Enhanced non-parametric sequence learning scheme for internet of things sensory data in cloud infrastructure

The Internet of Things (IoT) Cloud is an emerging technology that enables machine-to-machine, human-to-machine and human-to-human interaction through the Internet. IoT sensor devices tend to generate sensory data known for their dynamic and heterogeneous nature. Hence, it makes it elusive to be managed by the sensor devices due to their limited computation power and storage space. However, the Cloud Infrastructure as a Service (IaaS) leverages the limitations of the IoT devices by making its computation power and storage resources available to execute IoT sensory data. In IoT-Cloud IaaS, resource allocation is the process of distributing optimal resources to execute data request tasks that comprise data filtering operations. Recently, machine learning, non-heuristics, multi-objective and hybrid algorithms have been applied for efficient resource allocation to execute IoT sensory data filtering request tasks in IoT-enabled Cloud IaaS. However, the filtering task is still prone to some challenges. These challenges include global search entrapment of event and error outlier detection as the dimension of the dataset increases in size, the inability of missing data recovery for effective redundant data elimination and local search entrapment that leads to unbalanced workloads on available resources required for task execution. In this thesis, the enhancement of Non-Parametric Sequence Learning (NPSL), Perceptually Important Point (PIP) and Efficient Energy Resource Ranking- Virtual Machine Selection (ERVS) algorithms were proposed. The Non-Parametric Sequence-based Agglomerative Gaussian Mixture Model (NPSAGMM) technique was initially utilized to improve the detection of event and error outliers in the global space as the dimension of the dataset increases in size. Then, Perceptually Important Points K-means-enabled Cosine and Manhattan (PIP-KCM) technique was employed to recover missing data to improve the elimination of duplicate sensed data records. Finally, an Efficient Resource Balance Ranking- based Glow-warm Swarm Optimization (ERBV-GSO) technique was used to resolve the local search entrapment for near-optimal solutions and to reduce workload imbalance on available resources for task execution in the IoT-Cloud IaaS platform. Experiments were carried out using the NetworkX simulator and the results of N-PSAGMM, PIP-KCM and ERBV-GSO techniques with N-PSL, PIP, ERVS and Resource Fragmentation Aware (RF-Aware) algorithms were compared. The experimental results showed that the proposed NPSAGMM, PIP-KCM, and ERBV-GSO techniques produced a tremendous performance improvement rate based on 3.602%/6.74% Precision, 9.724%/8.77% Recall, 5.350%/4.42% Area under Curve for the detection of event and error outliers. Furthermore, the results indicated an improvement rate of 94.273% F1-score, 0.143 Reduction Ratio, and with minimum 0.149% Root Mean Squared Error for redundant data elimination as well as the minimum number of 608 Virtual Machine migrations, 47.62% Resource Utilization and 41.13% load balancing degree for the allocation of desired resources deployed to execute sensory data filtering tasks respectively. Therefore, the proposed techniques have proven to be effective for improving the load balancing of allocating the desired resources to execute efficient outlier (Event and Error) detection and eliminate redundant data records in the IoT-based Cloud IaaS Infrastructure

Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems

Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements. This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model. The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience. Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states. Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

AI meets CRNs : a prospective review on the application of deep architectures in spectrum management

The spectrum low utilization and high demand conundrum created a bottleneck towards ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an in-depth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin

Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Efficient Design, Training, and Deployment of Artificial Neural Networks

Over the last decade, artificial neural networks, especially deep neural networks, have emerged as the main modeling tool in Machine Learning, allowing us to tackle an increasing number of real-world problems in various fields, most notably, in computer vision, natural language processing, biomedical and financial analysis. The success of deep neural networks can be attributed to many factors, namely the increasing amount of data available, the developments of dedicated hardware, the advancements in optimization techniques, and especially the invention of novel neural network architectures. Nowadays, state-of-the-arts neural networks that achieve the best performance in any field are usually formed by several layers, comprising millions, or even billions of parameters. Despite spectacular performances, optimizing a single state-of- the-arts neural network often requires a tremendous amount of computation, which can take several days using high-end hardware. More importantly, it took several years of experimentation for the community to gradually discover effective neural network architectures, moving from AlexNet, VGGNet, to ResNet, and then DenseNet. In addition to the expensive and time-consuming experimentation process, deep neural networks, which require powerful processors to operate during the deployment phase, cannot be easily deployed to mobile or embedded devices. For these reasons, improving the design, training, and deployment of deep neural networks has become an important area of research in the Machine Learning field. This thesis makes several contributions in the aforementioned research area, which can be grouped into two main categories. The first category consists of research works that focus on designing efficient neural network architectures not only in terms of accuracy but also computational complexity. In the first contribution under this category, the computational efficiency is first addressed at the filter level through the incorporation of a handcrafted design for convolutional neural networks, which are the basis of most deep neural networks. More specifically, the multilinear convolution filter is proposed to replace the linear convolution filter, which is a fundamental element in a convolutional neural network. The new filter design not only better captures multidimensional structures inherent in CNNs but also requires far fewer parameters to be estimated. While using efficient algebraic transforms and approximation techniques to tackle the design problem can significantly reduce the memory and computational footprint of neural network models, this approach requires a lot of trial and error. In addition, the simple neuron model used in most neural networks nowadays, which only performs a linear transformation followed by a nonlinear activation, cannot effectively mimic the diverse activities of biological neurons. For this reason, the second and third contributions transition from a handcrafted, manual design approach to an algorithmic approach in which the type of transformations performed by each neuron as well as the topology of neural networks are optimized in a systematic and completely data-dependent manner. As a result, the algorithms proposed in the second and third contributions are capable of designing highly accurate and compact neural networks while requiring minimal human efforts or intervention in the design process. Despite significant progress has been made to reduce the runtime complexity of neural network models on embedded devices, the majority of them have been demonstrated on powerful embedded devices, which are costly in applications that require large-scale deployment such as surveillance systems. In these scenarios, complete on-device processing solutions can be infeasible. On the contrary, hybrid solutions, where some preprocessing steps are conducted on the client side while the heavy computation takes place on the server side, are more practical. The second category of contributions made in this thesis focuses on efficient learning methodologies for hybrid solutions that take into ac- count both the signal acquisition and inference steps. More concretely, the first contribution under this category is the formulation of the Multilinear Compressive Learning framework in which multidimensional signals are compressively acquired, and inference is made based on the compressed signals, bypassing the signal reconstruction step. In the second contribution, the relationships be- tween the input signal resolution, the compression rate, and the learning performance of Multilinear Compressive Learning systems are empirically analyzed systematically, leading to the discovery of a surrogate performance indicator that can be used to approximately rank the learning performances of different sensor configurations without conducting the entire optimization process. Nowadays, many communication protocols provide support for adaptive data transmission to maximize the data throughput and minimize energy consumption depending on the network’s strength. The last contribution of this thesis proposes an extension of the Multilinear Compressive Learning framework with an adaptive compression capability, which enables us to take advantage of the adaptive rate transmission feature in existing communication protocols to maximize the informational content throughput of the whole system. Finally, all methodological contributions of this thesis are accompanied by extensive empirical analyses demonstrating their performance and computational advantages over existing methods in different computer vision applications such as object recognition, face verification, human activity classification, and visual information retrieval