Flexible WSN Data Gathering through Energy-aware Adaptive Sensing

Silva, João Marco C.; Bispo, Kalil Araujo; Carvalho, Paulo; Lima, Solange Rito

doi:10.1109/saconet.2018.8585614

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present proposal extends previous work of the same authors in the sense that data acquisition rate now is dependent not only on the observed variation in the scalar physical quantities measured but also on the perceived devices residual battery level over time.…”

Section: Related Worksupporting

confidence: 78%

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Silva

Carvalho

Bispo

et al. 2019

Int J Communication

Self Cite

View full text Add to dashboard Cite

Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure.Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments. KEYWORDSadaptive sensing, data collection, energy-aware sensing, wireless sensor networks INTRODUCTIONAs a key enabler of the Internet of Things (IoT) and Smart Cities paradigms, wireless sensor networks (WSNs) are currently a technology capturing much attention, both from the academic community and industry. Sustained by the development of multifunctional low-cost wireless sensors, WSNs are applied in a large range of scenarios, many of then requiring operation without human intervention. 1,2 This versatility and difficult maintenance urge for mechanisms of self-management and power saving in order to allow a cost-effective adaptation of the sensing process to the application area, while optimising WSN lifetime. 3 Reducing energy consumption of sensor nodes for improving WSN lifetime has been a major topic of research in the last decade, being identified three major subsystems impacting on energy consumption-communication, sensing, and processing. The communication subsystem revealed to be the most demanding, even for devices able of energy harvesting. 4 This motivated solutions such as data gathering aggregation in order to reduce the number of transmission events.However, several works revealed that, depending on the operational scenario, acquiring and processing data may be more demanding than communicating. 5,6 This suggests that a versatile data gathering process should avoid acquiring redundant information, even when data aggregation strategies are in place.Int J Commun Syst. 2020;33:e4153.wileyonlinelibrary.com/journal/dac

show abstract

Section: Related Worksupporting

confidence: 78%

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Silva

Carvalho

Bispo

et al. 2019

Int J Communication

Self Cite

View full text Add to dashboard Cite

show abstract

“…To accommodate monitoring systems to changeable environments, Silva [ 10 ] proposed a flexible data gathering protocol that changes monitor attributes according to previous environments and sensor situations. Meyer [ 9 ] designed a feedback mechanism to control event notifications flexibly, under which feedback messages are made by a complex decision system according to a large amount of monitoring information.…”

Section: Related Studiesmentioning

confidence: 99%

“…In general, a shorter routing path means that events can be detected efficiently because notifying the sink of the event requires short time and less energy. For monitoring the environment efficiently, studies also developed flexible mechanisms to adjust sensors’ occasions of triggering or relaying event notifications [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Smart Sensing and Routing Mechanism for Wireless Sensor Networks

Hung

2020

Sensors

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on building energy-efficient transmission mechanisms to extend monitoring lifetimes, and others have focused on building additional systems to support monitoring for enhanced accuracy. Herein, we propose a distributed cooperative mechanism where neighboring sensors mutually confirm event occurrences for improved monitoring accuracy. Moreover, the mechanism transmits events in a time- and energy-efficient manner by using smart antennae to extend monitoring lifetimes. The results of the simulations reveal that monitoring lifetime is extended and time for event notifications is shortened under the proposed mechanism. The evaluations also demonstrate that the monitoring accuracy of the proposed mechanism is much higher than that of other existing mechanisms.

show abstract

“…The Quality of Service (QoS) and a lifetime of WSNs can be improved with a well-designed system [3]. Due to the limitations of the WSN node battery capacity, an energy-aware adaptive sensing technique can be applied to calculate the sustainable sensing period based on residual battery energy that yields an optimal data gathering process [4]. Besides the energy consumption management within the WSN infrastructure, energy harvesting is another solution that is being continuously researched.…”

Section: Introductionmentioning

confidence: 99%

Optimal Data Transfer of SEH-WSN Node via MDP Based on Duty Cycle and Battery Energy

2021

View full text Add to dashboard Cite

Applications in wireless sensor networks (WSNs) are rapidly spreading out over the world. The one critical point of WSNs is energy consumption, where the transmitted data is limited by battery energy. Solar energy is used to handle the depletion of the battery energy via photo voltaic (PV) panels. A solar energy harvesting WSN (SEH-WSN) node utilizes exponential decision-dynamic duty cycle scheduling based on prospective increase in energy (ED-DSP) to save battery energy by adjusting the duty cycle from an exponential curve and future solar energy. To estimate the prospective solar energy, a prediction technique is applied, but does not guarantee 100% accuracy. Hence, this paper proposes a Markov Decision Process (MDP) to schedule a duty cycle of an SEH-WSN node instead of the ED-DSP depending on the predicted energy. We evaluate its performance via MATLAB simulations with simple irradiance models and real annual irradiance data. The results show that the MDP policy outperforms the ED-DSP.

show abstract

Flexible WSN Data Gathering through Energy-aware Adaptive Sensing

Cited by 6 publications

References 17 publications

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

A Smart Sensing and Routing Mechanism for Wireless Sensor Networks

Optimal Data Transfer of SEH-WSN Node via MDP Based on Duty Cycle and Battery Energy

Contact Info

Product

Resources

About