{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T13:56:26Z","timestamp":1789826186715,"version":"4.0.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11276-022-03198-0","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:02:45Z","timestamp":1673913765000},"page":"1787-1797","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Multiple agent based reinforcement learning for energy efficient routing in WSN"],"prefix":"10.1007","volume":"29","author":[{"given":"D.","family":"Prabhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Alageswaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Miruna Joe Amali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"issue":"1","key":"3198_CR1","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/JSYST.2015.2460747","volume":"11","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2017). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88\u201395.","journal-title":"IEEE Systems Journal"},{"key":"3198_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.jnca.2019.04.002","volume":"137","author":"MU Younus","year":"2019","unstructured":"Younus, M. U., Islam, S. U., Ali, I., Khan, S., & Khan, M. K. (2019). A survey on software dened networking enabled smart buildings. Architecture, challenges and use cases. Journal of Network and Computer Applications, 137, 62\u201377.","journal-title":"Journal of Network and Computer Applications"},{"key":"3198_CR3","doi-asserted-by":"crossref","unstructured":"Al-Aubidy, K. M., Derbas, A. M., & Al-Mutairi, A. W. (Mar. 2016) Real-time patient health monitoring and alarming using wireless-sensor-network. In Proc. 13th Int. Multi-Conf. Syst., Signals Devices (SSD), pp. 416\u2013423.","DOI":"10.1109\/SSD.2016.7473672"},{"issue":"3","key":"3198_CR4","doi-asserted-by":"publisher","first-page":"16","DOI":"10.14569\/IJACSA.2018.090304","volume":"9","author":"M Usman","year":"2018","unstructured":"Usman, M. (2018). Analysis of the impact of different parameter settings on wireless sensor network lifetime. International Journal of Advanced Computer Science and Applications, 9(3), 16\u201321.","journal-title":"International Journal of Advanced Computer Science and Applications"},{"issue":"4","key":"3198_CR5","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1109\/TNSE.2020.3017751","volume":"7","author":"YR Chen","year":"2020","unstructured":"Chen, Y. R., Rezapour, A., Tzeng, W. G., & Tsai, S. C. (2020). RL-Routing: An SDN routing algorithm based on deep reinforcement learning. IEEE Transactions on Network Science and Engineering, 7(4), 3185\u20133199.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"3198_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2022.3186099","author":"A Mohajer","year":"2022","unstructured":"Mohajer, A., Daliri, M. S., Mirzaei, A., Ziaeddini, A., Nabipour, M., & Bavaghar, M. (2022). Heterogeneous computational resource allocation for NOMA: Toward green mobile edge-computing systems. IEEE Transactions on Services Computing. https:\/\/doi.org\/10.1109\/TSC.2022.3186099","journal-title":"IEEE Transactions on Services Computing"},{"issue":"10","key":"3198_CR7","doi-asserted-by":"publisher","first-page":"2234","DOI":"10.1109\/JSAC.2020.3000364","volume":"38","author":"Y Shao","year":"2020","unstructured":"Shao, Y., Rezaee, A., Liew, S. C., & Chan, V. W. (2020). Significant sampling for shortest path routing: A deep reinforcement learning solution. IEEE Journal on Selected Areas in Communications, 38(10), 2234\u20132248.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"3198_CR8","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1109\/ACCESS.2020.3046693","volume":"9","author":"MU Younus","year":"2020","unstructured":"Younus, M. U., Khan, M. K., Anjum, M. R., Afridi, S., Arain, Z. A., & Jamali, A. A. (2020). Optimizing the lifetime of software defined wireless sensor network via reinforcement learning. IEEE Access., 9, 259\u2013272.","journal-title":"IEEE Access."},{"key":"3198_CR9","doi-asserted-by":"publisher","first-page":"37109","DOI":"10.1109\/ACCESS.2019.2904539","volume":"7","author":"R Ding","year":"2019","unstructured":"Ding, R., Xu, Y., Gao, F., Shen, X., & Wu, W. (2019). Deep reinforcement learning for router selection in network with heavy traffic. IEEE Access, 7, 37109\u201337120.","journal-title":"IEEE Access"},{"key":"3198_CR10","doi-asserted-by":"publisher","first-page":"64533","DOI":"10.1109\/ACCESS.2018.2877686","volume":"6","author":"C Yu","year":"2018","unstructured":"Yu, C., Lan, J., Guo, Z., & Hu, Y. (2018). DROM: Optimizing the routing in software-defined networks with deep reinforcement learning. IEEE Access, 6, 64533\u201364539.","journal-title":"IEEE Access"},{"issue":"3","key":"3198_CR11","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/JSTSP.2021.3055957","volume":"15","author":"KC Tsai","year":"2021","unstructured":"Tsai, K. C., Zhuang, Z., Lent, R., Wang, J., Qi, Q., Wang, L. C., & Han, Z. (2021). Tensor-based reinforcement learning for network routing. IEEE Journal of Selected Topics in Signal Processing, 15(3), 617\u2013629.","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"issue":"1","key":"3198_CR12","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1109\/TNSM.2020.3036911","volume":"18","author":"DM Casas-Velasco","year":"2020","unstructured":"Casas-Velasco, D. M., Rendon, O. M. C., & da Fonseca, N. L. (2020). Intelligent routing based on reinforcement learning for software-defined networking. IEEE Transactions on Network and Service Management, 18(1), 870\u2013881.","journal-title":"IEEE Transactions on Network and Service Management"},{"issue":"4","key":"3198_CR13","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","volume":"21","author":"NC Luong","year":"2019","unstructured":"Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y. C., & Kim, D. I. (2019). Applications of deep reinforcement learning in communications and networking: A survey. IEEE Communications Surveys & Tutorials, 21(4), 3133\u20133174.","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"11","key":"3198_CR14","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1364\/JOCN.11.000547","volume":"11","author":"J Su\u00e1rez-Varela","year":"2019","unstructured":"Su\u00e1rez-Varela, J., Mestres, A., Yu, J., Kuang, L., Feng, H., Cabellos-Aparicio, A., & Barlet-Ros, P. (2019). Routing in optical transport networks with deep reinforcement learning. Journal of Optical Communications and Networking, 11(11), 547\u2013558.","journal-title":"Journal of Optical Communications and Networking"},{"issue":"5","key":"3198_CR15","doi-asserted-by":"publisher","first-page":"7103","DOI":"10.1109\/JSEN.2020.3044049","volume":"21","author":"H Al-Tous","year":"2020","unstructured":"Al-Tous, H., & Barhumi, I. (2020). Reinforcement learning framework for delay sensitive energy harvesting wireless sensor networks. IEEE Sensors Journal, 21(5), 7103\u20137113.","journal-title":"IEEE Sensors Journal"},{"issue":"7\u20138","key":"3198_CR16","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/08839514.2018.1486132","volume":"32","author":"F Nikjoo","year":"2018","unstructured":"Nikjoo, F., Mirzaei, A., & Mohajer, A. (2018). A novel approach to efficient resource allocation in NOMA heterogeneous networks: Multi-criteria green resource management. Applied Artificial Intelligence, 32(7\u20138), 583\u2013612.","journal-title":"Applied Artificial Intelligence"},{"key":"3198_CR17","doi-asserted-by":"crossref","unstructured":"Jha, A., Singh, K.K., Devi, K.V., & Manjula, V. (2021). Reinforcement learning based weighted multipath routing for datacenter networks. Materials Today: Proceedings.","DOI":"10.1016\/j.matpr.2021.01.252"},{"key":"3198_CR18","doi-asserted-by":"publisher","first-page":"107313","DOI":"10.1016\/j.comnet.2020.107313","volume":"178","author":"D Wang","year":"2020","unstructured":"Wang, D., Liu, J., Yao, D., & Member, I. E. E. E. (2020). An energy-efficient distributed adaptive cooperative routing based on reinforcement learning in wireless multimedia sensor networks. Computer Networks, 178, 107313.","journal-title":"Computer Networks"},{"key":"3198_CR19","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.eswa.2019.04.056","volume":"131","author":"MAL Silva","year":"2019","unstructured":"Silva, M. A. L., de Souza, S. R., Souza, M. J. F., & Bazzan, A. L. C. (2019). A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems. Expert Systems with Applications, 131, 148\u2013171.","journal-title":"Expert Systems with Applications"},{"issue":"10","key":"3198_CR20","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1016\/j.engappai.2013.07.017","volume":"26","author":"VG Rolla","year":"2013","unstructured":"Rolla, V. G., & Curado, M. (2013). A reinforcement learning-based routing for delay tolerant networks. Engineering Applications of Artificial Intelligence, 26(10), 2243\u20132250.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"3198_CR21","doi-asserted-by":"publisher","first-page":"102865","DOI":"10.1016\/j.jnca.2020.102865","volume":"177","author":"WX Liu","year":"2021","unstructured":"Liu, W. X., Cai, J., Chen, Q. C., & Wang, Y. (2021). DRL-R: Deep reinforcement learning approach for intelligent routing in software-defined data-center networks. Journal of Network and Computer Applications, 177, 102865.","journal-title":"Journal of Network and Computer Applications"},{"key":"3198_CR22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.asoc.2015.09.003","volume":"38","author":"S Chettibi","year":"2016","unstructured":"Chettibi, S., & Chikhi, S. (2016). Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks. Applied Soft Computing, 38, 321\u2013328.","journal-title":"Applied Soft Computing"},{"key":"3198_CR23","doi-asserted-by":"publisher","first-page":"106099","DOI":"10.1016\/j.asoc.2020.106099","volume":"89","author":"C Qu","year":"2020","unstructured":"Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020). A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied Soft Computing, 89, 106099.","journal-title":"Applied Soft Computing"},{"key":"3198_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2022.3154162","author":"A Mohajer","year":"2022","unstructured":"Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and Backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal. https:\/\/doi.org\/10.1109\/JSYST.2022.3154162","journal-title":"IEEE Systems Journal"},{"issue":"3","key":"3198_CR25","doi-asserted-by":"publisher","first-page":"3646","DOI":"10.1007\/s11227-021-04001-1","volume":"78","author":"T Mahmood","year":"2022","unstructured":"Mahmood, T., Li, J., Pei, Y., Akhtar, F., Butt, S. A., Ditta, A., & Qureshi, S. (2022). An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing, 78(3), 3646\u20133675.","journal-title":"The Journal of Supercomputing"},{"key":"3198_CR26","doi-asserted-by":"publisher","first-page":"10737","DOI":"10.1109\/ACCESS.2021.3051360","volume":"9","author":"WK Yun","year":"2021","unstructured":"Yun, W. K., & Yoo, S. J. (2021). Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access, 9, 10737\u201310750.","journal-title":"IEEE Access"},{"key":"3198_CR27","doi-asserted-by":"publisher","first-page":"2239","DOI":"10.1007\/s11277-016-3378-6","volume":"94","author":"T Senthil","year":"2017","unstructured":"Senthil, T., & Kannapiran, B. (2017). ECTMRA: Energy conserving trustworthy multipath routing algorithm based on cuckoo search algorithm. Wireless Personal Communications, 94, 2239\u20132258. https:\/\/doi.org\/10.1007\/s11277-016-3378-6","journal-title":"Wireless Personal Communications"},{"key":"3198_CR28","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.comcom.2019.11.033","volume":"150","author":"PS Ragavan","year":"2020","unstructured":"Ragavan, P. S., & Ramasamy, K. (2020). Software defined networking approach based efficient routing in multihop and relay surveillance using Lion Optimization algorithm. Computer Communications, 150, 764\u2013770.","journal-title":"Computer Communications"},{"issue":"3","key":"3198_CR29","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1504\/IJENM.2021.118056","volume":"12","author":"S Yasotha","year":"2021","unstructured":"Yasotha, S., & Gopalakrishnan, V. (2021). Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks. International Journal of Enterprise Network Management, 12(3), 221\u2013238.","journal-title":"International Journal of Enterprise Network Management"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-022-03198-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-022-03198-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-022-03198-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T11:11:36Z","timestamp":1682593896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-022-03198-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["3198"],"URL":"https:\/\/doi.org\/10.1007\/s11276-022-03198-0","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]},"assertion":[{"value":"21 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human or animal rights"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}