Existing multi-path routing protocols can meet the service requirements of end-to-end delay and reliability between nodes in the Internet of Things, but they consume more energy. Constrained QoS routing is a new research method for routing protocols, which keeps up with the current trend. By considering the three QoS constraints of end-to-end delay, reliability, and energy consumption, we innovatively use the related techniques of mobile edge computing with machine learning automata to construct a sensor network as a multi-constrained optimal path model in this paper. At the same time, introducing the energy-aware node wake-up mechanism and the reward and punishment mechanism of learning automata, we propose an oriented mobile edge computing node energy-aware optimized QoS constrained routing algorithm. The algorithm can optimize the network energy consumption, extend the network life cycle, and accelerate the convergence by using learning automata. By experimental testing and comparison, the novel constrained Multi-QoS routing approach of energy-aware optimization based on learning automata for mobile edge computing (MQEN) proposed in this paper can reduce end-to-end delay, improve reliability, and decrease energy consumption.
ZhangJZhangZZhangD, et al.Novel approach of vehicular cooperative communication based on strategy of interval type-2 fuzzy logic and cooperative game. IEEE Trans Sustain Comput2025; 10: 588–600.
2.
ZhangJZhangLZhangD-G, et al.New routing method based on sticky bacteria algorithm and link stability for VANET. Ad Hoc Netw2025; 166: 103682.
3.
ZhangDGZhangZHZhangJ, et al.UAV-assisted task offloading system using dung beetle optimization algorithm and deep reinforcement learning. Ad Hoc Netw2024; 156: 103434.
4.
RimalBPMaierMSatyanarayananM. Experimental testbed for edge computing in fiber-wireless broadband access networks. IEEE Commun Mag2018; 56: 160–167.
5.
ZhangDGAnHZZhangJ, et al.Novel privacy awareness task offloading approach based on privacy entropy. IEEE Trans Netw Serv Manage2024; 21: 3598–3608.
6.
ZhangDWangWZhangJ, et al.New method of vehicular network content distribution based on edge caching and catch fish optimization strategy. IEEE Trans Reliab2025; 74: 5190–5204.
7.
ZhangDWangXSongX. A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans Serv Comput2014; 7: 741–748.
8.
ZhangDGZhangX. Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterpr Inf Syst2012; 6: 473–489.
9.
ZhangD-GZhangTZhangJ, et al.A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP J Wirel Commun Netw2018; 2018: 1–15. DOI: 10.1186/s13638-018-1176-4.
10.
ZhangD-GZhangTDongY, et al.Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning. J Netw Comput Appl2018; 122: 37–49.
11.
ZhangDGLiuSZhangT, et al.Novel unequal clustering routing protocol considering energy balancing based on network partition and distance for mobile education. J Netw Comput Appl2017; 88: 1–9.
ZhangDChenCCuiY, et al.New method of energy efficient subcarrier allocation based on evolutionary game theory. Mobile Netw Appl2021; 26: 523–536.
14.
ZhouS. A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mobile Netw Appl2018; 23: 828–839.
15.
ZhangD-GLiuSLiuX-H, et al.Novel dynamic source routing protocol (DSR) based on genetic algorithm-bacterial foraging optimization (GA-BFO). Int J Commun Syst2018: 31, 1–20.
16.
ZhangDGTangYCuiY, et al.Novel reliable routing method for engineering of internet of vehicles based on graph theory. Eng Comput2018; 36: 226–247.
17.
ZhangDGZhangTLiuX. Novel self-adaptive routing service algorithm for application in VANET. Appl Intell2019; 49: 1866–1879.
18.
LiuSZhangDGLiuX, et al.Dynamic analysis for the average shortest path length of mobile ad hoc networks under random failure scenarios. IEEE Access2019; 7: 21343–21358.
19.
ZhangDGGaoJLiuX, et al.Novel approach of distributed and adaptive trust metrics for MANET. Wirel Netw2019; 25: 3587–3603.
20.
NingJZhangCZhangB. A novel artificial bee colony algorithm for the QoS based multicast route optimization problem. Optik (Stuttg)2016; 127: 2771–2779.
21.
ZhangDGDuJZhangT, et al.New algorithm of QoS constrained routing for node energy optimization of edge computing. In: 2020 IEEE 17th International conference on mobile Ad Hoc and sensor systems (MASS), Delhi NCR, India, 10–13 December 2020, pp. 482–490. New York: IEEE.
22.
GuckJWVan BemtenAReissleinM, et al.Unicast QoS routing algorithms for SDN: a comprehensive survey and performance evaluation. IEEE Commun Surv Tutorials2018; 20: 388–415.
23.
HuibinLJunZ. Intelligent QoS routing algorithm based on improved AODV protocol for ad hoc networks. J Phys: Conf Ser2016; 710: 012007.
24.
XuXGuYLZhangYJ. A QoS anycast routing algorithm for wireless sensor networks based on krill herd optimization. Chinese J. Sensors Actuators2016; 29: 1893–1898.
25.
ZhangDZhangJNiC, et al.New method of edge computing-based data adaptive return in internet of vehicles. IEEE Trans Ind Inf2024; 20: 2042–2052.
26.
El AttarHMEl-EmaryMI. Energy, delay and hop count multi-constraints QoS routing algorithm for wireless ad-hoc networks. In: 2017 IEEE 17th international conference on communication technology (ICCT), Beijing, China, 28–30 July 2017, pp. 568–573. New York: IEEE.
27.
WenJ. A new class of ɛ-optimal learning automata. Proc Int Conf Intell Comput.2011; 6838: 116–121.
28.
ZhangDGWangWJZhangJ, et al.Novel edge caching approach based on multi-agent deep reinforcement learning for internet of vehicles. IEEE Trans Intell Transp Syst2023; 24: 8324–8338.
29.
DongW-MSunG-XCaoY-H. New computing tasks offloading method for MEC based on prospect theory framework. IEEE Trans Comput Social Sys2024; 11: 770–781.
30.
PapadimitriouGI. A new approach to the design of reinforcement schemes for learning automata: stochastic estimator learning algorithms. IEEE Trans Knowl Data Eng1994; 6: 649–654.
31.
ZhangDGWangJXZhangJ, et al.A new method of fuzzy multicriteria routing in vehicle ad-hoc network. IEEE Trans Comput Social Syst2023; 10: 3181–3193.
32.
ZhangDGeHZhangT, et al.New multi-hop clustering algorithm for vehicular ad hoc networks. IEEE Trans Intell Transp Syst2019; 20: 1517–1530.
33.
BapuBRTGowdLCS. Link quality based opportunistic routing algorithm for QOS: aware wireless sensor networks security. Wirel Pers Commun2017; 97: 1563–1578.
34.
Zhang Zhang D. UAV-Assisted Geo-Edge Routing Protocol for Internet of Vehicles Based On Reinforcement Learning Strategy. IEEE Transactions on Vehicular Technology2026; 1: 1–14. DOI: 10.1109/TVT. 2026. 3653679.
35.
ZhangDLiGZhengK, et al.An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Trans Ind Inf2014; 10: 766–773.
36.
ChenLZhangDGZhangJ, et al.A novel offloading approach of IoT user perception task based on quantum behavior particle swarm optimization. Future Gener Comput Syst2023; 141: 577–594.
37.
ZhangDGWangSZhangJ, et al.A content distribution method of internet of vehicles based on edge cache and immune cloning strategy. Ad Hoc Netw2023; 138: 103012.
38.
ZhuHLZhangT. A new method of content distribution based on fuzzy logic and coalition graph games for VEC. Cluster Comput2023; 26: 701–717.
39.
ZhangD-GZhangJZhuX-M, et al.Novel approach of computational resource allocation in fog computing based on deep reinforcement learning strategies. IEEE Internet Things J2025; 12: 43829–43841.
40.
ZhangJPiaoMZhangD, et al.An approach of multi-objective computing task offloading scheduling based NSGS for IOV in 5G. Cluster Comput2022; 25: 4203–4219.
41.
ZhangD-GNiC-HZhangJ, et al.New method of vehicle cooperative communication based on fuzzy logic and signal game strategy. Fut Gener Comp Syst2023; 142: 131–149.
42.
ZhangJZhangD-GQiaoM, et al.New offloading method of computing task based on gray wolf hunting optimization mechanism for the IOV. IEEE Trans Netw Serv Manage2025; 22: 2264–2277.
