This chapter identifies the vulnerabilities associated with the operational paradigms currently employed by Wireless Sensor Networks. A survey of current WSN security research is presented. The security issues of Mobile Ad-Hoc Networks and infrastructure supported wireless networks are briefly compared and contrasted to the security concerns of Wireless Sensor Networks. A framework for implementing security in WSNs, which identifies the security measures necessary to mitigate the identified vulnerabilities is defined.

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Security for Sensor Networks

This paper reviews the state-of-the art in vibration energy harvesting for wireless, self-powered microsystems. Vibration-powered generators are typically, although not exclusively, inertial spring and mass systems. The characteristic equations for inertial-based generators are presented, along with the specific damping equations that relate to the three main transduction mechanisms employed to extract energy from the system. These transduction mechanisms are: piezoelectric, electromagnetic and electrostatic. Piezoelectric generators employ active materials that generate a charge when mechanically stressed. A comprehensive review of existing piezoelectric generators is presented, including impact coupled, resonant and human-based devices. Electromagnetic generators employ electromagnetic induction arising from the relative motion between a magnetic flux gradient and a conductor. Electromagnetic generators presented in the literature are reviewed including large scale discrete devices and wafer-scale integrated versions. Electrostatic generators utilize the relative movement between electrically isolated charged capacitor plates to generate energy. The work done against the electrostatic force between the plates provides the harvested energy. Electrostatic-based generators are reviewed under the classifications of in-plane overlap varying, in-plane gap closing and out-of-plane gap closing; the Coulomb force parametric generator and electret-based generators are also covered. The coupling factor of each transduction mechanism is discussed and all the devices presented in the literature are summarized in tables classified by transduction type; conclusions are drawn as to the suitability of the various techniques.

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Energy harvesting vibration sources for microsystems applications

Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks, it is important to be able to download code into the network dynamically. We present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.

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Contiki - a lightweight and flexible operating system for tiny networked sensors

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

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Energy conservation in wireless sensor networks: A survey

Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks it is important to be able to dynamically download code into t ...

Contiki - a Lightweight and Flexible Operating System for Tiny Networked Sensors

This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy-aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.

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Energy-aware wireless microsensor networks

The advent of wireless micro sensors promises many yet unrealized benefits. A network of such sensors or “sensor network” introduces a new set of challenges. Besides being able to communicate effectively, sensor networks have demanding sensing tasks. First, they must be aware of their environment and oftentimes are required to adapt to their surroundings. Second, they must coordinate among them to perform a greater group-sensing task. In this context, the study of sensor networks has numerous other aspects besides communication. To create a better understanding of sensor networks and to facilitate the development of new protocols and applications, detailed simulation and performance evaluation techniques need to be developed. In this paper, we introduce our ongoing efforts in the development of SensorSim, a simulation framework that introduces new models and techniques for the design and analysis of sensor networks. SensorSim inherits the core features of traditional event driven network simulators, and builds up new features that include ability to model power usage in sensor nodes, hybrid simulation that allows the interaction of real and simulated nodes, new communication protocols and real time user interaction with graphical data display. After discussing the details of SensorSim, we provide our current results, that demonstrate various capabilities of SensorSim.

SensorSim: a simulation framework for sensor networks

In this paper, we look at different battery capacity models that have been introduced in the literatures. These models describe the battery capacity utilization based on how the battery is discharged by the circuits that consume power. In an attempt to validate these models, we characterize a commercially available lithium coin cell battery through careful measurements of the current and the voltage output of the battery under different load profile applied by a micro sensor node. In the result, we show how the capacity of the battery is affected by the different load profile and provide analysis on whether the conventional battery models are applicable in the real world. One of the most significant finding of our work will show that DC/DC converter plays a significant role in determining the battery capacity, and that the true capacity of the battery may only be found by careful measurements.

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Battery capacity measurement and analysis using lithium coin cell battery

Recent advances in low-power embedded processors, radios, and micro-mechanical systems (MEMs) have made possible the development of networks of wirelessly interconnected sensors. With their focus on applications requiring tight coupling with the physical world, as opposed to the personal communication focus of conventional wireless networks, these wireless sensor networks pose significantly different design, implementation, and deployment challenges. In this paper, we present a set of models and techniques that are embodied in a simulation tool for modeling wireless sensor networks. Our work builds up on the infrastructure provided by the widely used ns-2 simulator, and adds a suite of new features and techniques that are specific to wireless sensor networks. These features introduce the notion of a sensing channel through which sensors detect targets, and provide detailed models for evaluating energy consumption and battery lifetime.

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Simulating networks of wireless sensors

We describe the system design of "Smart Kindergarten", a smart problem solving environment for early childhood education. Two crucial building blocks that make up smart kindergarten are iBadge, a lightweight power-aware sensor-instrumented badge, and Sylph, a sensor middleware infrastructure. Equipped with sensors, radio, and DSP, iBadge has been designed to be worn by kids and teachers to unobtrusively capture the interaction between the kids and their surroundings. The captured data is then wirelessly transmitted to Sylph, a middleware infrastructure, which provides means of accessing the data in coherent and user-friendly manner. In this paper, we present the design challenges of iBadge and Sylph and our efforts in integrating these two systems.

https://www2.computer.org/portal/web/csdl/doi/10.1109/ISWC.2002.1167252

Sung Park

Papers

Energy-aware wireless microsensor networks

SensorSim: a simulation framework for sensor networks

Battery capacity measurement and analysis using lithium coin cell battery

Simulating networks of wireless sensors

Design of a wearable sensor badge for smart kindergarten