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2020 | OriginalPaper | Chapter

Energy Efficient Data Collection in Smart Cities Using IoT

Authors : Tanuj Wala, Narottam Chand, Ajay K. Sharma

Published in: Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's

Publisher: Springer International Publishing

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Abstract

Wireless sensor network (WSN) has emerged as a major part of the Internet of Things (IoT) and their collaboration helps in the formation of a smart environment. The usability of WSN has gained impetus with the advancement in the field of wireless technology. The impact of this growth can be seen in expanded smart city applications that have enhanced the living standards of citizens. In a smart city, millions of sensors are deployed in various intelligent applications like smart homes, smart transportation, smart industries, smart parking, and so forth to make the city smarter and efficient. These applications continuously generate a huge amount of data to provide innovative services to citizens. Therefore, an efficient collection of this data is utmost important to make an effective decision for the betterment of society. The sensor nodes work in collaboration to send the collected data to the base station. In WSN, the sensor nodes have resource constraints like low power and limited communication range. So, as a result, in reference to sensor processing, the continuous stream of data generated by sensor nodes need to be processed and delivered to the end-user in the optimal lapse of time. One promising approach to accomplish these needs is to collaborate among the sensors and sink in an efficient way to reduce the transmission cost. Recent research showed the emergence of several data collection approaches as data aggregation, data compression, optimal deployment of sinks and sink mobility. These approaches mainly aim specifically to reduce consumption of energy, time delay management and maximize the network lifetime. Referring to this context, this chapter discusses challenges in the field of data collection approaches and proposes possible future directions.

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Literature
1.
go back to reference Ray, P.P.: A survey on Internet of Things architectures. J. King Saud Univ.-Comput. Inf. Sci. 30(3), 291–319 (2018) Ray, P.P.: A survey on Internet of Things architectures. J. King Saud Univ.-Comput. Inf. Sci. 30(3), 291–319 (2018)
2.
go back to reference Sheng, Z., et al.: Recent advances in industrial wireless sensor networks toward efficient management in IoT. IEEE Access 3(1), 622–637 (2015)CrossRef Sheng, Z., et al.: Recent advances in industrial wireless sensor networks toward efficient management in IoT. IEEE Access 3(1), 622–637 (2015)CrossRef
3.
go back to reference Ahmed, E., et al.: Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel. Commun. 23(5), 10–16 (2016)CrossRef Ahmed, E., et al.: Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel. Commun. 23(5), 10–16 (2016)CrossRef
4.
go back to reference Singh, S., Singh, P.K.: Performance investigation of energy efficient HetSEP for prolonging lifetime in WSNs. In: International Conference on Futuristic Trends in Network and Communication Technologies, pp. 496–509. Springer, Singapore (2018) Singh, S., Singh, P.K.: Performance investigation of energy efficient HetSEP for prolonging lifetime in WSNs. In: International Conference on Futuristic Trends in Network and Communication Technologies, pp. 496–509. Springer, Singapore (2018)
5.
go back to reference Talari, S., et al.: A review of smart cities based on the Internet of Things concept. Energies 10(4), 1–23 (2017)CrossRef Talari, S., et al.: A review of smart cities based on the Internet of Things concept. Energies 10(4), 1–23 (2017)CrossRef
6.
go back to reference Burhan, M., et al.: IoT elements, layered architectures and security issues: a comprehensive survey. Sensors 18(9), 1–37 (2018)CrossRef Burhan, M., et al.: IoT elements, layered architectures and security issues: a comprehensive survey. Sensors 18(9), 1–37 (2018)CrossRef
7.
go back to reference Cheng, S., Cai, Z., Li, J.: Approximate sensory data collection: a survey. Sensors 17(3), 1–16 (2017)CrossRef Cheng, S., Cai, Z., Li, J.: Approximate sensory data collection: a survey. Sensors 17(3), 1–16 (2017)CrossRef
8.
go back to reference Rathore, M.M., et al.: Exploiting IoT and big data analytics: defining smart digital city using real-time urban data. Sustain. Cities Soc. 40(1), 600–610 (2018)CrossRef Rathore, M.M., et al.: Exploiting IoT and big data analytics: defining smart digital city using real-time urban data. Sustain. Cities Soc. 40(1), 600–610 (2018)CrossRef
9.
go back to reference Randhawa, A., Kumar, A.: Exploring sustainability of smart development initiatives in India. Int. J. Sustain. Built Environ. 6(2), 701–710 (2017)CrossRef Randhawa, A., Kumar, A.: Exploring sustainability of smart development initiatives in India. Int. J. Sustain. Built Environ. 6(2), 701–710 (2017)CrossRef
10.
go back to reference Singh, P., Paprzycki, M., Bhargava, B., Chhabra, J., Kaushal, N., Kumar, Y. (eds.): FTNCT 2018. Communications in Computer and Information Science, vol. 958. Springer, Singapore (2018) Singh, P., Paprzycki, M., Bhargava, B., Chhabra, J., Kaushal, N., Kumar, Y. (eds.): FTNCT 2018. Communications in Computer and Information Science, vol. 958. Springer, Singapore (2018)
11.
go back to reference Su, K., Li, J., Fu, H.: Smart city and the applications. In: International Conference on Electronics, Communications and Control (ICECC), pp. 1028–1031. IEEE, China (2015) Su, K., Li, J., Fu, H.: Smart city and the applications. In: International Conference on Electronics, Communications and Control (ICECC), pp. 1028–1031. IEEE, China (2015)
12.
go back to reference Jawhar, I., Mohamed, N., Al-Jaroodi, J.: Networking architectures and protocols for smart city systems. J. Internet Serv. Appl. 9(1), 1–26 (2018)CrossRef Jawhar, I., Mohamed, N., Al-Jaroodi, J.: Networking architectures and protocols for smart city systems. J. Internet Serv. Appl. 9(1), 1–26 (2018)CrossRef
13.
go back to reference Mohbey, K.K.: An efficient framework for smart city using big data technologies and Internet of Things. In: Progress in Advanced Computing and Intelligent Engineering, pp. 319–328. Springer, Singapore (2019) Mohbey, K.K.: An efficient framework for smart city using big data technologies and Internet of Things. In: Progress in Advanced Computing and Intelligent Engineering, pp. 319–328. Springer, Singapore (2019)
14.
go back to reference Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5(1), 5247–5261 (2017) Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5(1), 5247–5261 (2017)
15.
go back to reference Randhawa, S., Jain, S.: Data aggregation in wireless sensor networks: previous research, current status and future directions. Wirel. Pers. Commun. 97(3), 3355–3425 (2017)CrossRef Randhawa, S., Jain, S.: Data aggregation in wireless sensor networks: previous research, current status and future directions. Wirel. Pers. Commun. 97(3), 3355–3425 (2017)CrossRef
16.
go back to reference Avinash, R.A., et al.: Data prediction in Wireless sensor networks using Kalman Filter. In: International Conference on Smart Sensors and Systems (IC-SSS), pp. 1–4. IEEE, Bangalore (2015) Avinash, R.A., et al.: Data prediction in Wireless sensor networks using Kalman Filter. In: International Conference on Smart Sensors and Systems (IC-SSS), pp. 1–4. IEEE, Bangalore (2015)
17.
go back to reference Wang, Q., Lin, D., Yang, P., Zhang, Z.: An energy-efficient compressive sensing-based clustering routing protocol for WSNs. IEEE Sens. J. 19(10), 3950–3960 (2019)CrossRef Wang, Q., Lin, D., Yang, P., Zhang, Z.: An energy-efficient compressive sensing-based clustering routing protocol for WSNs. IEEE Sens. J. 19(10), 3950–3960 (2019)CrossRef
18.
go back to reference Yu, S., Kim, J., Lee, J.: Lifetime improvement method using mobile sink for IoT service. In: Proceedings of the 10th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, pp. 145–150. ACM, New York (2013) Yu, S., Kim, J., Lee, J.: Lifetime improvement method using mobile sink for IoT service. In: Proceedings of the 10th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, pp. 145–150. ACM, New York (2013)
19.
go back to reference Akkaya, K., Guvenc, I., Aygun, R., Pala, N., Kadri, A.: IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In: Wireless Communications and Networking Conference Workshops (WCNCW), pp. 58–63. IEEE, USA (2015) Akkaya, K., Guvenc, I., Aygun, R., Pala, N., Kadri, A.: IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In: Wireless Communications and Networking Conference Workshops (WCNCW), pp. 58–63. IEEE, USA (2015)
20.
go back to reference Pandey, V., Kaur, A., Chand, N.: A review on data aggregation techniques in wireless sensor network. J. Electron. Electr. Eng. 1(2), 1–8 (2010) Pandey, V., Kaur, A., Chand, N.: A review on data aggregation techniques in wireless sensor network. J. Electron. Electr. Eng. 1(2), 1–8 (2010)
21.
go back to reference Virmani, D., Sharma, T., Sharma, R.: Adaptive energy aware data aggregation tree for wireless sensor networks. Int. J. Hybrid Inf. Technol. 6(1), 26–36 (2013) Virmani, D., Sharma, T., Sharma, R.: Adaptive energy aware data aggregation tree for wireless sensor networks. Int. J. Hybrid Inf. Technol. 6(1), 26–36 (2013)
22.
go back to reference Lachowski, R., et al.: An efficient distributed algorithm for constructing spanning trees in wireless sensor networks. Sensors 15(1), 1518–1536 (2015)CrossRef Lachowski, R., et al.: An efficient distributed algorithm for constructing spanning trees in wireless sensor networks. Sensors 15(1), 1518–1536 (2015)CrossRef
23.
go back to reference Qiu, T., et al.: An efficient tree-based self-organizing protocol for Internet of Things. IEEE Access 4(1), 3535–3546 (2016)MathSciNetCrossRef Qiu, T., et al.: An efficient tree-based self-organizing protocol for Internet of Things. IEEE Access 4(1), 3535–3546 (2016)MathSciNetCrossRef
24.
go back to reference Yin, B., Wei, X.: Communication-efficient data aggregation tree construction for complex queries in IoT applications. IEEE IoT J. 6(2), 3352–3363 (2018) Yin, B., Wei, X.: Communication-efficient data aggregation tree construction for complex queries in IoT applications. IEEE IoT J. 6(2), 3352–3363 (2018)
25.
go back to reference Najjar-Ghabel, S., Yousefi, S., Farzinvash, L.: Reliable data gathering in the Internet of Things using artificial bee colony. Turk. J. Electr. Eng. Comput. Sci. 26(4), 1710–1723 (2018)CrossRef Najjar-Ghabel, S., Yousefi, S., Farzinvash, L.: Reliable data gathering in the Internet of Things using artificial bee colony. Turk. J. Electr. Eng. Comput. Sci. 26(4), 1710–1723 (2018)CrossRef
26.
go back to reference Kumar, H., Singh, P.K.: Node energy based approach to improve network lifetime and throughput in wireless sensor networks. J. Telecommun. Electron. Comput. Eng. (JTEC) 9(3), 79–88 (2017) Kumar, H., Singh, P.K.: Node energy based approach to improve network lifetime and throughput in wireless sensor networks. J. Telecommun. Electron. Comput. Eng. (JTEC) 9(3), 79–88 (2017)
27.
go back to reference Lin, D., Wang, Q.: An energy-efficient clustering algorithm combined game theory and dual-cluster-head mechanism for WSNs. IEEE Access 7(1), 49894–49905 (2019)CrossRef Lin, D., Wang, Q.: An energy-efficient clustering algorithm combined game theory and dual-cluster-head mechanism for WSNs. IEEE Access 7(1), 49894–49905 (2019)CrossRef
28.
go back to reference Kalantari, M., Ekbatanifard, G.: An energy aware dynamic cluster head selection mechanism for wireless sensor networks. In: Annual IEEE International Systems Conference, pp. 1–8. IEEE, Canada (2017) Kalantari, M., Ekbatanifard, G.: An energy aware dynamic cluster head selection mechanism for wireless sensor networks. In: Annual IEEE International Systems Conference, pp. 1–8. IEEE, Canada (2017)
29.
go back to reference Mantri, D.S., Prasad, N.R., Prasad, R.: Bandwidth efficient cluster-based data aggregation for wireless sensor network. Comput. Electr. Eng. 41(1), 256–264 (2015)CrossRef Mantri, D.S., Prasad, N.R., Prasad, R.: Bandwidth efficient cluster-based data aggregation for wireless sensor network. Comput. Electr. Eng. 41(1), 256–264 (2015)CrossRef
30.
go back to reference Jia, D., et al.: Dynamic cluster head selection method for wireless sensor network. IEEE Sens. J. 16(8), 2746–2754 (2015)CrossRef Jia, D., et al.: Dynamic cluster head selection method for wireless sensor network. IEEE Sens. J. 16(8), 2746–2754 (2015)CrossRef
31.
go back to reference Mohapatra, A.D., et al.: Distributed fault diagnosis with dynamic cluster-head and energy efficient dissemination model for smart city. Sustain. cities Soc. 43(1), 624–634 (2018)CrossRef Mohapatra, A.D., et al.: Distributed fault diagnosis with dynamic cluster-head and energy efficient dissemination model for smart city. Sustain. cities Soc. 43(1), 624–634 (2018)CrossRef
32.
go back to reference Zhao, Y., et al.: Distributed dynamic cluster-head selection and clustering for massive IoT access in 5G networks. Appl. Sci. 9(1), 1–15 (2019) Zhao, Y., et al.: Distributed dynamic cluster-head selection and clustering for massive IoT access in 5G networks. Appl. Sci. 9(1), 1–15 (2019)
33.
go back to reference Kaur, S., Gangwar, R.: A study of tree based data aggregation techniques for WSNs. Int. J. Database Theory Appl. 9(1), 109–118 (2016)CrossRef Kaur, S., Gangwar, R.: A study of tree based data aggregation techniques for WSNs. Int. J. Database Theory Appl. 9(1), 109–118 (2016)CrossRef
34.
go back to reference Zhu, T., et al.: An architecture for aggregating information from distributed data nodes for industrial Internet of Things. Comput. Electr. Eng. 58(1), 337–349 (2017)CrossRef Zhu, T., et al.: An architecture for aggregating information from distributed data nodes for industrial Internet of Things. Comput. Electr. Eng. 58(1), 337–349 (2017)CrossRef
35.
go back to reference Ferrari, S., Zhang, G., Wettergren, T.A.: Probabilistic track coverage in cooperative sensor networks. IEEE Trans. Syst. Man Cybern. 40(6), 1492–1504 (2010)CrossRef Ferrari, S., Zhang, G., Wettergren, T.A.: Probabilistic track coverage in cooperative sensor networks. IEEE Trans. Syst. Man Cybern. 40(6), 1492–1504 (2010)CrossRef
36.
go back to reference Chu, D., et al.: Approximate data collection in sensor networks using probabilistic models. In: 22nd International Conference on Data Engineering (ICDE 2006), pp. 48. IEEE, USA (2006) Chu, D., et al.: Approximate data collection in sensor networks using probabilistic models. In: 22nd International Conference on Data Engineering (ICDE 2006), pp. 48. IEEE, USA (2006)
37.
go back to reference Wang, L., Deshpande, A.: Predictive modeling-based data collection in wireless sensor networks. In: European Conference on Wireless Sensor Networks, pp. 34–51. Springer, Berlin (2008) Wang, L., Deshpande, A.: Predictive modeling-based data collection in wireless sensor networks. In: European Conference on Wireless Sensor Networks, pp. 34–51. Springer, Berlin (2008)
38.
go back to reference Deshpande, A., et al.: Model-driven data acquisition in sensor networks. In: Proceedings of the Thirtieth International Conference on Very Large Databases, vol. 30, pp. 588–599. VLDB Endowment, Canada (2004) Deshpande, A., et al.: Model-driven data acquisition in sensor networks. In: Proceedings of the Thirtieth International Conference on Very Large Databases, vol. 30, pp. 588–599. VLDB Endowment, Canada (2004)
39.
go back to reference Wang, C., et al.: Adaptive approximate data collection for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(6), 1004–1016 (2012)CrossRef Wang, C., et al.: Adaptive approximate data collection for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(6), 1004–1016 (2012)CrossRef
40.
go back to reference Li, C., et al.: A novel energy-efficient k-Coverage algorithm based on probability driven mechanism of wireless sensor networks. Int. J. Distrib. Sens. Netw. 12(4), 1–9 (2016)MathSciNetCrossRef Li, C., et al.: A novel energy-efficient k-Coverage algorithm based on probability driven mechanism of wireless sensor networks. Int. J. Distrib. Sens. Netw. 12(4), 1–9 (2016)MathSciNetCrossRef
41.
go back to reference Schobel, J., et al.: Towards flexible mobile data collection in healthcare. In: IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 181–182. IEEE, Ireland (2016) Schobel, J., et al.: Towards flexible mobile data collection in healthcare. In: IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 181–182. IEEE, Ireland (2016)
42.
go back to reference Hao, P., et al.: Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data. IEEE Trans. Intell. Transp. Syst. 18(3), 701–711 (2016)CrossRef Hao, P., et al.: Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data. IEEE Trans. Intell. Transp. Syst. 18(3), 701–711 (2016)CrossRef
43.
go back to reference Liu, X.Y., et al.: CDC: compressive data collection for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 26(8), 2188–2197 (2015)CrossRef Liu, X.Y., et al.: CDC: compressive data collection for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 26(8), 2188–2197 (2015)CrossRef
44.
go back to reference Wu, X., et al.: An efficient compressive data gathering routing scheme for large-scale wireless sensor networks. Comput. Electr. Eng. 39(6), 1935–1946 (2013)CrossRef Wu, X., et al.: An efficient compressive data gathering routing scheme for large-scale wireless sensor networks. Comput. Electr. Eng. 39(6), 1935–1946 (2013)CrossRef
45.
go back to reference Zheng, H., et al.: Data gathering with compressive sensing in wireless sensor networks: a random walk based approach. IEEE Trans. Parallel Distrib. Syst. 26(1), 35–44 (2014)CrossRef Zheng, H., et al.: Data gathering with compressive sensing in wireless sensor networks: a random walk based approach. IEEE Trans. Parallel Distrib. Syst. 26(1), 35–44 (2014)CrossRef
46.
go back to reference Barcelo-Llado, J.E., Perez, A.M., Seco-Granados, G.: Enhanced correlation estimators for distributed source coding in large wireless sensor networks. IEEE Sens. J. 12(9), 2799–2806 (2012)CrossRef Barcelo-Llado, J.E., Perez, A.M., Seco-Granados, G.: Enhanced correlation estimators for distributed source coding in large wireless sensor networks. IEEE Sens. J. 12(9), 2799–2806 (2012)CrossRef
47.
go back to reference Aktas, M., et al.: D-DSC: decoding delay-based distributed source coding for internet of sensing things. PLoS ONE 13(3), 1–25 (2018)MathSciNetCrossRef Aktas, M., et al.: D-DSC: decoding delay-based distributed source coding for internet of sensing things. PLoS ONE 13(3), 1–25 (2018)MathSciNetCrossRef
48.
go back to reference Masoum, A., Meratnia, N., Havinga, P.J.: A distributed compressive sensing technique for data gathering in wireless sensor networks. Procedia Comput. Sci. 21, 207–216 (2013)CrossRef Masoum, A., Meratnia, N., Havinga, P.J.: A distributed compressive sensing technique for data gathering in wireless sensor networks. Procedia Comput. Sci. 21, 207–216 (2013)CrossRef
49.
go back to reference Li, S., Da Xu, L., Wang, X.: Compressed sensing signal and data acquisition in wireless sensor networks and Internet of Things. IEEE Trans. Ind. Inf. 9(4), 2177–2186 (2012)CrossRef Li, S., Da Xu, L., Wang, X.: Compressed sensing signal and data acquisition in wireless sensor networks and Internet of Things. IEEE Trans. Ind. Inf. 9(4), 2177–2186 (2012)CrossRef
50.
go back to reference Zhang, C., et al.: Dynamic clustering and compressive data gathering algorithm for energy-efficient wireless sensor networks. Int. J. Distrib. Sens. Netw. 13(10), 1–12 (2017)CrossRef Zhang, C., et al.: Dynamic clustering and compressive data gathering algorithm for energy-efficient wireless sensor networks. Int. J. Distrib. Sens. Netw. 13(10), 1–12 (2017)CrossRef
51.
go back to reference Wang, W., Wang, D., Jiang, Y.: Energy efficient distributed compressed data gathering for sensor networks. Ad Hoc Netw. 58(1), 112–117 (2017)MathSciNetCrossRef Wang, W., Wang, D., Jiang, Y.: Energy efficient distributed compressed data gathering for sensor networks. Ad Hoc Netw. 58(1), 112–117 (2017)MathSciNetCrossRef
52.
go back to reference Nguyen, M.T., Teague, K.A., Rahnavard, N.: CCS: energy-efficient data collection in clustered wireless sensor networks utilizing block-wise compressive sensing. Comput. Netw. 106(1), 171–185 (2016)CrossRef Nguyen, M.T., Teague, K.A., Rahnavard, N.: CCS: energy-efficient data collection in clustered wireless sensor networks utilizing block-wise compressive sensing. Comput. Netw. 106(1), 171–185 (2016)CrossRef
53.
go back to reference Yarinezhad, R., Sarabi, A.: Reducing delay and energy consumption in wireless sensor networks by making virtual grid infrastructure and using mobile sink. AEU-Int. J. Electron. Commun. 84(1), 144–152 (2018)CrossRef Yarinezhad, R., Sarabi, A.: Reducing delay and energy consumption in wireless sensor networks by making virtual grid infrastructure and using mobile sink. AEU-Int. J. Electron. Commun. 84(1), 144–152 (2018)CrossRef
54.
go back to reference Gupta, N., Gupta, V.: A review on sink mobility aware fast and efficient data gathering in wireless sensor networks. In: International Conference on Advances in Computing, Communication, & Automation (ICACCA), pp. 1–4. IEEE, Dehradun (2016) Gupta, N., Gupta, V.: A review on sink mobility aware fast and efficient data gathering in wireless sensor networks. In: International Conference on Advances in Computing, Communication, & Automation (ICACCA), pp. 1–4. IEEE, Dehradun (2016)
55.
go back to reference Zareei, M., et al.: Mobility-aware medium access control protocols for wireless sensor networks: a survey. J. Netw. Comput. Appl. 104(1), 21–37 (2018)CrossRef Zareei, M., et al.: Mobility-aware medium access control protocols for wireless sensor networks: a survey. J. Netw. Comput. Appl. 104(1), 21–37 (2018)CrossRef
56.
go back to reference Sharma, S., et al.: Rendezvous based routing protocol for wireless sensor networks with mobile sink. J. Supercomput. 73(3), 1168–1188 (2017)CrossRef Sharma, S., et al.: Rendezvous based routing protocol for wireless sensor networks with mobile sink. J. Supercomput. 73(3), 1168–1188 (2017)CrossRef
57.
go back to reference Zhao, H., et al.: Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink. Appl. Soft Comput. 34(1), 539–550 (2015)CrossRef Zhao, H., et al.: Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink. Appl. Soft Comput. 34(1), 539–550 (2015)CrossRef
58.
go back to reference Fitzgerald, E., Pióro, M., Tomaszewski, A.: Energy-optimal data aggregation and dissemination for the Internet of Things. IEEE IoT J. 5(2), 955–969 (2018) Fitzgerald, E., Pióro, M., Tomaszewski, A.: Energy-optimal data aggregation and dissemination for the Internet of Things. IEEE IoT J. 5(2), 955–969 (2018)
59.
go back to reference Liu, X., et al.: Optimizing trajectory of unmanned aerial vehicles for efficient data acquisition: a matrix completion approach. IEEE IoT J. 6(2), 1829–1840 (2019) Liu, X., et al.: Optimizing trajectory of unmanned aerial vehicles for efficient data acquisition: a matrix completion approach. IEEE IoT J. 6(2), 1829–1840 (2019)
60.
go back to reference Mohamed, S.M., et al.: Coverage in mobile wireless sensor networks (M-WSN): a survey. Comput. Commun. 110(1), 133–150 (2017)CrossRef Mohamed, S.M., et al.: Coverage in mobile wireless sensor networks (M-WSN): a survey. Comput. Commun. 110(1), 133–150 (2017)CrossRef
61.
go back to reference Jain, M., Patle, V.K., Kumar, S.: Performance of mobility models with different routing protocols by using simulation tools for WSN: a review. Int. J. Adv. Res. Comput. Commun. Eng. 4(1), 57–61 (2015)CrossRef Jain, M., Patle, V.K., Kumar, S.: Performance of mobility models with different routing protocols by using simulation tools for WSN: a review. Int. J. Adv. Res. Comput. Commun. Eng. 4(1), 57–61 (2015)CrossRef
62.
go back to reference Wang, J., et al.: Internet of vehicles: Sensing-aided transportation information collection and diffusion. IEEE Trans. Veh. Technol. 67(5), 3813–3825 (2018)CrossRef Wang, J., et al.: Internet of vehicles: Sensing-aided transportation information collection and diffusion. IEEE Trans. Veh. Technol. 67(5), 3813–3825 (2018)CrossRef
63.
go back to reference Juang, P., et al.: Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. ACM SIGARCH Comput. Architect. News 30(5), 96–107 (2002)CrossRef Juang, P., et al.: Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. ACM SIGARCH Comput. Architect. News 30(5), 96–107 (2002)CrossRef
64.
go back to reference Ayele, E.D., Meratnia, N., Havinga, P.J.: Towards a new opportunistic IoT network architecture for wildlife monitoring system. In: 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–5. IEEE, France (2018) Ayele, E.D., Meratnia, N., Havinga, P.J.: Towards a new opportunistic IoT network architecture for wildlife monitoring system. In: 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–5. IEEE, France (2018)
65.
go back to reference Maroto-Molina, F., et al.: A low-cost IoT-based system to monitor the location of a whole herd. Sensors 19(10), 1–15 (2019)CrossRef Maroto-Molina, F., et al.: A low-cost IoT-based system to monitor the location of a whole herd. Sensors 19(10), 1–15 (2019)CrossRef
66.
go back to reference Xu, J., et al.: Animal monitoring with unmanned aerial vehicle-aided wireless sensor networks. In: IEEE 40th Conference on Local Computer Networks (LCN), pp. 125–132. IEEE, USA (2015) Xu, J., et al.: Animal monitoring with unmanned aerial vehicle-aided wireless sensor networks. In: IEEE 40th Conference on Local Computer Networks (LCN), pp. 125–132. IEEE, USA (2015)
Metadata
Title
Energy Efficient Data Collection in Smart Cities Using IoT
Authors
Tanuj Wala
Narottam Chand
Ajay K. Sharma
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-40305-8_30