The smart industry is a significant realization of the internet of things. IPv6 over time-slotted channel hopping (6TiSCH) is the new standard that has emerged as a promising technology for industrial communications. The demanding issues of smart industries include scalability, latency, and energy efficiency. Existing studies have presented scheduling schemes for 6TiSCH-based industries. However, these works are not suitable for practical scenarios where emergencies frequently occur. Hence, this study aims to improve scalability, latency, and energy efficiency in 6TiSCH-based smart industries for practical scenarios. A new fog assisted 6TiSCH Tri-Layer architecture is designed with a network layer, smart communication layer, and cloud layer. Scalability and latency are realized by fog computing in the smart communication layer. The rough set-based root selection (R2S) algorithm improves the network layer’s energy efficiency by constructing an R2S and destination-oriented direct acyclic graph. Emergency cognizant distributed adaptive scheduling minimizes the latency for data transmission through fuzzy Bayesian learning-based parent selection and multiobjective gravitational search algorithm-based channel selection. In the fog layer, the rank-based Q-learning algorithm performs offloading to manage the energy consumption among fog nodes. Industrial data are stored in the cloud layer where they can be accessed by end users. The performance is evaluated by modeling the tri-layer 6TiSCH network in network simulator-3.26. Experiments show that the proposed work achieves satisfactory results in terms of energy consumption, latency, end-to-end delay, response time, and transmission efficiency.

The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.

With the advancement of IPv6 technology, many nodes in wireless sensor networks realize seamless connections with the Internet via IPv6 addresses. Security issues are a significant obstacle to the widespread adoption of IPv6 technology. Resource-constrained IPv6 nodes face dual attacks: local and Internet-based. Moreover, constructing an active cyber defense system for IPv6-based wireless sensor networks is difficult. In this article, we propose a K-nearest neighbor-based intrusion detection mechanism and design a secure network framework. This mechanism trains an intrusion detection algorithm using a feature data set to create a normal profile. The intrusion detection algorithm uses the normal profile to perform real-time detection of network traffic data to achieve rapid detections connecting many devices in a wireless sensor network. In addition, we develop a test platform to verify this mechanism. Experimental results show that this mechanism is appropriate for IPv6-based wireless sensor networks and achieves a low false-positive rate and good intrusion detection accuracy at an acceptable resource cost.

Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network’s operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.