Abstract
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.
Similar content being viewed by others
References
Agarwal V, Decarlo RA, Tsoukalas LH (2017) Modeling energy consumption and lifetime of a wireless sensor node operating on a contention-based MAC protocol. IEEE Sens J 17(16):5153–5168
Akyildiz et al (2002) Wireless sensor networks: a survey. Comput Netw 38:393–422
Anand JV (2020) Trust-value based wireless sensor network using compressed sensing. J Electron 2(02):88–95
Bokareva T, Hu W, Kanhere S, Ristic B, Gordon N, Bessell T, Rutten M, Jha S (2006) Wireless sensor networks for battlefield surveillance. In: Proceedings of the land warfare conference, LWC Brisbane, Australia, October 24–27
Erol-Kantarci M, Mouftah HT (2010) Wireless sensor networks for domestic energy management in smart grids. In: 2010 25th Biennial symposium on communications
Haoxiang W, Smys S (2020) Soft computing strategies for optimized route selection in wireless sensor network. J Soft Comput Paradigm (JSCP) 2(01):1–12
Hubert T, Grijalva S (2012) Modeling for residential electricity optimization in dynamic pricing environments. IEEE Trans Smart Grid 3(4):2224–2231
Jung D, Teixeira T, Savvides A (2009) Sensor node life time analysis: models and tools. ACM Trans Sens Netw 5(1):1–29
Kim DS, Chung BJ, Son S-Y, Lee J (2013) Developments of the in-home display systems for residential energy monitoring. In: 2013 IEEE international conference on consumer electronics (ICCE), vol 59, no 3, pp 492–498
Lee Y-T, Hsiao W-H, Huang C-M, Chou S-CT (2016) An integrated cloud-based smart home management system with community hierarchy. IEEE Trans Consum Electron 62(1):1–9
Mao S, Cheung MH, Wong VWS (2014) Joint energy allocation for sensing and transmission in rechargeable wireless sensor networks. IEEE Trans Veh Technol 63(6):2862–2875
Medina J, Muller N, Roytelman I (2010) Demand response and distribution grid operations: opportunities and challenges. IEEE Trans Smart Grid 1(2):193–198
Muruganathan SD, Ma DCF, Bhasin RI, Fapojuwo AO (2005) A centralized energy efficient routing protocol for wireless sensor networks. IEEE Commun Mag 43(3):S8–S15
Ortiz A, Al-Shatri H, Li X, Weber T, Klein A (2017) Reinforcement Learning for energy harvesting decode-and-forward two-hop communications. IEEE Trans Green Commun Netw 1(3):309–319
Ozturk Y, Senthilkumar D, Kumar S, Lee G (2013) An intelligent home energy management system to improve demand response. IEEE Trans Smart Grid 4(2):694–701
Peng S, Low C (2014) Prediction free energy neutral power management for energy harvesting wireless sensor nodes. Ad Hoc Netw 13:351–367
Raghavendra C, Sivalingam K, Znati T (2006) Wireless sensor networks, 1st edn. Springer, New York
Rahimi F, Ipakchi A (2010) Demand response as a market resource under the smart grid paradigm. IEEE Trans Smart Grid 1(1):82–88
Raj JS (2019) QoS optimization of energy efficient routing in IoT wireless sensor networks. J ISMAC 1(01):12–23
Rodriguez-Diaz E, Vasquez JC, Guerrero JM (2016) Intelligent DC homes in future sustainable energy systems: when efficiency and intelligence work together. IEEE Consum Electron Mag 5(1):74–80
Sathesh A (2019) Optimized multi-objective routing for wireless communication with load balancing. J Trends Comput Sci Smart Technol (TCSST) 1(02):106–120
Smys S, Raj JS (2019) Performance optimization of wireless adhoc networks with authentication. J Ubiquitous Comput Commun Technol (UCCT) 1(02):64–75
Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks: technology, protocols, and applications. Wiley, Hoboken
Tilak S, Abu-Ghazaleh NB, Heinzelman W (2002) A taxonomy of wireless micro-sensor network models. ACM SIGMOBILE Mob Comput Commun Rev 6(2):28–36
Tunca C, Isik S, Donmez MY, Ersoy C (2015) Ring routing: an energy-efficient routing protocol for wireless sensor networks with a mobile sink. IEEE Trans Mob Comput 14(9):1947–1960
Weng C-E, Zhang J-M, Hung H-L (2014) An efficient power control scheme and joint adaptive modulation for wireless sensor networks. Comput Electr Eng 40(2):641–650
Yi P, Dong X, Iwayemi A, Zhou C, Li S (2013) Real-time opportunistic scheduling for residential demand response. IEEE Trans Smart Grid 4(1):227–234
Zhang Y, Li W (2012) Modeling and energy consumption evaluation of a stochastic wireless sensor network. EURASIP J Wirel Commun Netw 2012(1):282
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
The authors used their data. The authors did not use any humans and animals in this research work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Prakash, J., Harshavardhan Naidu, S., Aziz, I.A. et al. Reward-based residential wireless sensor optimization approach for appliance monitoring. Soft Comput 25, 6947–6956 (2021). https://doi.org/10.1007/s00500-020-05525-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05525-z