The increasing importance of intravehicular health monitoring systems (IVHMSs) necessitates robust communication protocols within vehicles, especially with the integration of the Internet of Things (IoT). This integration presents challenges in enhancing vehicle health monitoring efficiency due to the constrained space for numerous sensing devices, leading to scalability and performance issues. Existing medium access control (MAC) schemes often result in congestion, decreased throughput, and increased delays. This study proposes a scalable hybrid MAC strategy to improve throughput and reduce congestion in IVHMS. The approach features a two-phase communication model—nonemergency and emergency phases—for scalability. Our hybrid MAC strategy combines a history-based approach for emergency communication and a priority-based approach for nonemergency Communication. A Markov chain model evaluates the expected throughput and delay of the proposed MAC strategy, with numerical analysis validating the approach. Results show the hybrid MAC achieves a 66.7% throughput improvement over the history-based strategy and a 16.7% improvement over the priority-based approach while effectively reducing data collisions and delays. Furthermore, the hybrid MAC demonstrates an 8.3% increase in throughput compared to a previously proposed distributed hybrid MAC. Implementing this hybrid MAC in industrial-scale vehicular health monitoring can enhance vehicle safety, benefiting manufacturers and passengers alike.

With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics such as trust, privacy, security, reliability, resilience and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this paper, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep learning-based Pyramidal Recurrent Units (PRU) and Decision Tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The non-linear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on fifteen datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

Massive data handling requirement in education Industry 4.0 has attracted interests in the research of microservice architectures due to their scalability, resilience, and elasticity characteristics. This development has been challenged by extensive data exchange required by a set of independent microservices to build a complete application, which could result in increasing risks and exposure to the security and privacy breaches of the data. It is imperative to see that educational data are highly sensitive, critical for ascertaining educational attainment and facilitating credentials for qualification verifications. This article puts forward a new proposal of devising a security and privacy-preserving design mechanism of data transactions in educational microservices leveraging the blockchain technology. The design comprises three phases, namely the blockchain framework, data sending–receiving, and confidentiality-integrity-availability over a secured platform with each phase having detailed mechanisms for algorithm implementation. The proposal is shown to exhibit favorable performance in terms of time cost of publishing, throughput, and latency, and shown to have high survey acceptance in terms of confidentiality, integrity, and availability with approximately 10% improvement from prior blockchain adoption.

While recent works on investigating renewable energy sources for powering the highway offer promising solutions for sustainable environments, they are often impeded by unequal distribution of sources across the region due to variations in solar exposure and road intensity that electromagnetically and mechanically generate the energy. By exploiting viable gathering of massive renewable energy data using the Internet of Things (IoT), this paper proposes a framework for improved highway-energy management based on the unmanned aerial vehicle-assisted wireless energy re-distribution of the harvested renewable energy. Combining both massive low-rate sensing with high-speed 6G-envisioned transmission for data aggregation, the IoT architecture is of multi-scale, consisting of: i) global data exchange and analytics for energy mapping, re-distribution planning and forecasting, and ii) local data sensing and processing at individual highway lampposts for micro-energy management. The feasibility of the networked energy system is analyzed via analytical cost-reliability analyses. The cost analysis demonstrates the cost-effectiveness through the lowest Requirement of Energy and Cost of Energy for the setup and maintenance. The reliability analysis reveals the energy plus (E+) feature of the system in certain conditions with enhanced reliability in adverse weathers that impact energy generation. With multi-scale data connectivity to intelligently manage standalone renewable energy, this work puts forward a viable idea of 6G use cases with massively networked energy sensors with a vision of achieving super-connected and intelligence-equipped highways.

The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

Recent years have seen continuous exposure of online social network (OSN) users to the security vulnerability due to socio-technical cyber hazards committed by OSN associates. Unfortunately, the current OSN platforms has lack of functionalities for automatic users protection in initiating online friendship and online users interaction within their circle. Moreover, the users cannot analyze their associates' time-varying and changing behavior, which is a strong indicator of malicious activities. To address this issue, this paper proposes a design of automatic two-phase pre- and post-filtering approach with the objective of mitigating the cyber threats in OSN platforms. In the pre-filtering phase, we propose a method where each user can access a reliable technique to select a friend after getting his/her details authenticity. In the post-filtering phase, a control mechanism is established to recognize malicious posts/actions of the associates in order to limit the spread of the corresponding cyber threats. Our empirical analysis shows some significant comparison data towards hybrid (combined pre- and post-filtering) method, where the users' consents for our proposed method exceed significantly over the other competing techniques. In fact, the proportional mean value of the hybrid method is increased almost twice (2.03) of the existing methods.

In this article, an industrial cyber-physical system (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth and energy), and environmental problems. This makes data collection in the ICPS untrustworthy, even the data are altered after the data forwarding. Without validating this before data aggregation, detection of an event through the aggregation in the ICPS can be difficult. This article introduces TrustData, a scheme for high-quality data collection for event detection in the ICPS, referred to as “Trust worthy and secured Data collection” scheme. It alleviates authentic data for accumulation at groups of sensor devices in the ICPS. Based on the application requirements, a reduced quantity of data is delivered to an upstream node, say, a cluster head. We consider that these data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this article is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data are trustworthy (unaltered) before transmission, and whether or not the transmitted data are secured (data privacy is preserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting toward upstream nodes. Finally, we provide an extensive performance analysis through a real-world dataset, and our results prove the effectiveness of TrustData.

As more knowledge discovery functions or sensing units for event detection are added to sensor devices in the Internet of Things (IoT), devices acquire big data that is bigger than they are able to deliver using their radios in a given time window. As a result, energy consumption for big data acquisition and transmission and real-time data processing are great challenges. In this paper, we introduce BigReduce, a low-cost IoT framework for event detection that reduces a big amount of data at the time of data acquisition and before the data transmission across the network. BigReduce works on the analysis of the frequency content of signals as they are acquired and efficiently adapts the frequency rate based on the sensitivity to a respective event, such as fire event. Instead of transmitting the entire set of acquired data, BigReduce transmits only the signals that have a high event-sensitivity. We provide a detailed algorithm for fire event sensitivity indication based on the frequency consents. Results achieved through a lab testbed show that BigReduce is able to reduce energy consumption by at least 78% and data volume by 82% in comparison to other frameworks.

Due to rapid population growth, the demand for food is also elevating, which inspires farmers to embrace precision agriculture to increase production by exploiting predictive analytics on relevant real-time data. The exactitude of a prediction is vital to decide the next course of actions to be taken to compensate current demands, which again relies on a competent data acquisition technique. The Media Access Control (MAC) protocols have significant contribution in designing data acquisition technique. In this paper, we propose a new Store-and-Delivery base MAC (SD-MAC) protocol for Air-Ground Collaborative Wireless Networks (AGCWNs) to acquire data efficiently from the sensing devices which are deployed in the agricultural field. Our proposed protocol takes into consideration of the factors of network architecture and transforms them into advantages to attain higher throughput. The performance of the proposed protocol is evaluated using simulations and involving another such protocol, where the proposed protocol outperforms the other protocol.