Exhaustive Search for Optimal Offline Spectrum Assignment in Elastic Optical Networks

Heuristic-based approaches are widely deployed in solving Spectrum Assignment problem. This causes the results to be unreliable in some test cases when the results are very far from the lowerbound. This paper presents an exhaustive search approach that starts with an initial seed of a solution achieved by a heuristic-based algorithm called “Longest First Fit” (LFF) and tries all possible permutations starting from this initial seed. The algorithm skips some branches and all its descendant permutations if it meets certain criteria that guarantees that those permutations will not lead to a better result. The experimental results show that the new algorithm has succeeded in achieving the lower-bound in 93% of the randomly generated test cases while the heuristic solver LFF can achieve 65%. The algorithm achieves these results in a reasonable running time of less than 10 seconds

Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)

AbstractInternet of Things (IoT) is set to revolutionize all aspects of our lives. The number of objects connected to IoT is expected to reach 50 billion by 2020, giving rise to an enormous amounts of valuable data. The data collected from the IoT devices will be used to understand and control complex environments around us, enabling better decision making, greater automation, higher efficiencies, productivity, accuracy, and wealth generation. Data mining and other artificial intelligence methods would play a critical role in creating smarter IoTs, albeit with many challenges. In this paper, we examine the applicability of eight well-known data mining algorithms for IoT data. These include, among others, the deep learning artificial neural networks (DLANNs), which build a feed forward multi-layer artificial neural network (ANN) for modelling high-level data abstractions. Our preliminary results on three real IoT datasets show that C4.5 and C5.0 have better accuracy, are memory efficient and have relatively higher processing speeds. ANNs and DLANNs can provide highly accurate results but are computationally expensive

IP/MPLS over OTN over DWDM multilayer networks: optimization models, algorithms, and analyses

Dissertation advisor: Deep MedhiVitaIncludes bibliographical references (p. 151-155)Thesis (Ph.D)--School of Computing and Engineering. University of Missouri--Kansas City, 2011Title from PDF of title page, viewed on May 24, 2011Over the past decade, multilayer network design has received significant attention in the scientific literature. However, the explicit modeling of IP/MPLS over OTN over DWDM in which the OTN layer is specifically considered has not been addressed before. This multilayer network architecture has been identified as promising that bridges integration and interaction between the IP and optical layers. In this dissertation, we consider four related problems. First, we present an integrated capacity network optimization model for the operational planning of such multilayer networks. The model considers the OTN layer as a distinct layer with its unique technological ODU sublayer constraints. Secondly, we present a design model to investigate the correlation effects of the IP and OTN layers when the physical DWDM layer capacity is a given constant. We also develop a heuristic algorithm to solve the models for large networks. We provide comprehensive numeric studies that consider various cost parameter values of each layer in the network and analyze the impact of varying the values on network layers and overall network cost. We have observed the significant impact of the IP/MPLS capacity module on each layer and the entire network. Generally, when this parameter size is above the average demand in the network, it leads to the best overall network design. Thirdly, we consider the problem of optimizing node capacity in this architecture as our design goal, since routers with more capacity and complex structure consume significant power. We present an explicit networking optimization model that aims to minimize the total capacity at the LSRs and the OXCs. Our assessment shows that the different weight ratios of LSR to OXC nodes do not generally affect the overall required capacity of each layer. However, the weight ratios influence differently required node capacity at nodes in each layer. Finally, we factor in the survivability of the multilayer network by considering a suitable protection mechanism for each network layer. We provide a phase-based heuristic approach, study and analysis. We have also examined the network performance from cost vs. protection capacity perspectives while varying the size of the IP/MPLS capacity module.Introduction -- Literature survey -- OTN technology overview -- An integrated capacity optimization model -- A heuristic approach to solve (P1) -- Study and results for (P1) -- IP/MPLS and OTN layer correlation effects -- Study and results for (P2) -- Optimizing node capacity -- Study and results of (P3) -- Multilayer network protection -- Study and results for (P4) -- conclusion and future work -- Appendix A. Sample input/output file

AI explainability and governance in smart energy systems: A review

Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems

Sustainable Participatory Governance: Data-Driven Discovery of Parameters for Planning Online and In-Class Education in Saudi Arabia During COVID-19

Everything about our life is complex. It should not be so. New approaches to governance are needed to tackle these complexities and the rising global challenges. Smartization of cities and societies has the potential to unite us, humans, on a sustainable future for us through its focus on the triple bottom line (TBL) – social, environmental, and economic sustainability. Data-driven analytics are at the heart of this smartization. This study provides a case study on sustainable participatory governance using a data-driven parameter discovery for planning online, in-class, and blended learning in Saudi Arabia evidenced during the COVID-19 pandemic. For this purpose, we developed a software tool comprising a complete machine learning pipeline and used a dataset comprising around 2 million tweets in the Arabic language collected during a period of over 14 months (October 2020 to December 2021). We discovered fourteen governance parameters grouped into four governance macro parameters. These discovered parameters by the tool demonstrate the possibility and benefits of our sustainable participatory planning and governance approach, allowing the discovery and grasp of important dimensions of the education sector in Saudi Arabia, the complexity of the policy, the procedural and practical issues in continuing learning during the pandemic, the factors that have contributed to the success of teaching and learning during the pandemic times, both its transition to online learning and its return to in-class learning, the challenges public and government have faced related to learning during the pandemic times, and the new opportunities for social, economical, and environmental benefits that can be drawn out of the situation created by the pandemic. The parameters and information learned through the tool can allow governments to have a participatory approach to governance and improve their policies, procedures, and practices, perpetually through public and stakeholder feedback. The data-driven parameter discovery approach we propose is generic and can be applied to the governance of any sector. The specific case study is used to elaborate on the proposed approach

Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

Saudi-KAU Coupled Global Climate Model: Description and Performance

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