Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

Using Travel Simulation to Investigate Driver Response to In-Vehicle Route Guidance Systems,

A major application for developed satellite navigation systems is the in-vehicle route guidance market. As systems become cheaper to purchase and easier to install and indeed car manufacturers begin to fit the equipment as standard in new vehicles, the potential market for such systems in the developed world is massive. But what are the consequences of giving navigational assistance to car drivers? How will drivers respond to this information? Such information is liable to have a big impact upon driver route choice behaviour and is also subject to their interpretation of the guidance and action upon receiving it. This response may change under different travel circumstances. The impact of collective response to driver guidance is also of importance to traffic engineers and city planners, since routing through environmentally sensitive areas or heavily congested corridors should be avoided. The overall network effects are therefore of key importance to ensure efficient routing and minimal disruption to the road network. It is quite difficult to observe real-life behaviour on a consistent basis, since there are so many confounding variables in the real-world, traffic is never the same two days running, let alone hour by hour and a rigorous experimental environment is required, since control of experimental conditions is paramount to being able to confidently predict driver behaviour in response to navigational aids. Also the take up of guidance systems is still in its infancy, so far available only to a niche market of specialist professionals and those with disposable income. A need to test the common publics’ response to route guidance systems is therefore required. The development of travel simulation techniques, using portable computers and specialist software, gives robust experimental advantages. Although not totally realistic of the driving task, these techniques are sufficient in their realism of the decision element of route selection, enough to conduct experimental studies into drivers’ route choice behaviour under conditions of receiving simulated guidance advice. In this manner driver response to in-vehicle route guidance systems can be tested under a range of hypothetical journey making travel scenarios. This paper will outline the development of travel simulation techniques as a tool for in-vehicle route guidance research, including different methods and key simulation design requirements. The second half of the paper will report in detail on the findings from a recently conducted experiment investigating drivers’ response to route guidance when in familiar and unfamiliar road networks. The results will indicate the importance of providing meaningful information to drivers under these two real-life circumstances and report on how demands for route guidance information may vary by type of journey. Findings indicate that the guidance acceptance need not only depend on the optimum route choice criteria, it is also affected by network familiarity, quality and credibility of guidance advice and personal attributes of the drivers

Using a high fidelity CCGT simulator for building prognostic systems

Pressure to reduce maintenance costs in power utilities has resulted in growing interest in prognostic monitoring systems. Accurate prediction of the occurrence of faults and failures would result not only in improved system maintenance schedules but also in improved availability and system efficiency. The desire for such a system has driven research into the emerging field of prognostics for complex systems. At the same time there is a general move towards implementing high fidelity simulators of complex systems especially within the power generation field, with the nuclear power industry taking the lead. Whilst the simulators mainly function in a training capacity, the high fidelity of the simulations can also allow representative data to be gathered. Using simulators in this way enables systems and components to be damaged, run to failure and reset all without cost or danger to personnel as well as allowing fault scenarios to be run faster than real time. Consequently, this allows failure data to be gathered which is normally otherwise unavailable or limited, enabling analysis and research of fault progression in critical and high value systems. This paper presents a case study of utilising a high fidelity industrial Combined Cycle Gas Turbine (CCGT) simulator to generate fault data, and shows how this can be employed to build a prognostic system. Advantages and disadvantages of this approach are discussed

Pressure-dependent EPANET extension

In water distribution systems (WDSs), the available flow at a demand node is dependent on the pressure at that node. When a network is lacking in pressure, not all consumer demands will be met in full. In this context, the assumption that all demands are fully satisfied regardless of the pressure in the system becomes unreasonable and represents the main limitation of the conventional demand driven analysis (DDA) approach to WDS modelling. A realistic depiction of the network performance can only be attained by considering demands to be pressure dependent. This paper presents an extension of the renowned DDA based hydraulic simulator EPANET 2 to incorporate pressure-dependent demands. This extension is termed “EPANET-PDX” (pressure-dependent extension) herein. The utilization of a continuous nodal pressure-flow function coupled with a line search and backtracking procedure greatly enhance the algorithm’s convergence rate and robustness. Simulations of real life networks consisting of multiple sources, pipes, valves and pumps were successfully executed and results are presented herein. Excellent modelling performance was achieved for analysing both normal and pressure deficient conditions of the WDSs. Detailed computational efficiency results of EPANET-PDX with reference to EPANET 2 are included as well