Jul 2026· International Journal of Communication Systems· Vol 39· 0 citations· 45 references
TL;DR
This paper modeled route planning for Audio–Video Bridging (AVB) streams is modeled as a multi‐criteria decision making problem for TSN with mixed criticality support and a K‐shortest paths based heuristic approach is proposed to solve this problem.
Abstract
The effective management of physical processes in cyber physical systems such as in‐vehicle platforms and industrial automation necessitates a deterministic real‐time communication. Although traditional time‐sensitive networks provide low‐delay data transmissions, there is a need for the real‐time variants of IEEE 802.3 Ethernet to satisfy the low‐cost and high‐bandwidth requirements. IEEE 802.1 Time‐Sensitive Networking (TSN) task group is the leading organization that aims to standardize Ethernet‐based deterministic communication technologies. In this paper, route planning for Audio–Video Bridging (AVB) streams is modeled as a multi‐criteria decision making problem for TSN with mixed criticality support and a K‐shortest paths based heuristic approach is proposed to solve this problem. Our heuristic is integrated into metaheuristic approaches relying on Greedy Randomized Adaptive Search Procedure to construct initial solutions, as generating good‐quality initial solutions can improve the performance of a metaheuristic by increasing the convergence speed and accuracy of the solution. Experimental results relying on realistic automotive and Industry 4.0 use cases show that our approach significantly improves the schedulability of AVB streams in presence of time‐triggered traffic and features a better computational efficiency compared with the other approaches in the literature.
The proposed SPN model enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment, and enables the identification of communication bottlenecks without requiring physical deployment.
José Miquéias Araújo, L. Lopes, Luiz Nelson Lima et al.· Journal of Internet Services...· 0 citations
Dynamic heterogeneous vehicle routing with time windows requires reoptimization whenever customer arrivals and network disruptions change the decision space and the set of feasible routes. A reoptimized plan is useful in practice only if it does not rewrite the schedule that crews are already executing. This paper presents DNSGA-II-ALNS, an epoch-based dynamic multi-objective evolutionary algorithm. It couples an event-conditioned warm-start projection with an exact marginal assignment cost, feasibility-aware destroy-and-repair search and NSGA-II selection. The projection is not claimed to be a new optimization paradigm: it is a deterministic map between consecutive decision spaces that is defined even when a customer arrival changes their dimension, that introduces no constraint violation, and that leaves every customer unaffected by the event on its current vehicle. The algorithm is compared with seven alternatives under a paired protocol. All 56 Solomon instances are used with ten independent runs, and every method sees the same stored event stream for a given instance and run, a population of 50 and 8000 objective evaluations per epoch. The main empirical finding concerns plan stability. DNSGA-II-ALNS reassigns 11.1% of the persisting customers after an event, whereas a cold restart reassigns 89.2%, and the two groups do not overlap on any of the 56 instances. The reduction is not accompanied by a loss of solution quality, since the eight methods differ by at most 2.6% in total distance and 0.6% in makespan and all of them serve every customer within its time window. In front quality, the algorithm is not separated from the best-ranked method by the applied tests: it obtains the second-best Friedman mean rank (3.000 against 2.446), and the difference is smaller than the Nemenyi critical difference of 1.403. Advantages over the cold restart, MOPSO-ALNS and a memory-MOEA/D control are statistically significant with rank-biserial effect sizes of 0.84–0.87, while the comparisons with MODE-ALNS and the memory-NSGA-II control are not significant. An ablation indicates that the destroy-and-repair operators govern front quality, exceeding a routing-specific genetic control by 6.8–8.7% and a generic real-coded control by 15.9–26.4%. Quality is retained up to 200 customers at a fixed fleet density, but the mean response time rises from 33 to 716 s per epoch, which limits applicability to real-time dispatching. The contribution is accordingly operational rather than a new optimization methodology: comparable front quality at an order of magnitude less plan churn.
Jia-Hao Tian, Min Zou, Zhi-Fei Li et al.· Applied Sciences· 0 citations
Wireless sensor network deployment is a fundamental design problem because the physical placement of relay nodes directly affects network connectivity, communication reliability, and end‐to‐end delay. Although many deployment studies focus on coverage, connectivity, and deployment cost, delay‐related quality‐of‐service (QoS) requirements are often addressed mainly at the routing or protocol stage after the network topology has been fixed. This paper investigates QoS‐aware relay node deployment by incorporating probabilistic link reliability, retransmission‐induced expected delay, and deadline satisfaction into the deployment optimization stage. The relay deployment problem is formulated as a constrained multiobjective optimization problem that minimizes deployment cost while maximizing the deadline satisfaction ratio (DSR). Rather than replacing the classical NSGA‐II algorithm, this work develops a QoS‐aware constrained deployment framework tailored to relay placement in wireless sensor networks. NSGA‐II is used as the multiobjective search backbone, and problem‐specific sequential repair mechanisms are embedded into the search process. Structural feasibility repair first enforces coverage and connectivity requirements, while QoS‐oriented delay repair then refines deadline‐violating communication paths. Simulation experiments are conducted under uniform, clustered, and obstacle‐based deployment scenarios. The proposed framework is compared with greedy relay placement, standard NSGA‐II, MOEA/D, and MOPSO. Ablation, scalability, and parameter sensitivity analyses are also performed to examine the contribution of the repair modules and the robustness of the framework. The results show that the repair‐guided framework improves deadline satisfaction and Pareto solution quality while maintaining structural feasibility under constrained deployment settings. These findings suggest that deployment‐stage QoS optimization is useful for latency‐sensitive WSN applications such as industrial monitoring, disaster surveillance, and mission‐critical IoT.
YongGuk So, KangSa Yun· International Journal of Com...· 0 citations
Results show that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks, particularly in dense deployments.
V. Nam, A. Chehri, Weiwei Jiang et al.· Expert Syst. J. Knowl. Eng.· 0 citations
A Centre of Mass‐Based Sorting algorithm (CMBS) is proposed that divides a network into multiple sub‐networks and deploys one controller in each sub‐network. CMBS has been derived by minimizing the average distance of the network nodes. The performance of the CMBS method has been evaluated on real‐world network topologies and compared with two state‐of‐the‐art approaches, such as Particle Swarm Optimization (PSO) and Density‐Based Controller Placement (DBCP). Experimental results show that CMBS requires fewer controllers and the lowest average Switch‐to‐Controller (S‐C) latency for a given network compared to others. It is shown that CMBS is not only able to minimize the (S‐C) latency but is also beneficial to reduce the system cost. The proposed method is a cost‐effective solution with the lowest average (S‐C) latency for the controller placement problem in Software‐Defined Networks compared to (PSO and DBCP).
Mili Dhar, B. Bhattacharyya· Concurrency and Computation· 0 citations
Recently, in‐network computing has been proposed as a new paradigm to integrate computation and communication at the network edge, enabling data to be processed while being transmitted toward its destination. To achieve this, the problem of joint optimization of computation path selection and workload allocation has gained extensive research interest, and many efficient algorithms have been proposed. However, existing studies assume fixed or static latency when selecting computation paths, and thus fail to capture the time‐varying and queueing‐induced latency experienced by tasks arriving dynamically in online environments. To address the above limitations, this paper investigates the online scheduling problem of time‐dependent path selection and workload allocation for in‐network computing. We formulate the problem as a mixed‐integer programming model that jointly minimizes server operating cost and end‐to‐end latency, and prove that it is NP‐hard. To enable efficient online decision‐making, we further transform the problem into a Markov decision process. Based on this formulation, we propose a reinforcement learning–based scheduling algorithm using proximal policy optimization (PPO), which incorporates an LSTM‐based temporal encoder to capture historical deployment information and dynamically evolving network states. This design allows the algorithm to make adaptive, queueing‐aware scheduling decisions in dynamic network environments. Extensive simulations on real networks demonstrate that the proposed algorithm is robust and outperforms state‐of‐the‐art baseline methods in terms of the joint cost–latency performance metric.
Junyu Mai, Tian Yang, Ouyang Sheng et al.· Concurrency and Computation· 0 citations
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