30,931 research outputs found
Energy neutral operation of vibration energy-harvesting sensor networks for bridge applications
greatly benefit from the use of wireless sensor networks
(WSNs), however energy harvesting for the operation of the
network remains a challenge in this setting. While solar and
wind power are possible and credible solutions to energy generation,
the need for positioning sensor nodes in shaded and
sheltered locations, e.g., under a bridge deck, is also often
precluding their adoption in real-world deployments. In some
scenarios vibration energy harvesting has been shown as an
effective solution, instead.
This paper presents a multihop vibration energy-harvesting
WSN system for bridge applications. The system relies on
an ultra-low power wireless sensor node, driven by a novel
vibration based energy-harvesting technology. We use a
receiver-initiated routing protocol to enable energy-efficient
and reliable connectivity between nodes with different energy
charging capabilities. By combining real vibration data with
an experimentally validated model of the vibration energy
harvester, a hardware model, and the COOJA simulator, we
develop a framework to conduct realistic and repeatable experiments
to evaluate the system before on-site deployment.
Simulation results show that the system is able to maintain
energy neutral operation, preserving energy with careful management
of sleep and communication times. We also validate
the system through a laboratory experiment on real hardware
against real vibration data collected from a bridge. Besides
providing general guidelines and considerations for the development
of vibration energy-harvesting systems for bridge
applications, this work highlights the limitations of the energy
budget made available by traffic-induced vibrations, which
clearly shrink the applicability of vibration energy-harvesting
technology for WSNs to applications that do not generate an
overwhelming amounts of data
An Energy Driven Architecture for Wireless Sensor Networks
Most wireless sensor networks operate with very limited energy sources-their
batteries, and hence their usefulness in real life applications is severely
constrained. The challenging issues are how to optimize the use of their energy
or to harvest their own energy in order to lengthen their lives for wider
classes of application. Tackling these important issues requires a robust
architecture that takes into account the energy consumption level of functional
constituents and their interdependency. Without such architecture, it would be
difficult to formulate and optimize the overall energy consumption of a
wireless sensor network. Unlike most current researches that focus on a single
energy constituent of WSNs independent from and regardless of other
constituents, this paper presents an Energy Driven Architecture (EDA) as a new
architecture and indicates a novel approach for minimising the total energy
consumption of a WS
Autonomous monitoring framework for resource-constrained environments
Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD
AI-Based Wireless Sensor IoT Networks for Energy-Efficient Consumer Electronics Using Stochastic Optimization
Wireless Sensor Networks (WSNs) integration with the Internet of Things (IoT) expands its potential by providing ideal communication and data sharing across devices, allowing more considerable monitoring and management in Consumer Electronics (CE). WSNs have an essential limitation in terms of energy resources since sensor nodes frequently run on limited power from batteries. This limitation necessitates the consideration of energy-efficient techniques to extend the network’s lifetime. In this article, an integrated approach has been presented to improve the energy efficiency of Wireless Sensor IoT Networks (WSINs) by leveraging modern machine learning algorithms with stochastic optimization. Recursive Feature Elimination (RFE) is utilized for the feature selection thus optimizing the input features for various machine learning models. These models are rigorously evaluated for their aptness to predict and mitigate energy consumption concerns inside WSINs. Subsequently, the stochastic optimization technique utilizes the uniform and normal distributions to model energy consumption situations. The results show that RFE-driven feature selection has significant effects on model performance and that Random Forest is effective at reaching higher accuracy. This research provides valuable perspectives for the design and implementation of WSINs in CE, supporting sustainable smart devices, by addressing energy consumption concerns using an optimized approach
A Comprehensive Experimental Comparison of Event Driven and Multi-Threaded Sensor Node Operating Systems
The capabilities of a sensor network are strongly influenced by the operating system used on the sensor nodes. In general, two different sensor network operating system types are currently considered: event driven and multi-threaded. It is commonly assumed that event driven operating systems are more suited to sensor networks as they use less memory and processing resources. However, if factors other than resource usage are considered important, a multi-threaded system might be preferred. This paper compares the resource needs of multi-threaded and event driven sensor network operating systems. The resources considered are memory usage and power consumption. Additionally, the event handling capabilities of event driven and multi-threaded operating systems are analyzed and compared. The results presented in this paper show that for a number of application areas a thread-based sensor network operating system is feasible and preferable
Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services
Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing
efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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