The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems.

The Internet of Medical Things (IoMT) effectively tackles several shortcomings of conventional healthcare systems. It includes medical personnel shortages, patient care quality, insufficient medical supplies, and healthcare expenditures. There are several advantages of using IoMT technology for enhanced treatment efficiency and quality, thus improving patient health. However, the frequency and magnitude of cyberattacks on IoMT are increasing at a breakneck pace. Therefore, this article proposes a cyberattack detection method for IoMT-based networks using ensemble learning and fog-cloud architecture to address security issues. The ensemble technique employs a set of long short-term memory (LSTM) networks as individual learners at the first level and stacks a decision tree on top of them to classify attack and normal events. In addition, we present a framework for deploying the proposed IoMT-based approach as Infrastructure as a Service in the cloud and Software as a Service in the fog. The proposed method is evaluated on the telemetry datasets of IoT and IIoT sensors (ToN-IoT) dataset, and the outcomes reveal that it surpasses the baseline approaches in terms of precision by 4%.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using artificial intelligence within the cyber–physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices’ authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive edge computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel artificial bee colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.