Forensic Aware Cyber-Physical System (FA-CPS) is an evolving core of digital forensic systems that discovers the integrity of biometric service platforms. Most forensic agencies use emerging technologies such as IoT, Cloud, etc., to integrate a few core elements (networking, communication, and distributed computing) to achieve sustainable memory forensics. This systematic process brings additional capabilities to the physical systems that capture device memories to discover the evidence of malicious tools. Therefore, this paper deals with the Internet of Things (IoT) to form an effective and economical interaction with evolving technologies, including B5G/6G, edge, and cloud computing, to uncover the context of security implications. Most precisely, to sense, collect, share, and analyze numerical data from information systems, the application domain, like healthcare, utilizes computing methods and communications technologies to collect and analyze physiological data from patients in a haphazard way. Since an insecure network has security issues such as information leakage, secret key loss, and fraudulent authentication in Telehealth and remote monitoring, this work applies elliptic curve cryptography (ECC) and a physical unclonable function (PUF) to construct an AI-driven privacy-preserving key authentication framework (AID-PPKAF). In the proposed AID-PPKAF, the PUF generates key information, and ECC encrypts the parameters generated by the system to establish session key agreement and proper mutual authentication. The security analyses (both formal and informal) prove that AID-PPKAF has greater security efficiency than other state-of-the-art approaches. Lastly, a performance analysis using NS3 and a pragmatic study using SVM demonstrate the significance of identity protection in designing a more reliable authentication model.

In recent times, cloud-enabled healthcare services have gained much attention in fulfilling the analysis of privacy risks associated with effective decision management systems including trustworthiness and secure data sharing. This system has revolutionized the medical architecture to evolve unprecedented opportunities in using the technologies of next-generation networks, including services, control, and signaling, to effectively improve content delivery to individuals and organizations. However, it is a highly complex matter to distribute confidential data over a public network because data privacy and device security are at risk. As a result, a cloud-based healthcare application is introduced that integrates the computation capabilities of ubiquitous computing to provide extensive communications over dedicated Internet access in order to store health records. To manage access control mechanisms and process sensitive data without extensive computation, the existing cloud-centric systems access remote servers via dedicated networks. Based on a centralized architecture, a dedicated network uses different application domains to deliver information to the healthcare industry. Unfortunately, computation complexity greatly deteriorates the performance of peer-to-peer (P2P) communications. Thus, this paper presents a cloud-assisted decentralized privacy preserving framework (CA-DPPF) using blockchain and key agreement (KA) mechanisms to achieve secure data storage and privacy. The detailed security analysis proves that the proposed scheme fulfills the desired security properties of healthcare supply chain management (H-SCM) such as conditional traceability, data immutability, and data integrity. Overall, exploratory analysis shows that CA-DPPF guarantees better transaction efficiencies such as less latency and more throughput in order to improve the service utilization factor.

B5G-enabled healthcare systems interconnect a wide range of Internet of Medical Things (IoMT) using supportive networks such as heterogeneous networks and cognitive radio networks to enhance the medical infrastructure. In healthcare, IoMT integrates access technologies, computing infrastructure, and services to connect healthcare systems to handle intensive computation without sharing private data. As a result, healthcare systems accessing a massive IoMT (mIoMT) utilize real-time data sharing to enhance the overall resource efficiency of remote patient monitoring. To optimize the IoT-generated data, the application interface of the computing device regulates self-management messaging systems with healthcare providers. By utilizing direct communication with the networks, they offer a long-lasting service, enhancing the performance trade-off. Since the network has more of a digital existence in the physical universe, a convergence of cloud-server integration with IoT inherently causes more security challenges to preserving the privacy of edge computing systems. Therefore, in this paper, we present privacy preserving based seamless authentication with provable key verification (PPSA-PKV) for securing B5G-enabled healthcare systems. To preserve the identities of the registered users, the proposed PPSA-PKV applies a collision-free cryptographic hash function and elliptic-curve arithmetic. Security analyses including formal and informal show high-level privacy protection for the proposed PPSA-PKV with seamless verification compared to other state-of-the-art approaches. The simulation analysis shows that the proposed PPSA-PKV incurs less delay ($\approx 0.14 sec$) and improves throughput ($\approx 1865 bits$) to fulfill the energy efficiency (at an average 0.294J) of B5G networks. Lastly, a learning model using a support vector machine (SVM) demonstrates the monitoring process of edge data centers to detect malicious authentication requests.

Abstract Internet of Things (IoT) and cyber‐physical systems demand proper real‐time data processing and analysis to fulfill the essential characteristics of seamless computing services such as less processing time, dynamic network management, and location awareness. With the evolution of mobile edge computing, numerous security and privacy issues have been addressed including policy management, authentication, access control, and confidentiality. The technologies such as big‐data, Internet of Things, and cloud have improved the development of modern healthcare systems to improve the quality of medical services. It is nowadays integrating the computing methods and communication technologies such as wireless sensor networks, radio frequency identification, smartphones, and location awareness to collect and analyze the physiological data of the patient. The e‐Health entities such as medical device, client, and server rely on analytical models to carry out early diagnosis and remote monitoring. As it is based on e‐Health applications, a real‐time healthcare system is preferred to integrate with mobile cloud computing. Since the medical data are hugely exposed to vulnerabilities, a lightweight privacy‐aware secure authentication (LPASA) scheme is presented. To achieve better secure efficiencies, this article is preferable to use lightweight cryptographic operations, namely, one‐way hashing function and bitwise exclusive‐OR. Moreover, the formal and informal security analysis shows that the proposed LPASA can withstand the possible active and passive attacks such as replay, man‐in‐the‐middle, privileged‐insider, online‐guessing, and so forth. The experimental analysis demonstrates that the proposed LPASA scheme achieves a better quality of services in terms of packet delivery ratio, energy consumption per data delivery bit, and percentage of packets with low latency to adopt in resource‐constrained environments.

Internet of Things (IoT) has led to significant advancements in communication technologies, specifically, concerning IoT-based sustainable information systems. Lately, industry-academic communities have made great strides for the development of security in IoT-based applications, such as traffic management, industrial automation systems, military surveillance systems, transportation, parking, etc. The sustainable IoT converges AI and blockchain technologies for enhancing quality of individual’s life. As a result, emerging IoT applications operate a distributed ledger technology to provide robust-level of encryption and execution for contractual agreement that resolves interoperability and security issues. Thus, this article proposes a blockchain-based remote mutual authentication (B-RMA) that considers smart devices and cloud networks to offer security and privacy. The proposed B-RMA can coexist with the IoT-based smart environment to decentralize the processing of user authentication requests. The prominence of the proposed strategies including security efficiency and privacy protection, is evaluated using informal security analysis. Moreover, a runtime platform “Node.js” was used to analyze the communication metrics, such as execution time, throughput, and overhead ratio, over the concurrent requests. The investigation results prove that the B-RMA achieves a scalable environment, accordingly.

The advancement of sensory technologies proliferates the development of low-cost electronics systems to operate the environmental features of smart cities. Global urbanization integrates networking systems to offer computing-based practical solutions for improving the quality of application-oriented services. Few existing studies have primarily focused on a single-point vulnerability for decentralized IoT applications. However, very few mechanisms address the issues concerning privacy-preserving and trust-aware authentication for IoT-enabled industrial applications. Moreover, the existing schemes are in fact not applicable to real-time scenarios, such as decentralized networks and long-term evolution advanced networks. Thus, this article presents a trust-aware blockchain-based seamless authentication with privacy-preserving (TAB-SAPP) to resolve the critical things, such as privacy, security, and packet delivery ratio. In the proposed TAB-SAPP, a novel data traffic pattern is utilized using identity management to show that the proposed mechanism can be more functional in expanding users’ connectivity to improve the communication metrics, such as packet delivery ratio and mobility speed.

The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital-twin for industrial practice. It has low-power wireless-enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital-twin-assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool-holder that integrates a k-type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool-holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools.

Artificial intelligence based Internet of Things enables autonomous communication among social networks and IoT to leverage the promising solution in the modern paradigms. It can provide an interactive platform across the globe to enrich the quality of networking services to the end users. Of late, the expansion of information-centric networking has brought an incredible technique, known as public auditing scheme, for IoT-enabled sensor technologies. It uses a cloud-based medical cyber-physical system (M-CPS) to rely on cloud computing that ensures fast computing and reliable data storage. Since the medical file is so vital to involve precise diagnoses, data integrity and verification have lately become the data auditing tool. To exploit proxy authorizer and trusted auditor, an identity-based proxy authorized outsourcing with public auditing (ID-PAOPA) is proposed. It uses proxy authorization and verification to upload medical data over cloud-based M-CPS. To substantiate the finding, this article provides security proof based on the EC-DLP assumption. Finally, the performance analysis proves that the proposed ID-PAOPA achieves less computation and auditing timing to fulfill the objectives of cloud-based M-CPS.

Cognitive-inspired Internet of Medical Things (CI-IoMT) combines cognitive science and artificial intelligence to interact with humans and ubiquitous digital environments. The Internet of Things devices generate massive amounts of data and process it with cognitive computing to perform efficient analysis at the edge nodes. Internet of Medical Things (IoMT) uses the said analysis to design smart communication systems to facilitate ubiquitous services. However, the protocols used in IoMT use conventional number theory systems that are vulnerable to quantum-computer attacks. Therefore, an efficient CI-IoMT scheme is required to handle access privacy, preservation, and trust guarantee. This article presents an identity-based seamless privacy preservation (IB-SPP) for CI-IoMT to authorize smart device communications. It is entirely based on fast user authentication to shorten access timing in an emergency situation. The simulation analysis shows that the proposed IB-SPP scheme consumes less response time and minimum data volume than other existing schemes.

Technological development expands the computation process of smart devices that adopt the telecare medical information system (TMIS) to fulfill the demands of the healthcare organization. It provides better medical identification to claim the features namely trustworthy, efficient, and resourceful. Moreover, the telecare services automate the remote healthcare monitoring process to ease professional workloads. Importantly, it is conceived to be more timesaving, economical, and easy healthcare access. Cloud-Based Medical Healthcare (CBMH) system is a standard platform that gives its support to the patients for emergency treatment from the medical experts over Internet communication. Since the medical records are very sensitive, security protection is much necessitated. In addition, patient anonymity should be well preserved. In 2016, Chiou et al. proposed a mutual authentication protocol for the Telecare Medical Information System (TMIS) using Cloud Environment (CE). They claim that their protocol satisfies patient anonymity. However, this paper proves that the Chiou et al. scheme is not only completely insecure against the patient anonymity, health-report revelation, health-report forgery, report confidentiality, and non-repudiation but also fails to validate the service access against verifiability, undeniability and unforgeability. In order to provide better mutual authenticity, this paper suggests the framework of smart service authentication to cross-examine the common secret session key among the communication entities. In order to examine the security properties, formal and informal verification was carried out. Lastly, to prove the security and performance efficiency of a system, the proposed SSA framework was implemented using FPGA and Moteiv TMote Sky-Mote. A proposed smart service authentication (SSA) framework is presented to ensure better data security between the patients and the physicians. The formal and informal security analysis proves the significance of the SSA framework model to withstand the security attacks such as health-report forgery, health-report revelation, server-spoofing etc. As a result, it is claimed that it can be well suited for TMIS.

Technological developments in communication technologies in the form of hardware and software have made unilateral sensor connectivity over Internet access that facilitates data observation and measurement of physical entities. A technology known as Internet of Things (IoT) is commonly referred to as the connectivity of Internet devices that provides the communication interactivity between the physical and the cyber objects. One of the key objectives of Internet computing is to simplify human activities and improve the user experience and device access. To explore its basic challenges, big data is somehow diversified into smart-data intelligence that transforms the raw semantic data into smart-data. The transformation approaches realize the significance of productivity and financial gain, which in turn offers a better decision-making process and privacy preservation. Moreover, the intelligent system collects raw data from different devices that analyze the extracted information. Since IoT plays a significant role in the development of a new source dataset, a seamless authentication protocol (SAP) is preferably chosen to coalesce data inference, algorithm development, and technological advancement. The comparative analysis proves that the proposed SAP consumes less computation and communication overhead as compared to other authentication schemes.

The perceptual experience of next-generation wireless networks is nowadays coexisting with a unified heterogeneous system. It uses advanced internet protocol (IP) features with scalable infrastructure to optimize the efficiency rate of the core networks. Of late, the Internet-of-Things has played a significant role in the growth of smart devices that derive a forward and backward interface to provide a low-rate data flow. Moreover, a software-defined network (SDN) is openly chosen to explore the promising features such as controller and switches to separate the control and data plane. To fulfill the standard constraints of 6G networks, this article presents dynamic-driven congestion control and segment rerouting. It has two essential purposes: 1) to mitigate the flow rate and signal congestion and 2) to enhance the monitoring process and path adjustment. The approach of Deleroi superimposed, forward-backward interface, and segment rerouting have been implemented and configured in the IMSCore platform that examines the quality metrics such as throughput rate and transmission delay. To probe the signaling traffic and congestion rate, two superposition principles were derived that can minimize the transmission rate and signaling congestion to achieve the demands of the media zone.