Chapter 1. Introduction and Overview of Wireless Sensor Networks. Chapter 2. Commercial and Scientific Applications of Wireless Sensor Networks. Chapter 3. Basic Wireless Sensor Technology. Chapter 4. Wireless Sensors Networks Protocols: Physical Layer. Chapter 5. Medium Access Control Protocols for Wireless Sensor Networks. Chapter 6. Sensors Network Protocols: Routing Protocols. Chapter 7. Transport Control Protocols for Wireless Sensors Networks. Chapter 8. Middleware for Sensor Networks. Chapter 9. Network Management for Wireless Sensor Networks. Chapter 10. Operating Systems for Sensor Networks. Chapter 11. Performance and Traffic Management Issues. Appendix A: Analysis. Appendix B: Discussions. Index.

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Wireless Sensor Networks: Technology, Protocols, and Applications

The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT’s characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.

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Middleware for Internet of Things: A Survey

Current trends in computing include increases in both distribution and wireless connectivity, leading to highly dynamic, complex environments on top of which applications must be built. The task of designing and ensuring the correctness of applications in these environments is similarly becoming more complex. The unified goal of much of the research in distributed wireless systems is to provide higher-level abstractions of complex low-level concepts to application programmers, easing the design and implementation of applications. A new and growing class of applications for wireless sensor networks require similar complexity encapsulation. However, sensor networks have some unique characteristics, including dynamic availability of data sources and application quality of service requirements, that are not common to other types of applications. These unique features, combined with the inherent distribution of sensors, and limited energy and bandwidth resources, dictate the need for network functionality and the individual sensors to be controlled to best serve the application requirements. In this article, we describe different types of sensor network applications and discuss existing techniques for managing these types of networks. We also overview a variety of related middleware and argue that no existing approach provides all the management tools required by sensor network applications. To meet this need, we have developed a new middleware called MiLAN. MiLAN allows applications to specify a policy for managing the network and sensors, but the actual implementation of this policy is effected within MiLAN. We describe MiLAN and show its effectiveness through the design of a sensor-based personal health monitor.

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Middleware to support sensor network applications

Using middleware to bridge the gap between applications and low-level constructs is a novel approach to resolving many wireless sensor network issues and enhancing application development. This survey discusses representative WSN middleware, presenting the state of the research

http://www.informatik.hs-furtwangen.de/~hanne/Pervasive/MiddelwareAndWirelessSensorNetworks.pdf

Middleware: middleware challenges and approaches for wireless sensor networks

Many sensor networks have been deployed to monitor Earth’s environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.

New Generation Sensor Web Enablement

Data fusion is an important component of applications for systems that use correlated data from multiple sources to determine the state ofa system. As the state of the system being monitored and available resources change, the general data fusion famework should change dynamically based on the current environment and available resources in the system. To achieve this goal, we have proposed a general Data Fusion Architecture (DFA) based on the Unfled Modeling Language (UML) and using a taxonomy based on the definitions of raw data and variables or tasks. The DFA can be reconfigured according to the measured environment and availability of the sensing units or data sources, providing a graceful degradation in the view of the environment as resources change. We have shown that we can apply the DFA to dflerent domains and applications. including a test bed health monitoring application.

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A general data fusion architecture

Power consumption became a crucial problem in the development of mobile devices, especially those that are communication intensive In these devices, it is imperative to reduce the power consumption devoted to maintaining a communication link during data transmission/reception The application of dynamic power management methodologies has contributed to the reduction of power consumption in general purpose computer systems However, to further reduce power consumption in communication intensive real-time embedded devices, we have to consider the state of the computation and external events in addition to power management policies In this paper we propose a model of an Extended Power State Machine (EPSM), where we adapt a Power State Machine to include the state of an embedded program in the power state machine formulation This EPSM model is used to adapt the Quality of Service (QoS) in communication intensive devices to ensure low power consumption In such development, a middleware layer fits in the system's architecture, being responsible for intercepting the data communication and implementing the EPSM Also, a software tool was developed, allowing the Middleware Code to be generated based on the State Machine A case study demonstrates the application of the proposed model to a real situation

Efficient power management in real-time embedded systems

Describes the design of a wearable multiparametric physiological signal monitor, a continuously running internetworking system for monitoring multiple physiological parameters of individuals during daily tasks. A prototype, measuring parameters such as electrocardiogram, oximetry and non-invasive blood pressure is now in test. The system performs real-time processing on the signals and can send these parameters to a central monitoring station over a computer network. The relevant points of the control operating system and applications are described, along with future directions for research.

Wearable computer as a multi-parametric monitor for physiological signals

Recent advances in wireless communication technologies, smart sensors and high-performance low-power microprocessors opened space for applications in many different areas, such as medicine. Putting together these technologies to meet functional specification of the applications and operation requirements still presents many challenges, mainly due to limited power supply. This paper presents research challenges and solutions for a biomedical sensor application. Novel features have been included in a system for real-time remote monitoring of physiological signals (electrocardiogram, blood oxygen level, and blood pressure), improving its functionality, performance, and power management. The concept of quality of service (QoS) is applied to power management, which is focused on the application parameters.

H.S. Carvalho

Papers

Middleware to support sensor network applications

A general data fusion architecture

Efficient power management in real-time embedded systems

Wearable computer as a multi-parametric monitor for physiological signals

A biomedical wearable device for remote monitoring of physiological signals