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Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide (\n                \n                  \n                \n                $$CO_2$$\n                \n              ), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.",{"EN":115},"VentQsys: Low-cost open IoT system for $$CO_2$$ monitoring in classrooms",{"VOID":117},"[\"13429109706701359557\"]",{"VOID":119},"Adafruit Industries: DHT22 temperature & humidity sensor (2016). URL: http:\u002F\u002Fwww.adafruit.com\u002Fdatasheets\u002FDHT22.pdf. (Accessed: 10\u002F01\u002F2021).\nArroyo, P., Herrero, J. L., Suárez, J. I., & Lozano, J. (2019). Wireless sensor network combined with cloud computing for air quality monitoring. Sensors (Switzerland). https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs19030691.\nAzuma, K., Yanagi, U., Kagi, N., Kim, H., Ogata, M., & Hayashi, M. (2020). Environmental factors involved in SARS-CoV-2 transmission: Effect and role of indoor environmental quality in the strategy for COVID-19 infection control. Environmental Health and Preventive Medicine. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12199-020-00904-2.\nBhagat, R. K., Wykes, M. S. D., Dalziel, S. B., & Linden, P. F. (2020). Effects of ventilation on the indoor spread of COVID-19. Journal of Fluid Mechanics. https:\u002F\u002Fdoi.org\u002F10.1017\u002Fjfm.2020.720.\nBoubrima, A., Bechkit, W., & Rivano, H. (2017). Optimal WSN deployment models for air pollution monitoring. IEEE Transactions on Wireless Communications. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTWC.2017.2658601.\nBrands, E. Energizer e91 datasheet (2020). http:\u002F\u002Fdata.energizer.com\u002Fpdfs\u002Fe91.pdf (Accessed: 17\u002F01\u002F2021).\nChaabouni, S., & Saidi, K. (2017). The dynamic links between carbon dioxide (CO2) emissions, health spending and GDP growth: A case study for 51 countries. Environmental Research. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.envres.2017.05.041.\nCressie, N. (1993). Statistics for spatial data. New York: John Wiley.\nDemanega, I., Mujan, I., Singer, B. C., Andelkovic, A. S., Babich, F., & Licina, D. (2021). Performance assessment of low-cost environmental monitors and single sensors under variable indoor air quality and thermal conditions. Building and Environment. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.buildenv.2020.107415.\nDiblasi, A., & Bowman, A. (2001). On the use of the variogram in checking for independence in spatial data. Biometrics, 57, 211–218.\nDuan, R. R., Hao, K., & Yang, T. (2020). Air pollution and chronic obstructive pulmonary disease. Chronic Diseases and Translational Medicine. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cdtm.2020.05.004.\nEspressif Systems: Nodemcu datasheet (2013). URL: https:\u002F\u002Fwww.elecrow.com\u002Fdownload\u002FESP8266_Specifications_English.pdf. (Accessed: 01\u002F02\u002F2021).\nEuropean Centre for Disease Prevention and Control: Heating, ventilation and air-conditioning systems in the context of COVID-19. Tech. rep., European Union, Stockholm (2020). https:\u002F\u002Fwww.ecdc.europa.eu\u002Fen\u002Fpublications-data\u002Fheating-ventilation-air-conditioning-systems-covid-19.\nJo, J., Jo, B., Kim, J., Kim, S., & Han, W. (2020). Development of an IoT-based indoor air quality monitoring platform. Journal of Sensors, 2020, 8749764. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2020\u002F8749764.\nLiu, Z., Ciais, P., Deng, Z., et al. (2020). Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-020-18922-7.\nMcKinney, K. R., Gong, Y. Y., & Lewis, T. G. (2006). Environmental transmission of SARS at amoy gardens. Journal of Environmental Health, 68, 26–52.\nMialdea-Flor, I., Segura-Garcia, J., Felici-Castell, S., Garcia-Pineda, M., Alcaraz-Calero, J. M., & Navarro-Camba, E. (2019). Development of a low-cost IoT system for lightning strike detection and location. Electronics (Switzerland). https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics8121512.\nMinguillón, M.C., Querol, X., Felisi, J.M.,&Garrido, T. Guía para ventilación de las aulas CSIC (2020). 10.20350\u002FDIGITALCSIC\u002F12677. https:\u002F\u002Fdigital.csic.es\u002Fhandle\u002F10261\u002F221538.\nNagaraj, S., & Biradar, R.V. Applications of wireless sensor networks in the real-Time ambient air pollution monitoring and air quality in metropolitan cities-A survey. In: Proceedings of the 2017 international conference on smart technology for smart nation, SmartTechCon 2017 (2018). 10.1109\u002FSmartTechCon.2017.8358594.\nNodeMCU documentation: Over-the-air updating (2018). URL: https:\u002F\u002Fnodemcu.readthedocs.io\u002Fen\u002Frelease\u002Fbuild\u002F. (Accessed: 10\u002F01\u002F2021).\nPastor-Aparicio, A., Segura-Garcia, J., Lopez-Ballester, J., Felici-Castell, S., Garcia-Pineda, M., & Perez-Solano, J. J. (2020). Psychoacoustic annoyance implementation with wireless acoustic sensor networks for monitoring in smart cities. IEEE Internet of Things Journal. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2019.2946971.\nPatil, D., Thanuja, T. C., & Melinamath, B. C. (2018). Air pollution monitoring system using wireless sensor network (WSN). In V. Balas, N. Sharma, & A. Chakrabarti (Eds.), Data management, analytics and innovation (pp. 391–400). Singapore: Springer.\nPerez, A. O., Bierer, B., Scholz, L., Wöllenstein, J., & Palzer, S. (2018). A wireless gas sensor network to monitor indoor environmental quality in schools. Sensors (Switzerland). https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs18124345.\nPetersen, S., Jensen, K. L., Pedersen, A. L., & Rasmussen, H. S. (2016). The effect of increased classroom ventilation rate indicated by reduced CO2 concentration on the performance of schoolwork by children. Indoor Air. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fina.12210.\nRuideng. UM34C User Manual (2020). http:\u002F\u002Fruidengkeji.com\u002Finst\u002FUM34C.pdf (Accessed: 10\u002F01\u002F2021).\nSegura-Garcia, J., Navarro-Ruiz, J., Perez-Solano, J., Montoya-Belmonte, J., Felici-Castell, S., Cobos, M., & Torres-Aranda, A. (2018). Spatio-temporal analysis of urban acoustic environments with binaural psycho-acoustical considerations for IoT-based applications. Sensors, 18, 690.\nSeppänen, O. A., Fisk, W. J., & Mendell, M. J. (1999). Association of ventilation rates and CO2 concentrations with health and other responses in commercial and institutional buildings. Indoor Air, 9(4), 226–252. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1600-0668.1999.00003.x.\nTuranjanin, V., Vučeć, B., Jovanović, M., Mirkov, N., & Lazović, I. (2014). Indoor CO2 measurements in Serbian schools and ventilation rate calculation. Energy, 77, 290–296. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.energy.2014.10.028.\nWackernagel, H. (2003). Ordinary kriging (pp. 79–88). Berlin: Springer.\nZhang, J. (2020). Integrating IAQ control strategies to reduce the risk of asymptomatic SARS CoV-2 infections in classrooms and open plan offices. Science and Technology for the Built Environment, 26(8), 1013–1018. https:\u002F\u002Fdoi.org\u002F10.1080\u002F23744731.2020.1794499.\nZhengzhou Winsen Electronics Technology Co. Ltd. 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The protocol – denoted EC‐MAC (energy conserving medium access control) – is designed to support different traffic types with quality‐of‐service (QoS) provisions. The network is based on the infrastructure model where a base station (BS) serves all the mobiles currently in its cell. A reservation‐based approach is proposed, with appropriate scheduling of the requests from the mobiles. This strategy is utilized to accomplish the dual goals of reduced energy consumption and quality of service provision over wireless links. A priority round robin with dynamic reservation update and error compensation scheduling algorithm is used to schedule the transmission requests of the mobiles. Discrete‐event simulation has been used to study the performance of the protocol. A comparison of energy consumption of the EC‐MAC to a number of other protocols is provided. This comparison indicates the EC‐MAC has, in general, better energy consumption characteristics. Performance analysis of the proposed protocol with respect to different quality‐of‐service parameters using video, audio and data traffic models is provided.",{"EN":298},"Design and analysis of low‐power access protocols for wireless and mobile ATM networks",{"VOID":300},"[\"9889549515500574840\"]",{"VOID":302},"citation_journal_title=IEEE Personal Communications; citation_title=SWAN: A mobile multimedia wireless network; citation_author=P. Agrawal, E. Hyden, P. Krzyzanowski, P. Mishra, M.B. Srivastava, J.A. Trotter; citation_volume=3; citation_issue=2; citation_publication_date=1996; citation_pages=18-33; citation_doi=10.1109\u002F98.490750; citation_id=CR1\ncitation_journal_title=IEEE Transactions on Circuits and Systems for Video Technology; citation_title=Packet loss resilience of MPEG-2 scalable video coding algorithms; citation_author=R. Aravind, M.R. Civanlar, A.R. 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citation_id=CR49",{"VOID":304},"10.1023\u002FA:1019152506607","2024-05-16T20:48:24.217+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1019152506607","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1023\u002FA:1019152506607.pdf",[309,326,343,358],{"id":310,"sortIndex":19,"researcher":18,"roles":311,"affiliations":312,"properties":321,"displayName":323,"givenName":18,"familyName":18},"3a8dec7e-c298-429c-b160-d0b5dd1e31c1",[131],[313],{"id":314,"sortIndex":19,"affiliation":315,"properties":18},"4abd9f56-bb01-4fa3-9650-ffa80fa0c544",{"id":314,"createTime":18,"updateTime":18,"relativeEntities":316,"slug":18,"properties":317,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":320,"statistic":18},[],{"title":318},{"VI":319},"School of Electrical Engineering & Computer Science, Washington State University, Pullman, USA",[],{"title":322,"gsAuthor":324},{"VI":323},"Sivalingam, Krishna M.",{"VOID":325},"[\"mn54pyMAAAAJ\"]",{"id":327,"sortIndex":96,"researcher":18,"roles":328,"affiliations":329,"properties":338,"displayName":340,"givenName":18,"familyName":18},"3b961873-cd35-4a51-8467-a98d14d970b7",[131],[330],{"id":331,"sortIndex":19,"affiliation":332,"properties":18},"b1997593-cb0d-4ee5-8a35-63fc37e9192a",{"id":331,"createTime":18,"updateTime":18,"relativeEntities":333,"slug":18,"properties":334,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":337,"statistic":18},[],{"title":335},{"VI":336},"Internet Architecture Research Lab, Telcordia Technologies, Morristown, USA",[],{"title":339,"gsAuthor":341},{"VI":340},"Chen, Jyh‐Cheng",{"VOID":342},"[\"qq7bmfIAAAAJ\"]",{"id":344,"sortIndex":163,"researcher":18,"roles":345,"affiliations":346,"properties":353,"displayName":355,"givenName":18,"familyName":18},"71560a8a-f1fb-4675-baa2-85ec23cdd191",[131],[347],{"id":331,"sortIndex":19,"affiliation":348,"properties":18},{"id":331,"createTime":18,"updateTime":18,"relativeEntities":349,"slug":18,"properties":350,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":352,"statistic":18},[],{"title":351},{"VI":336},[],{"title":354,"gsAuthor":356},{"VI":355},"Agrawal, Prathima",{"VOID":357},"[\"OSx0RzAAAAAJ\"]",{"id":359,"sortIndex":179,"researcher":18,"roles":360,"affiliations":361,"properties":370,"displayName":372,"givenName":18,"familyName":18},"b8c5c00b-69de-4d78-b56c-98727b2c34bb",[131],[362],{"id":363,"sortIndex":19,"affiliation":364,"properties":18},"6d050884-32e2-4bb5-8718-40725fe6681a",{"id":363,"createTime":18,"updateTime":18,"relativeEntities":365,"slug":18,"properties":366,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":369,"statistic":18},[],{"title":367},{"VI":368},"Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, USA",[],{"title":371,"gsAuthor":373},{"VI":372},"Srivastava, Mani B.",{"VOID":374},"[\"X2Qs7XYAAAAJ\"]",{"url":306,"publisher":376,"properties":425},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":377,"slug":10,"properties":378,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":381,"manageAffiliations":394,"indexDatabases":405,"url":18,"thumbnailPath":18,"statistic":420,"gsStatistic":18,"type":100,"analyzePriority":18},[],{"issn":379,"title":380},{"VOID":13},{"VOID":15},[382,386,390],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":383,"label":384,"description":385,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":387,"label":388,"description":389,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":391,"label":392,"description":393,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":37},{},[395,400],{"id":41,"createTime":18,"updateTime":18,"relativeEntities":396,"slug":18,"properties":397,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":399,"statistic":18},[],{"title":398},{"EN":45},[47],{"id":49,"createTime":18,"updateTime":18,"relativeEntities":401,"slug":18,"properties":402,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":404,"statistic":18},[],{"title":403},{"EN":53},[],[406,413],{"id":57,"indexDatabase":407,"url":70,"indexYears":18,"academicFieldIds":412,"indexDatabaseRanking":18},{"id":59,"createTime":18,"updateTime":18,"relativeEntities":408,"label":409,"description":410,"key":66,"publicationTags":411,"standard":18},[],{"EN":62,"VI":62},{"EN":64,"VI":65},[68,69],[72,73,74],{"id":76,"indexDatabase":414,"url":87,"indexYears":88,"academicFieldIds":419,"indexDatabaseRanking":93},{"id":78,"createTime":18,"updateTime":18,"relativeEntities":415,"label":416,"description":417,"key":84,"publicationTags":418,"standard":18},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":19,"impactFactorByYear":421,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":96,"totalPublicationByYear":422,"totalCitation":19,"totalCitationByYear":423,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":424,"hindexLast5Year":19,"hindex":19},{},{"2018":96},{},{},{"issue":426,"pages":428,"volume":430},{"VOID":427},"1",{"VOID":429},"73-87",{"VOID":431},"6",{"total":19,"publishYear":433,"statisticByYear":434},2000,{},"2000-02-01","2026-08-14T14:24:40.927+00:00",[68,93],{"id":439,"createTime":440,"updateTime":441,"relativeEntities":442,"slug":443,"properties":444,"entityType":122,"verifyStatus":123,"verifyTime":455,"verifyNote":125,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":456,"fullTextUrl":18,"authors":457,"publicationType":223,"publisherRelationship":475,"citationCount":530,"citationInfo":531,"publishDate":533,"publishYear":532,"citationAnalyzeStatus":284,"lastCitationAnalyze":534,"indexDatabases":535,"openAccess":18,"references":18,"isForceReanalyzing":287},"37c745e0-fef4-4ff6-8138-6b77c22935e0","2024-02-12T18:50:15.576+00:00","2026-08-14T08:20:32.784+00:00",[],"Evolutionary-intelligence-in-wireless-sensor-network-routing-clustering-localization-and-coverage",{"abstract":445,"title":447,"gsPaper":449,"references":451,"doi":453},{"EN":446},"Evolutionary intelligence has become one of the most important directions that improve the performance and effectiveness of automated systems such as communication systems, robotics and engineering industries. Today, there are many applications of evolutionary intelligence in many engineering fields and the most important fields related to computation and informatics engineering as a part of electrical and communication engineering, as modern engineering applications are involved in these fields. The sensor network is the main data source in the world of smart systems nowadays. Additionally, it has become a field of science used in the development of the rest of scientific applications. The need to use evolutionary intelligence in sensor networks has emerged because of the problems encountered by different types of sensor networks. This paper represents a comprehensive scientific review of the role of evolutionary intelligence in sensor networks and its implications for this important part of engineering applications. This paper discusses the theoretical, mathematical and practical application of evolutionary computing with the use of evolutionary algorithms and the improvements resulting from the application of evolutionary intelligence in sensor networks. The content of this paper will review the most important of the evolutionary intelligence from principles, algorithms and applications. The problems facing the types of sensor network has been solved using evolutionary algorithms. After reviewing the evolutionary intelligence and its details in the sensor network, a performance evaluation is presented in the paper at the end of each of the targeted areas of the sensor network. This performance evaluation represents the measure of the quality of improvements provided by evolutionary intelligence in sensor network field with graphical analysis studies to demonstrate the effect of evolutionary algorithms on the sensor network.",{"EN":448},"Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage",{"VOID":450},"[\"5372379850791858533\"]",{"VOID":452},"Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.\nAkyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.\nYu, X., Wu, P., Han, W., & Zhang, W. (2013). A survey on wireless sensor network infrastructure for agriculture. Computer Standards & Interfaces, 35(1), 59–64.\nWang, P., Hou, H., He, X., Wang, C., Xu, T., & Li, Y. (2015). Survey on application of wireless sensor network in smart grid. 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London: Academic Press.\nNguyen, T. T., Yang, S., & Branke, J. (2012). Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation, 6, 1–24.\nAhmed, Y. E. E., Adjallah, K. H., Stock, R., & Babikier, S. F. (2016). Wireless sensor network lifespan optimization with simple, rotated, order and modified partially matched crossover genetic algorithms. IFAC-PapersOnLine, 49(25), 182–187.\nAguilar-Rivera, R., Valenzuela-Rendón, M., & Rodríguez-Ortiz, J. J. (2015). Genetic algorithms and Darwinian approaches in financial applications: A survey. Expert Systems with Applications, 42(21), 7684–7697.\nYi, L., & Wanli, K. (2011). A new genetic programming algorithm for building decision tree. Procedia Engineering, 15(2011), 3658–3662.\nCai, J., & Thierauf, G. (1996). Evolution strategies for solving discrete optimization problems. Advances in Engineering Software, 25(2–3), 177–183.\nBalkaya, Ç. (2013). An implementation of differential evolution algorithm for inversion of geoelectrical data. Journal of Applied Geophysics, 98, 160–175.\nHolmes, J. H., Durbin, D. R., & Winston, F. K. (2000). The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence in Medicine, 19(1), 53–74.\nBensmaine, A., Dahane, M., & Benyoucef, L. (2013). A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computers & Industrial Engineering, 66(3), 519–524.\nSaranya, S., & Princy, M. (2012). Routing techniques in sensor network—A survey. Procedia Engineering, 38, 2739–2747.\nLiang, Z., Jianmin, X. U., & Lingxiang, Z. (2007). Application of genetic algorithm in dynamic route guidance system. Journal of Transportation Systems Engineering and Information Technology, 7(3), 45–48.\nGupta, S. K., Kuila, P., & Jana, P. K. (2016). Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Computers & Electrical Engineering, 56, 544–556.\nBhatia, T., Kansal, S., Goel, S., & Verma, A. K. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers & Electrical Engineering, 56, 441–455.\nBayraklı, S., & Erdogan, S. Z. (2012). Genetic algorithm based energy efficient clusters (GABEEC) in wireless sensor networks. Procedia Computer Science, 10, 247–254.\nYan, W., Xin-xin, S., & Yan-ming, S. U. (2011). Study on the application of genetic algorithms in the optimization of wireless network. Procedia Engineering, 16, 348–355.\nGong, G., Liu, Y., & Qian, M. (2001). An adaptive simulated annealing algorithm. Stochastic Processes and their Applications, 94(1), 95–103.\nShahi, B., Dahal, S., Mishra, A., Kumar, S. V., & Kumar, C. P. (2016). A review over genetic algorithm and application of wireless network systems. Procedia Computer Science, 78, 431–438.\nBari, A., Wazed, S., Jaekel, A., & Bandyopadhyay, S. (2009). A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks, 7, 665–676.\nAfsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46(2014), 198–226.\nNayebi, A., & Sarbazi-Azad, H. (2011). Performance modelling of the LEACH protocol for mobile wireless sensor networks. Journal of Parallel and Distributed Computing, 71, 812–821.\nGeetha, V., Kallapur, P. V., & Tellajeera, S. (2012). Clustering in wireless sensor networks: Performance comparison of leach & leach-C protocols using ns2. Procedia Technology, 4, 163–170.\nKuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.\nZhou, Y., Li, X., & Gao, L. (2013). A differential evolution algorithm with intersecting mutation operator. Applied Soft Computing, 13(1), 390–401.\nStorn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.\nPotthuri, S., Shankar, T., & Rajesh, A. (2016). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Engineering Journal, 9(4), 655–663.\nSumithra, S., & Victoire, T. A. A. (2015). Differential evolution algorithm with diversified vicinity operator for optimal routing and clustering of energy efficient wireless sensor networks. The Scientific World Journal, 2015, 3, 729634.\nRaguraman, P., Ramasundaram, M., & Balakrishnan, V. (2018). Localization in wireless sensor networks: A dimension based pruning approach in 3D environments. Applied Soft Computing, 68, 219–232.\nSun, W., & Su, X. (2011). Wireless sensor network node localization based on genetic algorithm. In 2011 IEEE 3rd international conference on communication software and networks (pp. 316–319).\nSchmitt, L. M. (2001). Theory of genetic algorithms. Theoretical Computer Science, 259(1–2), 1–61.\nCarter, J. N. (2003). Chapter 3, Introduction to using genetic algorithms. In M. Nikravesh, F. Aminzadeh, & L. A. Zadeh (Eds.), Developments in petroleum science (Vol. 51, pp. 51–76). Elsevier.\nBanzhaf, W. (2001). Artificial intelligence: Genetic programming. In International encyclopedia of the social & behavioral sciences (pp. 789–792). Pergamon.\nTam, V., Cheng, K.-Y., & Lui, K.-S. (2006). Improving localization in wireless sensor networks with an evolutionary algorithm. In IEEE consumer communications and networking conference (CCNC) 2006 (pp. 137–141). Las Vegas, NV, USA.\nLi, Z., Zhou, X., & Li, S. (2005). Issues of wireless sensor network management. Lecture notes in computer science (pp. 355–36 l).\nHightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57–66. https:\u002F\u002Fdoi.org\u002F10.1109\u002F2.940014.\nFlathagen, J., & Korsnes, R. (2010). Localization in wireless sensor networks based on Ad hoc routing and evolutionary computation. In 2010, Milcom military communications conference, CA (pp. 1062–1067).\nTam, V., Cheng, K. -Y., & Lui, K.-S. (2006). Improving localization in wireless sensor networks with an evolutionary algorithm. CCNC. In 2006 3rd IEEE consumer communications and networking conference, 2006 (pp. 137–141). Las Vegas, NV, USA, 2006.\nMohamed, S. M., Hamza, H. S., & Saroit, I. A. (2017). Coverage in mobile wireless sensor networks (M-WSN): A survey. Computer Communications, 110, 133–150.\nVecchio, M., & López-Valcarce, R. (2015). Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach. Journal of Network and Computer Applications, 48, 1–13.\nLi, X. -Y., Wan, P. -J., & Frieder, O. (2002). Coverage in wireless ad-hoc sensor networks. In 2002 IEEE international conference on communications. Conference proceedings. ICC 2002 (Cat. No.02CH37333), New York, NY, USA (Vol. 5, pp. 3174–3178).\nLi, M., Liu, S., Zhang, L., Wang, H., Meng, F., & Bai, L. (2012). Non-dominated sorting genetic algorithms-based on multi-objective optimization model in the water distribution system. Procedia Engineering, 37, 309–313.\nJie, J., Jian, C., Chang, G. R., & Ying-You, W. E. N. (2008). Efficient cover set selection in wireless sensor networks. Acta Automatica Sinica, 34(9), 1157–1162.",{"VOID":454},"10.1007\u002Fs11276-019-02008-4","2024-06-24T18:18:33.721+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11276-019-02008-4",[458],{"id":459,"sortIndex":19,"researcher":18,"roles":460,"affiliations":461,"properties":470,"displayName":472,"givenName":18,"familyName":18},"63313903-f876-49ac-85ee-1a751171e7ff",[131],[462],{"id":463,"sortIndex":19,"affiliation":464,"properties":18},"39ec828e-8afe-4223-9332-e6310bca2810",{"id":463,"createTime":18,"updateTime":18,"relativeEntities":465,"slug":18,"properties":466,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":469,"statistic":18},[],{"title":467},{"VI":468},"Department of Programming and Intelligent Applications, Information Technology Regulation Directory, Communication and Media Commission of Iraq (CMC), Baghdad, Iraq",[],{"title":471,"gsAuthor":473},{"VI":472},"Ali Jameel Al-Mousawi",{"VOID":474},"[\"qJfAtjwAAAAJ\"]",{"url":456,"publisher":476,"properties":525},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":477,"slug":10,"properties":478,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":481,"manageAffiliations":494,"indexDatabases":505,"url":18,"thumbnailPath":18,"statistic":520,"gsStatistic":18,"type":100,"analyzePriority":18},[],{"issn":479,"title":480},{"VOID":13},{"VOID":15},[482,486,490],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":483,"label":484,"description":485,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":487,"label":488,"description":489,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":491,"label":492,"description":493,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":37},{},[495,500],{"id":41,"createTime":18,"updateTime":18,"relativeEntities":496,"slug":18,"properties":497,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":499,"statistic":18},[],{"title":498},{"EN":45},[47],{"id":49,"createTime":18,"updateTime":18,"relativeEntities":501,"slug":18,"properties":502,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":504,"statistic":18},[],{"title":503},{"EN":53},[],[506,513],{"id":57,"indexDatabase":507,"url":70,"indexYears":18,"academicFieldIds":512,"indexDatabaseRanking":18},{"id":59,"createTime":18,"updateTime":18,"relativeEntities":508,"label":509,"description":510,"key":66,"publicationTags":511,"standard":18},[],{"EN":62,"VI":62},{"EN":64,"VI":65},[68,69],[72,73,74],{"id":76,"indexDatabase":514,"url":87,"indexYears":88,"academicFieldIds":519,"indexDatabaseRanking":93},{"id":78,"createTime":18,"updateTime":18,"relativeEntities":515,"label":516,"description":517,"key":84,"publicationTags":518,"standard":18},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":19,"impactFactorByYear":521,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":96,"totalPublicationByYear":522,"totalCitation":19,"totalCitationByYear":523,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":524,"hindexLast5Year":19,"hindex":19},{},{"2018":96},{},{},{"pages":526,"volume":528},{"VOID":527},"5595-5621",{"VOID":529},"26",43,{"total":530,"publishYear":532,"statisticByYear":18},2019,"2019-05-11","2026-08-14T08:20:32.783+00:00",[68,93],{"id":537,"createTime":538,"updateTime":539,"relativeEntities":540,"slug":541,"properties":542,"entityType":122,"verifyStatus":123,"verifyTime":553,"verifyNote":125,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":554,"fullTextUrl":18,"authors":555,"publicationType":223,"publisherRelationship":616,"citationCount":18,"citationInfo":18,"publishDate":671,"publishYear":672,"citationAnalyzeStatus":284,"lastCitationAnalyze":673,"indexDatabases":674,"openAccess":18,"references":18,"isForceReanalyzing":287},"c5639f84-e7bd-458e-8156-4dadc37eb826","2024-02-14T04:59:04.752+00:00","2026-07-29T14:45:29.769+00:00",[],"A-novel-spectrum-sensing-scheme-with-sensing-time-optimization-for-energy-efficient-CRSNs",{"abstract":543,"title":545,"gsPaper":547,"references":549,"doi":551},{"EN":544},"The cognitive radio technology enables secondary users (SUs) to occupy licensed bands when primary users (PUs) are not occupy them. Spectrum sensing is a key technology for SUs to detect PUs, and the sensing time is a critical parameter for spectrum sensing performance. Optimum sensing time tradeoffs between the spectrum sensing performance and the secondary throughput. This paper proposes a novel spectrum sensing scheme that performs spectrum sensing for either one period or two periods based on the previous sensing result. Due to the energy constraint in cognitive radio sensor networks, the energy efficiency is maximized by optimizing spectrum sensing time. In order to seek the optimal sensing time, the objective function is proven to be a concave function and the Golden Section Search method is employed. Our simulation study verifies that the proposed scheme improves the network energy efficiency, especially when PUs are more active.",{"EN":546},"A novel spectrum sensing scheme with sensing time optimization for energy-efficient CRSNs",{"VOID":548},"[\"3722555696174722888\"]",{"VOID":550},"Federal Communications Commission. (2003). FCC, ET Docket No 03-222 Notice of proposed rule making and order. Technical Report.\nMitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.\nSaifan, R., Kamal, A. E., & Guan, Y. (2012). Spectrum decision for efficient routing in cognitive radio network. In Mobile Adhoc and Sensor Systems (pp. 371–379).\nAlmasaeid, H. M., Jawadwala, T. H., & Kamal, A. E. (2010). On-demand multicast routing in cognitive radio mesh networks. In Global Telecommunications Conference.\nAskari, M., Kavian, Y. S., Kaabi, H., & Rashvand, H. F. (2012). A channel assignment algorithm for cognitive radio wireless sensor networks. In Wireless Sensor Systems (WSS) (pp. 1–4).\nPuccinelli, D., & Haenggi, M. (2005). Wireless sensor networks: Applications and challenges of ubiquitous sensing. IEEE Circuits and Systems Magazine, 5(3), 19–31.\nEwaisha, A., Sultan, A., & ElBatt, T. (2011). Optimization of channel sensing time and order for cognitive radios. In Wireless Communications and Networking Conference (WCNC) (pp. 1414–1419).\nHe, H., Li, G. Y., & Li, S. (2013). Adaptive spectrum sensing for time-varying channels in cognitive radios. IEEE Wireless Communications Letters, 2(2), 1–4.\nShokri-Ghadikolaei, H., Abdi, Y., & Nasiri-Kenari, M. (2012). Learning-based spectrum sensing time optimization in cognitive radio systems. In Telecommunications (IST) (pp. 249–254).\nSun, D., Song, T., Wu, M., Hu, J., Guo, J., & Gu, B. (2013). Optimal sensing time of soft decision cooperative spectrum sensing in cognitive radio networks. In Wireless Communication and Networking Conference (WCNC).\nLiu, X., Zhong, W., Ye, L., & Li, Q. (2013). Joint optimal sensing time and number of cooperative users in OR-RULE cooperative spectrum sensing. In Wireless Communications & Signal Processing (WCSP).\nYin, W., Ren, P., & Zhang, C. (2011). A joint sensing-time adaption and data transmission scheme in cognitive radio networks. In Global Telecommunications Conference (GLOBECOM).\nZhong, W., Chen, K., & Liu, X. (2017). Joint optimal energy-efficient cooperative spectrum sensing and transmission in cognitive radio. China Communications, 14(1), 98–110.\nLuo, L., & Roy, S. (2012). Efficient spectrum sensing for cognitive radio networks via joint optimization of sensing threshold and duration. IEEE Transactions on Communications, 60(10), 2851–2860.\nDeepak, G. C., & Navaie, K. (2013). On the sensing time and achievable throughput in sensor-enabled cognitive radio networks. In Wireless Communication Systems (ISWCS) (pp. 1–5).\nFu, J., Yibing, Z., Yi, L., Shuo, L., & Jun, P. (2015). The energy efficiency optimization based on dynamic spectrum sensing and nodes scheduling in cognitive radio sensor networks. In Control and Decision Conference (CCDC) (pp. 4371–4378).\nAwin, F., Abdel-Raheem, E., & Ahmadi, M. (2017). Joint optimal transmission power and sensing time for energy efficient spectrum sensing in cognitive radio system. IEEE Sensors Journal, 17(2), 369–376.\nZhang, H., Nie, Y., Cheng, J., Leung, V. C. M., & Nallanathan, A. (2017). Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing. IEEE Transactions on Wireless Communications, 16(2), 730–743.\nLi, X., Cao, J., Ji, Q., & Hei, Y. (2013). Energy efficient techniques with sensing time optimization in cognitive radio networks. In IEEE Wireless Communications and Networking Conference (WCNC) (pp. 25–28).\nGhosh, C., Cordeiro, C., Agrawal, D. P., & Rao, M. B. (2009). Markov chain existence and hidden Markov models in spectrum sensing. In Pervasive Computing and Communications.\nFarag, H. M., & Ehab, M. (2014). An efficient dynamic thresholds energy detection technique for cognitive radio spectrum sensing. In Computer Engineering Conference (ICENCO) (pp. 139–144).\nBai, X., Hao, M., & Wang, W. (2015). Frequency spectrum sensing of cognitive radio based on bayesian network. In International Congress on Image and Signal Processing (CISP) (pp. 1095–1099).\nLee, D.-J. (2015). Adaptive random access for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 14(2), 831–840.\nUrkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.\nProakis, J. G. (2006). Digital communication (4th ed.) (J Zhang etc., Trans.). Publishing House of Electronic Industry.\nLee, W.-Y., & Akyildiz, I. F. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(10), 3845–3857.\nPei, Y., Liang, Y. C., Teh, K. C., & Li, K. H. (2011). Energy-efficient design of sequential channel sensing in cognitive radio networks: Optimal sensing strategy, power allocation, and sensing order. IEEE Journal on Selected Areas in Communications, 29(8), 1648–1659.\nStevenson, C. R., Chouinard, G., Lei, Z., Hu, W., Shellhammer, S. J., & Caldwell, W. (2009). IEEE 802.22: The first cognitive radio wireless regional area network standard. Communications Magazine, IEEE (Vol. 47, No. 1, pp. 130–138).\nFu, L., & Wang, X. (2013). Multicast scaling law in multichannel multiradio wireless networks. IEEE Transactions on Parallel and Distributed Systems, 24(12), 2418–2428.",{"VOID":552},"10.1007\u002Fs11276-017-1634-7","2024-05-31T12:07:42.240+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11276-017-1634-7",[556,571,588,601],{"id":557,"sortIndex":19,"researcher":18,"roles":558,"affiliations":559,"properties":568,"displayName":570,"givenName":18,"familyName":18},"0b4ff096-4efe-464a-9390-88b17d59f06c",[131],[560],{"id":561,"sortIndex":19,"affiliation":562,"properties":18},"04980f4b-1c32-4532-9a9f-f426deeb2122",{"id":561,"createTime":18,"updateTime":18,"relativeEntities":563,"slug":18,"properties":564,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":567,"statistic":18},[],{"title":565},{"EN":566},"Department of Computer Science and Engineering, Kyung Hee University, Yongin, Republic of Korea",[],{"title":569},{"VI":570},"Fanhua Kong",{"id":572,"sortIndex":96,"researcher":18,"roles":573,"affiliations":574,"properties":583,"displayName":585,"givenName":18,"familyName":18},"1916e44d-f6d8-45ad-8dcc-93e27906872a",[131],[575],{"id":576,"sortIndex":19,"affiliation":577,"properties":18},"481cf9dc-54f5-49de-919c-422ff8d5481f",{"id":576,"createTime":18,"updateTime":18,"relativeEntities":578,"slug":18,"properties":579,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":582,"statistic":18},[],{"title":580},{"VI":581},"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China",[],{"title":584,"gsAuthor":586},{"VI":585},"Zilong 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Lupas and S. Verdù, Near-far resistance of multi-user detectors in asynchronous channels, IEEE Transactions on Communications 38 (April 1990) 496–508.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":1080},"10.1007\u002Fs10440-022-00541-7",{"id":1076,"text":1082,"url":1078,"identifiers":1083},"S.S.H. Wijayasuria, G.H. Norton and J.P. McGeehan, Sliding window decorrelating algorithm for DS-CDMA receivers, Electronics Letters 28(17) (August 1992).",{"doi":1080},{"id":1076,"text":1085,"url":1078,"identifiers":1086},"S.Y. Yoon, S.E. Hong, J. Ahn and H.S. Lee, Pilot symbol aided coherent decorrelating detector for up-link CDMA mobile radio communication, Electronics Letters 30(12) (June 1994).",{"doi":1080},{"id":1076,"text":1088,"url":1078,"identifiers":1089},"F. Zheng and S. Barton, Near–far resistant detection of CDMA signals via isolation bit insertion, IEEE Transactions on Communications 43 (February 1995).",{"doi":1080},{"id":1076,"text":1091,"url":1078,"identifiers":1092},"Z. Zvonar and D. Brady, Suboptimal multiuser detection for frequency-selective Rayleigh fading synchronous channels, IEEE Transactions on Communications 43 (February\u002FMarch\u002FApril 1995).",{"doi":1080},{"id":1094,"createTime":1095,"updateTime":1096,"relativeEntities":1097,"slug":1098,"properties":1099,"entityType":122,"verifyStatus":123,"verifyTime":1110,"verifyNote":125,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1111,"fullTextUrl":18,"authors":1112,"publicationType":223,"publisherRelationship":1211,"citationCount":1264,"citationInfo":1265,"publishDate":1268,"publishYear":1266,"citationAnalyzeStatus":1068,"lastCitationAnalyze":1096,"indexDatabases":1269,"openAccess":18,"references":18,"isForceReanalyzing":287},"4b351eec-efbd-46e3-8111-d98c8986cb95","2023-12-28T09:24:37.891+00:00","2026-07-26T06:09:45.370+00:00",[],"A-novel-fuzzy-clustering-based-method-for-human-activity-recognition-in-cloud-based-industrial-IoT-environment",{"abstract":1100,"title":1102,"gsPaper":1104,"references":1106,"doi":1108},{"EN":1101},"With the advancement of technology such as video monitoring, Internet-of-things, cloud, and machine learning, Industry 4.0 is working continuously to ensure the security of workers. The workers are equipped with sensors to analyze their activities. In general, the recognition of human activities in cloud-based industrial scenario is leveraged to monitor the safety of the workers. This paper introduced a new optimal clustering method for the activity recognition of workers in industry using cloud based IoT environment. The proposed method uses the temporal and spatial features of human workers in industry. The proposed method is tested on publicly available dataset of different activities maintained into three groups, namely movement, gestures, and object handling, in the context of the medium and small industrial environment. The experimental findings validate that the proposed method achieves \n              \n                \n              \n              $$80.2\\%$$\n              \n            , \n              \n                \n              \n              $$81.05\\%$$\n              \n             and \n              \n                \n              \n              $$80.19\\%$$\n              \n             of average accuracy for movement, gesture, and object handling activities, which clearly outperformed the fuzzy c-means, particle-swarm optimization, and HMM-based activity recognition methods.",{"EN":1103},"A novel fuzzy clustering-based method for human activity recognition in cloud-based industrial IoT environment",{"VOID":1105},"[\"15083891249638247610\"]",{"VOID":1107},"Abbasi, M., Tahouri, R., & Rafiee, M. (2019). Enhancing the performance of the aggregated bit vector algorithm in network packet classification using gpu. PeerJ Computer Science, 5, e185.\nAbbasi, M., Najafi, A., Rafiee, M., et al. (2020). Efficient flow processing in 5g-envisioned sdn-based internet of vehicles using gpus. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5283–5292.\nAhmed, I., Zhang, Y., & Jeon, G., et al. A blockchain-and artificial intelligence-enabled smart iot framework for sustainable city. International Journal of Intelligent Systems.\nChen, J., Sun, Y., & Sun, S. (2021). Improving human activity recognition performance by data fusion and feature engineering. Sensors, 21(3), 692.\nChen, K., Zhang, D., Yao, L., et al. (2021). Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities. ACM Computing Surveys (CSUR), 54(4), 1–40.\nChowdhury, A. K., Tjondronegoro, D., Chandran, V., et al. (2017). Physical activity recognition using posterior-adapted class-based fusion of multi-accelerometers data. IEEE Journal of Biomedical and Health Informatics, 99, 1–1.\nDallel, M., Havard, V., Baudry, D., et al. (2020). Inhard-industrial human action recognition dataset in the context of industrial collaborative robotics. In 2020 IEEE International Conference on Human-Machine Systems (ICHMS), IEEE (pp. 1–6).\nDang, L. M., Min, K., Wang, H., et al. (2020). Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition, 108(107), 561.\nEltaeib, T., & Mahmood, A. (2018). Differential evolution: A survey and analysis. Applied Sciences, 8(10), 1945.\nHu, J., Pan, Y., Li, T., et al. (2020). Tw-co-mfc: Two-level weighted collaborative fuzzy clustering based on maximum entropy for multi-view data. Tsinghua Science and Technology, 26(2), 185–198.\nKhosravi, M. R., & Samadi, S. (2019). Reliable data aggregation in internet of visar vehicles using chained dual-phase adaptive interpolation and data embedding. IEEE Internet of Things Journal, 7(4), 2603–2610.\nKhosravi, M. R., & Samadi, S. (2021). Bl-alm: A blind scalable edge-guided reconstruction filter for smart environmental monitoring through green iomt-uav networks. IEEE Transactions on Green Communications and Networking, 5(2), 727–736.\nKilany, M., Hassanien, AE., & Badr, A. (2015). Accelerometer-based human activity classification using water wave optimization approach. In 2015 11th International Computer Engineering Conference (ICENCO), IEEE (pp. 175–180).\nLiao, X., Zheng, D., & Cao, X. (2021). Coronavirus pandemic analysis through tripartite graph clustering in online social networks. Big Data Mining and Analytics, 4(4), 242–251.\nLiu, Y., Pei, A., Wang, F., et al. (2021). An attention-based category-aware gru model for the next poi recommendation. International Journal of Intelligent Systems, 36(7), 3174–3189.\nLiu, Y., Li, D., Wan, S., et al. (2022). A long short-term memory-based model for greenhouse climate prediction. International Journal of Intelligent Systems, 37(1), 135–151.\nMaitre, J., Bouchard, K., & Gaboury, S. (2021). Alternative deep learning architectures for feature-level fusion in human activity recognition. Mobile Networks and Applications 1–11.\nMittal, H., & Saraswat, M. (2019). An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm and Evolutionary Computation, 45, 15–32.\nMittal, H., & Saraswat, M. (2020). A new fuzzy cluster validity index for hyper-ellipsoid or hyper-spherical shape close clusters with distant centroids. IEEE Transactions on Fuzzy Systems.\nMittal, H., Pandey, A. C., Pal, R., et al. (2021). A new clustering method for the diagnosis of covid19 using medical images. Applied Intelligence, 51(5), 2988–3011.\nNandy, S., Adhikari, M., Khan, M. A., et al. (2021). An intrusion detection mechanism for secured iomt framework based on swarm-neural network. IEEE Journal of Biomedical and Health Informatics.\nNunes, U. M., Faria, D. R., & Peixoto, P. (2017). A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier. Pattern Recognition Letters, 99, 21–31.\nNweke, H. F., Teh, Y. W., Al-Garadi, M. A., et al. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233–261.\nPal, R., Mittal, H., & Saraswat, M. (2019). Optimal fuzzy clustering by improved biogeography-based optimization for leukocytes segmentation. In 2019 Fifth International Conference on Image Information Processing (ICIIP), IEEE (pp. 74–79).\nPal, R., Saraswat, M., & Mittal, H. (2021). Improved bag-of-features using grey relational analysis for classification of histology images. Complex & Intelligent Systems, 7(3), 1429–1443.\nPandey, AC., Tripathi, AK., Pal, R., et al. (2019). Spiral salp swarm optimization algorithm. In 2019 4th International Conference on Information Systems and Computer Networks (ISCON), IEEE (pp. 722–727).\nPant, M., Zaheer, H., Garcia-Hernandez, L., et al. (2020). Differential evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence, 90(103), 479.\nPrice, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: A practical approach to global optimization. Springer.\nRaju, P., Subash, Y., Rishabh, K., et al. (2020). Eewc: Energy-efficient weighted clustering method based on genetic algorithm for hwsns. Complex & Intelligent Systems, 6(2), 391–400.\nRoitberg, A., Perzylo, A., Somani, N., et al. (2014). Human activity recognition in the context of industrial human-robot interaction. Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 1–10). IEEE: Asia-Pacific.\nRonao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244.\nSaraswat, M., Arya, K., & Sharma, H. (2013). Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm and Evolutionary Computation, 11, 46–54.\nSingh, G., & Singh, A. (2020). A hybrid algorithm using particle swarm optimization for solving transportation problem. Neural Computing and Applications, 32(15), 11699–11716.\nStorn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.\nTripathi, A. K., Sharma, K., & Bala, M. (2018). Dynamic frequency based parallel k-bat algorithm for massive data clustering (dfbpkba). International Journal of System Assurance Engineering and Management, 9(4), 866–874.\nTripathi, A. K., Sharma, K., & Bala, M. (2018). A novel clustering method using enhanced grey wolf optimizer and mapreduce. Big Data Research, 14, 93–100.\nTripathi, A. K., Sharma, K., & Bala, M. (2019). Parallel hybrid bbo search method for twitter sentiment analysis of large scale datasets using mapreduce. International Journal of Information Security and Privacy (IJISP), 13(3), 106–122.\nTripathi, A. K., Sharma, K., Bala, M., et al. (2020). A parallel military-dog-based algorithm for clustering big data in cognitive industrial internet of things. IEEE Transactions on Industrial Informatics, 17(3), 2134–2142.\nTripathi, A. K., Mittal, H., Saxena, P., et al. (2021). A new recommendation system using map-reduce-based tournament empowered whale optimization algorithm. Complex & Intelligent Systems, 7(1), 297–309.\nTu, P., Li, J., Wang, H., et al. (2021). Non-linear chaotic features-based human activity recognition. Electronics, 10(2), 111.\nWeiss, G. M., & Lockhart, J. (2012). The impact of personalization on smartphone-based activity recognition. In Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence.\nXue, Z., & Wang, H. (2021). Effective density-based clustering algorithms for incomplete data. Big Data Mining and Analytics, 4(3), 183–194.\nZappi, P., Lombriser, C., Stiefmeier, T., et al. (2008). Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In European Conference on Wireless Sensor Networks (pp. 17–33). Springer.\nZdravevski, E., Lameski, P., Trajkovik, V., et al. (2017). Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering. IEEE Access, 5, 5262–5280.\nZhang, H., Babar, M., Tariq, M. U., et al. (2020). Safecity: Toward safe and secured data management design for iot-enabled smart city planning. IEEE Access, 8, 145256–145267.\nZheng, X., Wang, M., & Ordieres-Meré, J. (2018). Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. 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This allows the same time–frequency resources to be allocated to spatially separated D2D flows simultaneously, thus increasing the cell throughput. This paper presents a framework for: (1) selecting which communications should use the D2D mode, and when, and (2) allocating resources to D2D and non-D2D users, exploiting reuse for the former. We show that the two problems, although apparently similar, should be kept separate and solved at different timescales in order to avoid problems, such as excessive packet loss. We model both as optimization problems, and propose a heuristic solution to the second, which must be solved at millisecond timescales. Simulation results show that our framework is practically viable, it avoids the problem of packet losses, increases throughput and reduces delays.",{"EN":1280},"Resource allocation for network-controlled device-to-device communications in LTE-Advanced",{"VOID":1282},"[\"17342903280372592384\"]",{"VOID":1284},"3GPP. (2014). Study on LTE device to device proximity services: Radio aspects (release 12). TS 36.843 v12.0.1. March 2014.\n3GPP. (2015). Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); overall description; stage 2. TS 36.300 v12.5.0. March 2015.\nAsadi, A., Wang, Q., & Mancuso, V. (2014). A survey on device-to-device communication in cellular networks. IEEE Communications Surveys and Tutorials, 16(4), 1801–1819.\nDoppler, K., Yu, C. H., Ribeiro, C. B., & Janis, P. (2010). Mode selection for device-to-device communication underlaying an LTE-Advanced network. In Proceedings of WCNC, (pp. 1–6). April 18–21, 2010.\nGu, J., Bae, S. J., Choi, B. G., & Chung, M. Y. (2012). Mode selection scheme considering transmission power for improving performance of device-to-device communication in cellular networks. In Proceedings of ICUIMC. February 20–22, 2012, Kuala Lumpur, Malaysia.\nLee, D. H., Choi, K. W., Jeon, W. S., & Jeong, D. G. (2014). Two-Stage Semi-Distributed Resource Management for Device-to-Device Communication in Cellular Networks. IEEE Transactions on Wireless Communications, 13(4), 1908–1920.\nBelleschi, M., Fodor, G., & Abrardo, A. (2011). Performance analysis of a distributed resource allocation scheme for D2D communications. In IEEE GLOBECOM 2011 workshops, (pp. 358–362). December 5–9, 2011.\nXiao, X., Tao, X., & Lu, J. (2011). A QoS-aware power optimization scheme in OFDMA systems with integrated device-to-device (D2D) communications. In Proceedings of VTC Fall, (pp. 1–5). September 5–8, 2011.\nZhang, R., Cheng, X., Yang, L., & Jiao, B. (2013). Interference-aware graph based resource sharing for device-to-device communications underlaying cellular networks. In Proceedings of WCNC, (pp. 140–145). April 7–10, 2013.\nWen, S., Zhu, X., Zhang, X., & Yang, D. (2013). QoS-aware mode selection and resource allocation scheme for Device-to-Device (D2D) communication in cellular networks. In Proceedings of ICC, (pp. 101–105). June 9–13, 2013.\nXu, Y., Yin, R., Han, T., & Yu, G. (2012). Interference-aware channel allocation for Device-to-Device communication underlying cellular networks. In Proceedings of IEEE ICCC, (pp. 422–427). August 15–17, 2012.\nDahlman, E., Parkvall, S., & Skold, J. (2011). 4G: LTE\u002FLTE-Advanced for mobile broadband. Cambridge: Academic Press.\nLin, X., Andrews, J., Ghosh, A., & Ratasuk, R. (2014). An overview of 3GPP device-to-device proximity services. IEEE Communications Magazine, 52(4), 40–48. doi:10.1109\u002FMCOM.2014.6807945.\nFodor, G., et al. (2012). Design aspects of network assisted device-to-device communications. IEEE Communications Magazine, 50(3), 170–177.\nYu, C. H., Tirkkonen, O., Doppler, K., & Ribeiro, C. (2009). On the performance of device-to-device underlay communication with simple power control. In Proceedings of IEEE VTC Spring, (pp. 1–5). April 26–29, 2009.\nZhou, X., Zhang, Z., Wang, G., Yu, X., Zhao, B. Y., & Zheng, H. (2015). Practical conflict graphs in the wild. IEEE\u002FACM Transactions on Networking, 23(3), 824–835. doi:10.1109\u002FTNET.2014.2306416.\n3GPP. (2015). Evolved Universal Terrestrial Radio Access (E-UTRA); Packet Data Convergence Protocol (PDCP) specification. TS 36.323 v12.3.0. March 2015.\nLi, C., Sun, F., Cioffi, J. M., & Yang, L. (2014). Energy efficient MIMO relay transmissions via joint power allocations. IEEE Transactions on Circuits and Systems-II, 61(7), 531–535.\nLi, C., Yang, H. J., Sun, F., Cioffi, J. M., & Yang, L. (2014). Approximate closed-form energy efficient PA for MIMO relaying systems in the high SNR regime. IEEE Communications Letters, 18(8), 1367–1370.\nLi, C., Cioffi, J. M., & Yang, L. (2014). Optimal energy efficient joint power allocation for two-hop single-antenna relaying systems. European Transactions on Telecommunications, 25(7), 745–751.\nLi, C., Zhang, S., Liu, P., Sun, F., Cioffi, J. M., & Yang, L. (2015). Overhearing protocol design exploiting inter-cell interference in cooperative green networks. IEEE Transactions on Vehicular Technology, 99, 1–7. doi:10.1109\u002FTVT.2015.2389826.\nEARTH EU project website. https:\u002F\u002Fwww.ict-earth.eu\u002F.\nSimuLTE webpage. http:\u002F\u002Fwww.simulte.com.\nVirdis, A., Stea, G., & Nardini, G. (2014). SimuLTE: A modular system-level simulator for LTE\u002FLTE-A networks based on OMNeT++. SimulTech 2014, Vienna, AT, August 28–30, 2014.\nOMNeT++. http:\u002F\u002Fwww.omnetpp.org.\n3GPP. (2010). Further advancements for E-UTRA physical layer aspects (release 9). TR 36.814 v9.0.0. March 2010.\nILOG CPLEX Software. http:\u002F\u002Fwww.ilog.com.\nZulhasnine, M., Changcheng, H., & Srinivasan, A. (2010). Efficient resource allocation for device-to-device communication underlaying LTE network. In IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), (pp. 368–375). October 11–13, 2010. doi:10.1109\u002FWIMOB.2010.5645039.\nWu, Y., Wang, S., Guo, W., Chu, X., & Zhang, J. (2014). Optimal resource management for device-to-device communications underlaying SC-FDMA systems. In 9th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), (pp. 569–574). July 23–25, 2014. doi:10.1109\u002FCSNDSP.2014.6923893.",{"VOID":1286},"10.1007\u002Fs11276-016-1193-3","2024-05-07T06:46:01.603+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11276-016-1193-3",[1290,1307,1322,1337,1354],{"id":1291,"sortIndex":19,"researcher":18,"roles":1292,"affiliations":1293,"properties":1302,"displayName":1304,"givenName":18,"familyName":18},"6db5445b-7b25-4f18-8a2e-e1dca5897b11",[131],[1294],{"id":1295,"sortIndex":19,"affiliation":1296,"properties":18},"26fa2349-d1a5-4baa-8d40-4102be23b03f",{"id":1295,"createTime":18,"updateTime":18,"relativeEntities":1297,"slug":18,"properties":1298,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1301,"statistic":18},[],{"title":1299},{"VI":1300},"Dipartimento di Ingegneria dell’Informazione, University of Pisa, Pisa, Italy",[],{"title":1303,"gsAuthor":1305},{"VI":1304},"G. 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This paper presents two localized algorithms, fast localized Delaunay triangulation 1 (FLDT1) and fast localized Delaunay triangulation \t2 (FLDT2), that build a graph called planar localized Delaunay triangulation, PLDel, known to be a good spanner of the Unit Disk Graph, UDG. Our algorithms improve previous algorithms with similar theoretical bounds in the following aspects: unlike previous work, FLDT1 and FLDT2 build PLDel in a single communication step, maintaining a communication cost of O(n log n), which is within a constant of the optimal. Additionally, we show that FLDT1 is more robust than previous triangulation algorithms, because it does not require the strict UDG connectivity model to work. The small signaling cost of our algorithms allows us to improve routing performance, by efficiently using the PLDel graph instead of sparser graphs, like the Gabriel or the Relative Neighborhood graphs.",{"EN":1439},"Single-step creation of localized Delaunay triangulations",{"VOID":1441},"[\"3712323898071220715\"]",{"VOID":1443},"Araujo, F., & Rodrigues, L. (2004). Fast localized delaunay triangulation. In The 8th International Conference on Principles of Distributed Systems (OPODIS 2004) (pp. 81–93). Grenoble: Springer-Verlag, LNCS 3544.\nAraujo, F., & Rodrigues, L. (2006). Single-step creation of localized delaunay triangulations. Technical Report TR 06\u002F03, Centre of Informatics and Systems of the University of Coimbra, ISSN 0874-338X.\nAvin, C. (2005). Fast and efficient restricted delaunay triangulation in random geometric graphs. In Workshop on Combinatorial and Algorithmic Aspects of Networking (CAAN 2005).\nBhardwaj, M., Chandrakasan, A., & Garnett, T. (2001). Upper bounds on the lifetime of sensor networks. In IEEE International Conference on Communications (pp. 785–790).\nBoissonnat, J.-D., & Teillaud, M. (1993). On the randomized construction of the Delaunay tree. Theoretical Computer Science, 112(2), 339–354.\nBondy, J. A., & Murty, U. S. R. (1976). Graph Theory with Applications. North-Holland: Elsevier.\nBose, P., & Morin, P. (1999). Online routing in triangulations. In 10th Annual Internation Symposium on Algorithms and Computation (ISAAC).\nBose, P., Morin, P., Stojmenovic, I., & Urrutia, J. (1999). Routing with guaranteed delivery in ad hoc wireless networks. In International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIALM) (pp. 48–55).\nDobkin, D., Friedman, S. J., & Supowit, K. J. (1990). Delaunay graphs are almost as good as complete graphs. Discrete Computational Geometry, 5(1), 399–407.\nEppstein, D. (2000). Spanning trees and spanners. In Handbook of Computational Geometry (pp. 425–461). North-Holland: Elsevier.\nFinn, G. (1987). Routing and addressing problems in large metropolitan-scale internetworks. Technical Report ISU\u002FRR-87-180, Institute for Scientific Information, March.\nFortune, S. (1987). A sweepline algorithm for Voronoi diagrams. Algorithmica, 2, 153–174.\nFrey, H., & Stojmenovic, I. (2006). On delivery guarantees of face and combined greedy-face routing in ad hoc and sensor networks. In MobiCom ’06: Proceedings of the 12th Annual International Conference on Mobile Computing and Networking (pp. 390–401). New York: ACM Press.\nGao, J., Guibas, L., Hershberger, J., Zhang, L., & Zhu, A. (2001). Geometric spanners for routing in mobile networks. In 2nd ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 01).\nKarp, B., & Kung, H. (2000). GPRS: Greedy perimeter stateless routing for wireless networks. In ACM\u002FIEEE International Conference on Mobile Computing and Networking.\nKim, Y.-J., Govindan, R., Karp, B., & Shenker, S. (2005). On the pitfalls of geographic face routing. In DIALM-POMC ’05: Proceedings of the 2005 Joint Workshop on Foundations of Mobile Computing (pp. 34–43). New York: ACM Press.\nKozma, G., Lotker, Z., Sharir, M., & Stupp, G. (2004). Geometrically aware communication in random wireless networks. In PODC ’04: Proceedings of the Twenty-third Annual ACM Symposium on Principles of Distributed Computing (pp. 310–319). New York: ACM Press.\nKranakis, E., Singh, H., & Urrutia, J. (1999). Compass routing on geometric networks. In 11th Canadian Conference on Computation Geometry (CCCG 99).\nKuhn, F., Wattenhofer, R., Zhang, Y., & Zollinger, A. (2003). Geometric ad-hoc routing: Of theory and practice. In 22nd ACM Symposium on the Principles of Distributed Computing (PODC 2003), Boston, July.\nKuhn, F., Wattenhofer, R., & Zollinger, A. (2002). Asymptotically optimal geometric mobile ad-hoc routing. In 6th International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIALM’02).\nLan, L., & Wen-Jing, H. (2002). Localized Delaunay triangulation for topological construction and routing on manets. In 2nd ACM Workshop on Principles of Mobile Computing (POMC’02).\nLee, D.-T., & Schachter, B. (1980). Two algorithms for constructing a Delaunay triangulation. International Journal of Computer and Information Sciences, 9(3), 219–242\nLi, X.-Y., Calinescu, G., & Wan, P.-J. (2002). Distributed construction of a planar spanner and routing for ad hoc wireless networks. In The 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM).\nLi, X.-Y., Calinescu, G., Wan, P.-Jun, & Wang, Y. (2003). Localized delaunay triangulation with application in ad hoc wireless networks. IEEE Transactions on Parallel and Distributed Systems, 14(9), 1035–1047.\nLi, X.-Y., Stojmenovic, I., & Wang, Y. (2004). Partial delaunay triangulation and degree limited localized bluetooth scatternet formation. IEEE Transactions on Parallel and Distributed Systems, 15(4), 350–361.\nLiebeherr, J., Nahas, M., & Si, W. (2001). Application-layer multicasting with Delaunay triangulation overlays. Technical Report CS-2001-26, University of Virginia, Department of Computer Science, Charlottesville, VA 22904, 5.\nLynch, N. (1996). Distributed algorithms. In Data Link Protocols (Chap. 16, pp. 691–732). Morgan-Kaufmann.\nPreparata, F. P., & Shamos, M. I. (1985). Computational Geometry: An Introduction. New York: Springer-Verlag.\nRodoplu, V., & Meng, T. (1998). Minimum energy mobile wireless networks. In 1998 IEEE International Conference on Communications, ICC’98 (Vol. 3, pp. 1633–1639). Atlanta, June.\nSibson, R. (1977). Locally equiangular triangulations. The Computer Journal, 21(3), 243–245.\nStojmenovic, I., & Lin, X. (2001). Power-aware localized routing in wireless networks. IEEE Transactions on Parallel and Distributed Systems, 12(11), 1122–1133.",{"VOID":1445},"10.1007\u002Fs11276-007-0078-x","2024-06-27T01:02:45.756+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11276-007-0078-x",[1449,1466],{"id":1450,"sortIndex":19,"researcher":18,"roles":1451,"affiliations":1452,"properties":1461,"displayName":1463,"givenName":18,"familyName":18},"bd4da747-d8f6-4dd4-ac6b-a41f76a5de7b",[131],[1453],{"id":1454,"sortIndex":19,"affiliation":1455,"properties":18},"089d1434-bc92-4d88-9b1b-635ec4fc9eaa",{"id":1454,"createTime":18,"updateTime":18,"relativeEntities":1456,"slug":18,"properties":1457,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1460,"statistic":18},[],{"title":1458},{"VI":1459},"CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal",[],{"title":1462,"gsAuthor":1464},{"VI":1463},"Filipe 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order to deliver a qualitative Internet Protocol Television (IPTV) service over vehicular ad hoc networks (VANETs), a quality of service (QoS) mechanism is needed to manage the allocate of network resources to the diverse IPTV application traffic demands. Unlike other mobile network, VANETs have certain unique characteristic that presents several difficulties in providing an effective QoS. Similarly, IPTV requires a constant stream for QoS which at the moment is quite difficult due to the inherent VANET characteristics. To provide an effective QoS that will meet the IPTV application service demands, VANETs, must satisfy the compelling real-time traffic streaming QoS requirement (i.e., minimum bandwidth allocation, packet loss and jitter). In this report, we evaluate via simulation the feasibility of deploying quality IPTV services over VANETs, by characterizing the association between the IPTV streaming quality determining factors (i.e., throughput, delay, loss, jitter) and the IPTV quality degradation, with respect to node density and node velocity. Furthermore, we used an objective QoS metric (Media-Delivery-Index) to identify, locate and address the loss or out-of-order packet. We outline how, using these information’s can support in shaping network parameters to optimize service flows. The implementation assures a priority for handling IPTV traffic, such that maximise the usage of VANETs resources, and opens the possibility that loss and delay can be minimised to a degree that could guarantee quality IPTV service delivery among vehicle in a vehicular network system.",{"EN":1554},"Quality of service management for IPTV services support in VANETs: a performance evaluation study",{"VOID":1556},"[\"12495590729144913294\"]",{"VOID":1558},"Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.\nHeidari, E., Gladisch, A., Moshiri, B., & Tavangarian, D. (2013). Survey on location information services for Vehicular Communication Networks. Wireless Networks, 20(5), 1085–1105.\nAmadeo, M., Campolo, C., & Molinaro, A. (2012). Enhancing IEEE 802.11p\u002FWAVE to provide infotainment applications in VANETs. Ad Hoc Networks, 10(2), 253–269. doi:10.1016\u002Fj.adhoc.2010.09.013.\nGong, H., Liu, N., Yu, L., & Song, C. (2013). An efficient data dissemination protocol with roadside parked vehicles’ assistance in vehicular networks. International Journal of Distributed Sensor Networks, 2013(2013), 1–12.\nSpyropoulos, T., Rais, R. N., Turletti, T., Obraczka, K., & Vasilakos, A. (2010). Routing for disruption tolerant networks: Taxonomy and design. Wireless Networks, 16(8), 2349–2370.\nSalvo, P., De Felice, M., Cuomo, F., & Baiocchi, A. (2012). Infotainment traffic flow dissemination in an urban VANET. In Global communications conference (GLOBECOM), 2012 IEEE (pp. 67–72), IEEE.\nVishal Garg, C. K. (2011). A survey of QoS parameters through reactive routing in MANETs. International Journal of Computational Engineering & Management, 13, 22–27.\nYim, T., Nguyen, T. M., Hong, K., Kyung, Y., & Park, J. (2014). Mobile flow-aware networks for mobility and QoS support in the IP-based wireless networks. Wireless Networks, 20(6), 1639–1652.\nZhou, L., Zhang, Y., Song, K., Jing, W., & Vasilakos, A. V. (2011). Distributed media services in P2P-based vehicular networks. Vehicular Technology, IEEE Transactions on, 60(2), 692–703.\nSaleet, H., Langar, R., Naik, K., Boutaba, R., Nayak, A., & Goel, N. (2011). Intersection-based geographical routing protocol for VANETs: A proposal and analysis. Vehicular Technology, IEEE Transactions on, 60(9), 4560–4574.\nTianbo Kuang, C. L. W. (2004). Hierarchical analysis of realmedia streaming traffic on an IEEE 802.11b Wireless LAN. Computer Communications, 27, 538–548.\nMinghua, C., & Avideh, Z. (2005). Rate control for streaming video over wireless. Wireless Communications, IEEE, 12(4), 32–41. doi:10.1109\u002Fmwc.2005.1497856.\nIsmail, D., & Toufik, A. (2007). A cross-layer interworking of DVB-T and WLAN for mobile IPTV service delivery. Broadcasting, IEEE Transactions on, 53(1), 382–390. doi:10.1109\u002Ftbc.2006.889111.\nShihab, E., Fengdan, W., Lin, C., Gulliver, A., & Tin, N. (2007). Performance analysis of IPTV traffic in home networks. In Global telecommunications conference, 2007. GLOBECOM ‘07. IEEE, 26–30 Nov. 2007 (pp. 5341–5345). doi:10.1109\u002Fglocom.2007.1012.\nQinghe, D., & Xi, Z. (2009). Statistical QoS provisionings for wireless unicast\u002Fmulticast of layered video streams. In INFOCOM 2009, IEEE, 19–25 April 2009 (pp. 477–485). doi:10.1109\u002Finfcom.2009.5061953.\nDeer, L., & Jianping, P. (2010). Performance evaluation of video streaming over multi-hop wireless local area networks. Wireless Communications, IEEE Transactions on, 9(1), 338–347. doi:10.1109\u002Ftwc.2010.01.090556.\nMeng, G., Ammar, M. H., & Zegura, E. W. (2005). V3: A vehicle-to-vehicle live video streaming architecture. In Pervasive computing and communications, 2005. PerCom 2005. Third IEEE international conference on, 8–12 March 2005 (pp. 171–180). doi:10.1109\u002Fpercom.2005.53.\nPark, A. H., & Choi, J. K. (2007). QoS guaranteed IPTV service over Wireless Broadband network. In Advanced communication technology, the 9th international conference on, 12–14 Feb. 2007 (Vol. 2, pp. 1077–1080). doi:10.1109\u002Ficact.2007.358545.\nWinkler, S., & Mohandas, P. (2008). The evolution of video quality measurement: From PSNR to hybrid metrics. Broadcasting, IEEE Transactions on, 54(3), 660–668. doi:10.1109\u002Ftbc.2008.2000733.\nKrejci, J. (2008). MDI measurement in the IPTV. In Systems, signals and image processing, 2008. IWSSIP 2008. 15th international conference on, 25–28 June 2008 (pp. 49–52). doi:10.1109\u002Fiwssip.2008.4604364.\nYu, H., Ahn, S., & Yoo, J. (2013). A stable routing protocol for vehicles in urban environments. International Journal of Distributed Sensor Networks, 2013(2013), 1–9.\nYoussef, M., Ibrahim, M., Abdelatif, M., Chen, L., & Vasilakos, A. (2013). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys & Tutorials, 16(1), 92–109.\nPunchihewa, A., & De Silva, A. M. (2010). Tutorial on IPTV and its latest developments. In Information and automation for sustainability (ICIAFs), 2010 5th international conference on, 17–19 Dec. 2010 (pp. 45–50). doi:10.1109\u002Ficiafs.2010.5715633.\nSchierl, T., Gruneberg, K., & Wiegand, T. (2009). Scalable video coding over RTP and MPEG-2 transport stream in broadcast and IPTV channels. Wireless Communications, IEEE, 16(5), 64–71.\nLi, P., Guo, S., Yu, S., & Vasilakos, A. V. (2012) CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In INFOCOM, 2012 proceedings IEEE 2012 (pp. 100–108), IEEE.\nWan, Z., Xiong, N., & Yang, L. T. (2013). Cross-layer video transmission over IEEE 802.11 e multihop networks. Multimedia Tools and Applications, 67(3), 1–19.\nWen, C.-C., & Wu, C.-S. (2012). QoS supported IPTV service architecture over hybrid-tree-based explicit routed multicast network. International Journal of Digital Multimedia Broadcasting, 2012(2012), 1–11.\nAcuta, S., Buzila, G. L., Blaga, T., & Dobrota, V. (2007). Evaluation of QoS parameters for IPTV. ATN, 48(3), 9–14.\nVasilakos, A. V., Zhang, Y., & Spyropoulos, T. (2012). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.\nKaram, M. J., & Tobagi, F. A. (2001) Analysis of the delay and jitter of voice traffic over the Internet. In INFOCOM 2001. Twentieth annual joint conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2001 (Vol. 2, 822 pp. 824–833). doi:10.1109\u002Finfcom.2001.916273.\nHamodi, J., Salah, K., & Thool, R. (2013). Evaluating the Performance of IPTV over Fixed WiMAX. International Journal of Computer Applications, 84(6), 35–43.\nITU-T P.910. (2008). Subjective video quality assessment methods for multimedia applications. International Telecommunication Union Recommendation. http:\u002F\u002Fhandle.itu.int\u002F11.1002\u002F1000\u002F9317.\nYuehui, J., Yidong, C., Jun, S., Zhi, J., Lunyong, Z., & Hongqi, L. (2010). An experimental study on measurement and evaluation of IPTV video quality. In Broadband network and multimedia technology (IC-BNMT), 2010 3rd IEEE international conference on, 26–28 Oct. 2010 (pp. 149–153). doi:10.1109\u002Ficbnmt.2010.5704885.\nWelch, J., & Clark, J. (2006). A proposed Media Delivery Index (MDI). http:\u002F\u002Fwww.rfc-editor.org\u002Frfc\u002Frfc4445.txt. Accessed March 13, 2012.\nFall, K., & Varadhan, K. (2007). The network simulator NS-2. http:\u002F\u002Fwww.isi.edu\u002Fnsnam\u002Fns.\nTelecommunication Networks Group, Technische Universität Berlin. (2014). MPEG-4 and H.263 Video Traces for Network Performance Evaluation. http:\u002F\u002Fwww.tkn.tu-berlin.de\u002Fmenue\u002Fsofthardware_components\u002Ftraces\u002Fmpeg--4_and_h263_video_traces_for_network_performance_evaluation\u002F. 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