An authentic-based privacy preservation protocol for smart e-healthcare systems in iot

© 2013 IEEE. Emerging technologies rapidly change the essential qualities of modern societies in terms of smart environments. To utilize the surrounding environment data, tiny sensing devices and smart gateways are highly involved. It has been used to collect and analyze the real-time data remotely in all Industrial Internet of Things (IIoT). Since the IIoT environment gathers and transmits the data over insecure public networks, a promising solution known as authentication and key agreement (AKA) is preferred to prevent illegal access. In the medical industry, the Internet of Medical Things (IoM) has become an expert application system. It is used to gather and analyze the physiological parameters of patients. To practically examine the medical sensor-nodes, which are imbedded in the patient\u27s body. It would in turn sense the patient medical information using smart portable devices. Since the patient information is so sensitive to reveal other than a medical professional, the security protection and privacy of medical data are becoming a challenging issue of the IoM. Thus, an anonymity-based user authentication protocol is preferred to resolve the privacy preservation issues in the IoM. In this paper, a Secure and Anonymous Biometric Based User Authentication Scheme (SAB-UAS) is proposed to ensure secure communication in healthcare applications. This paper also proves that an adversary cannot impersonate as a legitimate user to illegally access or revoke the smart handheld card. A formal analysis based on the random-oracle model and resource analysis is provided to show security and resource efficiencies in medical application systems. In addition, the proposed scheme takes a part of the performance analysis to show that it has high-security features to build smart healthcare application systems in the IoM. To this end, experimental analysis has been conducted for the analysis of network parameters using NS3 simulator. The collected results have shown superiority in terms of the packet delivery ratio, end-to-end delay, throughput rates, and routing overhead for the proposed SAB-UAS in comparison to other existing protocols

EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats

Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient’s body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease

Analyzing enhanced real-time uplink scheduling algorithm in 3GPP LTE-advanced networks using multimedia systems

Third Generation Partnership Project (3GPP) standardizes the Long-Term Evolution (LTE) to improve the quality of service in modern communication systems using 3GPP LTE-advanced (LTE-A) networks. As this technology is converging with modern devices, efficient resource allocation schemes are essential for minimization of the communication delay for the sensitive real-time devices. To achieve the demands of latest technologies, this paper proposes two novel mechanisms, enhanced real-time polling system with continuous time-batch Markovian arrival process and proactive resource allocation framework. The objective of the former mechanism is to improvise the service connectivity between the multimedia devices in LTE-A networks and the latter is to exploit the transmission bits in each user's buffer in addition to the state information of wireless channel. The proposed mechanisms consider the constraints enforced by 3GPP standard in terms of radio resource blocks allocation for LTE-A networks. In addition, the proposed mechanisms use LTE-A networks to investigate the Voice over LTE traffic in order to analyze various quality-of-service measures, namely, sum throughput, session setup delay, packet delivery ratio, resource utilization ratio, and throughput improvement via real-time multimedia client-server systems. The probing results show that the proposed algorithm is superior in comparison to other typical services of IEEE 802.16d/e-based systems as well as LTE-A system with 7 orthogonal frequency-division multiplexing symbols over a duration slot better throughput performance in comparison with existing mechanisms

IoT-BSFCAN: A smart context-aware system in IoT-Cloud using mobile-fogging

© 2020 Elsevier B.V. Internet of Things (IoT) leverages the sensor inter-connectivity that offers a wide range of real-time monitoring opportunities for smart environmental systems. As a consequence, it is witnessed that IoT plays an important role in the next generation of smart communication networks. In the future, the evolution of smart environment becomes a pillar of 2020 society for the various innovative services. In order to deal with the challenges, such as supporting urban development and improving the quality of people\u27s life, an interdisciplinary approach is preferred. It is evident that the infrastructure of smart information and communication technology (ICT) claims to be a de facto standard to realize the smart vision of emerging technologies. Thus, an IoT-Based Smart CAN (IoT-BSFCAN) framework is proposed to monitor the smart environment continuously through smart computing devices over cloud-enabled networks. The objective of this framework is to minimize computation cost along with communication fairness, while it uses different kinds of user applications. Moreover, the illustrative result proves that the proposed IoT-BSFCAN framework yields better successful execution results than the other alternative solutions

Improving the physical layer security of IoT-5G systems

Ensuring the security of the Internet of Things (IoT) is deemed as one of the most critical challenges and needs that have to be addressed in order to guarantee the successful deployment of IoT in emerging technologies like 5G. In an effort to address this challenge, in this work, an improved and flexible physical layer security technique, referred to as orthogonal frequency-division multiplexing with subcarrier index selection and artificially interfering signals (OFDM-SIS-AIS), is developed for protecting the transmission of OFDM-based waveforms against eavesdropping in 5G and beyond wireless networks. In this technique, the frequency response of correlated subchannels is first converted into a completely randomized and independent response by means of adaptive interleaving. Then, the whole OFDM block is divided into small subblocks, each containing a set of subcarriers, from which a subset of these subcarriers, which are corresponding to high subchannel gains, are selected and used for data transmission, while the remaining ones, which are corresponding to low subchannel gains, are used for sending artificially interfering signals. The selected subcarriers are determined through an optimization problem that can effectively maximize the signal-to-noise ratio (SNR) at only the legitimate receiver. The obtained results demonstrate a significant improvement in the secrecy gap performance without considering the knowledge of the eavesdropper’s channel nor sharing any keys while maintaining low complexity and high reliability at the legitimate user. These numerous advantages have the potential to make the proposed scheme a consistent candidate technique for secure IoT-5G based services.No sponso