IoT-based intelligent irrigation management and monitoring system using arduino

Plants, flowers and crops are living things around us that makes our earth more productive and beautiful. In order to growth healthy, they need water, light and nutrition from the soil in order to effect cleaning air naturally and produce oxygen to the world. Therefore, a technology that manage to brilliantly control plants watering rate according to its soil moisture and user requirement is proposed in this paper. The developed system included an Internet of Things (IoT) in Wireless Sensor Network (WSN) environment where it manages and monitors the irrigation system either manually or automatically, depending on the user requirement. This proposed system applied Arduino technology and NRF24L01 as the microprocessor and transceiver for the communication channel, respectively. Smart agriculture and smart lifestyle can be developed by implementing this technology for the future work. It will save the budget for hiring employees and prevent from water wastage in daily necessities

Forest Fire Detection Using New Routing Protocol

The Mobile Ad-Hoc Network (MANET) has received significant interest from researchers for several applications. In spite of developing and proposing numerous routing protocols for MANET, there are still routing protocols that are too inefficient in terms of sending data and energy consumption, which limits the lifetime of the network for forest fire monitoring. Therefore, this paper presents the development of a Location Aided Routing (LAR) protocol in forest fire detection. The new routing protocol is named the LAR-Based Reliable Routing Protocol (LARRR), which is used to detect a forest fire based on three criteria: the route length between nodes, the temperature sensing, and the number of packets within node buffers (i.e., route busyness). The performance of the LARRR protocol is evaluated by using widely known evaluation measurements, which are the Packet Delivery Ratio (PDR), Energy Consumption (EC), End-to-End Delay (E2E Delay), and Routing Overhead (RO). The simulation results show that the proposed LARRR protocol achieves 70% PDR, 403 joules of EC, 2.733 s of E2E delay, and 43.04 RO. In addition, the performance of the proposed LARRR protocol outperforms its competitors and is able to detect forest fires efficiently

Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows

Imperialist competitive algorithm for increasing the lifetime of wireless sensor network

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper presents an energy-efficient cluster head selection algorithm for the clustering of heterogeneous WSN, inspired by Imperialist Competitive Algorithm (ICA). In order to reduce the network energy consumption and subsequently increases the sensor network lifetime, the clustering problem is transformed into an optimization problem and the specific cost function is used to select the cluster heads in a way that the energy utilization of the network is optimized. Extensive simulation works are done based on MATLAB to test the algorithm in various network scenarios, with different network sizes and number of nodes. Simulation results have shown that the proposed algorithm is able to extend the network lifetime compared to its comparative by up to 154 percent in terms of first node death. Furthermore, choosing the optimum set of cluster heads at every round has proved that our proposed algorithm not only could reduce the network energy consumption, but also improves the total data delivery at the base station up to 59 percent compared to the well-known algorithm

Eco-friendly smart renewable microgrid system

Smart grid is a backbone for power supply system. Thus, an eco-friendly smart microgrid is developed which able to benefit the community. Smart microgrid increase integration of usage of renewable energy where it leans towards greener energy and healthier environment. The most common renewable energy source used in this smart microgrid is solar but since it is weather dependent sources, it will not be used during rainy weather. Therefore, rainwater harvesting system is implemented in this project by using micro turbine hydro-generator. A decision making algorithm is developed in this system using C++ so that it can effectively decide selected optimum power supply to the smart microgrid. Users also able to access their load usage data and power supplied data from application in their smartphones. This is by applying Internet of Things (IoT) communication where it is leading to a new level of lifestyle and environment. In this paper, a total eco-friendly condition is achieved in condition 1: hot weather at case 2: 2pm and condition 2: rainy weather at case 2: 2pm

Industry revolution 4.0 knowledge assessment in Malaysia

The impacts of the Fourth Industrial Revolution (IR4.0) on our life is well noted. The future job market landscape will drastically change and needs to be addressed by the Higher Education Institutes (HEIs) for graduate employability. This paper presents engineering education for IR4.0 in the context of Malaysia. The initiatives taken by the Ministry of Education Malaysia (MoEM) in addressing the challenges of IR4.0 for the higher education sector is discussed. This work assessed the IR4.0 knowledge amongst industries, the adoption, the readiness and the skill sets required for future-ready engineers. This is followed by an overview on the approach taken by one of the public universities in Malaysia in addressing the needs of IR4.0 through its undergraduate engineering programmes

Link capacity based channel assignment (LCCA) for cognitive radio wireless mesh networks

Cognitive radio wireless mesh network (CRWMN) is expected as an upcoming technology with the potential advantages of both cognitive radio (CR) and the wireless mesh networks (WMN). In CRWMN, co-channel interference is one of the key limiting factors that affect the reception capabilities of the client and reduce the achievable transmission rate. Furthermore, it increases the frame loss rate and results in underutilization of resources. To maximize the performance of such networks, interference related issues need to be considered. Channel assignment (CA) is one of the key techniques to overcome the performance degradation of a network caused by the interferences. To counter the interference issues, we propose a novel CA technique which is based on link capacity, primary user activity and secondary user activity. These three parameters are fed to the proposed weightage decision engine to get the weight for each of the stated parameters. Thus, the link capacity based channel assignment (LCCA) algorithm is based on the weightage decision engine. The end-to-end delay, packet delivery ratio and the throughput is used to estimate the performance of the proposed algorithm. The numerical results demonstrate that the proposed algorithm is closer to the optimum resource utilizatio

Forest Fire Detection Using New Routing Protocol

The Mobile Ad-Hoc Network (MANET) has received significant interest from researchers for several applications. In spite of developing and proposing numerous routing protocols for MANET, there are still routing protocols that are too inefficient in terms of sending data and energy consumption, which limits the lifetime of the network for forest fire monitoring. Therefore, this paper presents the development of a Location Aided Routing (LAR) protocol in forest fire detection. The new routing protocol is named the LAR-Based Reliable Routing Protocol (LARRR), which is used to detect a forest fire based on three criteria: the route length between nodes, the temperature sensing, and the number of packets within node buffers (i.e., route busyness). The performance of the LARRR protocol is evaluated by using widely known evaluation measurements, which are the Packet Delivery Ratio (PDR), Energy Consumption (EC), End-to-End Delay (E2E Delay), and Routing Overhead (RO). The simulation results show that the proposed LARRR protocol achieves 70% PDR, 403 joules of EC, 2.733 s of E2E delay, and 43.04 RO. In addition, the performance of the proposed LARRR protocol outperforms its competitors and is able to detect forest fires efficientl

Voice pathology detection using machine learning technique

Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively