Remote Sensing Study of Mobile Networks: An Assessment of Technological Challenges

Abdul Basit Butt; M Usman Tanveer; Neelam Javed; Syed Aamir Mehmood Shah; Aamir Mustafa

Authors

Abdul Basit Butt Tribe Consulting PVT (Ltd), Islamabad
M Usman Tanveer Department of Space science, Punjab university Lahore
Neelam Javed Department of Space science, Punjab university Lahore, Regional Meteorological Centre, Pakistan Meteorological Department, 46-Jail Road, Lahore
Syed Aamir Mehmood Shah Department of Space science, Punjab university Lahore, Punjab Municipal Development Fund Company Lahore
Aamir Mustafa Department of Space Science, University of the Punjab, Lahore, Pakistan

Keywords:

Advanced Technologies, Innovative Technologies, Mobile Networks

Abstract

The current era is witnessing a notable shift in the logistics and transportation sector due to the advent of Advanced Technologies (ATs). ATs, or smart technologies, encompass the use of artificial intelligence and data science methodologies, including machine learning and big data analysis, to establish cognitive comprehension and autonomous capabilities in relation to an entity. This study investigates the efficacy of remote sensing techniques in analyzing mobile network coverage for optimizing logistic applications. With the proliferation of mobile technologies, seamless connectivity has become integral for efficient logistical operations. Presently, numerous implementations of ATs have exhibited considerable potential in augmenting the efficiency and efficacy of diverse logistical operations and transportation systems. Moreover, the emergence of these innovative technologies presents significant modelling complexities for conventional optimization techniques, hence offering promising avenues for the exploration and development of novel optimization strategies within the realm of logistics and transportation research. The study aims to provide insights into areas with limited or inadequate network coverage, facilitating strategic planning for logistical operations. By integrating remote sensing findings with logistic frameworks, this research contributes to enhancing the efficiency, reliability, and responsiveness of logistical networks in regions with varying degrees of mobile network connectivity. The focus of our investigation is to thoroughly examine and engage in discourse regarding the technological challenges faced by researchers during the creation of optimization approaches as a result of the use of ATs.

References

M. M. Parast and N. Subramanian, “An examination of the effect of supply chain disruption risk drivers on organizational performance: evidence from Chinese supply chains,” Supply Chain Manag., vol. 26, no. 4, pp. 548–562, 2020, doi: 10.1108/SCM-07-2020-0313.

E. Bayraktar, M. Demirbag, S. C. L. Koh, E. Tatoglu, and H. Zaim, “A causal analysis of the impact of information systems and supply chain management practices on operational performance: Evidence from manufacturing SMEs in Turkey,” Int. J. Prod. Econ., vol. 122, no. 1, pp. 133–149, Nov. 2009, doi: 10.1016/J.IJPE.2009.05.011.

F. D. G. Solfa, “Impacts of Cyber Security and Supply Chain Risk on Digital Operations: Evidence from the Pharmaceutical Industry,” Int. J. Technol. Innov. Manag., vol. 2, no. 2, Oct. 2022, doi: 10.54489/IJTIM.V2I2.98.

I. A. Omar, R. Jayaraman, K. Salah, M. Debe, and M. Omar, “Enhancing vendor managed inventory supply chain operations using blockchain smart contracts,” IEEE Access, vol. 8, pp. 182704–182719, 2020, doi: 10.1109/ACCESS.2020.3028031.

G. Li, J. Tan, and S. S. Chaudhry, “Industry 4.0 and big data innovations,” Enterp. Inf. Syst., vol. 13, no. 2, pp. 145–147, Feb. 2019, doi: 10.1080/17517575.2018.1554190.

S. Sukathong, P. Suksawang, and T. Naenna, “Analyzing the importance of critical success factors for the adoption of advanced manufacturing technologies,” Int. J. Eng. Bus. Manag., vol. 13, 2021, doi: 10.1177/18479790211055057.

S. K. Sardar, B. Sarkar, and B. Kim, “Integrating machine learning, radio frequency identification, and consignment policy for reducing unreliability in smart supply chain management,” Processes, vol. 9, no. 2, pp. 1–16, Feb. 2021, doi: 10.3390/PR9020247.

E. Erdfelder, F. FAul, A. Buchner, and A. G. Lang, “Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses,” Behav. Res. Methods, vol. 41, no. 4, pp. 1149–1160, 2009, doi: 10.3758/BRM.41.4.1149.

L. K. Loon, Z. M. Udin, M. G. Hassan, Z. A. Bakar, and J. R. Hanaysha, “The power of organizational innovativeness in shaping supply chain operational performance,” Adv. Sci. Lett., vol. 23, no. 9, pp. 8579–8585, Sep. 2017, doi: 10.1166/ASL.2017.9933.

D. Ivanov and B. B. Keskin, “Post-pandemic adaptation and development of supply chain viability theory,” Omega (United Kingdom), vol. 116, Apr. 2023, doi: 10.1016/J.OMEGA.2022.102806.

K. Butner, “The smarter supply chain of the future,” Strateg. Leadersh., vol. 38, no. 1, pp. 22–31, Jan. 2010, doi: 10.1108/10878571011009859.

S. Gupta, V. A. Drave, S. Bag, and Z. Luo, “Leveraging Smart Supply Chain and Information System Agility for Supply Chain Flexibility,” Inf. Syst. Front., vol. 21, no. 3, pp. 547–564, Jun. 2019, doi: 10.1007/S10796-019-09901-5.

Cahyaningratri and M. Naylah, “The effect of supply chain operational capabilities in consolidating organizational compatibility of supply chain process integration and business performance,” Uncertain Supply Chain Manag., vol. 11, no. 1, pp. 95–102, Dec. 2023, doi: 10.5267/J.USCM.2022.11.006.

M. T. Alshurideh, E. K. Alquqa, H. M. Alzoubi, B. Al Kurdi, and S. Hamadneh, “The effect of information security on e-supply chain in the UAE logistics and distribution industry,” Uncertain Supply Chain Manag., vol. 11, no. 1, pp. 145–152, Dec. 2023, doi: 10.5267/J.USCM.2022.11.001.

L. Wu, X. Yue, A. Jin, and D. C. Yen, “Smart supply chain management: A review and implications for future research,” Int. J. Logist. Manag., vol. 27, no. 2, pp. 395–417, Aug. 2016, doi: 10.1108/IJLM-02-2014-0035.

A. Shahzad, A. Gherbi, and K. Zhang, “Enabling Fog–Blockchain Computing for Autonomous-Vehicle-Parking System: A Solution to Reinforce IoT–Cloud Platform for Future Smart Parking,” Sensors, vol. 22, no. 13, Jul. 2022, doi: 10.3390/s22134849.

H. M. Alzoubi, H. Elrehail, J. R. Hanaysha, A. Al-Gasaymeh, and R. Al-Adaileh, “The Role of Supply Chain Integration and Agile Practices in Improving Lead Time During the COVID-19 Crisis,” Int. J. Serv. Sci. Manag. Eng. Technol., vol. 13, no. 1, Jan. 2022, doi: 10.4018/IJSSMET.290348.

H. Mori, J. Kundaliya, K. Naik, and M. Shah, “IoT technologies in smart environment: security issues and future enhancements,” Environ. Sci. Pollut. Res., vol. 29, no. 32, pp. 47969–47987, Jul. 2022, doi: 10.1007/s11356-022-20132-1.

E. Kavaliauskaitė, “APPLICATION OF SMART TECHNOLOGY SOLUTIONS IN LOGISTICS ENTERPRISES,” Young Sci. 2023, Conf. / Jaun. Moksl. 2023, Konf., pp. 106–111, Jun. 2022, Accessed: Nov. 16, 2023. [Online]. Available: https://ejournals.vdu.lt/index.php/jm2022/article/view/3202

K. Micko, P. Papcun, and I. Zolotova, “Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure.,” Sensors (Basel)., vol. 23, no. 9, May 2023, doi: 10.3390/s23094469.

K. L. Lee, S. Y. Wong, H. M. Alzoubi, B. Al Kurdi, M. T. Alshurideh, and M. El Khatib, “Adopting smart supply chain and smart technologies to improve operational performance in manufacturing industry,” Int. J. Eng. Bus. Manag., vol. 15, Jan. 2023, doi: 10.1177/18479790231200614/ASSET/IMAGES/LARGE/10.1177_18479790231200614-FIG2.JPEG.

R. Low, Z. D. Tekler, and L. Cheah, “Predicting commercial vehicle parking duration using generative adversarial multiple imputation networks,” Transp. Res. Rec., vol. 2674, no. 9, pp. 820–831, Jul. 2020, doi: 10.1177/0361198120932166.

Y. Kandogan and S. D. Johnson, “Role of economic and political freedom in the emergence of global middle class,” Int. Bus. Rev., vol. 25, no. 3, pp. 711–725, Jun. 2016, doi: 10.1016/J.IBUSREV.2015.02.005.

A. F. AlMulhim, “Smart supply chain and firm performance: the role of digital technologies,” Bus. Process Manag. J., vol. 27, no. 5, pp. 1353–1372, 2021, doi: 10.1108/BPMJ-12-2020-0573.

Y. Song, F. R. Yu, L. Zhou, X. Yang, and Z. He, “Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey,” IEEE Internet Things J., vol. 8, no. 6, pp. 4250–4274, Mar. 2021, doi: 10.1109/JIOT.2020.3034385.

H. S. Kang et al., “Smart manufacturing: Past research, present findings, and future directions,” Int. J. Precis. Eng. Manuf. - Green Technol., vol. 3, no. 1, pp. 111–128, Jan. 2016, doi: 10.1007/S40684-016-0015-5.

M. Talal et al., “Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review,” J. Med. Syst., vol. 43, no. 3, Mar. 2019, doi: 10.1007/s10916-019-1158-z.

M. Whaiduzzaman et al., “A Review of Emerging Technologies for IoT-Based Smart Cities,” Sensors, vol. 22, no. 23, Dec. 2022, doi: 10.3390/s22239271.

Z. D. Tekler, R. Low, C. Yuen, and L. Blessing, “Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings,” Build. Environ., vol. 223, Sep. 2022, doi: 10.1016/J.BUILDENV.2022.109472.

D. Oladimeji, A. Rasheed, C. Varol, M. Baza, H. Alshahrani, and A. Baz, “CANAttack: Assessing Vulnerabilities within Controller Area Network.,” Sensors (Basel)., vol. 23, no. 19, Oct. 2023, doi: 10.3390/s23198223.