Zaid Hasan Nasralla

As the Internet grows in capacity, the energy consumption of Information and Communication Technologies (ICT) are significantly increasing. Significant research efforts on energy conservation have been devoted to devise different technological solutions to address raised concerns surrounding the power consumption of networking equipment and its impact firstly on the emission of greenhouse gases and secondly on electricity bills. In this work, we investigate energy-efficient physical topologies for NSF IP over wavelength-division multiplexing (WDM) network for the purpose of minimizing energy consumption by redesigning its current physical connectivity. We implement different network topologies, such as implementing the small-world, scale-free (SFN), and random networks on the NSF network, then evaluate and compare its physical properties and network power consumption with the current NSF topology design using a mixed-integer linear programming model, all with the aim of minimizing the network total power consumption. The evaluation shall optimize and minimize the embodied energy consumption of network equipment in the IP and optical layers. Results have demonstrated that the implementation of the proposed energy-minimized topology designs can significantly improve the node’s clustering coefficient, reduce network’s diameter, and reduce energy consumption of the NSF IP over WDM network to 28% if compared with the current design implementation.

Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.

A disaster may not necessarily demolish the telecommunications infrastructure, but instead it might affect the national grid and cause blackouts, consequently disrupting the network operation unless there is an alternative power source(s). In disaster-resilient networks, fiber cut, datacenter destruction, and node isolation have been studied before with different scenarios, but the power outage impact has not been investigated before. In this paper, power outages are considered, and the telecommunication network performance is evaluated during a blackout. A mixed Integer Linear Programming (MILP) model is developed to evaluate the network performance for a single node blackout under two scenarios: minimization of blocking and minimization of renewable and battery energy consumption. Insights analyzed from the MILP model results have demonstrated the trade-off between the two evaluated optimization cost functions and shown that the proposed scheme can extend the network lifetime while minimizing the required amount of backup energy.

In this paper, we present three heuristics for mitigating post-disaster traffic floods. First exploiting the excess capacity, second rerouting backup paths, finally redistributing the whole traffic by rerouting the working and protection paths to accommodate more floods. Using these mitigation approaches can reduce the blocking by up to 30%.