The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient’s information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient’s information in the IoT enabled healthcare system.

The technological progression is raised as a hybrid ecosystem with the industrial Internet of Things (IIoT). Among them, healthcare is also broadly unified with the Internet of Things to develop an industrial forthcoming system. Utilizing this type of system can be facilitating optimum patient monitoring, competent diagnosis, intensive care, and including the appropriate operation against the existing critical diseases. Due to enormous data theft or privacy leakage, security, and privacy towards patient-based informative data, the preservation of personal patients’ informative data has now become a necessity in the digitized community. The produced article is underlined on handsomely monitoring, perceptively extracted keyframe, and further processed lightweight cosine functions using hybrid way chaotic map keyframe image encryption. Initially, a regular concept of extracted keyframe is deployed to salvage meaningful detected frames by transmitting an alert autonomously to the administration. Then, lightweight cosine function for encryption is employed. This encryption incorporates keyframe exceedingly secure and safe from the outside world or any adversary. Our proposed methodology validates effectiveness throughout the IIoT ecosystem. The produced outcome is highly acceptable and has minimum execution time, robustness, and reasonably adopted cost-effective, secure parameter than any other (keyframes) image encryption methods. Furthermore, this methodology has optimally reduced bandwidth, essential communicating price, transmission cost, storage, and immediately judicious analysis of each occurred activity from the outside world or any adversary to remain secure and confident about the real patient-based data in the smartly developed environment.

The diagnosis of heart diseases through invoice based techniques as well as ordinary medical based methods are not reliable. On other hand, non-invoice based techniques are more effective for heart disease diagnosis. Therefore, we check the capability of various Machine Learning (ML) classifiers and deep learning classifier for heart disease identification in this paper. Six machine-learning classifiers and BPNN were used in order to check which one classifier is more effective for diagnosis the heart disease. The feature selection algorithm Relief was used for selection of important features and on these selected features, classifiers performances were also computed. Ensemble machine learning techniques (boosting, bagging, stacking) were used to further increase the classifiers performance. Furthermore, cross-validation techniques k-folds was also used. Additionally backward propagation neural network (BPNN) was also used for classification purpose because deep learning algorithm not need feature selection algorithms and it automatically select important features for good result. Based on model performance evaluation metrics the SVM (RBF) performed excellently on full features achieved accuracy 86%, and 88% accuracy on selected features as compared other classifiers. Through Ensemble learning techniques, SVM obtained the classification accuracy 92.30%. The BPNN achieved 93% classification accuracy. Thus the performance of deep neural networks learning algorithm is better than traditional machine learning algorithms. As per our experimental results shows that the performance of BPNN based diagnosis system is more effective for heart disease diagnosis

Internet of Things (loT)is the fast-growing emerging technology and it is making our daily life (activities)easier and better. The loT is connecting millions of sensors and heterogeneous devices having different computation, communication, and storage capabilities which are increasing rapidly. The loT network is dissimilar and larger from the conventional network. However, different natures of the connected devices raising security concerns and challenges which affect the importance of loT technology. In this paper, we proposed an authentication scheme based on the OAuth 2.0 protocol to secure access loT network by providing authentication service. An OAuth 2.0 protocol is used to propose authentication mechanism to allow only authorized and authentic users by comparing user information and access tokens in the security manager local database and denies the access to the loT network. It also keeps safe loT network from different types of attacks like impersonation and replays attacks etc.

The security and privacy of medical data are highly required when it involved in connecting with smart and intelligent sensor devices in the hospital ecosystem. The Internet of things is mutually integrated with healthcare to produce effective and smart working system but it still needs better security mechanism which can protect patient’s privacy. This paper is focusing to protect medical data by using image encryption techniques. In which we are applying three rounds of high-speed scrambling as well as pixel adaptive diffusion concept for shuffling random neighboring pixels. In addition, implementing pixel adaptive diffusion our mechanism is used modulo arithmetic operation. Simulations and experiments demonstrate that the encryption mechanism employing modulo arithmetic operation have comparatively higher levels of security. It can adapt better than several traditional as well as state-of - the-art image encryption.

Due to the exceptional growth of the electronic industry and the cause of technological advancement which is generated an exceptionally high rate of electronic waste in India particularly the Delhi national capital region. This research is focused on E-waste generation and awareness of its managing disposal practices based on a high standard questionnaire. This study focusses only on mobile, laptop, notebook, computer and Television as an E-waste. The structured questionnaire is widely spread and received around 200 households of the different working sectors as well as different income groups individual’s reactions. The findings of individual awareness of generation and managing disposal practices are considerably lacking. It should be negative impact of health issues in the Delhi (NCR) area.