The distributed nature of Internet of Things (IoT) and its rapid increase on a large scale raises many security and privacy issues. Access control is one of the major challenges currently addressed through centralized approaches that may rely on a third party and they are constrained by availability and scalability, which may result in a performance bottleneck. Therefore, this paper proposes a novel solution to manage the delivery of lightweight and decentralized secure access control of an IoT system based on a multi-agent system and a blockchain. The main objective of the proposed solution is to build Blockchain Managers (BCMs) for securing IoT access control, as well as allowing for secure communication between local IoT devices. Moreover, the solution also enables secure communication between IoT devices, fog nodes and cloud computing.

The rapid growth of the Internet of Things (IoT) and its attributes of constrained devices and a distributed environment make it difficult to manage such a huge and growing network of devices on a global scale. Existing traditional access-control systems provide security and management to the IoT system. However, these mechanisms are based on central authority management, which introduces issues such as a single point of failure, low scalability, and a lack of privacy. In order to address these problems, many researchers have proposed using blockchain technology to achieve decentralized access control. However, such models are still faced with problems such as a lack of scalability and high computational complexity. In this paper, we propose a light-weight hierarchical blockchain-based multi-chaincode access control to protect the security and privacy of IoT systems. A clustering concept with BC managers enables the extended scalability of the proposed system. The architecture of the proposed solution contains three main components: an Edge Blockchain Manager (EBCM), which is responsible for authenticating and authorizing constrained devices locally; an Aggregated Edge Blockchain Manager (AEBCM), which contains various EBCMs to control different clusters and manage ABAC policies, and a Cloud Consortium Blockchain Manager (CCBCM), which ensures that only authorized users access the resources. In our solution, smart contracts are used to self-enforce decentralized AC policies. We implement a proof of concept for our proposed system using the permissioned Hyperledger Fabric. The simulation results and the security analysis show the efficiency and effectiveness of the proposed solution.

The Internet of Things paradigm is growing rapidly. In fact, controlling this massive growth of IoT globally raises new security and privacy issues. The traditional access control mechanisms provide security to IoT systems such as DAC (discretionary access control) and mandatory access control (MAC). However, these mechanisms are based on central authority management, which raises some issues such as absence of scalability, single point of failure, and lack of privacy. Recently, the decentralized and immutable nature of blockchain technology integrated with access control can help to overcome privacy and security issues in the IoT. This paper presents a review of different access control mechanisms in IoT systems. We present a comparison table of reviewed access control mechanisms. The mechanisms’ scalability, distribution, security, user-centric, privacy and policy enforcing are compared. In addition, we provide access control classifications. Finally, we highlight challenges and future research directions in developing decentralized access control mechanisms for IoT systems.

With the advancement of new technology, security is the biggest issue nowadays. To solve security problems, blockchain technology will be used. In recent work, most of the work has been done on homogeneous systems, but in our research, the primary focus is on the security of wireless sensor networks using blockchain. Over the last few decades, the Internet of Things (IoT) has been the most advancing technology due to the number of intelligent devices and associated technologies that have rapidly grown in every field of the world, such as smart cities, education, agriculture, banking, healthcare, etc. Many of the applications are developing by using IoT technologies for real-time monitoring. Because of storage capacity or low processing power, smart devices or gadgets are vulnerable to attack as existing cryptography techniques or security are insufficient. In this research work, firstly, we review and identify the privacy and security issues in the IoT system. Secondly, there is a solution for the security issues, which is resolved by blockchain technology. We will check the wireless sensor network to see how data work on distributed or decentralized network architecture. Wireless sensor network clustering technique was introduced by researchers for network efficiency because when the workload spreads, the system will work faster and more efficiently. A cluster comprises a number of nodes, and the cluster head manages the local interactions between the nodes in the cluster (CH). In general, cluster members connect with the cluster head, and the cluster head aggregates and fuses the data acquired in order to save energy. Before approaching the sink, the cluster heads may additionally create another layer of clusters among themselves. The clustering concept divides data traffic into several groups similar to the other data points in the same data point. In contrast, this data point is dissimilar from other data points in another group. All results are presented at the end of this study paper, in which we will see the network or nodes’ performance in the specific area of the network, how it works, and how efficient it is. Likewise, Blockchain also works in a distributed manner.

Software-defined networking (SDN) has emerged as a flexible and programmable network architecture that takes advantage of the benefits of global visibility and centralized control over a network. One of the main properties of the SDN architecture is the ability to offer a northbound interface (NBI), which enables network applications to access the SDN controller resources. However, the NBI can be compromised by a malicious application due to the lack of standardization and security aspects in the most current NBI designs. Therefore, in this paper, we propose a novel comprehensive security solution for securing the application–controller interface, named BCNBI. We propose a controller-independent lightweight blockchain architecture and exploit the security features of blockchain while limiting the blockchain’s computational overhead. BCNBI automatically verifies application and SDN controller credentials through token-based authentication. The proposed solution enforces fine-grained access control for each application’s API request and classifies the permission set into strict and normal policies, in order to add an extra level of security. In addition, the trustworthiness of applications is evaluated in order to prevent malicious activities. We implemented our blockchain-based solution to analyze its security, based on the confidentiality–integrity–availability model criteria, and evaluated the introduced overhead in terms of processing time and packet overhead. The experimental results demonstrate that the BCNBI can effectively secure the NBI, based on the fundamental security goals, while introducing insignificant overhead.

The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.