensor can communicate measurements directly to an instrument or a system. The networking of transducers (sensors or actuators) in a system can provide flexibility, improve system performance, and make it easier to install, upgrade and maintain systems.
The sensor market is extremely diverse and sensors are used in most industries. Sensor manufacturers are seeking ways to add new technology in order to build low-cost, smart sensors that are easy to use and which meet the continuous demand for more sophisticated applications. Networking is becoming pervasive in various industrial settings, and decisions about the use of sensors, networks, and application software can all be made independently, based on application requirements.
The IEEE (Institute of Electrical and Electronics Engineers) 1451 smart transducer interface standards provide the common interface and enabling technology for the connectivity of transducers to microprocessors, control and field networks, and data acquisition and instrumentation systems. The standardized Transducer Electronic Data Sheet (TEDS) specified by IEEE 1451.2 allows for self-description of sensors. The interfaces provide a standardized mechanism to facilitate the plug and play of sensors to networks. The network-independent smart transducer object model, defined by IEEE 1451.1, allows sensor manufacturers to support multiple networks and protocols. This way, transducer-to-network interoperability can be supported. IEEE standards P1451.3 and P1451.4 will meet the needs of analog transducer users for high-speed applications. Transducer vendors and users, system integrators, as well as network providers can benefit from the IEEE 1451 interface standards. Networks of distributed microsensors are emerging as a solution for a wide range of data gathering applications. Perhaps the most substantial challenge faced by designers of small but long-lived microsensor nodes, is the need for significant reductions in energy consumption. A power-aware design methodology emphasizes the graceful scalability of energy consumption with factors such as available resources, event frequency, and desired output quality, at all levels of the system hierarchy. The architecture for a power-aware microsensor node highlights the collaboration between software that is capable of energy-quality tradeoffs and hardware with scalable energy consumption.
Power-aware methodology uses an embedded micro-operating system to reduce node energy consumption by exploiting both sleep state and active power management. Wireless distributed microsensor networks have gained importance in a wide spectrum of civil and military applications. Advances in MEMS (Micro Electro Mechanical Systems) technology, combined with low-power, low-cost, Digital Signal Processors (DSPs) and Radio Frequency (RF) circuits have resulted in feasible, inexpensive, wireless microsensor networks. A distributed, self-configuring network of adaptive sensors has significant benefits. They can be used for remote monitoring in inhospitable and toxic environments. A large class of benign environments also requires the deployment of a large number of sensors, such as intelligent patient monitoring, object tracking, and assembly line sensing. The massively distributed nature of these networks provides increased resolution and fault tolerance as compared with a single sensor node. Networking a large number of low-power mobile nodes involves routing, addressing and support for different classes of service at the network layer. Self-configuring wireless sensor networks consist of hundreds or thousands of small, cheap, battery-driven, spread-out nodes, bearing a wireless modem to accomplish a monitoring or control task jointly. Therefore, an important concern is the network lifetime: as nodes run out of power, the connectivity decreases and the network can finally be partitioned and become dysfunctional.
Deployment of large networks of sensors requires tools to collect and query data from these networks. Of particular interest are aggregates whose operations summarize current sensor values in part or all of an entire sensor network. Given a dense network of a thousand sensors querying for example, temperature, users want to know temperature patterns in relatively large regions encompassing tens of sensors, and individual sensor readings are of little value.
Networks of wireless sensors are the result of rapid convergence of three key technologies: digital circuitry, wireless communications, and MEMS. Advances in hardware technology and engineering design have led to reductions in size, power consumption, and cost. This has enabled compact, autonomous nodes, each containing one or more sensors, computation and communication capabilities, and a power supply. Ubiquitous computing is based on the idea that future computers merge with their environment until they become completely invisible to the user. Ubiquitous computing envisions everyday objects as being augmented with computation and communication capabilities. While such artifacts retain their original use and appearance, their augmentation can seamlessly enhance and extend their usage, thus opening up novel interaction patterns and applications. Distributed wireless microsensor networks are an important component of ubiquitous computing, and small dimensions are a design goal for microsensors. The energy supply of the sensors is a main constraint of the intended miniaturization process. It can be reduced only to a specific degree since energy density of conventional energy sources increases slowly. In addition to improvements in energy density, energy consumption can be reduced. This approach includes the use of energy-conserving hardware. Moreover, a higher lifetime of sensor networks can be accomplished through optimized applications, operating systems, and communication protocols. Particular modules of the sensor hardware can be turned off when they are not needed. Wireless distributed microsensor systems enable fault-tolerant monitoring and control of a variety of applications. Due to the large number of microsensor nodes that may be deployed, and the long system lifetimes required, replacing the battery is not an option. Sensor systems must utilize minimal energy while operating over a wide range of operating scenarios. These include power-aware computation and communication component technology, low-energy signaling and networking, system partitioning considering computation and communication trade-offs, and a power-aware software infrastructure. Routing and data dissemination in sensor networks requires a simple and scalable solution. The topology discovery algorithm for wireless sensor networks selects a set of distinguished nodes, and constructs a reachability map based on their information. The topology discovery algorithm logically organizes the network in the form of clusters and forms a tree of clusters rooted at the monitoring node. The topology discovery algorithm is completely distributed, uses only local information, and is highly scalable.
To achieve optimal performance in a wireless sensor network, it is important to consider the interactions among the algorithms operating at the different layers of the protocol stack. For sensor networks, one question is how the self-organization of the network into clusters affects the sensing performance. Thousands to millions of small sensors form self-organizing wireless networks, and providing security for these sensor networks is not easy since the sensors have limited processing power, storage, bandwidth, and energy. A set of Security Protocols for Sensor Networks (SPINS), explores the challenges for security in sensor networks. SPINS include: TESLA (the micro version of the Timed, Efficient, Streaming, Loss-tolerant Authentication Protocol), providing authenticated streaming broadcast, and SNEP (Secure Network Encryption Protocol) providing data confidentiality, two-party data authentication, and data freshness, with low overhead. An authenticated routing protocol uses SPINS building blocks. Wireless networks, in general, are more vulnerable to security attacks than wired networks, due to the broadcast nature of the transmission medium. Furthermore, wireless sensor networks have an additional vulnerability because nodes are often placed in a hostile or dangerous environment, where they are not physically protected. The essence of ubiquitous computing is the creation of environments saturated with computing and communication in an unobtrusive way. WWRF (Wireless World Research Forum) and ISTAG (Information Society Technologies Advisory Group) envision a vast number of various intelligent devices, embedded in the environment, sensing, monitoring and actuating the physical world, communicating with each other and with humans. The main features of the IEEE 802.15.4 standard are network flexibility, low cost, and low power consumption. This standard is suitable for many applications in the home requiring low data rate communications in an ad hoc self-organizing network.
The IEEE 802.15.4 standard defines a low-rate wireless personal area network (LR-WPAN) which has ultra-low complexity, cost, and power, for low data rate wireless connectivity among inexpensive fixed, portable, and moving devices. The IEEE 802.15.4 standard defines the physical (PHY) layer and Media Access Control (MAC) layer specifications. In contrast to traditional communication networks, the single major resource constraint in sensor networks is power, due to the limited battery life of sensor devices. Data-centric methodologies can be used to solve this problem efficiently. In Data Centric Storage (DCS) data dissemination frameworks, all event data are stored by type at designated nodes in the network and can later be retrieved by distributed mobile access points in the network. Resilient Data-Centric Storage (R-DCS) is a method of achieving scalability and resilience by replicating data at strategic locations in the sensor network. Various wireless technologies, like simple RF, Bluetooth, UWB (ultrawideband) or infrared can be used for communication between sensors. Wireless sensor networks require low-power, low-cost devices that accommodate powerful processors, a sensing unit, wireless communication interface and power source, in a robust and tiny package. These devices have to work autonomously, to require no maintenance, and to be able to adapt to the environment. Wireless Sensor Network Designs focuses on the newest technology in wireless sensor networks, networked embedded systems, and their applications. A real applications-oriented approach to solving sensor network problems is presented. The book includes a broad range of topics from networked embedded systems and smart sensor networks, to power-aware wireless sensor networks, routing, clustering, security, and operating systems along with networks support. The book is organized into ten chapters, with the goal to explain the newest sensor technology, design issues, protocols, and solutions to wireless sensor network architectures.
As previously discussed, Chapter 1 describes networked embedded systems, their design, prototyping, and application support. Chapter 2 introduces smart sensor networks and their applications. Chapter 3 introduces power-aware wireless sensor networks. Routing in wireless sensor networks and the aggregation techniques are discussed in Chapter 4. Distributed sensor networks are presented in Chapter 5, and clustering techniques in wireless sensor networks are introduced in Chapter 6. Chapter 7 presents security protocols in sensor networks. Operating systems for embedded applications are discussed in Chapter 8. Chapter 9 presents network support for embedded applications. Applications of wireless sensor networks are studied in Chapter 10.