Data In Internet Of Things Environment Research Articles

Offline/Online Outsourced Attribute-Based Encryption with Partial Policy Hidden for the Internet of Things

In the Internet of Things (IoT) environment, the intelligent devices collect and share large-scale sensitive personal data for a wide range of application. However, the power of storage and computing of IoT devices is limited, so the mass perceived data will be encrypted and transmitted to a cloud platform-interconnected IoT devices. Therefore, the concern how to save the encryption/decryption cost and preserve the privacy of the sensitive data in IoT environment is an issue that deserves research. To mitigate these issues, an offline/online attribute-based encryption scheme that supports partial policy hidden and outsourcing decryption will be proposed. This scheme adopts offline/online attribute-based encryption algorithms; then, the key generation algorithm and encryption algorithm are divided into two stages: offline stage and online stage. Meanwhile, in order to solve the problem of policy disclosure under the cloud platform, the policy hidden is supported, that is, the attribute is divided into the attribute value and the attribute name. For the pairing operation involved in decryption process, a verifiable outsourced decryption is implemented. Our scheme is constructed based on composite bilinear groups, which meets full security under the standard model. Finally, by comparing with other schemes in terms of functionality and computational overhead, it is shown that the proposed scheme is more efficient and applicable to the mobile devices with limited computing and storage functions in the Internet of Things environment.

A Novel Information Processing in IoT Based Real Time Health Care Monitoring System

The recent technology developments and innovations improves the life style of people through smart applications, sensors, wireless communication networks, etc., for all those technologies internet is the backbone and the information processing like accessing, distributing the necessary information is achieved through Internet of Things (IoT). IoT supports multi-disciplinary applications as an active entity in engineering, science and business discipline. Based on the user preference these applications and its services could be framed in IoT. On contrary to the development, IoT has flaws in information processing as huge volume of data is need to be handled in a single environment. Considering these facts, the proposed research work is aimed to develop a novel information processing system in IoT platform through a reliable health care monitoring system. The effective utilization of big data in IoT environment is analysed through the proposed architecture to attain minimum delay in a real time environment. Conventional models are used to compare the performance of proposed design and the experimentation is performed to verify the superior performance of proposed approach using accuracy, cost functions in terms of transmission and storage, f-measure, sensitivity and specificity.

Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)

The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits.

An Authentication Scheme for an IoT Environment using Advanced Multiple Encryption System

Internet of Things (IoT) has become one of the most important fields of research as it plays a vital role in performing an assigned task with various smart devices. These connected devices work together to achieve a common goal. Numerous researchers have been working in various sectors of IoT for obtaining a betterment in the entire function of an IoT environment. Securing the data being transmitted or stored in these smart devices in one of the major fields of research. Some of the security challenges faced in an IoT environment are in the areas of user authentication, user privacy, access control, information confidentiality, trust, mobile security and policy enforcement. To overcome all these challenges. In this paper, we have proposed a system called AMES (Advanced Multiple Encryption System) which also secures the overall communication of the network. The paper briefs an overview of an IoT environment, consisting of some hackers where the user connection with the controller is blocked without the presence of integrity and confidentiality. The paper also discusses the various security benefits of RSA and SHA. The proposed AMES system is based on Hardy Wall Algorithm and MQTT protocol. The differentiation of the proposed protocol and HTTP protocol is stated clearly The experimental results are obtained for the proposed authentication scheme using NS3 simulator by showing the comparison between existing hybrid security system with our proposed authentication system. The results also state that the designed AMES authentication system using MQTT protocol can minimize the level of security threats and other complications in transmission of the data in IoT environment.

Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms From Big Data Repositories

The advanced computational capabilities of many resource constrained devices, such as smartphones have enabled various research areas including image retrieval from big data repositories for numerous Internet of Things (IoT) applications. The major challenges for image retrieval using smartphones in an IoT environment are the computational complexity and storage. To deal with big data in IoT environment for image retrieval, this paper proposes a light-weighted deep learning-based system for energy-constrained devices. The system first detects and crops face regions from an image using Viola–Jones algorithm with additional face and nonface classifier to eliminate the miss-detection problem. Second, the system uses convolutional layers of a cost effective pretrained CNN model with defined features to represent faces. Next, features of the big data repository are indexed to achieve a faster matching process for real-time retrieval. Finally, Euclidean distance is used to find similarity between query and repository images. For experimental evaluation, we created a local facial images dataset, including both single and group facial images. This dataset can be used by other researchers as a benchmark for comparison with other real-time facial image retrieval systems. The experimental results show that our proposed system outperforms other state-of-the-art feature extraction methods in terms of efficiency and retrieval for IoT-assisted energy-constrained platforms.