TY  - JOUR
AV  - none
SP  - 6947
TI  - Reward-based residential wireless sensor optimization approach for appliance monitoring
N1  - cited By 1
PB  - Springer Science and Business Media Deutschland GmbH
SN  - 14327643
EP  - 6956
ID  - scholars14980
KW  - Automation; Cost effectiveness; Domestic appliances; Energy harvesting; Energy management; Energy utilization; Information management; Learning systems; Mobile telecommunication systems; Reinforcement learning; Rhenium compounds; Wireless sensor networks
KW  -  End-user applications; Energy conservation rates; Grid-based applications; Home automation systems; Information sharing; Integrated system architecture; Lifetime of networks; Variable communication
KW  -  Energy management systems
N2  - Sensor network-based home automation systems are familiar over the recent decades. Incorporating the benefits of the sensor network, energy management systems (EMS), is introduced to benefit end-user through periodic information sharing and remote access. WSN opted for energy harvesters to reduce the maintenance costs and maximize the lifetime of network. It is a perfect match for wireless devices and WSNs. Energy management system designed for effective use of harvested energy. Wireless sensor networks (WSN) coupled with EMS and grid-based applications serve as a support for smart home appliances. The integrated system architectures are cost effective and are energy harvesting that is profitable for end-user applications. Identifying optimal devices and defining an energy management policy are a tedious task as the devices are interfaced through different application support. This manuscript proposes a reward-based energy harvesting (REH) approach for identifying reliable devices in order to frame minimal-allocation energy for its operation. The rewards for the devices are estimated through observations carried out using reinforced learning that determines the operation state of the device. The reward function is computed using a variant function evaluated using the enduring energy and storage metrics of a device. Unlike the other learning methods, this approach operates in variable communication interval retaining the reward from the previous history of the devices. With a distributed WSN support and recursive knowledge of the sensor devices, REH is intended to improve the energy conservation rate with lesser retransmissions. The curtailed number of retransmissions minimizes delay with more preferable ideal devices in a home management system. The performance of the proposed REH is evaluated through simulations considering the following metrics: end-to-end delay, energy utilization, packets forwarded, expected TTL and number of retransmissions. © 2020, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
IS  - 10
Y1  - 2021///
VL  - 25
A1  - Prakash, J.
A1  - Harshavardhan Naidu, S.
A1  - Aziz, I.A.
A1  - Jaafar, J.
JF  - Soft Computing
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104228573&doi=10.1007%2fs00500-020-05525-z&partnerID=40&md5=3c5feb0a6c9ce9dde11e7da22e96ea11
ER  -