Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche

Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process

Security Features in Fingerprint Biometric System

Nowadays, embedded systems run in every setting all around the globe. Recent advances in technology have created many sophisticated applications rich with functionality we have never seen. Nonetheless, security and privacy were a common issue for these systems, whether or not sensitive data can be protected from malicious attacks. These concerns are justified on the grounds that the past of security breaches and the resulting consequences narrate horrific stories concerning embedded systems. The attacks are now evolving, becoming more complex with technological advancements. Therefore, a new way of implementing security in embedded systems must be pursued. This paper attempts to demonstrate the incorporation of security features in fingerprint biometric system in the requirements analysis phase, ensuring the same throughout the system life cycle of embedded systems based on case study. The comparison of various biometric technologies such as face, fingerprint, iris, palm print, hand geometry gait, signature, and keystroke is presented. The aim of this paper includes analyzing, decomposing and transforming the threats and counter-measures identified during the requirements analysis using the abuse case into more specific safety requirements or functions. Furthermore, we have shown that the incorporation of security features into the biometric fingerprint system by analyzing the requirements of the system and providing the main steps for the protection of the biometric system in this paper

System for improving sleep quality by using internet of things

Disorders pertaining to the quantification of sleep and sleeping patterns have significantly gained momentum. The result of such disorders in the worst case has led to an alarming rate of fatal deaths. There are numerous causes for sleep deprivation. One of the causes is found to be the environmental condition of the location of sleeping. The lighting, humidity and temperature of the room affects sleep. Using Internet of Things and Artificial Intelligence, the proposed system aims at tracking the sleeping patterns, finding the causes, relating them with environmental parameters, i.e., temperature, humidity and light intensity and then controlling the affecting parameters to optimize the environment for a better quality of sleep

Algorithms and methods for energy harvesting in wireless networks: a review

The element of energy harvesting in the wireless network is popular nowadays due to its effectiveness in terms of performance. The effectiveness can be seen in the way it can prolong the battery life as well as increase the performance of the network. In order to have a better understanding of how energy harvesting happens in a wireless network, this paper will present different algorithms and methods used for energy harvesting on the wireless network. The algorithms such as EHWA, SSPCA, NEEC, ANCAEE, QL-SEP and PSO are discussed and reviewed. The methods discussed and reviewed in this paper are spectrum and energy-harvesting technique, Fair Packet Ratio Distributed Computation Method, Power Feedback Control MPPT, TEH technique and Electric Field Energy Harvesting technique. The review on energy harvesting will also include simulation approaches, methods and the result of the different energy harvesting simulations. By conducting this paper review, it will help researchers to see the energy harvesting in different aspects of algorithms and methods

IoT enabled wireless health monitoring system using textile antenna

Health monitoring systems have predominantly been in the limelight in recent years. This progressive field has seen innovative approaches and breathtaking features introduced by means of the Internet of Things (IoT). This paper takes a step forward as an effort to prioritize user's comfort incorporating Wemos D1 Mini and Textile Antenna, ruling out the possibility of forgetting the wearable at home. This essentially introduces how sensors clubbed with a textile antenna could be the new face of IoT in the coming years. The health parameters, including heart rate, pulse rate and body temperature, can conveniently be accessed by the guardian through an application designed exclusively to take immediate action. The project's testing phase delivered coherent results with the textile antenna fixed on the patient's outfit. With the successful deployment, the concerned doctor could receive unhampered notifications about the patient's health condition without further ado

Non-intrusive driver drowsiness detection based on face and eye tracking

The rate of annual road accidents attributed to drowsy driving are significantly high. Due to this, researchers have proposed several methods aimed at detecting drivers' drowsiness. These methods include subjective, physiological, behavioral, vehicle-based, and hybrid methods. However, recent reports on road safety are still indicating drowsy driving as a major cause of road accidents. This is plausible because the current driver drowsiness detection (DDD) solutions are either intrusive or expensive, thus hindering their ubiquitous nature. This research serves to bridge this gap by providing a test-bed for achieving a non-intrusive and low-cost DDD solution. A behavioral DDD solution is proposed based on tracking the face and eye state of the driver. The aim is to make this research an inception to DDD pervasiveness. To achieve this, National Tsing Hua University (NTHU) Computer Vision Lab's driver drowsiness detection video dataset was utilized. Several video and image processing operations were performed on the videos so as to detect the drivers' eye state. From the eye states, three important drowsiness features were extracted: percentage of eyelid closure (PERCLOS), blink frequency (BF), and Maximum Closure Duration (MCD) of the eyes. These features were then fed as inputs into several machine learning models for drowsiness classification. Models from the K-nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Networks (ANN) machine learning algorithms were experimented. These models were evaluated by calculating their accuracy, sensitivity, specificity, miss rate, and false alarm rate values. Although these five metrics were evaluated, the focus was more on getting optimal accuracies and miss rates. The result shows that the best models were a KNN model when k = 31 and an ANN model that used an Adadelta optimizer with 3 hidden layer network of 3, 27, and 9 neurons respective. The KNN model obtained an accuracy of 72.25% with a miss rate of 16.67%, while the ANN model obtained 71.61% and 14.44% accuracy and miss rate respectively