جستجوی مقالات مرتبط با کلیدواژه « Internet of Things » در نشریات گروه « علوم پایه »

This study investigates how the current academic literature discusses Wireless Sensor Network (WSN) applications in agriculture. The WSN is widely used to build decision support systems to overcome many problems in the real world. Using the basic principles of Internet and WSN technology, precision agriculture systems based on the Internet of Things (IoT) technology are explained in detail, especially on the hardware architecture, network architecture, and software process control of the precision agriculture system. The software monitors data from the wireless sensors, but implementing a WSN will optimize the usage of water fertilizers and maximize crop yield. Nowadays, the climatic conditions are not the same and predictable. There are many ways to cultivate healthy crops in a year. But it requires a lot of human resources, which is a burden nowadays. We are designing a WSN  for smart agriculture to make it smart and straightforward and give correct input to the corp.

Environmentally sustainable metropolitan environments are characterized by their ability to effectively produce and distribute power while reducing their impact on the environment. Smart homes are essential in smart cities since they enhance sustainability and efficiency in urban settings. A key advantage of smart homes is their capacity to diminish energy use and carbon emissions. This is accomplished by optimizing energy consumption in home appliances, which is customized to fulfill the individual requirements and preferences of consumers. However, there is still a need for further academic research to investigate and improve the functioning of intelligent residential homes in microgrids. To efficiently manage microgrids, it is crucial to gather and analyze large amounts of electrical data related to power production from microgrid sources and energy consumption of the loads. This study examines the use of Non-Intrusive Load Monitoring (NILM) methods to monitor electrical parameters of different loads in microgrids. The research focuses on the application of affordable smart meters that are equipped with Internet of Things (IoT) capabilities. An empirical study showcases the possibility of collecting significant data on microgrid operation via the deployment of an operational microgrid that integrates a hybrid wind-solar power source with a variety of home appliances.

Currently, cloud computing provides the necessary infrastructure and software services to provide the requested services needed by users on the Internet. Due to the spectacular growth of cloud computing, the number of users and the number of demands are increasing rapidly, which creates a high workload on servers and computing resources. In this situation, for the optimal use of resources, the need for an efficient and effective management approach is fully felt. For this purpose, game theory has been used. The game structure is designed in such a way that the leader (leader) owns a large number of resources and plans the allocation of resources based on the request received from mobile users. The goal from the leader's point of view is to minimize the cost of using the resources located in the fog nodes, on the other hand, the goal considered from the mobile users' point of view is to minimize the cost of responding and transmitting the message to the desired fog node. For this purpose, the entire region is divided into regions and a fog node is considered for each region. The main goal of this research is to reduce the average delay in the provision of services related to Internet of Things applications in cloud computing platforms. For this purpose, an attempt is made to provide a new method for allocating multiple tasks in mobile collective monitoring based on fog computing in the Internet of Things using the inverse Stackelberg game theory with the help of fuzzy logic and deep reinforcement learning algorithm.

In this article, Multitrial vector-based differential evolution algorithm (MTDE) is proposed as energy and cost management controller under Time of Use (TOU) tariff in grid associated domestic PV-wind power system tied with battery storages. To enrich the energy efficiency of a proposed power system, two optimisation algorithms are proposed in the scheduling operation. TOU billing is a cost- reflective power pricing strategy that has been found as an effective way to reduce peak energy consumption in the residential segment everywhere the world, mainly in industrialised nations. In the optimisation maximizing the cost benefit of the household energy is taken as the objective and the dispatching ratio of electricity sold to the grid and used locally is treated as the optimisation variable. Using MATLAB, the performance of proposed MTDE in the aspect of daily cost benefit and revenue growth rate are presented with the comparative analysis of gravitational search algorithm and conventional self-made for self-consumed and rest for sale (SFC&RFS) mode -based energy management controller. In comparison to the SFC&RFS mode, the GSA-based cost optimization offers a 21.46% increase in revenue growth, while it is improved to 38.7% using proposed MTDE algorithm. The paper also addresses the significance of fuzzy logic based maximum power point tracking (MPPT) of solar PV in enhancing the energy management of the proposed system. The prototype of fuzzy logic MPPT for 10 W solar panel is presented and the tracked maximum power is visualized using Thingspeak IoT cloud server. The tracking speed of the MPPT can be increased by introducing machine learning algorithms at the cost of memory and complexity. In this article, the linear regression-based machine learning algorithm is implemented in the hardware prototype by utilizing the dataset aggregated from fuzzy MPPT and hence the proposed MPPT inherits the characteristics of the fuzzy MPPT with increased tracking speed. Around 75 % data are used for training, 25% of data are used to test the model and it is observed that the root mean square error (rmse) is 5.1334 and mean square error is 26.3521 and the model is utilized as MPPT for the real-time inputs.

This study investigates integrating and optimizing an intelligent rural management system leveraging advanced technologies, including the Internet of Things (IoT), blockchain, and social networks. Initially, it identifies and scrutinizes prevailing issues in rural development processes, emphasizing the role of rural cultural dissemination, with a specific focus on the impact of social networks. Subsequently, by incorporating fuzzy set theory and adopting an enhanced blockchain consensus algorithm—the Byzantine Fault Tolerance (BFT) algorithm— a comprehensive rural management system is established, combining fuzzy sets and blockchain. Lastly, an intelligent traffic management system is developed to address logistics and distribution challenges in rural revitalization, facilitating efficient interurban delivery. The system automatically invokes the Matlab dynamic link library and employs a hybrid genetic algorithm to plan delivery routes. This study presents key findings on implementing an intelligent rural management system incorporating fuzzy sets, blockchain, and IoT technology. The system notably enhances transparency and traceability in agricultural production and supply chain processes, optimizes logistics and distribution efficiency, and reduces operational costs through intelligent management techniques. Additionally, the integrated application significantly bolsters consumer trust in the quality and safety of agricultural products, leading to heightened overall user satisfaction.

Land-based aquaculture operations, at present,  are intensively conducted to meet the ever-growing demand for food consumption. Floating net cages are one of the traditional methods commonly used by Indonesian fishermen for river fish farming. Increased human activities along the Musi River and coastline have resulted in pollution and waste in the river waters and fluctuating water quality. Yet, floating net cage owners still manually assess the water quality. This study aims to develop an early warning system for water quality and create a decision-making program as a reference for fishermen to relocate floating net cages when the river water quality deteriorates.

The device was tested at 39 locations within a radius of approximately 3400 meters, and the distance between locations varied between 55 and 334 meters. The river was divided into three sections: the river coast, the middle section, and the other river coast. Water quality sensors were placed at a depth of 0–20 centimeters from the surface of the Musi River, with measurement durations at each location ranging from 1 to 40 minutes. Direct measurements of the Musi River's water quality were obtained by monitoring the water quality using an internet-based computer application. A decision-making Python program utilizing fuzzy logic was then executed to evaluate the suitability of the river water quality for fish cultivation. The program's input variables comprise water temperature, potential of hydrogen, and dissolved oxygen sensor data. Meanwhile, the program output recommends floating net cage owners to either "Stay in position" or "Move." Water quality warnings that exceed the upper and lower threshold limits are displayed using light-emitting diode indicators and a buzzer.

Overall, the water quality values of the Musi River at the test locations generally indicated stable and suitable conditions for river fish cultivation. The average water quality values were 29.20 degrees Celsius for temperature, 3.98 milligrams per liter for dissolved oxygen, and a potential of hydrogen of 6.42. From all the data obtained during the decision-making program, 36 locations suggested that the floating net cages should "Stay in position." Meanwhile, the three remaining locations were recommended to "Move" as they exhibited poor water quality, with potential of hydrogen values below 6. Field observations indicated that these locations were situated near residential areas, factories/industries, and tributaries, which are highly susceptible to waste and pollution. The output of the decision-making program correlated with the issued warnings by the water quality warning indicators when the pH value exceeded the lower threshold limit.

The fuzzy logic method implemented in the Python program for decision-making regarding the relocation of floating net cages in river fish farming revealed the fluctuating water quality conditions of the Musi River within a specific time duration. These conditions correlated with the proximity of the water bodies to pollution sources such as residential areas, factories, and tributaries. The program's output classified the status of the floating net cages into two conditions: "Stay in position" or "Move." The decision-making application to relocate floating net cages for fish farming in rivers provides a solution for fishermen as the resulting program decisions give the same indication as the reading value of the water quality sensor.

Nowadays, the IoT attracts a multitude of research and industrial interests. Smaller and smarter devices are being implemented daily in multiple IoT domains. However, protecting IoT devices from cyber-attacks is critical to their operation. Confidential data is leaked as a result of malicious acts. As a result, device performance becomes crucial. Security risks are frequently made in IoT-based structures that impact their standard work. Therefore, to eliminate and mitigate these issues (attacks), the Intrusion Detection System (IDS) was proposed to fulfill this purpose. This paper aims to study the state of the art of the proposed IDS. Moving on, we critically review the proposed IDS-based machine learning algorithms. Based on this evaluation criteria, the solutions covering architectures, intelligent prediction and algorithms are critically reviewed. To achieve our goals, the paper presents challenges and open research areas in IoT design.

The Internet of Things (IOT) and Artificial Intelligence (AI) are being increasingly utilized in industrial, household and business sectors expected to change many aspects of the human life. As the nearly infinite possibilities unveil, many people even the expert ones, feel threatened [1, 2]. Calls to manage the risks or restrict the growth of these new trends, make us think about how severely the implications of this growth may change our lives. Human is being surrounded by AI enabled devices which are capable of communicating to each other through IOT. This combination sounds even more threatening than AI or IOT alone. In fact, we are at the edge of believing to be manipulated rather than making use of these newly emerging technologies. This note concerns the role standardization bodies have in this situation. Standards have been widely used to ensure that specific goals (including user safety, environmental compatibility, sustainable development [3], etc.) are fulfilled for a product or service. With IOT and AI appearing in almost any technological product, we have new concerns. Some of the most important of these concerns are listed below [4-6].

This review also looks at the various environmental characteristics that can be used to achieve precision agriculture, such as irrigation, monitoring, soil qualities, and temp, Furthermore, a thorough investigation is carried out on various crops that are covered by WSN technology. The numerous communication technologies and sensors available for PA are also highlighted in this review. Several research questions were developed to examine the influence of WSNs in the agricultural area, and we attempted to discover answers to these research questions through this revised. Compared to the being IoT grounded husbandry and husbandry results, the propose result reduces network quiescence up to a certain extent. In this, a cross sub caste grounded channel access and routing answer for looking and actuating is proposed. We dissect the network structure grounded on content range, and quiescence. In the process of improving GDP of the country agriculture is the main part.

The emerging and revolutionary technology called Internet of Things (IoT), presenting the future of computing, communication, and network, has made a number of tough jobs easier with the help of Internet, sensor, and embedded systems. Agriculture is said to be the backbone of Indian Economy and is of utmost importance in the present scenario. Today’s agriculture is data-centered, precise, and smarter than ever. The research paper explores the various issue and challenges which are vital in the field of smart agriculture using IoT devices. The paper also highlights the importance and usefulness of wireless network and other related terminology in this area. The paper explores the various advances in the field of smart agriculture, IoT, and wireless network and draws attention to the various future research directions that the research community can take to further improve the system in terms of economics, operations, and technical feasibility.

BlockChain (BC) has gotten a lot of interest because of its immutability and accompanying security and privacy benefits. BC has the potential to address the Internet of Things' security and privacy issues (IoT). BC, on the other hand, is computationally expensive, has limited scalability, and incurs significant bandwidth overheads and delays, making it unsuitable for use in the IoT. In this study, we propose a method that optimizes BC for use in IOT environment. we present a hierarchical structure that uses a private BC to increase scalability, reduce network overhead and delay. In the proposed method, devices with high-level resources build a network referred to as the overlay network on devices with low-level resources. The members of the overlay network manage the BC. The arrangement of the block managers at the overlay network level is two-dimensional which removes the focus from IOT management. We use trust technique and voting from direct neighbors to reduce network traffic and overhead costs. The use of factors such as trust, encouragement and penalty of block managers in the overlay network ensures accurate transactions in IOT. We use the new block manager authentication algorithm for authentication in the overlay network. The simulation results show that the proposed algorithm reduces packet overhead and delay in service delivery and increases the scalability of the BC in comparison to the system that uses the base BC. Furthermore, because in the proposed algorithm, the number of effective block managers in voting is limited to direct neighbors, the average time to confirm a block is significantly reduced.

One of the most important and essential requirements for Internet of things is security of its limited resources. The simple nature of many devices on the internet of things makes them the main purpose of a variety of attacks .To deal with these attacks, there are many protocols for authentication for internet of things .In fact, an appropriate authentication protocol plays an important role in ensuring secure communications for internet of things. In this paper, we propose an authentication scheme with key agreement on elliptic curve cryptography (ECC).the simulation results using SCYTHER show that our protocol is secure against active and passive attacks.

By using Internet of Things (IoT) we can make the things easier for our livelihood. The concept of smart parking in sensors is the combination of IoT. I.E By sending the status of the parking IoT through the internet, So that any person can see that which parking IoT is empty through the map and can easily park the vehicle. By using IoT the user can track the availability of the parking IoT through the web application to park the vehicle. In the existing systems the parking done manually and the user will probably had cases for looking for parking IoT for long time and there will be a chance for mismatching of parking IoT for the user. Smart parking provides parking efficiency and it reduces the time of searching for parking. The main concept of smart parking is to reduce searching for the parking IoT. Smart parking can optimize the traffic flows in the entire cities. The application uses IoT sensors just to receive the information about the parking IoT ’s.

Agriculture is the foundation of India and almost 70% of individuals in our nation rely upon agribusiness. The yield of horticulture ought to be expanded quickly to satisfy the food necessities of populace all through the world. Present day’s remote sensor organization (WSN) utilized for taking care of many on-going issues. Horticulture is one of the significant hotspots for every living thing. Yet, these days’ agribusiness crops are impacted because of numerous natural changes. WSN assists the rancher with creating the harvest with high amount and diminish the expense of yield. Agribusiness gets impacted by climatic change, ecological change, and cataclysmic event. Utilizing WSN the dirt and water the executives should be possible. To conquer this WSN takes significant job in the field of agribusiness. In horticulture WSN utilized for checking, estimating temperature, water system framework, estimating water supply, etc. Here remote sensors are utilized so the expense of execution is exceptionally low.

The internet of things is an emerging paradigm that will change the way we interact with objects and computers in the future. It envisions a global network of devices interacting with each other, over the internet, to perform a useful action. Firstly, we provided the overview of the internet of things and then the relevant technologies that can help in large-scale development of internet of things, then the security issues in internet of things and its challenging. Secondly, we analyzed some of the lightweight authentication protocol in internet of things based on different techniques such as RFID authentication and continuous authentication to evaluate their vulnerability. Finally, we proposed the solution for one of RFID authentication protocol by using physically unclonable functions. In this protocol, the valid authentication time period is proposed to enhance robustness of authentication between internet of things devices and used the authentication token to authenticate the message which transmits from sensor node to the gateway and at the end the security analysis is conducted to evaluate the security strength of the proposed protocol.

In this paper, the study detects the nutritional deficiencies from these leaves using Internet of Thing (IoT) based image acquisition and nutrition analyser devices. The former captures the color of the leaf and the latter helps in finding the nutrients in each zone based on the image captured by the device. The study uses an improved convolutional neural network to detect automatically the nutrients present in a leaf. The type of leaf is considered from the plants including coriander, tomato, pepper, chili, etc. The Convolutional Neural Network (CNN) is used to extract the patterns of leaf images from the data capturing IoT devices and nutrition analyser device. The system stores and process the data in cloud, where the CNN integrated in Virtual Machines enables the process of input data and process it and sends the report to the authority. A total of 3000 images are collected out of various disorders in five different plants. A 5 fold cross-validation is conducted on training and testing dataset. The system is tested in terms of accuracy, sensitivity, specificity, f-measure, geometric mean and percentage error. The comparison made with existing models shows an improved detection accuracy by CNN than other deep learning models.