302 research outputs found
Modelling of reliable service based operations support system (MORSBOSS)
Philosophiae Doctor - PhDThe underlying theme of this thesis is identification, classification, detection and prediction of cellular network faults using state of the art technologies, methods and algorithms
Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
The advancements in electronic devices have increased the demand for the internet of
things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected
electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The
advancements in smart grid technologies have enabled to monitor every moment of energy consumption
in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary
buildings. Due to smart cities and smart homes’ growth rates, the demand for efficient resource management
is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that,
scientists and researchers are working on optimizing energy usage, especially in smart cities, besides
providing a comfortable environment. The central focus of this paper is on energy consumption optimization
in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used
three parameters, i.e., Temperature (◦F), illumination (lx), and CO2 (ppm). The major problem with the
previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when
they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter
has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data.
We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat
algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted
user parameters have improved the system’s overall performance in terms of ease of use of smart systems,
energy consumption, and comfort index management. The comfort index after optimization remained near
to 1, which proves the significance of the system. After optimization, the power consumption also reduced
and stayed around the maximum of 15-18w
On the role of metaheuristic optimization in bioinformatics
Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics
An improved dynamic load balancing for virtualmachines in cloud computing using hybrid bat and bee colony algorithms
Cloud technology is a utility where different hardware and software resources are
accessed on pay-per-user ground base. Most of these resources are available
in virtualized form and virtual machine (VM) is one of the main elements
of visualization. In virtualization, a physical server changes into the virtual machine
(VM) and acts as a physical server. Due to the large number of users sometimes the
task sent by the user to cloud causes the VM to be under loaded or overloaded. This
system state happens due to poor task allocation process in VM and causes the
system failure or user tasks delayed. For the improvement of task allocation, several
load balancing techniques are introduced in a cloud but stills the system failure
occurs. Therefore, to overcome these problems, this study proposed an improved
dynamic load balancing technique known as HBAC algorithm which dynamically
allocates task by hybridizing Artificial Bee Colony (ABC) algorithm with Bat
algorithm. The proposed HBAC algorithm was tested and compared with other stateof-the-art
algorithms on 200 to 2000 even tasks by using CloudSim on standard
workload format (SWF) data sets file size (200kb and 400kb). The proposed HBAC
showed an improved accuracy rate in task distribution and reduced the makespan of
VM in a cloud data center. Based on the ANOVA comparison test results, a 1.25
percent improvement on accuracy and 0.98 percent reduced makespan on task
allocation system of VM in cloud computing is observed with the proposed HBAC
algorithm
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Capacity Enhancement Approaches for Long Term Evolution networks: Capacity Enhancement-Inspired Self-Organized Networking to Enhance Capacity and Fairness of Traffic in Long Term Evolution Networks by Utilising Dynamic Mobile Base-Stations
The long-term evolution (LTE) network has been proposed to provide better network capacity than the earlier 3G network. Driven by the market, the conventional LTE (3G) network standard could not achieve the expectations of the international mobile telecommunications advanced (IMT-Advanced) standard. To satisfy this gap, the LTE-Advanced was introduced with additional network functionalities to meet up with the IMT-Advanced Standard. In addition, due to the need to minimize operational expenditure (OPEX) and reduce human interventions, the wireless cellular networks are required to be self-aware, self-reconfigurable, self-adaptive and smart. An example of such network involves transceiver base stations (BTSs) within a self-organizing network (SON).
Besides these great breakthroughs, the conventional LTE and LTE-Advanced networks have not been designed with the intelligence of scalable capacity output especially in sudden demographic changes, namely during events of football, malls, worship centres or during religious and cultural festivals. Since most of these events cannot be predicted, modern cellular networks must be scalable in terms of capacity and coverage in such unpredictable demographic surge. Thus, the use of dynamic BTSs is proposed to be used in modern and future cellular networks for crowd and demographic change managements.
Dynamic BTSs are complements of the capability of SONs to search, determine and deploy less crowded/idle BTSs to densely crowded cells for scalable capacity management. The mobile BTSs will discover areas of dark coverages and fill-up the gap in terms of providing cellular services. The proposed network relieves the LTE network from overloading thus reducing packet loss, delay and improves fair load sharing.
In order to trail the best (least) path, a bio-inspired optimization algorithm based on swarm-particle optimization is proposed over the dynamic BTS network. It uses the ant-colony optimization algorithm (ACOA) to find the least path. A comparison between an optimized path and the un-optimized path showed huge gain in terms of delay, fair load sharing and the percentage of packet loss
Swarm Intelligence
Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence
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