Data Mining - Concepts and Techniques.

Linked: The New Science of Networks

A review of smart home applications based on Internet of Things

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.

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Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

In the recent era, security is the major problem in sensor networks. Wireless sensor networks (WSNs) are mostly used for various real-world applications. However, WSNs face a lot of insider and outsider attacks, and it is complex to identify and protect towards insider attacks. Generally, an insider attack, in which the intruders choose several received data packets to drop, threatens the clustered WSNs. This situation has occurred because of the unattended clustered environments in the network. To overcome this problem, this paper proposes a trustable and secure routing scheme using two-stage security mechanism, and dual assurance scheme, for selecting the node and securing the data packet for WSNs. Both schemes are based on Active Trust to protect several kinds of attacks, such as black hole attack, and selective forwarding attack, during routing. Therefore, this paper identifies the trusted path and provides the secure routing paths using trust and Cuckoo search algorithm. Energy is the performance parameter utilized in the proposed scheme. The experimental result proves that proposed system provides the assurance to prolong the network lifespan and the probability of secure routing path in the network.

Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust

Internet of Things (IoT) is extension of current internet to provide communication, connection, and inter-networking between various devices or physical objects also known as “Things.” In this paper we have reported an effective use of IoT for Environmental Condition Monitoring and Controlling in Homes. We also provide fault detection and correction in any devices connected to this system automatically. Home Automation is nothing but automation of regular activities inside the home. Now a day's due to huge advancement in wireless sensor network and other computation technologies, it is possible to provide flexible and low cost home automation system. However there is no any system available in market which provide home automation as well as error detection in the devices efficiently. In this system we use prediction to find out the required solution if any problem occurs in any device connected to the system. To achieve that we are applying Data Mining concept. For efficient data mining we use Naive Bayes Classifier algorithm to find out the best possible solution. This gives a huge upper hand on other available home automation system, and we actually manage to provide a real intelligent system.

i-learning IoT: An intelligent self learning system for home automation using IoT

Applied Machine Learning for Smart Data Analysis

Crop production quality and quantity can be enhanced using precision agriculture. One of the major products in India is grape. Major threats for grape farming are downy mildew and meal bug which spreads instantly and are hard to control. As the grape is very costly, Farmer needs to be very careful from the initial stage to harvesting to avoid infection of fungal disease and pest. To prevent disease farmer has to apply fungicides and pesticides in huge quantity, it results in increased cost of production. Also another disadvantage is that grapes may carry toxins. To reduce cost of production and enhance the quality and quantity of the grapes, it is important to propose some mechanism that will help the farmer to detect the disease in early stage. Existing systems for forecasting the disease mostly depends on image processing technologies. Drawback of the existing system is that they wait till symptoms appear and then only the disease can be detected. That’s why such type of systems is unable to help treating the disease at an early stage. Grape diseases like downy mildew is mostly dependent upon weather based parameter like humidity, temperature and wind speed. When any favorable weather condition occurs zoospores in downy start generating spores that enters into the leaves of grape via stomata of the leaves. If favorable weather condition and the probability of disease is detected then it it very helpful for farmers to prevent infection of disease and reduce the cost of production.

/pdf/monitoring-and-detection-of-agricultural-disease-using-2nlfteto48.pdf

Monitoring and Detection of Agricultural Disease using Wireless Sensor Network

Wireless sensor networks (WSNs) have become one of the important areas in networking field. This is because sensors as compared to others are cheaper, efficient, adaptable and smaller. These sensors are having wireless interfaces to communicate with each other. The design of wireless sensor networks is application specific. Change in the application leads to change in the design of the network. Also network topologies vary if some physical changes in the network occur. The paper gives overview of the WSN. Also broad classification of applications of WSN is given briefly in this paper. Also the overview has been given on types on which these applications are based on.

Sanjeev Wagh

Papers

Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust

i-learning IoT: An intelligent self learning system for home automation using IoT

Applied Machine Learning for Smart Data Analysis

Monitoring and Detection of Agricultural Disease using Wireless Sensor Network

A Quick Survey on Wireless Sensor Networks