Optimization of Wireless Sensor Node Parameters by Differential Evolution and Particle Swarm Optimization

2014 IEEE Symposium on Intelligent Embedded Systems (IES)

2014

Self Cite

Environmental sensing is necessary for air quality monitoring, assessment of ecosystem health, or climate change tracking. Environmental monitoring systems can take a form of standalone monitoring stations or networks of individual sensor nodes with wireless connectivity. The latter approach allows high resolution mapping of spatiotemporal characteristics of the environment. To allow their autonomous operation and to minimize their maintenance costs, such systems are often powered using energy harvested from the environment itself. Due to the scarcity and intermittency of the environmental energy, operation of energy harvesting monitoring systems is not a trivial task. Their sensing, transmitting, and housekeeping activities must be carefully managed to extend their lifetime while providing desired quality of service. As the environmental conditions change with the region of deployment, the strategies for energy management must change accordingly to match the energy availability. In this work, we examine how geographic location affects the operations and quality of data collected by a solarpowered monitoring system. In particular, we use node/network simulation tools to follow the performance of energy-harvesting environmental monitoring sensor nodes at different latitudes, from equator to the pole. Static parameters of the simulated sensor nodes are determined for each latitude using an intelligent optimization method. The results show a clear dependence of the monitoring system performance on its deployment location. This encourages location-specific optimization of sensor node properties and parameters.

Section: B Resultsmentioning

confidence: 99%

“…The results presented in this work were obtained using a software sensor node simulator [8], [9]. The simulator uses a lightweight yet realistic energy consumption model based on a hardware prototype of a new low-power wireless sensor architecture with support for energy harvesting.…”

Section: Simulation Of Sensor Node Operationmentioning

confidence: 99%

Location-specific optimization of energy harvesting environmental monitoring systems

Musílek

Krömer

2014 IEEE Symposium on Intelligent Embedded Systems (IES)

2014

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“…Another research challenge considers a measurement node and its focus on operating cycle. A previous study [ 83 ] provided evidence that monitoring nodes can be operated using a set of static parameters optimized off-line. These parameters typically include measurement rate and data transmission cycle equidistantly distributed in the time domain.…”

Section: Research Challengesmentioning

confidence: 99%

Energy Harvesting Sources, Storage Devices and System Topologies for Environmental Wireless Sensor Networks: A Review

Konecny

Borova

et al. 2018

Sensors

Self Cite

182

132

The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to (partially) power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered.

“…A load, such as a sensor node, may consist of multiple sub-systems and energy consumption may be variable for its different modes of operation (Kansal et al (2007), Krömer et al (2014a)). If the harvesting system is meant to operate indefinitely, it cannot consume more power than the harvesting source can provide -in the long run.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Energy Management of Autonomous Weather Station∗∗This work was supported by project SP2015/154, Development of algorithms and systems for control, measurement and safety applications. of Student Grant System, VSB-TU Ostrava.

Konecny

Hamel³

et al. 2015

IFAC-PapersOnLine

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The importance of renewable energy is increasing rapidly nowadays. Solar energy is one of the most powerful resources that can be used to provide power supply for remote electrical devices. Such remote devices often need batteries replacements and maintenance that raises expenses. This contribution describes energy management and power harvesting implementation in a low-power microcontroller integrated within an autonomous solar-powered device with supercapacitor that significantly reduces maintenance needs. The energy management implements a fuzzy logic-based system that allows it to adapt to ambient temperature and sunlight intensity in deployment location. Fuzzy energy management sets device's functional mode that affects the measurement rate of the weather station and subsequent storage rate in SD Card. Both simulation results and real data results obtained in a laboratory located in Ostrava, Czech Republic, are presented in this contribution.