Empowering smart cities: High-altitude platforms based Mobile Edge Computing and Wireless Power Transfer for efficient IoT data processing

被引:11
作者
Nauman, Ali [1 ]
Alruwais, Nuha [2 ]
Alabdulkreem, Eatedal [3 ]
Nemri, Nadhem [4 ]
Aljehane, Nojood O. [5 ]
Dutta, Ashit Kumar [6 ]
Assiri, Mohammed [7 ]
Khan, Wali Ullah [8 ]
机构
[1] Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan, South Korea
[2] King Saud Univ, Coll Appl Studies & Community Serv, Dept Comp Sci & Engn, POB 22459, Riyadh 11495, Saudi Arabia
[3] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Comp Sci, POB 84428, Riyadh 11671, Saudi Arabia
[4] King Khalid Univ, Coll Sci & Art Mahayil, Dept Informat Syst, Abha, Saudi Arabia
[5] Univ Tabuk, Fac Comp & Informat Technol, Dept Comp Sci, Tabuk, Saudi Arabia
[6] AlMaarefa Univ, Coll Appl Sci, Dept Comp Sci & Informat Syst, Riyadh 11597, Saudi Arabia
[7] Prince Sattam bin Abdulaziz Univ, Coll Sci & Humanities Aflaj, Dept Comp Sci, Aflaj 16273, Saudi Arabia
[8] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, Luxembourg City 1855, Luxembourg
关键词
Smart cities; Internet of Things (ioT); Mobile Edge Computing (MEC); High Altitude Platforms (HAPs); Wireless Power Transfer; Resource allocation; Task offloading; OPTIMIZATION; MODEL;
D O I
10.1016/j.iot.2023.100986
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work presents an efficient framework that combines High Altitude Platform (HAP)-based Mobile Edge Computing (MEC) networks with Wireless Power Transfer (WPT) to optimize resource allocation and task offloading. With the proliferation of smart sensor nodes (IoT) generating real-time data, there is a pressing need to overcome device limitations, including finite battery life and computational resources. Our proposed framework leverages HAP-based MEC servers, offering on-demand computation and communication resources without extensive physical infrastructure. Additionally, WPT, through terrestrial networks, addresses IoT device battery constraints by enabling energy harvesting from nearby access points. The primary focus is joint optimization, aiming to maximize computing bits while minimizing energy consumption under system constraints. Given the optimization problem's complexity, we employ a decomposition approach, breaking it into sub-problems. The first part handles mode selection and task segmentation, determining optimal placement and mode selection variables. The second part addresses resource allocation, optimizing transmission power, offloading time, energy harvesting time, and device computational resources. Numerical results demonstrate the framework's effectiveness compared to relevant benchmark schemes. This approach holds promise for enhancing IoT device performance and energy efficiency in smart city applications.
引用
收藏
页数:17
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