A Secure Framework for IoT Smart Home by Resolving Session Hijacking

IoT is a blessing in the field of information and technology. It is developing and deploying day by day. It is working for our betterment in the section of home, environment, retail, security, factory, industry, agriculture, education, energy, healthcare, and so on. In the Smart Home section, there are a numerous inventions. Vast analysis and working can be possible if needed. We have worked with session hijacking and implement it in our Smart Home Prototype. This paper represents the basic concept of IoT in Smart Home with Security like Session Hijacking

A smart fire detection system using iot technology with automatic water sprinkler

House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable

An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture

The usage Internet of Things (IoT) was maximized throughout the entire world. Hence, the different core processors incorporated microarchitecture makes this IoT communication system. However, the rise of faults due to the malicious event and the data overload might maximize energy and power utilization. So, the current study has proposed a novel Chimp-based Domain adaptation Alert System (CbDAAS) for the dynamic IoT communication microarchitecture. Before initiating the communication sharing process, the present fault in the designed IoT dynamic core microarchitecture was predicted, and those cores were removed for the current data broadcasting process. Henceforth, the designed fault alert microarchitecture is tested in the MATLAB platform. The reliability was valued using different metrics like power usage, energy consumption and detection exactness value. Finally, the validated metrics were compared with the associated studies and scored the finest outcome in fault detection score as 98% and less energy usage at 0.025mj

IoT Networks: Using Machine Learning Algorithm for Service Denial Detection in Constrained Application Protocol

The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed

Design and Implementation of a Low-Cost Cloud-Powered Home Automation System

synergy of these technologies such as intelligent lighting, entertainment (audio and video), security, heating, ventilation and air conditioning in the home for the automation of control and monitoring activities within that home is typically known as Home Automation today. The Home global home automation market size was approximated to be about USD 45.8 billion in just 2017 and is projected to reach as high as USD 114 Billion at the tail of 2025 with a compound annual growth rate of 12.1% in this forecast period [1]. North America is projected to emerge as the leading region in the global landscape during the forecast period, but West African countries such as Nigeria, Ghana, Cameroon are generally seen to have a much lower adoption rate when it comes to emerging technologies like these. The low adoption rate is due in part to the high cost of implementing a home Automation solution coupled with the general economic state of these developing countries. The interest and investment of the tech industry within the countries also play a role in this context. This paper explores the design and implementation of a relatively less costly cloud-based home automation architecture built partly on open-source technology and widely available resources. The solution was realized using a raspberry pi as the field gateway and primary controller. Arduino Uno microcontroller were used as the secondary controller. The highly robust Microsoft Azure cloud was used, enabling the representation, testing, deploying, and managing applications and systems and services on the cloud and at the edge. In demonstrating the feasibility of the proposed system, three systems were integrated: intelligent lighting, basic access security and remote monitoring. These would be monitored and controlled by a simple mobile app whose communication with the field devices is made possible through the Microsoft Azure IoT solution

Smart Monitoring and Controlling of Appliances using LoRa Based IoT System

In the era of Industry 4.0, remote monitoring and controlling appliance/equipment at home, institute, or industry from a long distance with low power consumption remains challenging. At present, some smart phones are being actively used to control appliances at home or institute using Internet of Things (IoT ) systems. This paper presents a novel smart automa-tion system using long range (LoRa) technology. The proposed LoRa based system consists of wireless communication system and different types of sensors, operated by a smart phone ap-plication and powered by a low-power battery, with an operating range of 3–12 km distance. The system established a connection between an android phone and a microprocessor (ESP32) through Wi-Fi at the sender end. The ESP32 module was connected to a LoRa module. At the re-ceiver end, an ESP32 module and LoRa module without Wi-Fi was employed. Wide Area Net-work (WAN ) communication protocol was used on the LoRa module to provide switching functionality of the targeted area. The performance of the system was evaluated by three real-life case studies through measuring environmental temperature and humidity, detecting fire, and controlling the switching functionality of appliances. Obtaining correct environmental data, fire detection with 90% accuracy, and switching functionality with 92.33% accuracy at a distance up to 12 km demonstrated the high performance of the system. The proposed smart system with modular design proved to be highly effective in controlling and monitoring home appliances from a longer distance with relatively lower power consumption