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

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  • INESC TEC & University of Minho
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Abstract and Figures

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. e‐LiteSense is an adaptive, energy‐aware sensing solution for WSNs, capable of auto‐regulate data sensing, adjusting it to each applicational scenario. e‐LiteSense is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. The adaptive scheme relies on low‐complexity rules to establish the sensing frequency weighting the drifts of the physical parameter and the levels of remaining energy in the sensor. This constitutes a step‐forward for implementing self‐adaptive energy‐aware data sensing in dynamic WSN environments.
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Received: 15 March 2019 Revised: 7 July 2019 Accepted: 29 July 2019
DOI: 10.1002/dac.4153
SPECIAL ISSUE ARTICLE
e-LiteSense: Self-adaptive energy-aware data sensing in
WSN environments
João Marco Silva1Paulo Carvalho2Kalil Araujo Bispo3Solange Rito Lima2
1INESC TEC, HASLab, Universidade do
Minho, Braga, Portugal
2Centro Algoritmi, Universidade do
Minho, Braga, Portugal
3Departamento de Computação,
Universidade Federal de Sergipe, São
Cristóvão, Brazil
Correspondence
Paulo Carvalho, Centro Algoritmi,
Universidade do Minho, 4750-057 Braga,
Portugal.
Email: pmc@di.uminho.pt
Funding information
Fundação para a Ciˆ
cncia e Tecnologia,
Grant/Award Number:
UID/CEC/00319/2019; The EU
Framework Programme for Research and
Innovation 2014-2020, Grant/Award
Number: 732505
Summary
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 phe-
nomena, 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 consti-
tutes a step-forward for implementing self-adaptive energy-aware data sensing
in dynamic WSN environments.
KEYWORDS
adaptive sensing, data collection, energy-aware sensing, wireless sensor networks
1INTRODUCTION
As 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 pro-
cessing. 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 © 2019 John Wiley & Sons, Ltd. 1of14
https://doi.org/10.1002/dac.4153
... Thus, apart from implementing a regular time interval to keep the solution robust, data is only sent when: (i) it is necessary to turn on or off the sprinklers; and (ii) sudden changes on the parameter readings are noticed. In addition, the adaptive sensing algorithm presented and tested in [13], is suggested to be applied in sensor nodes. This algorithm allows adapting the frequency of sensing in multivariate sensor nodes, such as in the present work, so that the energy wasted in sensing, processing and communicating is reduced. ...
... Nesta situaçãoé lançado um aviso para a praia fluvial mais próxima com o intuito de informar a população de que aágua está imprópria para uso balnear. O algoritmo de adaptação temporal da recolha de medidas, inicialmente proposto e testado em [11],é aqui sugerido para aplicação aos multi-parâmetros de interesse para o caso de estudo do rio Cávado. ...
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