Subject Areas : journal of Artificial Intelligence in Electrical Engineering
Majid Samad Zamini
1
,
Solmaz Abdollahizad
2
,
Kayvan Asghari
3
1 -
2 -
3 -
Keywords:
Abstract :
References:
1. Parihar, A., et al., Role of IOT in healthcare: Applications, security & privacy concerns. Intelligent Pharmacy, 2024. 2(5): p. 707-714.
2. Anurag, et al. Pervasive health monitoring based on Internet of Things: Two case studies. in 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH). 2014.
3. Li, C., et al., A review of IoT applications in healthcare. Neurocomputing, 2024. 565: p. 127017.
4. Pourghebleh, B. and N.J. Navimipour, Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research. Journal of Network and Computer Applications, 2017. 97: p. 23-34.
5. Montori, F., et al., The Curse of Sensing: Survey of techniques and challenges to cope with sparse and dense data in mobile crowd sensing for Internet of Things. Pervasive and Mobile Computing, 2018. 49: p. 111-125.
6. Nahrstedt, K., et al. Internet of Mobile Things: Mobility-Driven Challenges, Designs and Implementations. in 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). 2016.
7. Li, S., L.D. Xu, and S. Zhao, 5G Internet of Things: A survey. Journal of Industrial Information Integration, 2018. 10: p. 1-9.
8. Hernandez-Jaimes, M.L., et al., Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud-Fog-Edge architectures. Internet of Things, 2023: p. 100887.
9. Iannacci, J., Internet of things (IoT); internet of everything (IoE); tactile internet; 5G – A (not so evanescent) unifying vision empowered by EH-MEMS (energy harvesting MEMS) and RF-MEMS (radio frequency MEMS). Sensors and Actuators A: Physical, 2018. 272: p. 187-198.
10. Hamzei, M. and N.J. Navimipour, Towards Efficient Service Composition Techniques in the Internet of Things. IEEE Internet of Things Journal, 2018: p. 1-1.
11. Ray, P.P., A survey on Internet of Things architectures. Journal of King Saud University - Computer and Information Sciences, 2018. 30(3): p. 291-319.
12. Čolaković, A. and M. Hadžialić, Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 2018. 144: p. 17-39.
13. Chiregi, M. and N. Jafari Navimipour, Cloud computing and trust evaluation: A systematic literature review of the state-of-the-art mechanisms. Journal of Electrical Systems and Information Technology, 2017.
14. Yin, Y., et al., The internet of things in healthcare: An overview. Journal of Industrial Information Integration, 2016. 1: p. 3-13.
15. Srinidhi, N.N., S.M. Dilip Kumar, and K.R. Venugopal, Network optimizations in the Internet of Things: A review. Engineering Science and Technology, an International Journal, 2018.
16. Zarpelão, B.B., et al., A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications, 2017. 84: p. 25-37.
17. Zahoor, S. and R.N. Mir, Resource management in pervasive Internet of Things: A survey. Journal of King Saud University - Computer and Information Sciences, 2018.
18. Aloi, G., et al., Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. Journal of Network and Computer Applications, 2017. 81: p. 74-84.
19. Alaba, F.A., et al., Internet of Things security: A survey. Journal of Network and Computer Applications, 2017. 88: p. 10-28.
20. da Costa, C.A., et al., Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards. Artificial Intelligence in Medicine, 2018. 89: p. 61-69.
21. Chango, W., T. Olivares, and F. oDelicado, Topologies in the Internet of Medical Things (IoMT), literature review. arXiv preprint arXiv:2404.02908, 2024.
22. Swamy s, N., An Empirical Study on System Level Aspects of Internet of Things (IoT). IEEE Access, 2020. VOLUME 8: p. 188082-188134.
23. Subramaniam, E.V.D., et al., Interoperable IoMT approach for remote diagnosis with privacy-preservation perspective in edge systems. Sensors, 2023. 23(17): p. 7474.
24. Areia, J., et al., IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things. IEEE Access, 2024.
25. Zakeri, F., M. Golsorkhtabaramiri, and M. Hosseinzadeh, Optimizing Radio Frequency Identification Networks Planning by using Particle Swarm Optimization Algorithm with Fuzzy Logic Controller and Mutation. IETE Journal of Research, 2017. 63(5): p. 728-735.
26. Elijah, O., et al., An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet of Things Journal, 2018: p. 1-1.
27. Dudhe, P.V., et al. Internet of Things (IOT): An overview and its applications. in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). 2017.
28. Rath, K.C., A. Khang, and D. Roy, The role of Internet of Things (IoT) technology in Industry 4.0 economy, in Advanced IoT technologies and applications in the industry 4.0 digital economy. 2024, CRC Press. p. 1-28.
29. Do, T.M.T. and D. Gatica-Perez, The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data. IEEE Transactions on Mobile Computing, 2014. 13(3): p. 638-648.
30. Rahmani, A.M., et al., A novel offloading strategy for multi-user optimization in blockchain-enabled Mobile Edge Computing networks for improved Internet of Things performance. Computers and Electrical Engineering, 2024. 119: p. 109514.
31. Isaacman, S., et al. Identifying Important Places in People’s Lives from Cellular Network Data. 2011. Berlin, Heidelberg: Springer Berlin Heidelberg.
32. Mohimani, G.H., et al., Mobility Modeling, Spatial Traffic Distribution, and Probability of Connectivity for Sparse and Dense Vehicular Ad Hoc Networks. IEEE Transactions on Vehicular Technology, 2009. 58(4): p. 1998-2007.
33. Calabrese, F., et al., Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 2013. 26: p. 301-313.
34. Aouedi, O., et al., A survey on intelligent Internet of Things: Applications, security, privacy, and future directions. IEEE Communications Surveys & Tutorials, 2024.
35. Ghaleb, S.M., et al., Mobility management for IoT: a survey. EURASIP Journal on Wireless Communications and Networking, 2016. 2016(1): p. 165.
36. Goudos, S.K., et al., A Survey of IoT Key Enabling and Future Technologies: 5G, Mobile IoT, Sematic Web and Applications. Wireless Personal Communications, 2017. 97(2): p. 1645-1675.
37. Moffat, S., M. Hammoudeh, and R. Hegarty, A Survey on Ciphertext-Policy Attribute-based Encryption (CP-ABE) Approaches to Data Security on Mobile Devices and its Application to IoT, in Proceedings of the International Conference on Future Networks and Distributed Systems. 2017, ACM: Cambridge, United Kingdom.
38. Juyal, S., N. Joshi, and R. Chauhan. Mobile sensing and Internet of Things: A survey. in 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT). 2017.
39. Elazhary, H., Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 2018.
40. Ma, H.-D., Internet of Things: Objectives and Scientific Challenges. Journal of Computer Science and Technology, 2011. 26(6): p. 919-924.
41. Perera, C., C.H. Liu, and S. Jayawardena, The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey. IEEE Transactions on Emerging Topics in Computing, 2015. 3(4): p. 585-598.
42. Perera, C., et al., Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 2014. 16(1): p. 414-454.
43. Adelantado, F., et al., Understanding the Limits of LoRaWAN. IEEE Communications Magazine, 2017. 55(9): p. 34-40.
44. Ullah, I., M. Sohail Khan, and D. Kim, IoT Services and Virtual Objects Management in Hyperconnected Things Network. Mobile Information Systems, 2018. 2018: p. 19.
45. Elkhodr, M., S.A. Shahrestani, and H. Cheung, Emerging Wireless Technologies in the Internet of Things: a Comparative Study. CoRR, 2016. abs/1611.00861.
46. Vandikas, K. and V. Tsiatsis. Performance Evaluation of an IoT Platform. in 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies. 2014.
47. da Silva, A.F., et al., A Cloud-based Architecture for the Internet of Things targeting Industrial Devices Remote Monitoring and Control. IFAC-PapersOnLine, 2016. 49(30): p. 108-113.
48. Al-Fuqaha, A., et al., Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 2015. 17(4): p. 2347-2376.
49. Aloi, G., et al., Software defined radar: synchronization issues and practical implementation. 2011. 48.
50. Costanzo, S., et al., Potentialities of USRP-based software defined radar systems. Vol. 53. 2013. 417-435.
51. Talavera, L.E., et al. The Mobile Hub concept: Enabling applications for the Internet of Mobile Things. in 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). 2015.
52. González, M.C., C.A. Hidalgo, and A.-L. Barabási, Understanding individual human mobility patterns. Nature, 2008. 453: p. 779.
53. Isaacman, S., et al. Identifying Important Places in People’s Lives from Cellular Network Data. in Pervasive Computing. 2011. Berlin, Heidelberg: Springer Berlin Heidelberg.
54. Lewis, P., et al., RFC6206: The Trickle Algorithm. 2011.
55. Cobârzan, C., J. Montavont, and T. Noël. Analysis and performance evaluation of RPL under mobility. in 2014 IEEE Symposium on Computers and Communications (ISCC). 2014.
56. Oliveira, L.M.L., A.F. de Sousa, and J.J.P.C. Rodrigues, Routing and mobility approaches in IPv6 over LoWPAN mesh networks. International Journal of Communication Systems, 2011. 24(11): p. 1445-1466.
57. Thubert, P., et al., RPL: IPv6 Routing Protocol for Low power and Lossy Networks. Vol. RFC 6550. 2012.
58. Iova, O., F. Theoleyre, and T. Noel, Using multiparent routing in RPL to increase the stability and the lifetime of the network. Ad Hoc Networks, 2015. 29: p. 45-62.
59. Bouaziz, M., A. Rachedi, and A. Belghith, EKF-MRPL: Advanced mobility support routing protocol for internet of mobile things: Movement prediction approach. Future Generation Computer Systems, 2017.
60. Kotenko, I., I. Saenko, and A. Branitskiy, Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning. IEEE Access, 2018. 6: p. 72714-72723.
61. Aggarwal, C., N. Ashish, and A. Sheth, The Internet of Things: A Survey from the Data-Centric Perspective. 2013. p. 383-428.
62. Charband, Y. and N. Jafari Navimipour, Knowledge sharing mechanisms in the education: A systematic review of the state of the art literature and recommendations for future research. Kybernetes, 2018. 47(7): p. 1456-1490.
63. Du, W., F. Mieyeville, and D. Navarro. Modeling Energy Consumption of Wireless Sensor Networks by SystemC. in 2010 Fifth International Conference on Systems and Networks Communications. 2010.
64. Huang, P., et al., The Evolution of MAC Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 2013. 15(1): p. 101-120.
65. Demirkol, I., C. Ersoy, and F. Alagoz, MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 2006. 44(4): p. 115-121.
66. IEEE Standard for Low-Rate Wireless Networks. IEEE Std 802.15.4-2015 (Revision of IEEE Std 802.15.4-2011), 2016: p. 1-709.
67. Ryoo, I., et al., A 3-dimensional group management MAC scheme for mobile IoT devices in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2018. 9(4): p. 1223-1234.
68. Francesco, M.D., S.K. Das, and G. Anastasi, Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey. ACM Trans. Sen. Netw., 2011. 8(1): p. 1-31.
69. Gia, T.N., et al., Fog Computing Approach for Mobility Support in Internet-of-Things Systems. IEEE Access, 2018. 6: p. 36064-36082.
70. Al-Nidawi, Y., H. Yahya, and A.H. Kemp. Impact of mobility on the IoT MAC infrastructure: IEEE 802.15.4e TSCH and LLDN platform. in 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). 2015.
71. Al-Nidawi, Y. and A.H. Kemp, Mobility Aware Framework for Timeslotted Channel Hopping IEEE 802.15.4e Sensor Networks. IEEE Sensors Journal, 2015. 15(12): p. 7112-7125.
72. Barcelo, M., et al. Novel Routing Approach for the TSCH Mode of IEEE 802.15.14e in Wireless Sensor Networks with Mobile Nodes. in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). 2014.
73. Accettura, N., et al. Performance analysis of the RPL Routing Protocol. in 2011 IEEE International Conference on Mechatronics. 2011.
74. Peng, B. and A.H. Kemp, Energy-efficient geographic routing in the presence of localization errors. Computer Networks, 2011. 55(3): p. 856-872.
75. Chowdhury, A.H., et al., Route-over vs mesh-under routing in 6LoWPAN, in Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly. 2009, ACM: Leipzig, Germany. p. 1208-1212.
76. Palattella, M.R., et al., Standardized Protocol Stack for the Internet of (Important) Things. IEEE Communications Surveys & Tutorials, 2013. 15(3): p. 1389-1406.
77. Goyal, S. and T. Chand, Improved Trickle Algorithm for Routing Protocol for Low Power and Lossy Networks. IEEE Sensors Journal, 2018. 18(5): p. 2178-2183.
78. Ko, J. and M. Chang, MoMoRo: Providing Mobility Support for Low-Power Wireless Applications. IEEE Systems Journal, 2015. 9(2): p. 585-594.
79. Fotouhi, H., D. Moreira, and M. Alves, mRPL: Boosting mobility in the Internet of Things. Ad Hoc Networks, 2015. 26: p. 17-35.
80. Fotouhi, H., et al., mRPL+: A mobility management framework in RPL/6LoWPAN. Computer Communications, 2017. 104: p. 34-54.
81. Ishino, M., Y. Koizumi, and T. Hasegawa, A Routing-Based Mobility Management Scheme for IoT Devices in Wireless Mobile Networks. IEICE Transactions on Communications, 2015. E98.B(12): p. 2376-2381.
82. Broder, A. and M. Mitzenmacher, Network Applications of Bloom Filters: A Survey. Internet Math., 2003. 1(4): p. 485-509.
83. Ancillotti, E., et al., A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management. Computer Communications, 2017. 112: p. 1-13.
84. Santos, B.P., et al., Mobile Matrix: Routing under mobility in IoT, IoMT, and Social IoT. Ad Hoc Networks, 2018. 78: p. 84-98.
85. Al-Kashoash, H.A.A., et al., Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things. Wireless Networks, 2018.
1. Parihar, A., et al., Role of IOT in healthcare: Applications, security & privacy concerns. Intelligent Pharmacy, 2024. 2(5): p. 707-714.
2. Anurag, et al. Pervasive health monitoring based on Internet of Things: Two case studies. in 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH). 2014.
3. Li, C., et al., A review of IoT applications in healthcare. Neurocomputing, 2024. 565: p. 127017.
4. Pourghebleh, B. and N.J. Navimipour, Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research. Journal of Network and Computer Applications, 2017. 97: p. 23-34.
5. Montori, F., et al., The Curse of Sensing: Survey of techniques and challenges to cope with sparse and dense data in mobile crowd sensing for Internet of Things. Pervasive and Mobile Computing, 2018. 49: p. 111-125.
6. Nahrstedt, K., et al. Internet of Mobile Things: Mobility-Driven Challenges, Designs and Implementations. in 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). 2016.
7. Li, S., L.D. Xu, and S. Zhao, 5G Internet of Things: A survey. Journal of Industrial Information Integration, 2018. 10: p. 1-9.
8. Hernandez-Jaimes, M.L., et al., Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud-Fog-Edge architectures. Internet of Things, 2023: p. 100887.
9. Iannacci, J., Internet of things (IoT); internet of everything (IoE); tactile internet; 5G – A (not so evanescent) unifying vision empowered by EH-MEMS (energy harvesting MEMS) and RF-MEMS (radio frequency MEMS). Sensors and Actuators A: Physical, 2018. 272: p. 187-198.
10. Hamzei, M. and N.J. Navimipour, Towards Efficient Service Composition Techniques in the Internet of Things. IEEE Internet of Things Journal, 2018: p. 1-1.
11. Ray, P.P., A survey on Internet of Things architectures. Journal of King Saud University - Computer and Information Sciences, 2018. 30(3): p. 291-319.
12. Čolaković, A. and M. Hadžialić, Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 2018. 144: p. 17-39.
13. Chiregi, M. and N. Jafari Navimipour, Cloud computing and trust evaluation: A systematic literature review of the state-of-the-art mechanisms. Journal of Electrical Systems and Information Technology, 2017.
14. Yin, Y., et al., The internet of things in healthcare: An overview. Journal of Industrial Information Integration, 2016. 1: p. 3-13.
15. Srinidhi, N.N., S.M. Dilip Kumar, and K.R. Venugopal, Network optimizations in the Internet of Things: A review. Engineering Science and Technology, an International Journal, 2018.
16. Zarpelão, B.B., et al., A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications, 2017. 84: p. 25-37.
17. Zahoor, S. and R.N. Mir, Resource management in pervasive Internet of Things: A survey. Journal of King Saud University - Computer and Information Sciences, 2018.
18. Aloi, G., et al., Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. Journal of Network and Computer Applications, 2017. 81: p. 74-84.
19. Alaba, F.A., et al., Internet of Things security: A survey. Journal of Network and Computer Applications, 2017. 88: p. 10-28.
20. da Costa, C.A., et al., Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards. Artificial Intelligence in Medicine, 2018. 89: p. 61-69.
21. Chango, W., T. Olivares, and F. oDelicado, Topologies in the Internet of Medical Things (IoMT), literature review. arXiv preprint arXiv:2404.02908, 2024.
22. Swamy s, N., An Empirical Study on System Level Aspects of Internet of Things (IoT). IEEE Access, 2020. VOLUME 8: p. 188082-188134.
23. Subramaniam, E.V.D., et al., Interoperable IoMT approach for remote diagnosis with privacy-preservation perspective in edge systems. Sensors, 2023. 23(17): p. 7474.
24. Areia, J., et al., IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things. IEEE Access, 2024.
25. Zakeri, F., M. Golsorkhtabaramiri, and M. Hosseinzadeh, Optimizing Radio Frequency Identification Networks Planning by using Particle Swarm Optimization Algorithm with Fuzzy Logic Controller and Mutation. IETE Journal of Research, 2017. 63(5): p. 728-735.
26. Elijah, O., et al., An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet of Things Journal, 2018: p. 1-1.
27. Dudhe, P.V., et al. Internet of Things (IOT): An overview and its applications. in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). 2017.
28. Rath, K.C., A. Khang, and D. Roy, The role of Internet of Things (IoT) technology in Industry 4.0 economy, in Advanced IoT technologies and applications in the industry 4.0 digital economy. 2024, CRC Press. p. 1-28.
29. Do, T.M.T. and D. Gatica-Perez, The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data. IEEE Transactions on Mobile Computing, 2014. 13(3): p. 638-648.
30. Rahmani, A.M., et al., A novel offloading strategy for multi-user optimization in blockchain-enabled Mobile Edge Computing networks for improved Internet of Things performance. Computers and Electrical Engineering, 2024. 119: p. 109514.
31. Isaacman, S., et al. Identifying Important Places in People’s Lives from Cellular Network Data. 2011. Berlin, Heidelberg: Springer Berlin Heidelberg.
32. Mohimani, G.H., et al., Mobility Modeling, Spatial Traffic Distribution, and Probability of Connectivity for Sparse and Dense Vehicular Ad Hoc Networks. IEEE Transactions on Vehicular Technology, 2009. 58(4): p. 1998-2007.
33. Calabrese, F., et al., Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 2013. 26: p. 301-313.
34. Aouedi, O., et al., A survey on intelligent Internet of Things: Applications, security, privacy, and future directions. IEEE Communications Surveys & Tutorials, 2024.
35. Ghaleb, S.M., et al., Mobility management for IoT: a survey. EURASIP Journal on Wireless Communications and Networking, 2016. 2016(1): p. 165.
36. Goudos, S.K., et al., A Survey of IoT Key Enabling and Future Technologies: 5G, Mobile IoT, Sematic Web and Applications. Wireless Personal Communications, 2017. 97(2): p. 1645-1675.
37. Moffat, S., M. Hammoudeh, and R. Hegarty, A Survey on Ciphertext-Policy Attribute-based Encryption (CP-ABE) Approaches to Data Security on Mobile Devices and its Application to IoT, in Proceedings of the International Conference on Future Networks and Distributed Systems. 2017, ACM: Cambridge, United Kingdom.
38. Juyal, S., N. Joshi, and R. Chauhan. Mobile sensing and Internet of Things: A survey. in 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT). 2017.
39. Elazhary, H., Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 2018.
40. Ma, H.-D., Internet of Things: Objectives and Scientific Challenges. Journal of Computer Science and Technology, 2011. 26(6): p. 919-924.
41. Perera, C., C.H. Liu, and S. Jayawardena, The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey. IEEE Transactions on Emerging Topics in Computing, 2015. 3(4): p. 585-598.
42. Perera, C., et al., Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 2014. 16(1): p. 414-454.
43. Adelantado, F., et al., Understanding the Limits of LoRaWAN. IEEE Communications Magazine, 2017. 55(9): p. 34-40.
44. Ullah, I., M. Sohail Khan, and D. Kim, IoT Services and Virtual Objects Management in Hyperconnected Things Network. Mobile Information Systems, 2018. 2018: p. 19.
45. Elkhodr, M., S.A. Shahrestani, and H. Cheung, Emerging Wireless Technologies in the Internet of Things: a Comparative Study. CoRR, 2016. abs/1611.00861.
46. Vandikas, K. and V. Tsiatsis. Performance Evaluation of an IoT Platform. in 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies. 2014.
47. da Silva, A.F., et al., A Cloud-based Architecture for the Internet of Things targeting Industrial Devices Remote Monitoring and Control. IFAC-PapersOnLine, 2016. 49(30): p. 108-113.
48. Al-Fuqaha, A., et al., Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 2015. 17(4): p. 2347-2376.
49. Aloi, G., et al., Software defined radar: synchronization issues and practical implementation. 2011. 48.
50. Costanzo, S., et al., Potentialities of USRP-based software defined radar systems. Vol. 53. 2013. 417-435.
51. Talavera, L.E., et al. The Mobile Hub concept: Enabling applications for the Internet of Mobile Things. in 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). 2015.
52. González, M.C., C.A. Hidalgo, and A.-L. Barabási, Understanding individual human mobility patterns. Nature, 2008. 453: p. 779.
53. Isaacman, S., et al. Identifying Important Places in People’s Lives from Cellular Network Data. in Pervasive Computing. 2011. Berlin, Heidelberg: Springer Berlin Heidelberg.
54. Lewis, P., et al., RFC6206: The Trickle Algorithm. 2011.
55. Cobârzan, C., J. Montavont, and T. Noël. Analysis and performance evaluation of RPL under mobility. in 2014 IEEE Symposium on Computers and Communications (ISCC). 2014.
56. Oliveira, L.M.L., A.F. de Sousa, and J.J.P.C. Rodrigues, Routing and mobility approaches in IPv6 over LoWPAN mesh networks. International Journal of Communication Systems, 2011. 24(11): p. 1445-1466.
57. Thubert, P., et al., RPL: IPv6 Routing Protocol for Low power and Lossy Networks. Vol. RFC 6550. 2012.
58. Iova, O., F. Theoleyre, and T. Noel, Using multiparent routing in RPL to increase the stability and the lifetime of the network. Ad Hoc Networks, 2015. 29: p. 45-62.
59. Bouaziz, M., A. Rachedi, and A. Belghith, EKF-MRPL: Advanced mobility support routing protocol for internet of mobile things: Movement prediction approach. Future Generation Computer Systems, 2017.
60. Kotenko, I., I. Saenko, and A. Branitskiy, Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning. IEEE Access, 2018. 6: p. 72714-72723.
61. Aggarwal, C., N. Ashish, and A. Sheth, The Internet of Things: A Survey from the Data-Centric Perspective. 2013. p. 383-428.
62. Charband, Y. and N. Jafari Navimipour, Knowledge sharing mechanisms in the education: A systematic review of the state of the art literature and recommendations for future research. Kybernetes, 2018. 47(7): p. 1456-1490.
63. Du, W., F. Mieyeville, and D. Navarro. Modeling Energy Consumption of Wireless Sensor Networks by SystemC. in 2010 Fifth International Conference on Systems and Networks Communications. 2010.
64. Huang, P., et al., The Evolution of MAC Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 2013. 15(1): p. 101-120.
65. Demirkol, I., C. Ersoy, and F. Alagoz, MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 2006. 44(4): p. 115-121.
66. IEEE Standard for Low-Rate Wireless Networks. IEEE Std 802.15.4-2015 (Revision of IEEE Std 802.15.4-2011), 2016: p. 1-709.
67. Ryoo, I., et al., A 3-dimensional group management MAC scheme for mobile IoT devices in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2018. 9(4): p. 1223-1234.
68. Francesco, M.D., S.K. Das, and G. Anastasi, Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey. ACM Trans. Sen. Netw., 2011. 8(1): p. 1-31.
69. Gia, T.N., et al., Fog Computing Approach for Mobility Support in Internet-of-Things Systems. IEEE Access, 2018. 6: p. 36064-36082.
70. Al-Nidawi, Y., H. Yahya, and A.H. Kemp. Impact of mobility on the IoT MAC infrastructure: IEEE 802.15.4e TSCH and LLDN platform. in 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). 2015.
71. Al-Nidawi, Y. and A.H. Kemp, Mobility Aware Framework for Timeslotted Channel Hopping IEEE 802.15.4e Sensor Networks. IEEE Sensors Journal, 2015. 15(12): p. 7112-7125.
72. Barcelo, M., et al. Novel Routing Approach for the TSCH Mode of IEEE 802.15.14e in Wireless Sensor Networks with Mobile Nodes. in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). 2014.
73. Accettura, N., et al. Performance analysis of the RPL Routing Protocol. in 2011 IEEE International Conference on Mechatronics. 2011.
74. Peng, B. and A.H. Kemp, Energy-efficient geographic routing in the presence of localization errors. Computer Networks, 2011. 55(3): p. 856-872.
75. Chowdhury, A.H., et al., Route-over vs mesh-under routing in 6LoWPAN, in Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly. 2009, ACM: Leipzig, Germany. p. 1208-1212.
76. Palattella, M.R., et al., Standardized Protocol Stack for the Internet of (Important) Things. IEEE Communications Surveys & Tutorials, 2013. 15(3): p. 1389-1406.
77. Goyal, S. and T. Chand, Improved Trickle Algorithm for Routing Protocol for Low Power and Lossy Networks. IEEE Sensors Journal, 2018. 18(5): p. 2178-2183.
78. Ko, J. and M. Chang, MoMoRo: Providing Mobility Support for Low-Power Wireless Applications. IEEE Systems Journal, 2015. 9(2): p. 585-594.
79. Fotouhi, H., D. Moreira, and M. Alves, mRPL: Boosting mobility in the Internet of Things. Ad Hoc Networks, 2015. 26: p. 17-35.
80. Fotouhi, H., et al., mRPL+: A mobility management framework in RPL/6LoWPAN. Computer Communications, 2017. 104: p. 34-54.
81. Ishino, M., Y. Koizumi, and T. Hasegawa, A Routing-Based Mobility Management Scheme for IoT Devices in Wireless Mobile Networks. IEICE Transactions on Communications, 2015. E98.B(12): p. 2376-2381.
82. Broder, A. and M. Mitzenmacher, Network Applications of Bloom Filters: A Survey. Internet Math., 2003. 1(4): p. 485-509.
83. Ancillotti, E., et al., A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management. Computer Communications, 2017. 112: p. 1-13.
84. Santos, B.P., et al., Mobile Matrix: Routing under mobility in IoT, IoMT, and Social IoT. Ad Hoc Networks, 2018. 78: p. 84-98.
85. Al-Kashoash, H.A.A., et al., Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things. Wireless Networks, 2018.
