Recent Advances in Computer Science and Communications - Volume 15, Issue 7, 2022

The technologies involved in wireless communication networks aim to integrate smartness and automation into these networks. This survey paper presents the literature on the rising field of self-organization as a new concept applied to wireless communication networks over the last two decades. We conduct an in-depth survey of the current literature on standards as well as projects in self-organizing communication networks. Also, our objective is to show deep knowledge of this active field of research, by defining a simple taxonomy, algorithms, architectures, routing protocols, and security aspects for the design and implementation of selforganizing techniques. Also, we discuss existing solutions, compare their strength and weakness, point out assessment metrics and measurements, and mention the main research scope for subsequent evolution. This article may act as a starting point and guidance for anybody who seeks to investigate the research done on self-organization in the area of wireless communication networks.

Objective: In order to better understand the research results of AC load prediction and carry out new research, the Air Conditioning (AC) load forecasting method plays an important role in the energy consumption of AC. Methods: This paper summarizes the methods of building AC load prediction, mainly from the impact factors of AC operating load and the methods of AC system operating load forecasting to introduce the current status of load prediction. This paper describes some studies on load influencing factors, compares the advantages and disadvantages of modeling methods for AC operation load prediction and points out the research direction of AC load forecasting. Results: The current research methods are summarized and analyzed. Traditional forecasting methods are no longer applicable to air conditioning systems. From the current research, combinatorial prediction has become a hot research object. This method combines two or more methods to reduce the prediction error and shorten the prediction time. Conclusion: This paper points out some shortcomings of the present research and future research suggestions are given in the three aspects of sharing AC operation data, selecting the key factors of AC, and exploring the new methods.

Introduction: Accurate branch prediction technique has become compulsory in superscalar and deep pipeline processors. The conditional instructions can break the continuous flow of execution in the pipeline stages, thereby decreasing processor performance. Discussion: This paper highlights the concept of branch prediction, some issues and challenges, and techniques for improving processor performance. Further, this paper also presents the role of branch prediction in different processors and their features. Conclusion: The concept of the branch prediction used in parallel processors to enhance the execution speed of the conditional branch instructions and improve the processor's performance is highlighted in this paper. Further, this paper highlights the branch predictor techniques with their features and presents the challenges, issues, and future techniques related to the branch prediction.

The human visual system is encompassed with three components that help to produce a color sensation. The color display devices use this concept and create their full range of colors by incorporating all three primary color components. Any displayable color can be created with the help of these primaries. This paper explains the concept of color space, which helps to understand a particular device’s color proficiencies. Since colors are a more robust descriptor, color spaces are considered to play a significant role in representing an image appropriately. The performance of many algorithms depends on the selection of an appropriate color space. There are many kinds of color spaces that can be used in real-time applications. These are RGB, nRGB, HSV, TSL, YCbCr, YUV, YES, CIE-XYZ, CMYK, etc. In this paper, color spaces and their vast classification are described in brief. Apart from these, the characteristics, applicability, limitations, mathematical conversion, and other essential factors are also explained for each color space.

The ever-increasing dependency of the utilities on networking brought several cyber vulnerabilities and burdened them with dynamic networking demands like QoS, multihoming, and mobility. As the existing network was designed without security in context, it poses several limitations in mitigating the unwanted cyber threats and struggling to provide an integrated solution for the novel networking demands. These limitations resulted in the design and deployment of various add-on protocols that made the existing network architecture a patchy and complex network. The proposed work introduces one of the future internet architectures, which seem to provide abilities to mitigate the above limitations. Recursive internetworking architecture (RINA) is one of the future internets and appears to be a reliable solution with its promising design features. RINA extended inter-process communication to distributed inter-process communication and combined it with recursion. RINA offered unique inbuilt security and the ability to meet novel networking demands with its design. It has also provided integration methods to make use of the existing network infrastructure. The present work reviews the unique architecture, abilities, and adaptability of RINA based on various research works of RINA. The contribution of this article is to expose the potential of RINA in achieving efficient networking solutions among academia and industry.

Objective: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) in recent years has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously is putting tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. This will help in optimizing the data at the device, edge/fog level for better performance of the network in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Objective: The mobility model is the basis of simulation experiments in the Mobile Ad-hoc Network. A composite model for mobility for city scenarios which includes a realistic model of obstacle avoidance, and movement in the vertical direction, is proposed. The comparison of its performance with those of other available mobility models is encouraging. We believe that it can upgrade the routing performance. Methods: Here, we discuss the synthetic mobility models (Gauss-Markov, Random Waypoint, Manhattan Grid), and trace-based mobility models (Truncated Levy Walk, Self-Similar Least Action Walk). Then we propose a new mobility model by replacing a speed calculating formula using Bonnmotion-3.0.1 on simulator NS2. The proposed mobility model, named Enhanced Manhattan Mobility Model, is compared with the existing Manhattan Grid mobility model in a tabulated form. AODV, DSR, and DSDV are analysed for above-mentioned mobility models against the proposed one. Furthermore, the accuracy of the best protocol over the best mobility model is investigated through Packet Delivery Ratio (PDR), throughput, average end-to-end delay, packet overhead, and packet drop rate performance metrics. Result: Due to the smooth movements created by the proposed model, it shows an improvement of 1 percent to 7 percent in throughput, 0.8 percent to 1.7 percent in packet overhead, 1 percent to 7 percent in PDR, and 1 percent in dropped packets. Conclusion: It may be attempted in the future to reduce the delay and analyse parameters (load balancing and power consumption).