Analysis of Data Aggregation Techniques of IOT

The internet of things is the self configuring network in which sensor nodes can join or leave the network when they want. The security, data aggregation are the two major issues of IOT. In the previous years various techniques are designed to improve data aggregation rate of IOT. The clustering is the technique which can increase data aggregation rate and reduce lifetime of the network. In this paper, various techniques are reviewed to improve lifetime of the network and increase data aggregation rate

Design and Implementation of an Innovative Internet of Things (IOT) based Smart Energy Meter

Energy meter is very essential measuring instrument for measuring the power in domestic, industrial etc. environment. Correct and appropriate measuring of power without any error is important in order to calculate the total power consumption and then for tariff calculation. In view of this, in this paper design and implementation on an innovative smart energy meter is proposed. The proposed smart energy meter is based on Internet of Things (IoT) applications. The paper describes its design along with its working

Internet of Things (IoT): Research, Architectures and Applications

Internet of Things is the concept of connecting any device (so long as it has an on/off switch) to the Internet and to other connected devices. The IoT is a giant network of connected things and people, all of which collect and share data about the way they are used and about the environment around them. Experts estimate that the IoT will consist of about 30 billion objects by 2020. This paper presents a study based on IoT and its applications in different field of science and technology. Along with the introduction of the IoT literature review is also provided. The paper also discusses the architecture and elements of the IoT along with its different applications

A smart campus design: data-driven and evidence-based decision support solution design

The growth and the availability of the smart devices is becoming ubiquitous today and inter-networking of these devices make up what is commonly called the Internet of Things (IoT). IoT is being used to update, enhance, simplify and automate individual lives and communities. Most of the cities in general and universities in special are adopting IoT technologies in order to create a smart sustainable living and working environments. Based on the existing literature of smart campus domain, it can be observed that there is only a small number of models as such. This study attempts to bridge the following knowledge gaps of smart campus domain. This project falls into the concept of Smart Campus and aims to design a Smart Campus solution for Staffordshire University. The primarily goal is to design a solution architecture able to collect data from remote sensor networks and analyse them with the support of data analytics and machine learning techniques for sound business decision making. The project has two stages. The first stage is the business side of the project where a business requirement study has been done to extract the exact business requirements and once this complete the second stage was the technical implementation of one or many requirements and evaluation of the solution. The scope of this paper limits to the first stage of the project. A quantitative approach was chosen by considering the nature of this study. A self-administered online questionnaire was developed around several key challenges and directed especially to the staff members, in order to identify what are the expectations of university staff in relation to thematic topics. Subsequently, business requirements under each key challenge were ranked based on MoSCoW prioritisation method. Energy management, space utilisation and occupancy, cleanliness recognition, smarter car parking, internet enabled café, network and physical security and environment (temperature) control are the key business challenges identified. Moreover, intended system qualities and specific project benefits were also identified to scope the project well

Internet of Things (IoT): The most up-to-date challenges, architectures, emerging trends and potential opportunities

Internet of Things (IoT) is nowadays the most profound buzzword in Information Technology science. IoT is the evolution of Information Technology which aims to build a mutual infrastructure that integrates, connects and telecommunicates every ‘Things’ (Objects) with each other on the face of the earth. This interconnected infrastructure provides humans with fully control of things. Projections and potential estimates about incomings of IoT are spectacular for future directions. IoT encompasses such a wide range of spectrum that its influences are anyone’s guess. Not only the profits of IoT but also the forfeits and fears of IoT are unpredictable for yet. However, with the increase in research, academic studies and technological developments the atmosphere will be clear for IoT. This research paper proposes a novel comprehensive reference source for those who are interested in IoT, ubiquitous sensing, pervasive computing and smart objects. The paper explains IoT emergence and IoT history in detail, current IoT usage areas, the most up-to-date potential opportunities and future IoT directions, overall IoT architecture and well-liked architectures, security and privacy concerns about IoT. The latest ongoing IoT projects are discussed and the latest burning issues are presented. Critical and turning points of IoT are given in tabular. As compared to similar survey papers in the area, to the best of our knowledge contributions of this paper are unique

Network Intrusion Detection Using Autoencode Neural Network

In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and identifying anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to create effective intrusion detection models that can distinguish between benign and malicious network behavior.The paper provides an in-depth analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also explores various data preprocessing techniques and feature engineering approaches to enhance the model's performance. Additionally, the research evaluates the robustness and scalability of autoencoder-based NIDS in real-world network environments. Furthermore, ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are discussed. It addresses the need for a balanced approach that ensures network security while respecting user privacy and minimizing disruptions. operation. This approach compresses the majority samples & increases the minority sample count in tough samples so that the IDS can achieve greater classification accuracy

Benefits and Challenges of Internet of Things for Telecommunication Networks

Recently, Internet of things (IoTs) has become the main issue in designing monitoring systems such as smart environments, smart cars, and smart wearable devices. IoTs has transformed the life of people to be more adaptable and intelligent. For example, in a healthcare monitoring system, using smart devices will improve the performance of doctors, nurses, patients, and the healthcare industry. The IoTs revolution is known as the fourth industrial revolution and would change the way humans interact with machines and lead the way to a high-technology machine-to-machine interaction. In fact, almost every device around us would be connected to Internet, collecting and exchanging data with other devices on the cloud. In this chapter, we will introduce the benefits of IoTs on telecommunication networks and its challenges to give a complete overview for researchers to know how to improve our life and society by building smart IoTs systems

Estimation computational models of the cyber-physical systems functioning

This paper reviews the use of computational models to support the functioning of cyber-physical systems (CPS) in the parallel world of the Internet of Things (IoT). Existing models, methods, techniques and their implementation in this direction are studied. The necessity of using machine learning methods due to inaccuracy, fuzziness, incompleteness of the transmitted data from sensors of physical systems is substantiated. The task is to make informed decisions in a timely manner to support the functioning of real objects of a particular cyber-physical system in real time conditions

APLIKASI DAN ANALISA SISTEM KOMUNIKASI PUBLISH-SUBSCRIBE PADA SISTEM MONITORING RADIASI DAN LINGKUNGAN

APLIKASI DAN ANALISA SISTEM KOMUNIKASI PUBLISH-SUBSCRIBE PADA SISTEM MONITORING RADIASI DAN LINGKUNGAN Sebagai salah satu bagian dari RAMONA (Radiation and Meteorological Monitoring Analysis System), telah dirancang sebuah sistem komunikasi antara alat sensor Ultrasonic Maretron WS0100 yang terhubung dengan komputer client dengan server Sistem Pemantauan Lingkungan Kawasan Nuklir, RAMONA. Sensor Ultrasonic ini terhubung dengan alat lainnya melalui antar muka NMEA2000 (National Maritime Electrical Association), yang merupakan standard komunikasi yang biasa menghubungkan antara sensor-sensor di kapal-kapal laut dengan tampilannya. Dalam alat ini terdapat beberapa sensor yakni sensor tekanan, arah angin, kecepatan angin dan suhu udara. Sistem komunikasi yang digunakan berbasis Message Queeing Telemetry Transport (MQTT). Prinsip dari sistem MQTT ini menggunakan publish/subscribe protocol, dimana Client, dalam hal ini Maretron, akan mengirim (publish) data-datanya ke data bus, yang kemudian di ambil oleh computer (baik itu client maupun server) yang sudah ‘berlanggganan’ (subscribe) jenis-jenis data dengan format tertentu yang diperlukannya. Format data yang digunakan dalam sistem komunikasi ini adalah format JSON (JavaScript Object Notation), yang kemudian diambil oleh server untuk kemudian hasilnya di simpan ke dalam database ataupun di tampilkan di website bagi pengguna maupun admin dari sistem ini. Program untuk kedua sistem client dan server telah berhasil ditulis, dan menghasilkan komunikasi data yang lanca