Performance evaluation of switched ethernet for real-time industrial communications
Computer Standards & Interfaces
Switched LAN simulation by colored petri nets
Mathematics and Computers in Simulation
International Journal of Communication Systems
Network partition for switched industrial Ethernet using genetic algorithm
Engineering Applications of Artificial Intelligence
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
Guaranteed real-time communication in packet-switched networks with FCFS queuing
Computer Networks: The International Journal of Computer and Telecommunications Networking
A design process of switched Ethernet architectures according to real-time application constraints
Engineering Applications of Artificial Intelligence
Network calculus: a theory of deterministic queuing systems for the internet
Network calculus: a theory of deterministic queuing systems for the internet
Journal of Network and Computer Applications
Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics
Engineering Applications of Artificial Intelligence
Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks
Engineering Applications of Artificial Intelligence
A calculus for network delay. I. Network elements in isolation
IEEE Transactions on Information Theory
Dual path communications over multiple spanning trees for networked control systems
Engineering Applications of Artificial Intelligence
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In this paper, we present an approach to evaluate end-to-end delays in packets switching networked automation systems. Since Client-Server paradigm is considered for communication between the field devices, the existing methods of network delays evaluation are hardly applicable to assess realistic upper bounds of these delays. In an effort to enhance these delays evaluation, we propose an alternative method. Two algorithms, usually used for optimization problems, exhaustive and genetic algorithms, are then developed to achieve this purpose. While a formal proof about the capacity of the former one to ensure the worst delay overestimation is given, the latter proves to provide faster and more accurate results at the same time. This is shown on a practical case study while comparing the results of the two methods.