Enhancing Evacuation Planning in Public Buildings: Optimising Egress Location and Protection

Effective evacuation strategies are crucial for ensuring the safety of individuals during emergencies and disasters. Despite significant progress in evacuation planning, the intricate dynamics of disaster scenarios and uncertainties inherent in such situations need to be better incorporated in planning egress locations to enhance safety in buildings. This work focuses on strategically locating egress points within public buildings, acknowledging their pivotal role in facilitating secure evacuations. Optimising egress points improves evacuation efficiency and minimises associated risks, significantly improving evacuation. This research introduces an innovative approach that integrates optimisation models, addresses decision-making complexities, explores practical applications, and considers potential attack scenarios. The study explores evacuation dynamics across diverse scenarios, elevating preparedness, and safety protocols to protect public assets and lives. Developing mixedinteger programming models establishes a foundation for optimising egress locations. MCDM is then employed, leveraging the F-AHP to address uncertainties in egress selection. Practicality is realised through integrating Revit and AnyLogic software, facilitating assessment through BIM and ABM. A stochastic BP model is formulated, addressing both Defender and Attacker perspectives for enhanced egress strategies. This model strategically allocates resources to fortify egresses, ensuring occupant safety during evacuations. Contributions further optimisation approaches, fortification strategies, and progressive enhancements in evacuation planning. These collectively address key challenges and gaps in existing literature, enhancing evacuation efficiency and public safety during emergencies. The research bridges gaps in existing approaches, providing a framework for future investigations into optimising evacuation strategies, enhanced disaster preparation, and further advancements in the field

Adaptive and Scalable Controller Placement in Software-Defined Networking

Software-defined networking (SDN) revolutionizes network control by externalizing and centralizing the control plane. A critical aspect of SDN is Controller Placement (CP), which involves identifying the ideal number and location of controllers in a network to fulfill diverse objectives such as latency constraints (node-to-controller and controller-controller delay), fault tolerance, and controller load. Existing optimization techniques like Multi-Objective Particle Swarm Optimisation (MOPSO), Adapted Non-Dominating Sorting Genetic Algorithm-III (ANSGA-III), and Non-Dominating Sorting Genetic Algorithm-II (NSGA-II) struggle with scalability (except ANSGA-III), computational complexity, and inability to predict the required number of controllers. This thesis proposes two novel approaches to address these challenges. First, an enhanced version of NSGA-III with a repair operator-based approach (referred to as ANSGA-III) is introduced, enabling efficient CP in SD-WAN by optimizing multiple conflicting objectives simultaneously. Second, a Stochastic Computational Graph Model with Ensemble Learning (SCGMEL) is developed, overcoming scalability and computational inefficiency associated with existing methods. SCGMEL employs stochastic gradient descent with momentum, a learning rate decay, a computational graph model, a weighted sum approach, and the XGBoost algorithm for optimization and machine learning. The XGBoost predicts the number of controllers needed and a supervised classification algorithm called Learning Vector Quantization (LVQ) is used to predict the optimal locations of controllers. Additionally, this research introduces the Improved Switch Migration Decision Algorithm (ISMDA) as part of the holistic contribution. ISMDA is implemented on each controller to ensure even load distribution throughout the controllers. It functions as a plug-and-play module, periodically checking if the load surpasses a certain limit. ISMDA improves controller throughput by approximately 7.4% over CAMD and roughly 1.1% over DALB. ISMDA also outperforms DALB and CAMD with a decrease of 5.7% and 1%, respectively, in terms of controller response time. Additionally, ISMDA outperforms DALB and CAMD with a decrease of 1.7% and 5.6%, respectively, in terms of the average frequency of migrations. The established framework results in fewer switch migrations during controller load imbalance. Finally, ISMDA proves more efficient than DALB and CAMD, with an estimated 1% and 6.4% lower average packet loss, respectively. This efficiency is a result of the proposed migration efficiency strategy, allowing ISMDA to handle higher loads and reject fewer packets. Real-world experiments were conducted using the Internet Zoo topology dataset to evaluate the proposed solutions. Six objective functions, including worst-case switch-to-controller delay, load balancing, reliability, average controller-to-controller latency, maximum controller-to-controller delay, and average switch-to-controller delay, were utilized for performance evaluation. Results demonstrated that ANSGA-III outperforms existing algorithms in terms of hypervolume indicator, execution time, convergence, diversity, and scalability. SCGMEL exhibited exceptional computational efficiency, surpassing ANSGA-III, NSGA-II, and MOPSO by 99.983%, 99.985%, and 99.446% respectively. The XGBoost regression model performed significantly better in predicting the number of controllers with a mean absolute error of 1.855751 compared to 3.829268, 3.729883, and 1.883536 for KNN, linear regression, and random forest, respectively. The proposed LVQ-based classification method achieved a test accuracy of 84% and accurately predicted six of the seven controller locations. To culminate, this study presents a refined and intelligent framework designed to optimize Controller Placement (CP) within the context of SD-WAN. The proposed solutions effectively tackle the shortcomings associated with existing algorithms, addressing challenges of scalability, intelligence (including the prediction of optimal controller numbers), and computational efficiency in the pursuit of simultaneous optimization of multiple conflicting objectives. The outcomes underscore the supremacy of the suggested methodologies and underscore their potential transformative influence on SDN deployments. Notably, the findings validate the efficacy of the proposed strategies, ANSGA-III and SCGMEL, in enhancing the optimization of controller placement within SD-WAN setups. The integration of the XGBoost regression model and LVQ-based classification technique yields precise predictions for both optimal controller quantities and their respective positions. Additionally, the ISMDA algorithm emerges as a pivotal enhancement, enhancing controller throughput, mitigating packet losses, and reducing switch migration frequency—collectively contributing to elevated standards in SDN deployments

A Soft Computing Based Approach for Multi-Accent Classification in IVR Systems

A speaker's accent is the most important factor affecting the performance of Natural Language Call Routing (NLCR) systems because accents vary widely, even within the same country or community. This variation also occurs when non-native speakers start to learn a second language, the substitution of native language phonology being a common process. Such substitution leads to fuzziness between the phoneme boundaries and phoneme classes, which reduces out-of-class variations, and increases the similarities between the different sets of phonemes. Thus, this fuzziness is the main cause of reduced NLCR system performance. The main requirement for commercial enterprises using an NLCR system is to have a robust NLCR system that provides call understanding and routing to appropriate destinations. The chief motivation for this present work is to develop an NLCR system that eliminates multilayered menus and employs a sophisticated speaker accent-based automated voice response system around the clock. Currently, NLCRs are not fully equipped with accent classification capability. Our main objective is to develop both speaker-independent and speaker-dependent accent classification systems that understand a caller's query, classify the caller's accent, and route the call to the acoustic model that has been thoroughly trained on a database of speech utterances recorded by such speakers. In the field of accent classification, the dominant approaches are the Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). Of the two, GMM is the most widely implemented for accent classification. However, GMM performance depends on the initial partitions and number of Gaussian mixtures, both of which can reduce performance if poorly chosen. To overcome these shortcomings, we propose a speaker-independent accent classification system based on a distance metric learning approach and evolution strategy. This approach depends on side information from dissimilar pairs of accent groups to transfer data points to a new feature space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum, respectively. Finally, a Non-dominated Sorting Evolution Strategy (NSES)-based k-means clustering algorithm is employed on the training data set processed by the distance metric learning approach. The main objectives of the NSES-based k-means approach are to find the cluster centroids as well as the optimal number of clusters for a GMM classifier. In the case of a speaker-dependent application, a new method is proposed based on the fuzzy canonical correlation analysis to find appropriate Gaussian mixtures for a GMM-based accent classification system. In our proposed method, we implement a fuzzy clustering approach to minimize the within-group sum-of-square-error and canonical correlation analysis to maximize the correlation between the speech feature vectors and cluster centroids. We conducted a number of experiments using the TIMIT database, the speech accent archive, and the foreign accent English databases for evaluating the performance of speaker-independent and speaker-dependent applications. Assessment of the applications and analysis shows that our proposed methodologies outperform the HMM, GMM, vector quantization GMM, and radial basis neural networks

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications