Over the past two decades, the rapid growth of the Internet of Things (IoT) has begun to transform traditional healthcare systems into intelligent systems; however, hospitals have encountered challenges in securely storing patient data within centralized architectures due to their lack of efficiency and security features. Blockchain technology offers a secure and reliable decentralized framework for storing and sharing healthcare data among various stakeholders, including patients, doctors, nurses, insurance companies, and pharmaceutical firms. In this article, we propose a blockchain-based data-protection scheme deployed at edge nodes. The proposed scheme uses the interplanetary file system (IPFS) model to address storage and data-protection issues in an IoT-edge-enabled smart healthcare system. First, the security issues in smart healthcare systems are identified, and the impact of these issues on patient privacy and hospital infrastructure is considered. Then, a technique based on the 128-bit Advanced Encryption Standard is proposed to encrypt patient information and store it in an IPFS-based decentralized network. Edge-computing techniques are used to perform computations at the edge level within a decentralized architecture, thereby addressing the computational challenges associated with cloud computing. Lastly, the encryption keys are stored using blockchain technology to address the issue of restricted computational power on low-end devices through off-chain and on-chain business processes. The experimental results demonstrate that the proposed scheme achieves a key management time of 0.2 ms, file retrieval time of 0.57 s, throughput of 0.11 Mb/s, encryption time of 1.96 ms, and decryption time of 1.91 ms. These findings indicate that the proposed scheme outperforms previously reported approaches with respect to key management time, file retrieval efficiency, and its potential for edge deployment and off-chain capabilities. Consequently, the proposed scheme is highly suited for efficiently securing patient data within IoT-enabled smart healthcare systems.

The work consists of two subapproaches. In the first approach, an analytical model is developed using trapezium fuzzy numbers in decision-making problems for an Internet of Things-based water distribution network. The second phase explains the integration of the previous phase with the MCDM-based location routing protocol (M-LRP). The water distribution network has three components static water source, the utility center (UC) which can be located in the proper position, and the consumer. The objective of this work is to select an optimal route between the UC and the consumer by considering multiple criteria. The simulation result shows that the proposed multicriterion-based decision-making (MCDM)-based routing protocol outperforms both existing MCDM-based and non-MCDM-based routing schemes. The proposed model outperforms the existing models like non-MCDM-based and MCDM-based routing protocols by 51% and 11%, respectively.

Internet of Things (IoT) has been the most emerging technology in the last decade because the number of smart devices and its associated technologies has rapidly grown in both industrial and research prospectives. The applications are developed using IoT techniques for real-time monitoring. Due to low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography techniques are not suitable. In this study, we initially review and identify the security and privacy issues that exist in the IoT system. Second, as per blockchain technology, we provide some security solutions. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. Finally, a case study is implemented using the Ethererum-based blockchain system in a smart IoT system and the results are discussed.

The exponential growth in the number of connected devices as well as the data produced from these devices call for a secure and efficient access control mechanism that can ensure the privacy of both users and data. Most of the conventional key management mechanisms depend upon a trusted third party like a registration center or key generation center for the generation and management of keys. Trusting a third party has its own ramifications and results in a centralized architecture; therefore, this article addresses these issues by designing a Blockchain-based distributed IoT architecture that uses hash chains for secure key management. The proposed architecture exploits the key characteristics of the Blockchain technology, such as openness, immutability, traceability, and fault tolerance, to ensure data privacy in IoT scenarios and, thus, provides a secure environment for communication. This article also proposes a scheme for secure and efficient key generation and management for mutual authentication between communication entities. The proposed scheme uses a one-way hash chain technique to provide a set of public and private key pairs to the IoT devices that allow the key pairs to verify themselves at any time. Experimental analysis confirms the superior performance of the proposed scheme to the conventional mechanisms.

Low maintenance of the roads is one of the extensive cause of increasing road accidents and vehicle breakage. Mostly roads contain potholes, wreckage or detritus which is best to be bypassed however if not sometimes lead to severe road causalities. Many car or bus accident happens over a wrecked bridge, as it slips and overturns. To avoid this kind of mishap and improve the safety of the road we have proposed a machine learning based model, MagTrack, to detect these road conditions and inform apriori to the responsible authority for fast reparation as well as other passers-by to drive carefully or take an alternative route. We have collected various road surface conditions such as smooth roads, uneven roads, potholes, speed breakers, and rumble strips data using magnetometer and accelerometer sensor embedded in our Smartphone and analyzed using various classification algorithms like Random Forest (RF), Random Tree (RT) and Support Vector Machine (SVM). The classification has done after performing the feature selection using GreedyStepwise, Ranker, and BestFirst Algorithms considering minimum, maximum, median and standard deviation as statistical features. 92% of accuracy to detect the road surface condition has been achieved by MagTrack.

Internet of Things (IoT) has lots of attention in the last decade. The connected IoT devices are more than the total world population. Due to its low cost, easy to deploy, and simple to implement, application areas are large like smart city, smart home, smart transportation, environment monitoring, agriculture and many more. There exists some security and privacy challenges in IoT system. The device identification is one of the challenges in any IoT application. Authentication is one of the processes to identify the device. Though some work has been done on this problem, most of these are using a centralized system. In this paper, we have proposed a distributed authentication system using the Blockchain technology The implementation of the proposed authentication is done on Ethereum platform for its better results in order to justify it as a superior scheme.

Blockchain is the backbone technology behind crypto-currency and Bitcoin. By concept, Blockchain is a distributed database where transactions are recorded in an incorruptible and non-modifiable manner. Currently, Blockchain technology is envisioned as a powerful framework for open-access networks, decentralized information processing and sharing systems, etc. This review is motivated due to the lack of an extensive survey on the existing decentralized consensus mechanisms in Blockchain technology. So in this paper, an in-depth review of the distributed consensus mechanisms has been presented. In addition to this, a comparative analysis of the consensus protocols based on the type of Blockchain is also demonstrated.

Side-channel attacks, which exploit deficiencies in the implementations of theoretically secure systems, have been known to take a variety of forms on the mobile platforms. In this work, we present Magneto, a magnetic field based app classification mechanism. Magneto captures the Hall effect due to energy consumption by different components in a smartphone, and fingerprints apps based on data captured with a Hall sensor, and a phone magnetometer. We demonstrate that our mechanism can identify magnetic field changes due to varying levels of energy consumption. We further show that Magento can not only classify between apps in the same scenario, but also can tell apart scenarios when the phone is being charged (with an AC adapter, wireless charger, or powerbank) or not. We perform validation experiments with 5 different apps, and achieve ~85% accuracy with 3 Android apps, subject to 3 different charging scenarios.