{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:25:07Z","timestamp":1773833107754,"version":"3.50.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T00:00:00Z","timestamp":1636156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T00:00:00Z","timestamp":1636156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11276-021-02804-x","type":"journal-article","created":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T18:02:26Z","timestamp":1636221746000},"page":"125-136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["An energy-aware clustering method in the IoT using a swarm-based algorithm"],"prefix":"10.1007","volume":"28","author":[{"given":"Mahyar","family":"Sadrishojaei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5514-5536","authenticated-orcid":false,"given":"Nima","family":"Jafari Navimipour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Midia","family":"Reshadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Hosseinzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehmet","family":"Unal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,6]]},"reference":[{"issue":"7","key":"2804_CR1","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s11265-021-01666-y","volume":"93","author":"N Xiao","year":"2021","unstructured":"Xiao, N., et al. (2021). A diversity-based selfish node detection algorithm for socially aware networking. Journal of Signal Processing Systems, 93(7), 811\u2013825.","journal-title":"Journal of Signal Processing Systems"},{"issue":"12","key":"2804_CR2","doi-asserted-by":"publisher","first-page":"9531","DOI":"10.1109\/JIOT.2020.3007130","volume":"8","author":"Z Lv","year":"2020","unstructured":"Lv, Z., Qiao, L., Li, J., & Song, H. (2020). Deep-learning-enabled security issues in the internet of things. IEEE Internet of Things Journal, 8(12), 9531\u20139538.","journal-title":"IEEE Internet of Things Journal"},{"issue":"7","key":"2804_CR3","doi-asserted-by":"publisher","first-page":"5350","DOI":"10.1109\/JIOT.2021.3056128","volume":"8","author":"Z Lv","year":"2021","unstructured":"Lv, Z., Lou, R., Li, J., Singh, A. K., & Song, H. (2021). Big data analytics for 6G-enabled massive internet of things. IEEE Internet of Things Journal, 8(7), 5350\u20135359.","journal-title":"IEEE Internet of Things Journal"},{"key":"2804_CR4","doi-asserted-by":"publisher","first-page":"15620","DOI":"10.1109\/JIOT.2021.3074499","volume":"8","author":"S Sefati","year":"2022","unstructured":"Sefati, S., & Navimipour, J. N. (2022). A QoS-aware service composition mechanism in the Internet of things using a hidden Markov model-based optimization algorithm. IEEE Internet of Things Journal, 8, 15620\u201315627.","journal-title":"IEEE Internet of Things Journal"},{"key":"2804_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shen, C., Su, H., Arafin, M. T., and Qu, G. (2021). Voltage over-scaling-based lightweight authentication for IoT security. IEEE Transactions on Computers.","DOI":"10.1109\/TC.2021.3049543"},{"key":"2804_CR6","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TII.2021.3076513","volume":"18","author":"K Cai","year":"2021","unstructured":"Cai, K., Chen, H., Ai, W., Miao, X., Lin, Q., & Feng, Q. (2021). Feedback convolutional network for intelligent data fusion based on near-infrared collaborative IoT technology. IEEE Transactions on Industrial Informatics, 18, 1200\u20131209.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2804_CR7","doi-asserted-by":"crossref","unstructured":"Li, B., Liang, R., Zhou, W., Yin, H., Gao, H., and Cai, K. (2021). LBS meets blockchain: an efficient method with security preserving trust in SAGIN. IEEE Internet of Things Journal.","DOI":"10.1109\/JIOT.2021.3064357"},{"issue":"3","key":"2804_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3397765","volume":"16","author":"Z Lv","year":"2020","unstructured":"Lv, Z., Qiao, L., & Song, H. (2020). Analysis of the security of internet of multimedia things. ACM Transactions on Multimedia Computing, Communications, and Applications (ToMM), 16(3), 1\u201316.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (ToMM)"},{"key":"2804_CR9","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.neucom.2021.05.010","volume":"455","author":"L Weng","year":"2021","unstructured":"Weng, L., He, Y., Peng, J., Zheng, J., & Li, X. (2021). Deep cascading network architecture for robust automatic modulation classification. Neurocomputing, 455, 308\u2013324.","journal-title":"Neurocomputing"},{"key":"2804_CR10","doi-asserted-by":"crossref","unstructured":"Yi, H. (2021) Secure social internet of things based on post-quantum blockchain. IEEE Transactions on Network Science and Engineering.","DOI":"10.1109\/TNSE.2021.3095192"},{"issue":"2","key":"2804_CR11","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1109\/COMST.2020.2973314","volume":"22","author":"YA Qadri","year":"2020","unstructured":"Qadri, Y. A., Nauman, A., Zikria, Y. B., Vasilakos, A. V., & Kim, S. W. (2020). The future of healthcare internet of things: A survey of emerging technologies. IEEE Communications Surveys & Tutorials, 22(2), 1121\u20131167.","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"2804_CR12","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.comnet.2019.05.014","volume":"160","author":"S Hajiheidari","year":"2019","unstructured":"Hajiheidari, S., Wakil, K., Badri, M., & Navimipour, N. J. (2019). Intrusion detection systems in the Internet of things: A comprehensive investigation. Computer Networks, 160, 165\u2013191.","journal-title":"Computer Networks"},{"key":"2804_CR13","unstructured":"Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., and Hosseinzadeh, M. Clustered routing method in the internet of things using a moth-flame optimization algorithm. International Journal of Communication Systems, e4964."},{"key":"2804_CR14","doi-asserted-by":"crossref","unstructured":"Mansoor, K., Ghani, A., Chaudhry, S. A., Shamshirband, S., Ghayyur, S. A. K., and Mosavi, A. (2019). Securing IoT-based RFID systems: A robust authentication protocol using symmetric cryptography. Sensors (Switzerland) 19(21), Art. no. 4752.","DOI":"10.3390\/s19214752"},{"key":"2804_CR15","doi-asserted-by":"crossref","unstructured":"Sarkeshikian, A., Shafia, M., Zakery, A., and Aliahmadi, A. J. K. (2020). Simulation of stakeholders\u2019 consensus on organizational technology acceptance (case study: Internet of Things). Kybernetes.","DOI":"10.1108\/K-06-2020-0352"},{"issue":"5","key":"2804_CR16","doi-asserted-by":"publisher","first-page":"3774","DOI":"10.1109\/JIOT.2018.2861742","volume":"5","author":"M Hamzei","year":"2018","unstructured":"Hamzei, M., & Navimipour, N. J. (2018). Toward efficient service composition techniques in the internet of things. IEEE Internet of Things Journal, 5(5), 3774\u20133787.","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"2804_CR17","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1007\/s10586-019-02910-8","volume":"22","author":"Z Ghanbari","year":"2019","unstructured":"Ghanbari, Z., Navimipour, N. J., Hosseinzadeh, M., & Darwesh, A. (2019). Resource allocation mechanisms and approaches on the Internet of Things. Cluster Computing, 22(4), 1253\u20131282.","journal-title":"Cluster Computing"},{"key":"2804_CR18","unstructured":"Rad, H. J., Abolhassani, B., & Abdizadeh, M. Mathematical analysis of optimal tracking interval management for power efficient target tracking wireless sensor networks."},{"key":"2804_CR19","doi-asserted-by":"crossref","unstructured":"Sadrishojaei, M., et al. (2021). A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Computing, 1\u201311.","DOI":"10.1007\/s10586-021-03394-1"},{"key":"2804_CR20","doi-asserted-by":"crossref","unstructured":"Choudhury, N., Matam, R., Mukherjee, M., Lloret, J., and Kalaimannan, E. (2020). NCHR: A Non-threshold-based cluster-head rotation scheme for IEEE 802.15. 4 Cluster-tree Networks. IEEE Internet of Things Journal. 1\u20136.","DOI":"10.1109\/JIOT.2020.3003320"},{"key":"2804_CR21","doi-asserted-by":"crossref","unstructured":"Amirinasab, M., Shamshirband, S., Chronopoulos, A. T., Mosavi, A., and Nabipour, N. (2020). Energy-efficient method for wireless sensor networks low-power radio operation in internet of things. (in English). Electronics (Switzerland), 9(2), Art. no. 320.","DOI":"10.3390\/electronics9020320"},{"issue":"5","key":"2804_CR22","doi-asserted-by":"publisher","first-page":"347","DOI":"10.32604\/csse.2020.35.347","volume":"35","author":"AA Hady","year":"2020","unstructured":"Hady, A. A. (2020). Duty cycling centralized hierarchical routing protocol with content analysis duty cycling mechanism for wireless sensor networks. Computer Systems Science And Engineering, 35(5), 347\u2013355.","journal-title":"Computer Systems Science And Engineering"},{"key":"2804_CR23","doi-asserted-by":"publisher","first-page":"10652","DOI":"10.1109\/JIOT.2021.3049631","volume":"8","author":"M Sadrishojaei","year":"2021","unstructured":"Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2021). A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things. IEEE Internet of Things Journal, 8, 10652\u201310664.","journal-title":"IEEE Internet of Things Journal"},{"key":"2804_CR24","doi-asserted-by":"crossref","unstructured":"Latiff, N. A., Tsimenidis, C. C, and Sharif, B. S. (2007). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In 2007 IEEE 18th international symposium on personal, indoor and mobile radio communications, pp. 1\u20135: IEEE.","DOI":"10.1109\/PIMRC.2007.4394521"},{"issue":"11","key":"2804_CR25","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1109\/LWC.2020.3010156","volume":"9","author":"R Aslani","year":"2020","unstructured":"Aslani, R., & Rasti, M. (2020). A distributed power control algorithm for energy efficiency maximization in wireless cellular networks. IEEE Wireless Communications Letters, 9(11), 1975\u20131979.","journal-title":"IEEE Wireless Communications Letters"},{"issue":"7","key":"2804_CR26","doi-asserted-by":"publisher","first-page":"1736","DOI":"10.1109\/TVLSI.2020.2995094","volume":"28","author":"T Ni","year":"2020","unstructured":"Ni, T., Liu, D., Xu, Q., Huang, Z., Liang, H., & Yan, A. (2020). Architecture of cobweb-based redundant TSV for clustered faults. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28(7), 1736\u20131739.","journal-title":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems"},{"key":"2804_CR27","unstructured":"Rajaram, V., and Kumaratharan, N. (2020). An optimized clustering using hybrid meta-heuristic approach for wireless sensor networks. International Journal of Communication Systems, 33(18)."},{"key":"2804_CR28","doi-asserted-by":"crossref","unstructured":"Rani, S., and Ahmed, S. H. (2015). Multi-hop routing in wireless sensor networks: An overview, taxonomy, and research challenges.","DOI":"10.1007\/978-981-287-730-7"},{"key":"2804_CR29","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.compbiomed.2015.02.003","volume":"59","author":"L Hu","year":"2015","unstructured":"Hu, L., Hong, G., Ma, J., Wang, X., & Chen, H. (2015). An efficient machine learning approach for diagnosis of paraquat-poisoned patients. Computers in Biology and Medicine, 59, 116\u2013124.","journal-title":"Computers in Biology and Medicine"},{"key":"2804_CR30","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2017.04.060","volume":"267","author":"M Wang","year":"2017","unstructured":"Wang, M., et al. (2017). Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing, 267, 69\u201384.","journal-title":"Neurocomputing"},{"key":"2804_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y. and Wang, Y. (2020) A novel energy-aware bio-inspired clustering scheme for IoT communication. Journal of Ambient Intelligence and Humanized Computing, pp. 1\u201310.","DOI":"10.1007\/s12652-020-01704-w"},{"key":"2804_CR32","doi-asserted-by":"crossref","unstructured":"Neshat, M., Adeli, A., Sepidnam, G., Sargolzaei, V., and Toosi, A. N. (2017). A review of artificial fish swarm optimization methods and applications. International Journal on Smart Sensing and Intelligent Systems, 5(1).","DOI":"10.21307\/ijssis-2017-474"},{"issue":"6","key":"2804_CR33","first-page":"13095","volume":"22","author":"MPK Reddy","year":"2019","unstructured":"Reddy, M. P. K., & Babu, M. R. (2019). A hybrid cluster head selection model for Internet of Things. Cluster Computing, 22(6), 13095\u201313107.","journal-title":"Cluster Computing"},{"key":"2804_CR34","doi-asserted-by":"crossref","unstructured":"Hriez, S., Almajali, S., Elgala, H., Ayyash, M., and Salameh, H. B. (2021). A novel trust-aware and energy-aware clustering method that uses stochastic fractal search in IoT-enabled wireless sensor networks. IEEE Systems Journal, 1\u201312.","DOI":"10.1109\/JSYST.2021.3065323"},{"key":"2804_CR35","doi-asserted-by":"crossref","unstructured":"Shukla, A., and Tripathi, S. (2020). A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network. Wireless Networks, pp. 1\u201323.","DOI":"10.1007\/s11276-020-02277-4"},{"key":"2804_CR36","doi-asserted-by":"crossref","unstructured":"Aziz, A., Osamy, W., Khedr, A. M., El-Sawy, A. A., and Singh, K. (2020). Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs. Wireless Networks, pp. 1\u201324.","DOI":"10.1007\/s11276-020-02265-8"},{"key":"2804_CR37","doi-asserted-by":"crossref","unstructured":"Amutha, S., Kannan, B., and Kanagaraj, M.(2020). Energy\u2010efficient cluster manager\u2010based cluster head selection technique for communication networks. International Journal of Communication Systems, pp. e4741.","DOI":"10.1002\/dac.4741"},{"key":"2804_CR38","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.comnet.2019.01.024","volume":"151","author":"K Thangaramya","year":"2019","unstructured":"Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211\u2013223.","journal-title":"Computer Networks"},{"issue":"3","key":"2804_CR39","doi-asserted-by":"publisher","first-page":"5132","DOI":"10.1109\/JIOT.2019.2897119","volume":"6","author":"TM Behera","year":"2019","unstructured":"Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2019). Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Internet of Things Journal, 6(3), 5132\u20135139.","journal-title":"IEEE Internet of Things Journal"},{"key":"2804_CR40","doi-asserted-by":"crossref","unstructured":"Saini, T. K., and Sharma, S. (2019). Self-managed access scheme for demand request in TDM\/TDMA Star Topology Network. Defence Science Journal, 69(1).","DOI":"10.14429\/dsj.69.11992"},{"key":"2804_CR41","doi-asserted-by":"publisher","first-page":"101804","DOI":"10.1016\/j.flowmeasinst.2020.101804","volume":"75","author":"M Roshani","year":"2020","unstructured":"Roshani, M., et al. (2020). Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter. Journal of Flow Measurement and Instrumentation, 75, 101804.","journal-title":"Journal of Flow Measurement and Instrumentation"},{"key":"2804_CR42","doi-asserted-by":"publisher","first-page":"108474","DOI":"10.1016\/j.measurement.2020.108474","volume":"168","author":"MA Sattari","year":"2021","unstructured":"Sattari, M. A., Roshani, G. H., Hanus, R., & Nazemi, E. (2021). Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique. Measurement, 168, 108474.","journal-title":"Measurement"},{"key":"2804_CR43","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.future.2020.04.008","volume":"111","author":"H Chen","year":"2020","unstructured":"Chen, H., Heidari, A. A., Chen, H., Wang, M., Pan, Z., & Gandomi, A. H. (2020). Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Future Generation Computer Systems, 111, 175\u2013198.","journal-title":"Future Generation Computer Systems"},{"key":"2804_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, R., Wang, X., Chen, H., and Li, C. (2020). Boosted binary Harris hawks optimizer and feature selection. Engineering with Computers, pp. 1\u201330.","DOI":"10.1007\/s00366-020-01028-5"},{"issue":"4","key":"2804_CR45","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s00500-013-1089-4","volume":"18","author":"X Xu","year":"2014","unstructured":"Xu, X., & Chen, H.-L. (2014). Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Computing, 18(4), 797\u2013807.","journal-title":"Soft Computing"},{"key":"2804_CR46","doi-asserted-by":"crossref","unstructured":"Yu, C et al. (2021). SGOA: Annealing-behaved grasshopper optimizer for global tasks. Engineering with Computers, pp. 1\u201328.","DOI":"10.1007\/s00366-020-01234-1"},{"key":"2804_CR47","doi-asserted-by":"publisher","first-page":"106684","DOI":"10.1016\/j.knosys.2020.106684","volume":"213","author":"J Hu","year":"2021","unstructured":"Hu, J., et al. (2021). Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowledge-Based Systems, 213, 106684.","journal-title":"Knowledge-Based Systems"},{"key":"2804_CR48","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.compbiolchem.2018.11.017","volume":"78","author":"X Zhao","year":"2019","unstructured":"Zhao, X., et al. (2019). Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Computational biology and chemistry, 78, 481\u2013490.","journal-title":"Computational biology and chemistry"},{"key":"2804_CR49","doi-asserted-by":"publisher","first-page":"106728","DOI":"10.1016\/j.knosys.2020.106728","volume":"214","author":"W Shan","year":"2021","unstructured":"Shan, W., Qiao, Z., Heidari, A. A., Chen, H., Turabieh, H., & Teng, Y. (2021). Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowledge-Based Systems, 214, 106728.","journal-title":"Knowledge-Based Systems"},{"key":"2804_CR50","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.ins.2019.04.022","volume":"492","author":"Y Xu","year":"2019","unstructured":"Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S., & Zhang, X. (2019). Enhanced Moth-flame optimizer with mutation strategy for global optimization. Information Sciences, 492, 181\u2013203.","journal-title":"Information Sciences"},{"key":"2804_CR51","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.knosys.2016.01.002","volume":"96","author":"L Shen","year":"2016","unstructured":"Shen, L., et al. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61\u201375.","journal-title":"Knowledge-Based Systems"},{"key":"2804_CR52","doi-asserted-by":"crossref","unstructured":"Yu, H et al. (2020). Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis. Engineering with Computers, pp. 1\u201329.","DOI":"10.1007\/s00366-020-01174-w"},{"key":"2804_CR53","doi-asserted-by":"crossref","unstructured":"Sun, G., Li, C., and Deng, L. (2021). An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Computing and Applications, pp. 1\u201317.","DOI":"10.1007\/s00521-021-05708-1"},{"key":"2804_CR54","doi-asserted-by":"publisher","first-page":"106642","DOI":"10.1016\/j.knosys.2020.106642","volume":"212","author":"J Tu","year":"2021","unstructured":"Tu, J., et al. (2021). Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowledge-Based Systems, 212, 106642.","journal-title":"Knowledge-Based Systems"},{"key":"2804_CR55","doi-asserted-by":"publisher","first-page":"105946","DOI":"10.1016\/j.asoc.2019.105946","volume":"88","author":"M Wang","year":"2020","unstructured":"Wang, M., & Chen, H. (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing, 88, 105946.","journal-title":"Applied Soft Computing"},{"key":"2804_CR56","doi-asserted-by":"publisher","first-page":"104094","DOI":"10.1016\/j.rinp.2021.104094","volume":"24","author":"S-Q Zhong","year":"2021","unstructured":"Zhong, S.-Q., Zhao, S.-C., & Zhu, S.-N. (2021). Photovoltaic properties enhanced by the tunneling effect in a coupled quantum dot photocell. Results in Physics, 24, 104094.","journal-title":"Results in Physics"},{"key":"2804_CR57","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.asoc.2014.07.024","volume":"24","author":"X Zhao","year":"2014","unstructured":"Zhao, X., Li, D., Yang, B., Ma, C., Zhu, Y., & Chen, H. (2014). Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Applied Soft Computing, 24, 585\u2013596.","journal-title":"Applied Soft Computing"},{"issue":"11","key":"2804_CR58","first-page":"32","volume":"22","author":"X-L Li","year":"2002","unstructured":"Li, X.-L. (2002). An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering-Theory & Practice, 22(11), 32\u201338.","journal-title":"Systems Engineering-Theory & Practice"},{"issue":"4","key":"2804_CR59","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s10462-012-9342-2","volume":"42","author":"M Neshat","year":"2014","unstructured":"Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2014). Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial intelligence review, 42(4), 965\u2013997.","journal-title":"Artificial intelligence review"},{"key":"2804_CR60","doi-asserted-by":"crossref","unstructured":"Gorgich, S., and Tabatabaei, S. (2021). Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in WSN (Wireless Sensor Networks). Wireless Personal Communications, pp. 1\u201321.","DOI":"10.1007\/s11277-021-08312-7"},{"issue":"10","key":"2804_CR61","doi-asserted-by":"publisher","first-page":"3351","DOI":"10.3390\/s18103351","volume":"18","author":"X Li","year":"2018","unstructured":"Li, X., Keegan, B., & Mtenzi, F. (2018). Energy efficient hybrid routing protocol based on the artificial fish swarm algorithm and ant colony optimisation for WSNs. Sensors, 18(10), 3351.","journal-title":"Sensors"},{"key":"2804_CR62","doi-asserted-by":"crossref","unstructured":"Mechta, D., Harous, S. (2019). Clustering in WSNs based on Artificial Fish Swarming Algorithm. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 161\u2013167: IEEE.","DOI":"10.1109\/IWCMC.2019.8766737"},{"key":"2804_CR63","doi-asserted-by":"publisher","first-page":"253","DOI":"10.4028\/www.scientific.net\/AMM.815.253","volume":"815","author":"N Zainal","year":"2015","unstructured":"Zainal, N., Zain, A. M., & Sharif, S. (2015). Overview of artificial fish swarm algorithm and its applications in industrial problems. Applied Mechanics and Materials, 815, 253\u2013257.","journal-title":"Applied Mechanics and Materials"},{"issue":"7","key":"2804_CR64","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1007\/s11276-016-1270-7","volume":"23","author":"PS Rao","year":"2017","unstructured":"Rao, P. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless networks, 23(7), 2005\u20132020.","journal-title":"Wireless networks"},{"issue":"1","key":"2804_CR65","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s10586-017-1628-3","volume":"22","author":"MPK Reddy","year":"2019","unstructured":"Reddy, M. P. K., & Babu, M. R. (2019). Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things. Cluster Computing, 22(1), 1361\u20131372.","journal-title":"Cluster Computing"},{"key":"2804_CR66","doi-asserted-by":"crossref","unstructured":"Bounceur, A. et al. (2018). Cupcarbon-lab: An iot emulator. In 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1\u20132: IEEE.","DOI":"10.1109\/CCNC.2018.8319313"},{"key":"2804_CR67","doi-asserted-by":"crossref","unstructured":"Bounceur, A. (2016). CupCarbon: a new platform for designing and simulating smart-city and IoT wireless sensor networks (SCI-WSN). In Proceedings of the International Conference on Internet of things and Cloud Computing, pp. 1\u20131.","DOI":"10.1145\/2896387.2900336"},{"key":"2804_CR68","doi-asserted-by":"crossref","unstructured":"Sun, G., Cong, Y., Dong, J., Liu, Y., Ding, Z., and Yu, H. (2021). What and How: Generalized lifelong spectral clustering via dual memory. IEEE Transactions on Pattern Analysis and Machine Intelligence.","DOI":"10.1109\/TPAMI.2021.3058852"},{"key":"2804_CR69","doi-asserted-by":"crossref","unstructured":"Sun, G., Cong, Y., Wang, Q., Zhong, B., and Fu, Y. (2020). Representative task self-selection for flexible clustered lifelong learning. IEEE Transactions on Neural Networks and Learning Systems, 1\u201315.","DOI":"10.1109\/TNNLS.2020.3042500"},{"key":"2804_CR70","doi-asserted-by":"publisher","first-page":"60676","DOI":"10.1109\/ACCESS.2020.2983483","volume":"8","author":"W Osamy","year":"2020","unstructured":"Osamy, W., El-Sawy, A. A., & Salim, A. (2020). CSOCA: Chicken swarm optimization based clustering algorithm for wireless sensor networks. IEEE Access, 8, 60676\u201360688.","journal-title":"IEEE Access"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02804-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-021-02804-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02804-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T08:47:06Z","timestamp":1642582026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-021-02804-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,6]]},"references-count":70,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2804"],"URL":"https:\/\/doi.org\/10.1007\/s11276-021-02804-x","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,6]]},"assertion":[{"value":"21 September 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}