ICT for Engineering & Critical Infrastructures

Solar lighting system offers a clean environment and therefore able to continue for a long period. In this paper presented is a neural network predictive control technique for a solar-powered wireless networked control system. Solar wireless networked control systems model is created by using back-propagation. Adaptive learning rate approach is used to identify the stochastic time delays in back-propagation neural network. The performance of the controller is based on minimisation of the mean square error. The simulation results demonstrate that the proposed design methodologies can achieve the prescribed performance requirements.

The 4th Industrial Revolution incorporates the digital revolution in several fields, including artificial intelligence, autonomous vehicles, IoT, manufacturing, etc. Due to the advancement in difficult-to-cut materials, technology like abrasive waterjet machine (AWJM) in non-traditional manufacturing has been a benefit to the industry, and it can almost cut any material and is also considered environmentally friendly. The machine uses water, abrasives and electricity which are natural resources, and the purpose of the study is to optimize these resources for the AWJM which are relatively very complex considering the different varying parameters and material properties. The methodology is discussed with a case study of NC 3015S AWJ machine for studying sustainability using different method/tools like SCLM, ABCD and TSPDs approach which will illustrate the combined technical and sustainability assessment. An approach for working with abrasive waterjet machine on reducing cost and machining time which focusses on the four pillars of sustainability (social, economic, environmental and technology) has been presented. The discussions are demonstrated by cost/hour calculations, i.e., 45$/h for machining Ti6AL4V material, and how different machine and cutting parameters affect the total process economy was understood with abrasives contributed almost 65% of the total cost. Furthermore, a cloud-based knowledge sharing model is proposed by linking the sustainability with technology and how it can benefit the current SMEs to improve their productivity with abrasive waterjet machines.

The study examines the effect of COVID-19, plunge in oil prices and stock market returns of Gulf Council Co-operation. As the demand and supply shock of oil has disrupted the global economy with the impact of COVID-19, our study attempts to trace out the relationship existence between these variables. To investigate the effect of this pandemic and the drop in oil prices and stock returns, the study employed a panel regression approach using two measurements: (1) daily growth in total confirmed cases and (2) daily growth in total deaths caused by COVID-19. There is no research available analyzing the relationship between COVID-19, oil price fluctuations and stock market index of GCC. It is this gap that the current study tries to fill by examining the effect of COVID-19, the fall of oil prices and stock market returns of GCC. The study found that there was bi-directional causality relationship between death cases as well as confirmed cases of COVID-19 to stock market returns, also it was also found that there was a uni-directional relationship of confirmed cases of COVID-19 and oil prices in world market, leading to an effect on the stock market returns, which shows that increased confirmed cases resulted to the reduced oil prices in world market affecting the investments in the stock market.

In water resources allocation problem, different stakeholders are faced with conflicting objectives and an optimal allocation of shared water resources in water transfer projects became an important task, taking into account the utilities of the different stakeholders. In Iran, there is unevenly distribution of water resources as well as increasing water demands, which have brought a need to transfer water from the Karoon river basin to the Rafsanjan basin in central Iran. Therefore, cooperative game theory can be used to analyze these problems, taking the advantage of cooperative games in maximizing the total net benefit of the shared stakeholders as well as increasing the individual shares of the allocated water to each stakeholder. In this paper, two cooperative game models are developed for modeling efficient water allocation among water stakeholders in water allocation problems in southern Iran. The main objective is to reallocate the water resources to achieve an equitable way of distributing these benefits among the water stakeholders to let them have the economic incentives for cooperation. Depending on the type of cooperation and the nature of the payoffs values, a fuzzy and a stochastic cooperative models are presented. The results showed that the models are appropriate in modeling real-world uncertain problems and efficient in allocating benefits specifically when considering the stochastic nature for payoffs.

This paper proposes embedded three pass protocol (TPP) for securing TCP streams through the worldwide interoperability microwave access (WiMAX) link. TPP messages are embedded into either WiMAX frame headers or TCP header to secure TCP connection. The embedded TPP in WiMAX MAC layer headers ensure any messages flow through WiMAX link is protected. Even though TCP packets experience average delay up to 47.8 ms, TCP streams are well protected as all signals are encrypted dynamically. On the other hand, by embedding the TPP messages into TCP header, TCP delay decreases significantly to about 29.4 ms. However, only TCP streams are protected. If existing WiMAX link security is sufficient, the later method is preferred. However, embedded TPP in TCP header has more security risks as TCP connection can be failed if other unprotected WiMAX signal is attacked and existing WiMAX security is weak. Multiple links connection prefers embedded TPP in TCP header as TCP streams are protected end to end.

Researchers attempt to develop more competent anti-ransomware tools that assisted by machine learning classifiers to mitigate the threats of ransomware, protect the privacy of cyber users, and survive the security of cyberspace. However, their achievements remain insufficient in their efficacy against zero-day variants and/or imperviously detected families of ransomware during real-time practice. This is due to their partial characterizing sets of traits, limited analysis of static and dynamic actions, lacking the semi-realistic testbeds, heavy use of computer footprints, indecisive classification margins, and unavailability of representative datasets, inactive learning mechanisms. To emphasize the causality between their contributions and shortages, this paper analyzes the applied machine learning classifier in the existing anti-ransomware tools empirically. Also, it qualifies their attributes and shortages to affirm their efficacy and sufficiency for real-time ransomware detection. Empirical outcomes yield notable facets to study in the future work that will boost up the current achievements in the anti-ransomware domain.

Increment of cybersecurity became one of the most important problems that researchers have to deal with it. However, simple passwords or smart cards are not appropriate due to their disadvantages—they are easy to forget or lose. The solution for the above-mentioned problem can be biometrics. In this case the human and their traits are the secret key. Nevertheless, it also has one main drawback that is low efficiency in data collection and preprocessing. This disadvantage can be easily removed by the use of FPGA. By this solution, not only can we speed up the processes but also create one fully automated, consistent biometrics system that will be fast and precise. Moreover, this technology can guarantee higher safety for system internal components. Performed analysis has shown that it is clearly possible to create this kind of system for human identity recognition.