Cluster head selection algorithm using fuzzy logic in multi-tier Wireless Sensor Network for energy efficiency / Wan Isni Sofiah Wan Din

Energy deficiency is one of the most critical aspects of Wireless Sensor Network (WSN). The network performance can be affected when a small network grows larger, and this is related to the energy deficiency of WSN. Therefore, it is essential to manage sensor node energy efficiently, so as to ensure that it would be sufficient to complete WSN applications. Clustering is an established approach which emphasized on cluster head to prolong the lifetime of WSN. However, there is still a lack of effective techniques to determine and select the cluster head. Currently, the selection of cluster head is based on residual energy and several parameters. The data routing to the based station is solely relying on cluster head. These have resulted ineffective of energy usage of sensor node which causes restrict on a lifetime of the sensor network. Hence, this study proposes a new algorithm called Multi-Tier Protocol (MAP). MAP introduced clustering scheme to reduce the energy consumption of wireless sensor network in which, Fuzzy Logic used as tools to select the cluster head and multi-hop communication is used to route the data from the cluster head to the base station. Initially, the combinations of parameters which are residual energy, centrality and communication cost are determined for cluster head selection and utilized in MAP

Analysis of energy consumption in a wireless sensor network using fuzzy memberships function

The use of sensors in every application as well as in today's life has become a huge demand. Since that, the public sphere is starting to move to mobile applications that are easy to access. For example, child monitoring at school, health monitoring, tracking system, fire detection and so on. Thus, the Wireless Sensor Network (WSN) is the preferred choice to meet these needs. However, despite the enthusiasm of building various applications using sensors, the WSN itself is still hampered by limited battery usage. Due to some applications that require long battery life, various types of research have been conducted to address this problem. One of these is the involvement of artificial intelligent (AI) in extending the life of a battery on a sensor. Fuzzy Logic (FL) is one of the preferred AI that have been chosen by researchers to be implemented with WSN especially to protract the lifetime of WSN. In Fuzzy Logic, there are three membership functions that need to investigate the capability of it towards the WSN applications. The proposed approach is by combining different types of Fuzzy Logic membership functions which are Triangular with Gaussian, Gaussian with Trapezoidal and Trapezoidal with Triangular to get the best results for analysing the use of sensor batteries. The parameters involved for the cluster head selection are communication cost, centrality and residual energy as Fuzzy inputs. This approach will use an existing algorithm which is Multi- Tier Algorithm (MAP) and this is a part of the MAP enhancement towards the WSN lifetime. The results will compare, discuss and analyst the number of dead nodes and energy usage of the sensor node during data transmission. In conclusion, through this approach, it able to prolong the lifetime for the sensor network since the proposed technique can reduce the energy usage of the sensor nodes

Internet of Things an implementation and its challenges in Malaysia

To date, the Internet becomes one of the technologies that is rapidly evolving and changing. It has become trending over the world. The Internet of Thing is a mechanism composed of devices, sensors, networks, cloud storage, and application. Each device able to communicate with another device over the Internet to share the information and accomplished some objectives. IoT is known as one of the new future technologies and were gaining attention from various fields over the countries. Malaysia is one of the countries in the planning stage to increase the development of IoT, which is equivalent to other countries with the emerging IoT applications development. However, it was not easy to develop IoT devices due to some issues and challenges in implementing IOT devices. This paper addresses the major concern and challenges in IOT and the solution how to overcome these issues. The future trends and applications of IoT were also briefly discussed in this paper for gaining more in-depth knowledge about the IOT technology

Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun

Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF) that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of 3.0 percent increased from 82.94% to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Internet of Things: An Implementation and Its Challenges in Malaysia

to date, the Internet becomes one of the technologies that is rapidly evolving and changing. It has become trending over the world. The Internet of Thing is a mechanism composed of devices, sensors, networks, cloud storage, and application. Each device able to communicate with another device over the Internet to share the information and accomplished some objectives. IoT is known as one of the new future technologies and were gaining attention from various fields over the countries. Malaysia is one of the countries in the planning stage to increase the development of IoT, which is equivalent to other countries with the emerging IoT applications development. However, it was not easy to develop IoT devices due to some issues and challenges in implementing IOT devices. This paper addresses the major concern and challenges in IOT and the solution how to overcome these issues. The future trends and applications of IoT were also briefly discussed in this paper for gaining more in-depth knowledge about the IOT technology

Exploring Machine Learning in IoT Smart Home Automation

The Internet of Things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. These applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. “Smart Homes” has emerged as one the latest Internet of Things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. This research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail

Fuzzy-Logic-RSSI based approach for cluster heads selection in wireless sensor networks

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way in order to sense and control the surrounding environment. Several WSNs algorithms have been proposed by utilizing the Fuzzy Logic technique to select the cluster heads (CHs). Each technique employs a different combination of input parameters such as nodes density, communication cost, and residual energy. CHs determination is critical towards this goal, whereas the combination of input parameters is expected to play an important role. Nevertheless, the received signal strength (RSSI) is one of the main criteria which get little attention from researchers on the topic of CHs selection. In this study, an RSSI based scheme was proposed which utilizes Fuzzy Logic approach in order to be combined with residual energy and centrality of the fuzzy descriptor. In order to evaluate the proposed scheme, the performance Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) were compared. The simulation results show that the proposed approach has significantly prolonged the survival time of the network against SEP and MAP, while effectively decelerating the dead process of sensor nodes

The relevance of bibliometric analysis to discover the area's research efforts: Root exploit evolution

Malware steals, encrypts, and damages data of the targeted machines for private, money, or fame purposes. The types of malwares are root exploit, cryptojacking, Trojan, worms, viruses, spyware, ransomware, and adware. Among these types, root exploit is one of the most destructive malware types since it disguises and obscures all types of malwares and provides a mechanism for other malware to carry out malicious acts invisibly. To review the progress of root exploitation efforts globally, there is a need to inspect all publications that involve root exploitation. Among all malware reviews previously, to date, there is still no trace of any bibliometric analysis that demonstrates the research impacts of root exploit and trends in bibliometric analysis. Hence, this paper adopts bibliometric analysis specifically on root exploit studies which evaluate: (1) Wordcloud; (2) WordTreeMap; (3) Three fields plot; (4) Thematic evolution; (5) Thematic maps; (6) Correspondence analysis (CA); (7) Dendrogram; and (8) Multiple correspondence analysis (MCA). To conclude, our bibliometric discovers that; 1) Linux and Android become the main interest in root exploit studies. 2) Types of root exploit in the virtualization layer and studies to detect this area are increasing. 3) USA and China have become the leaders in root exploitation research. 4) Research studies are more towards memory forensics to detect root exploit, which is more promising. 5) Instead of researching new methods of root exploit in compromising victims, root exploits researchers were more focused on detecting root exploits

Taxonomy of SQL Injection: ML Trends & Open Challenges

SQL injections are a significant and ever-present threat to web applications and database security. During these attacks, malicious SQL statements are injected into input fields of data-driven systems, leading to unauthorized access and data breaches. Consequently, a need is generated to understand the nature of the attacks, detection, and effective prevention techniques. This research paper focuses on providing a taxonomy and comprehensive survey of SQL injection attacks, detection, and prevention, including their various types and techniques. Additionally, it explores the current state-of-the-art and evaluation for attacks, detection, and prevention techniques. This research paper also discusses and provides a taxonomy of current machine learning (ML) trends (Taxonomy) and their open challenges for detection purposes. Finally, this paper ends with a discussion aiming to equip system administrators, researchers, scientists and practitioners with the knowledge and strategies to mitigate the risks associated with SQL injection attacks effectively. Eventually, this will help to enhance the security and resilience of web applications and databases in the face of this significant threat

Ergonomic wireless digital notice board

This work presents the prototype of ergonomic wireless digital notice board that can be controlled by mobile application installed in Android phone. The prototype is Wi-Fi based and support long distance. Blynk application is used for the development of mobile application. The functionality and performance of the prototype is used to display messages by receiving user input texts and displays input remotely on notice board. At the end of the receiver, a low cost microcontroller board (NodeMCU) is programmed to accept and display messages on notice board. The developed system will therefore aim to share information with intended users and also to save time and cost for paper and printing equipment