29,409 research outputs found
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
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
Implementation of RTOS to the WSN node
Bezdrátové senzorické sieťe zväčša používajú `event-driven` operačné systémy. Táto práca diskutuje výhody nevýhody použitia RTOS v bezdrátových senzorických sieťach. Najvhodnejší RTOS je vybratý a sú podniknuté všetky kroky aby bolo možne demonštrovať schopnosť mikrokontrolérov Gecko od EnergyMicro prevádzkovať tento RTOS s nízkou spotrebou energie a demonštrovať jednoduchú bezdrátovú komunikáciu s Atmel AT86RF212 rádiami.Wireless sensors networks mostly use event-driven OSes. This works discusses pros and cons of using RTOS in wirless sensors networks. A most appropriate RTOS is chosen and all necessary steps are undergone to demonstrate EnergyMicro Gecko MCU's ability to run the RTOS with low energy consumption and demonstrate wireless simple communication with Atmel AT86RF212 radios.
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks
This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201
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