Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices †
Abstract
:1. Introduction
- (1)
- We conducted simulations on a SAGIN model, encompassing the ground IoT terminal-UAV layer and the UAV-LEO satellite layer. Diverse categories of IoT devices relay collected data to LEO satellites through drone relays. Within this model, we formulated a mixed-integer multi-objective optimization problem, wherein the deployment location of drones, bandwidth allocation, and connection selection between IoT devices and drones are collectively optimized. This optimization, in turn, maximizes the minimum channel capacity of various types of IoT devices.
- (2)
- To address this non-convex problem, we propose a multi-objective evolutionary algorithm based on MOEA/D. The enhanced algorithm efficiently addresses the issue of equitable resource allocation among IoT devices of the same category within the SAGIN framework, while also exhibiting adaptability to the dynamic demand variations characteristic across various IoT device types.
- (3)
- The robustness and superiority of the proposed algorithm are substantiated. We devised various scenarios of IoT device distribution, altering the number of IoT device types and UAV relays to validate the algorithm’s robustness. Additionally, the algorithm was compared with three other classic multi-objective optimization algorithms, affirming its superiority.
2. Related Works
- (1)
- Equity in the allocation of communication resources among devices is not considered. Current methods optimize resource allocation based on the channel capacity of all IoT devices, potentially leading to substantial discrepancies in channel capacity among devices and inadequate capacities for certain IoT devices.
- (2)
- Variations in the demand for channel capacity among different device types are overlooked. As different categories of IoT devices collect diverse data types, such as video data, audio data, infrared signal data, radar signal data, etc., distinct requirements for channel capacity exist among IoT device categories.
- (3)
- The dynamic nature of channel capacity requirements for terminal equipment is not taken into account. The channel capacity needs of IoT devices are not static and may fluctuate with the rate of data collection. As demands change, new resource allocation schemes should be promptly adjusted.
3. Syetem Model and Problem Formulation
3.1. System Model
3.2. Channel Model
3.3. Problem Formulation
4. Algorithm Design
4.1. Problem Analysis
4.2. Parameter Initialization in MOEA/D-MFAR
- (1)
- Q is defined as the number of objective functions.
- (2)
- , where is the best performance value of the q-th objective function.
- (3)
- , where , and is the value of the m-th individual on the q-th objective function.
- (4)
- The target value calculation formula of the m-th sub-problem is as follows:
- (5)
- EP is defined as the set of feasible solutions eventually obtained by the algorithm, and PF is the set of target values corresponding to the solutions in EP.
4.3. Resource Adjustment Among Categories
4.3.1. Mutation in UAVs’ Positions
4.3.2. Mutation in Bandwidth Allocation
- If , the t-th breakpoint moves left:
- Otherwise, the t-th breakpoint moves right:
4.4. Resource Adjustment Within a Category
4.4.1. Fine-Tuning of UAVs’ Positions
4.4.2. Fine-Tuning of Bandwidth Allocation
4.4.3. Crossover–Mutation of UAV-IoT Device Association
Crossover
Mutation
4.5. Decay Rate Mechanism
4.6. Evaluating Solution Dominance
- (1)
- Non-inferiority: The solution is not worse than the solution in all objectives, that is, .
- (2)
- Dominance: The solution is strictly better than the solution in at least one objective, that is, .
5. Overview of Algorithm
Algorithm 1 The flow of MOEA/D-MFAR algorithm. |
|
6. Simulation Results
6.1. Experimental Settings
- NSGA-II [47]: The Non-dominated Sorting Genetic Algorithm II (NSGA-II), which includes an elite screening mechanism and a rapid non-dominated sorting technique, is utilized to ensure convergence while preserving search efficiency. This algorithm is also integrated into comparative experiments within the context of airship observation ground missions [39].
6.2. Verification of Decay Rate Mechanism
6.3. Algorithm Comparison Experiment
6.4. Sensitivity Analysis
6.5. Scenario with a Large Number of Devices
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Wei, Z.; Zhu, M.; Zhang, N.; Wang, L.; Zou, Y.; Meng, Z.; Wu, H.; Feng, Z. UAV-assisted data collection for internet of things: A survey. IEEE Internet Things J. 2022, 9, 15460–15483. [Google Scholar] [CrossRef]
- He, S.; Shi, K.; Liu, C.; Guo, B.; Chen, J.; Shi, Z. Collaborative sensing in internet of things: A comprehensive survey. IEEE Commun. Surv. Tutorials 2022, 24, 1435–1474. [Google Scholar] [CrossRef]
- Jacob, P.M.; Moni, J.; Robins, R.B.; Varghese, M.E.; Babu, S.S.; Bose, V.K. An intelligent fire detection and extinguishing assistant system using internet of things (IoT). In Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 23–25 March 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1057–1061. [Google Scholar]
- Tomar, R.; Tiwari, R.; Sarishma. Information delivery system for early forest fire detection using internet of things. In Advances in Computing and Data Sciences: Third International Conference, ICACDS 2019, Ghaziabad, India, 12–13 April 2019; Revised Selected Papers, Part I 3; Springer: Berlin/Heidelberg, Germany, 2019; pp. 477–486. [Google Scholar]
- Vo, D.T.; Nguyen, X.P.; Nguyen, T.D.; Hidayat, R.; Huynh, T.T.; Nguyen, D.T. A review on the internet of thing (IoT) technologies in controlling ocean environment. Energy Sources Part A Recover. Util. Environ. Eff. 2021, 1–19. [Google Scholar] [CrossRef]
- Qiu, T.; Zhao, Z.; Zhang, T.; Chen, C.; Chen, C.P. Underwater internet of things in smart ocean: System architecture and open issues. IEEE Trans. Ind. Inform. 2019, 16, 4297–4307. [Google Scholar] [CrossRef]
- Kao, Y.W.; Samani, H.; Tasi, S.C.; Jalaian, B.; Suri, N.; Lee, M. Intelligent search, rescue, and disaster recovery via internet of things. In Proceedings of the 2019 Global IoT Summit (GIoTS), Aarhus, Denmark, 17–21 June 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–7. [Google Scholar]
- Bianco, G.M.; Giuliano, R.; Marrocco, G.; Mazzenga, F.; Mejia-Aguilar, A. Lora system for search and rescue: Path-loss models and procedures in mountain scenarios. IEEE Internet Things J. 2020, 8, 1985–1999. [Google Scholar] [CrossRef]
- Zhang, Z.; Li, Y.; Huang, C.; Guo, Q.; Liu, L.; Yuen, C.; Guan, Y.L. User activity detection and channel estimation for grant-free random access in LEO satellite-enabled internet of things. IEEE Internet Things J. 2020, 7, 8811–8825. [Google Scholar] [CrossRef]
- Zhen, L.; Bashir, A.K.; Yu, K.; Al-Otaibi, Y.D.; Foh, C.H.; Xiao, P. Energy-efficient random access for LEO satellite-assisted 6G internet of remote things. IEEE Internet Things J. 2020, 8, 5114–5128. [Google Scholar] [CrossRef]
- Cui, H.; Zhang, J.; Geng, Y.; Xiao, Z.; Sun, T.; Zhang, N.; Liu, J.; Wu, Q.; Cao, X. Space-air-ground integrated network (SAGIN) for 6G: Requirements, architecture and challenges. China Commun. 2022, 19, 90–108. [Google Scholar] [CrossRef]
- Guo, Y.; Li, Q.; Li, Y.; Zhang, N.; Wang, S. Service coordination in the space-air-ground integrated network. IEEE Netw. 2021, 35, 168–173. [Google Scholar] [CrossRef]
- Cui, J.; Ng, S.X.; Liu, D.; Zhang, J.; Nallanathan, A.; Hanzo, L. Multiobjective optimization for integrated ground-air-space networks: Current research and future challenges. IEEE Veh. Technol. Mag. 2021, 16, 88–98. [Google Scholar] [CrossRef]
- Poudel, S.; Moh, S. Medium access control protocols for unmanned aerial vehicle-aided wireless sensor networks: A survey. IEEE Access 2019, 7, 65728–65744. [Google Scholar] [CrossRef]
- Zong, J.; Shen, C.; Cheng, J.; Gong, J.; Chang, T.H.; Chen, L.; Ai, B. Flight time minimization via UAV’s trajectory design for ground sensor data collection. In Proceedings of the 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland, 27–30 August 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 255–259. [Google Scholar]
- Wei, Z.; Feng, Z.; Zhou, H.; Wang, L.; Wu, H. Capacity and delay of unmanned aerial vehicle networks with mobility. IEEE Internet Things J. 2018, 6, 1640–1653. [Google Scholar] [CrossRef]
- Cai, Y.; Wei, Z.; Li, R.; Ng, D.W.K.; Yuan, J. Joint trajectory and resource allocation design for energy-efficient secure UAV communication systems. IEEE Trans. Commun. 2020, 68, 4536–4553. [Google Scholar] [CrossRef]
- Shi, Y.; Liu, J. Inter-segment gateway selection for transmission energy optimization in space-air-ground converged network. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas, MO, USA, 20–24 May 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar]
- Jia, Z.; Sheng, M.; Li, J.; Niyato, D.; Han, Z. LEO-satellite-assisted UAV: Joint trajectory and data collection for internet of remote things in 6G aerial access networks. IEEE Internet Things J. 2020, 8, 9814–9826. [Google Scholar] [CrossRef]
- Li, Z.; Wang, Y.; Liu, M.; Sun, R.; Chen, Y.; Yuan, J.; Li, J. Energy efficient resource allocation for UAV-assisted space-air-ground internet of remote things networks. IEEE Access 2019, 7, 145348–145362. [Google Scholar] [CrossRef]
- Shi, Y.; Xia, Y.; Gao, Y. Joint gateway selection and resource allocation for cross-tier communication in space-air-ground integrated IoT networks. IEEE Access 2020, 9, 4303–4314. [Google Scholar] [CrossRef]
- Zhao, J.; Mei, Y.; Gao, X.; Yang, J.; Shang, J. Multi-objective optimization for EE-SE tradeoff in space-air-ground internet of things networks. Electronics 2023, 12, 2585. [Google Scholar] [CrossRef]
- Li, X.; Feng, W.; Chen, Y.; Wang, C.X.; Ge, N. Maritime coverage enhancement using UAVs coordinated with hybrid satellite-terrestrial networks. IEEE Trans. Commun. 2020, 68, 2355–2369. [Google Scholar] [CrossRef]
- Sun, G.; Zheng, X.; Sun, Z.; Wu, Q.; Li, J.; Liu, Y.; Leung, V.C. UAV-enabled secure communications via collaborative beamforming with imperfect eavesdropper information. IEEE Trans. Mob. Comput. 2023, 23, 3291–3308. [Google Scholar] [CrossRef]
- Li, J.; Sun, G.; Duan, L.; Wu, Q. Multi-objective optimization for UAV swarm-assisted IoT with virtual antenna arrays. IEEE Trans. Mob. Comput. 2023, 23, 4890–4907. [Google Scholar] [CrossRef]
- Ma, T.; Zhou, H.; Qian, B.; Cheng, N.; Shen, X.; Chen, X.; Bai, B. UAV-LEO integrated backbone: A ubiquitous data collection approach for B5G internet of remote things networks. IEEE J. Sel. Areas Commun. 2021, 39, 3491–3505. [Google Scholar] [CrossRef]
- Cao, X.; Yang, B.; Yuen, C.; Han, Z. Hap-reserved communications in space-air-ground integrated networks. IEEE Trans. Veh. Technol. 2021, 70, 8286–8291. [Google Scholar] [CrossRef]
- Alsharoa, A.; Alouini, M.S. Improvement of the global connectivity using integrated satellite-airborne-terrestrial networks with resource optimization. IEEE Trans. Wirel. Commun. 2020, 19, 5088–5100. [Google Scholar] [CrossRef]
- Wang, Y.; Li, Z.; Chen, Y.; Liu, M.; Lyu, X.; Hou, X.; Wang, J. Joint resource allocation and UAV trajectory optimization for space–air–ground internet of remote things networks. IEEE Syst. J. 2020, 15, 4745–4755. [Google Scholar] [CrossRef]
- Sun, Y.; Xu, D.; Ng, D.W.K.; Dai, L.; Schober, R. Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems. IEEE Trans. Commun. 2019, 67, 4281–4298. [Google Scholar] [CrossRef]
- Ji, J.; Zhu, K.; Niyato, D.; Wang, R. Joint cache placement, flight trajectory, and transmission power optimization for multi-UAV assisted wireless networks. IEEE Trans. Wirel. Commun. 2020, 19, 5389–5403. [Google Scholar] [CrossRef]
- Gong, M.; Wang, Z.; Zhu, Z.; Jiao, L. A similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication system. IEEE Trans. Evol. Comput. 2017, 21, 878–897. [Google Scholar] [CrossRef]
- Yao, Y.; Dong, D.; Huang, S.; Pan, C.; Chen, S.; Li, X. Optimization of the internet of remote things data acquisition based on satellite UAV integrated network. China Commun. 2023, 20, 15–28. [Google Scholar] [CrossRef]
- Chen, J.; Xu, Y.; Sun, W.; Huang, L. Joint sparse neural network compression via multi-application multi-objective optimization. Appl. Intell. 2021, 51, 7837–7854. [Google Scholar] [CrossRef]
- Chen, Y.; Lin, Q.; Wei, W.; Ji, J.; Wong, K.C.; Coello, C.A.C. Intrusion detection using multi-objective evolutionary convolutional neural network for internet of things in fog computing. Knowl.-Based Syst. 2022, 244, 108505. [Google Scholar] [CrossRef]
- Shao, W.; Shao, Z.; Pi, D. An ant colony optimization behavior-based MOEA/D for distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs. IEEE Trans. Autom. Sci. Eng. 2021, 19, 3379–3394. [Google Scholar] [CrossRef]
- Zhang, H.; Yue, D.; Yue, W.; Li, K.; Yin, M. MOEA/D-based probabilistic PBI approach for risk-based optimal operation of hybrid energy system with intermittent power uncertainty. IEEE Trans. Syst. Man, Cybern. Syst. 2019, 51, 2080–2090. [Google Scholar] [CrossRef]
- Xu, X.; Zhang, X.; Liu, X.; Jiang, J.; Qi, L.; Bhuiyan, M.Z.A. Adaptive computation offloading with edge for 5G-envisioned internet of connected vehicles. IEEE Trans. Intell. Transp. Syst. 2020, 22, 5213–5222. [Google Scholar] [CrossRef]
- Xu, Z.; Liu, J.; Qiao, B.; Cao, Y. MOEA/D using dynamic weight vectors and stable matching schemes for the deployment of multiple airships in the earth observing system. In Proceedings of the 2021 IEEE Congress on Evolutionary Computation (CEC), Krakow, Poland, 28 June–1 July 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 177–184. [Google Scholar]
- Zhang, Q.; Li, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 2007, 11, 712–731. [Google Scholar] [CrossRef]
- Jing, Y.; Yang, Z.; Zhou, P.; Wang, Y.; Yan, H. Cooperative deployment multi-objective optimization approach for high-resolution multi-airship earth-observation coverage network. IEEE Trans. Netw. Sci. Eng. 2023, 10, 3435–3449. [Google Scholar] [CrossRef]
- Wang, J.; Jiang, C.; Wei, Z.; Pan, C.; Zhang, H.; Ren, Y. Joint UAV hovering altitude and power control for space-air-ground IoT networks. IEEE Internet Things J. 2018, 6, 1741–1753. [Google Scholar] [CrossRef]
- Jiang, H.; Xiao, Z.; Li, Z.; Xu, J.; Zeng, F.; Wang, D. An energy-efficient framework for internet of things underlaying heterogeneous small cell networks. IEEE Trans. Mob. Comput. 2020, 21, 31–43. [Google Scholar] [CrossRef]
- Wu, Q.; Zeng, Y.; Zhang, R. Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Trans. Wirel. Commun. 2018, 17, 2109–2121. [Google Scholar]
- Wang, J.; Jiang, C.; Zhang, H.; Ren, Y.; Chen, K.C.; Hanzo, L. Thirty years of machine learning: The road to Pareto-optimal wireless networks. IEEE Commun. Surv. Tutorials 2020, 13, 1472–1514. [Google Scholar] [CrossRef]
- Xu, Y.; Tang, X.; Deng, X.; Xie, X.; Ning, Q.; Huang, L. Multi-objective Resource Allocation across Multiple IoT Device Categories in Space-Air-Ground Integrated Network. In Proceedings of the 2023 9th International Conference on Computer and Communications (ICCC), Chengdu, China, 8–11 December 2023; pp. 260–265. [Google Scholar]
- Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6, 182–197. [Google Scholar] [CrossRef]
- Coello, C.C.; Lechuga, M.S. MoPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, HI, USA, 12–17 May 2002. CEC’02 (Cat. No. 02TH8600). [Google Scholar]
- Guan, S.; Wang, J.; Jiang, C.; Hou, X.; Fang, Z.; Ren, Y. Efficient on-demand UAV deployment and configuration for off-shore relay communications. In Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), Harbin, China, 28 June–2 July 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 997–1002. [Google Scholar]
Related Work | Scenario | Method | Objectives | Drawbacks |
---|---|---|---|---|
Ref. [26] | The UAV-LEO integrated data collection scenario in the B5G IoRT networks. | Successive convex approximation and block coordinate descent techniques. | The weighted function of the total data uploaded by UAVs, energy consumption, and the minimum channel capacity among IoT devices. | 1. Ignoring the differences in device requirements. 2. Lacking the ability to quickly respond to changes in demand. |
Ref. [27] | HAP-reserved communications in SAGIN. | Decompose into two sub-problems and solve them using convex optimization and linear programming, respectively. | Maximize total system throughput. | 1. Ignoring the differences in device requirements. 2. Lacking the ability to quickly respond to changes in demand. |
Ref. [28] | Integrated satellite–airborne–terrestrial network for downlink communication. | Fix HAP position, solve binary linear optimization with Taylor expansion, then optimize HAP location via recursive contraction and relocation. | Maximize the downlink channel capacity for users. | 1. Ignoring the differences in device requirements. 2. Lacking the ability to quickly respond to changes in demand. 3. Ignoring fairness among similar users. |
Ref. [29] | UAV-based SAG-IoRT uplink channel transmission. | Joint iterative optimization using LP, variable transformation, continuous convex, and block coordinate descent. | Maximize the system’s channel capacity. | 1. Ignoring the differences in device requirements. 2. Lacking the ability to quickly respond to changes in demand. 3. Ignoring fairness among similar devices. |
Ref. [30] | UAVs provide sustainable communication services for ground devices. | Low-complexity iterative suboptimal algorithm based on continuous convex approximation. | Maximize the system’s channel capacity. | 1. Ignoring the differences in device requirements. 2. Lacking the ability to quickly respond to changes in demand. 3. Ignoring fairness among similar devices. |
Ref. [31] | UAVs provide sustainable communication services for ground devices. | Iterative joint optimization method based on continuous convex optimization and block coordinate descent algorithm. | Maximize the minimum throughput of users. | 1. Ignoring the differences in device requirements. 2. Lacking the ability to quickly respond to changes in demand. |
Ref. [32] | SAGIN base on airship for ground signal coverage. | Multi-objective optimization algorithm-based MOEA/D. | Maximize coverage range and network speed. | 1. Ignoring fairness among similar users. |
Symbol | Description |
---|---|
The number of types of IoT devices, the t-th category of IoT devices. | |
The number of the t-th category of IoT devices, the total number of IoT devices, the number of UAVs. | |
Coordinate of the n-th device in , coordinate of the n-th UAV. | |
B | The total available bandwidth of the system. |
The set of bandwidth ratios occupied by different types of IoT devices, the set of the bandwidth ratios occupied by each device in the t-th category of IoT devices, the set of for each category of IoT devices. | |
The proportion of total bandwidth occupied by the t-th category of IoT devices, the proportion of bandwidth occupied by the n-th device in the t-th category of IoT devices. | |
The set of UAV serial numbers selected by each device in the t-th category of IoT devices, the set of for each category of IoT devices. | |
The UAV sequence number selected by the n-th device in the t-th-category IoT devices, the number of IoT devices associated with the UAV selected by the n-th device in the t-th-category IoT devices. | |
Signal transmission power of IoT devices, total forwarding power per UAV. | |
The three-dimensional coordinate of an LEO satellite. | |
The fixed height at which the drone flies, the altitude of the LEO satellite. | |
The channel gain per unit distance. | |
The channel gain between the n-th device in and the selected UAV, the channel gain between the UAV selected by the n-th device in and LEO satellite. | |
The signal-to-noise ratio between the n-th device in and the selected UAV, the signal-to-noise ratio between the UAV selected by the n-th device in and LEO satellite. | |
The power spectral density of additive white Gaussian noise. | |
The bandwidth occupied by the n-th device in , the forwarding power obtained by the n-th device in from the selected UAV. | |
The channel rate of the n-th device in forwarded to the LEO satellite via a UAV. |
Scenario | Number of Types of IoT Devices | Number of UAV | Distribution Characteristics of IoT Devices | The Respective Quantities of Different Types of IoT Devices |
---|---|---|---|---|
1 | 2 | 1 | sparse | 10, 10 |
2 | 2 | 3 | sparse | 10, 10 |
3 | 2 | 1 | dense | 10, 6 |
4 | 2 | 3 | dense | 10, 6 |
5 | 3 | 1 | sparse | 10, 10, 10 |
6 | 3 | 3 | sparse | 10, 10, 10 |
7 | 3 | 1 | dense | 10, 8, 6 |
8 | 3 | 3 | dense | 10, 8, 6 |
Parameter | Value |
---|---|
Height of UAV and LEO satellite | 100 m, 1000 km |
System bandwidth B | 1 MHz |
Channel gain per unit distance | |
Total transmission power of UAV | 50 W |
Transmission power of IoT device | 1 W |
Noise power spectral density | −174 dBm/Hz |
Population size M | 100 for scenarios 1,2,3,4 |
120 for scenarios 5,6,7,8 | |
Iteration steps E | 100 |
Neighborhood Size S | 5 |
0.1, 0.5, 0.5, 0.5 | |
Base of decay rate b | 0.8 for scenarios 1,3,5,7 |
0.7 for scenarios 2,4,6,8 | |
0.5, 0.5, 0.1 |
Algorithm | MOEA/D-MFAR | MOEA/D-MFAR-ndr | MOEA/D | NSGA-II | MOPSO | MOEA/D-MFAR | MOEA/D-MFAR-ndr | MOEA/D | NSGA-II | MOPSO |
---|---|---|---|---|---|---|---|---|---|---|
Scenario | Scenario 1 | Scenario 2 | ||||||||
HV | ||||||||||
Number of solution | 4290 | 113 | 197 | 165 | 26 | 977 | 102 | 134 | 64 | 39 |
Scenario | Scenario 3 | Scenario 4 | ||||||||
HV | ||||||||||
Number of solution | 4620 | 106 | 252 | 97 | 44 | 346 | 80 | 166 | 77 | 24 |
Scenario | Scenario 5 | Scenario 6 | ||||||||
HV | ||||||||||
Number of solution | 3639 | 383 | 679 | 642 | 113 | 3908 | 380 | 729 | 661 | 83 |
Scenario | Scenario 7 | Scenario 8 | ||||||||
HV | ||||||||||
Number of solution | 3612 | 370 | 888 | 509 | 56 | 2731 | 431 | 747 | 584 | 23 |
ASDR (kb/s) | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | Scenario 7 | Scenario 8 |
---|---|---|---|---|---|---|---|---|
MOEA/D-MFAR | 0.00472 | 4.88970 | 0.00638 | 6.06020 | 0.78157 | 3.81755 | 0.55242 | 4.39425 |
MOEA/D-MFAR-ndr | 6.71880 | 21.21205 | 15.34665 | 24.34125 | 24.09956 | 32.22496 | 32.87763 | 53.12022 |
MOEA/D | 37.91355 | 48.36469 | 27.09743 | 45.89225 | 63.04678 | 74.17693 | 54.01430 | 78.72201 |
NSGA-II | 36.54003 | 105.26025 | 33.97831 | 89.76500 | 94.81778 | 112.85205 | 114.71659 | 154.59454 |
MOPSO | 164.07773 | 140.23824 | 169.52251 | 189.42786 | 89.40870 | 163.86959 | 148.69868 | 173.26047 |
Number of IoT Devices per Category | 10 | 30 | 50 | 70 | 100 | 200 | 300 | 400 | 1000 |
---|---|---|---|---|---|---|---|---|---|
ADSR(kb/s) scenario A | 0.03453 | 0.00859 | 0.00519 | 0.00388 | 0.00318 | 0.00199 | 0.73537 | 1.86991 | 2.28121 |
ADSR(kb/s) scenario B | 1.47435 | 3.94150 | 1.15200 | 1.44833 | 3.28922 | 4.67727 | 5.10086 | 3.94150 | 2.08684 |
Number of UAV | 1 | 2 | 3 | 5 | 10 | 15 | 20 | 25 |
---|---|---|---|---|---|---|---|---|
ADSR(kb/s) scenario C | 0.00859 | 2.29062 | 1.74026 | 0.96567 | 1.63528 | 2.24172 | 2.57945 | 2.30610 |
ADSR(kb/s) scenario D | 3.94150 | 3.35178 | 1.67385 | 1.82613 | 4.42636 | 6.20919 | 6.19233 | 9.56433 |
Iterations | 10 | 20 | 30 | 40 | 50 |
---|---|---|---|---|---|
ADSR(kb/s) | 13.81754 | 7.44094 | 5.24779 | 4.38385 | 2.54977 |
Number of solutions | 915 | 1811 | 2876 | 4021 | 5044 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xu, Y.; Tang, X.; Huang, L.; Ullah, H.; Ning, Q. Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices. Sensors 2025, 25, 274. https://doi.org/10.3390/s25010274
Xu Y, Tang X, Huang L, Ullah H, Ning Q. Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices. Sensors. 2025; 25(1):274. https://doi.org/10.3390/s25010274
Chicago/Turabian StyleXu, Yongnan, Xiangrong Tang, Linyu Huang, Hamid Ullah, and Qian Ning. 2025. "Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices" Sensors 25, no. 1: 274. https://doi.org/10.3390/s25010274
APA StyleXu, Y., Tang, X., Huang, L., Ullah, H., & Ning, Q. (2025). Multi-Objective Optimization for Resource Allocation in Space–Air–Ground Network with Diverse IoT Devices. Sensors, 25(1), 274. https://doi.org/10.3390/s25010274