ABSTRACT The Internet of Things (IoT) has revolutionised the maintenance of parking systems. By incorporating IoT technology, parking systems can assist drivers in quickly locating appropriate parking spaces, alleviating traffic congestion, and minimising emissions from vehicles meandering in search of parking. This paper proposes a machine learning multilayer model that efficiently handles data generated by the sensors; it works at two layers to accurately predict appropriate parking spaces according to the vehicle type. Based on the survey conducted by the researchers, we use different models at different layers. At the first layer, an ensemble technique predicts the available parking spot; in the second layer, a random forest technique detects the type of vehicle. The integration of these techniques makes the proposed multilayer model novel, time‐efficient, and cost‐effective. These techniques are selected because they can handle complex data patterns, use the model's different strengths, and achieve high accuracy in prediction and classification tasks. The proposed multilayer model is implemented on the sensor dataset extracted from the Harvard Dataverse. The model is implemented using Python in a Jupyter notebook, and the evaluation metrics include accuracy, recall, precision, F1 score, and ROC curve. The proposed multilayer model obtained an accuracy of 97.88%, a precision of 96.11%, a recall of 95.69%, an F1 score of 96.04%, and an AUC score of 0.89. The results prove that the proposed multilayer model achieves the highest accuracy among all existing models in predicting the parking space with appropriate vehicle detection for the most effective parking space allocation in real‐time scenarios.

ABSTRACT Functional requirements (FRs) prioritization is process of ranking of software FRs from development perspective such that which requirement to be implemented first and which should not. FRs prioritization is necessary as these requirements are interrelated such that one requirement is necessary for the implementation of another requirement. Also, when two parallel developers work on interrelated dependent requirements, requirements must be prioritized. Prioritizing small size requirements is not a big issue due to a fewer number of comparisons but when developers implement large size requirements such as enterprise resource planning (ERP), it requires a huge number of comparisons. Numerous techniques are suggested for FRs prioritization such as AHP, which yield more accurate results, but these techniques are not scalable for large size software requirements. In this research paper, a new prioritization approach based on graph and k‐means clustering is suggested that will capture all dependencies from a list of FRs using a directed graph and then prioritize it with a clustering technique with fewer comparisons. The proposed technique based on directed graph and clustering approach is validated on ODOO ERP, which shows that with n‐1 pairwise comparisons, requirements can be prioritized.

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.

Impressive technological advancements over the past decades commenced significant advantages in the maritime industry sector and elevated commercial, operational, and financial benefits. However, technological development introduces several novel risks that pose serious and potential threats to the maritime industry and considerably impact the maritime industry. Keeping in view the importance of maritime cyber security, this study presents the cyber security threats to understand their impact and loss scale. It serves as a guideline for the stakeholders to implement effective preventive and corrective strategies. Cyber security risks are discussed concerning maritime security, confidentiality, integrity, and availability, and their impact is analyzed. The proneness of the digital transformation is analyzed regarding the use of internet of things (IoT) devices, modern security frameworks for ships, and sensors and devices used in modern ships. In addition, risk assessment methods are discussed to determine the potential threat and severity along with the cyber risk mitigation schemes and frameworks. Possible recommendations and countermeasures are elaborated to alleviate the impact of cyber security breaches. Finally, recommendations about the future prospects to safeguard the maritime industry from cyber-attacks are discussed, and the necessity of efficient security policies is highlighted.

Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA‐WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi‐Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi‐Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.

Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson’s motor symptoms appear on the loss of 60% to 80% of cells, due to the non-production of appropriate dopamine. Recent research found a close connection between the speech impairment and PD. Many researchers have developed a classification algorithm to identify the PD from speech signals. In this article, Adaptive Crow Search Algorithm (ACSA) and Deep Learning (DL)–based optimal feature selection method are introduced. The proposed model is the combination of CROW Search and Deep learning (CROWD) stack sparse autoencoder neural network. Parkinson’s dataset is taken for the experiment from the Irvine dataset repository at the University of California (UCI). In the first phase, dataset cleaning is performed to handle the missing values in the dataset. After that, the proposed ACSA algorithm is employed to find the scrunched feature vector. Furthermore, stack spare autoencoder with seven hidden layers is employed to generate the compressed feature vector. The performance of the proposed CROWD autoencoder model is compared with three feature selection approaches for six supervised classification techniques. The experiment result demonstrates that the performance of the proposed CROWD autoencoder feature selection model has outperformed the benchmarked feature selection techniques: (i) Maximum Relevance (mRMR) (ii) Recursive Feature Elimination (RFE), and (iii) Correlation-based Feature Selection (CFS), to classify Parkinson’s disease. This research has significance in the healthcare sector for the enhancement of classification accuracy up to 0.96%.

The Internet of Health Things (IoHT) is an extended version of the Internet of Things that is acting a starring role in data sharing remotely. These remote data sources consist of physiological processes, such as treatment progress, patient monitoring, and consultation. The main purpose of IoHT platform is to intervene independently from geographically remote areas by providing low‐cost preventive or active healthcare services. Several low‐power biomedical sensors with limited computing capabilities provide IoHT’s communication, integration, computation, and interoperability. However, IoHT transfers IoT data via IP‐centric Internet, which has implications for security and privacy. To address this issue, in this paper, we suggest using named data networking (NDN), a future Internet model that is well suited for mobile patients and caregivers. As the IoHT contains a lot of personal information about a user’s physical condition, which can be detrimental to users’ finances and health if leaked, therefore, data protection is important in the IoHT. Experts and scholars have researched this area, but the reconstruction of existing schemes could be further improved. Also, doing computing‐intensive tasks leads to slower response times, which further worsens the performance of IoHT. We are trying to resolve such an error, so a new NDN‐based certificateless signcryption scheme is proposed for IoHT using the security hardness of the hyperelliptic curve cryptosystem. Security analysis and comparisons with existing schemes show the viability of the designed scheme. The final results confirm that the designed scheme provides better security with minimal computational and communicational resources. Finally, we validate the security of the designed scheme against man‐in‐the‐middle attacks and replay attacks using the AVISPA tool.

Machine‐to‐machine communication (M2M) has obtained increasing interest in recent years. However, its enhancement and broadcasting characteristics produced a new security challenge. We have suggested a novel dynamic Quadrature Amplitude Modulation (QAM) scheme for a totally elastic and dynamic mapping of user data by using chaos. This paper analyses physical layer security methods in Orthogonal Frequency Division Multiplexing‐based Nonorthogonal Multiple Access (OFDM‐NOMA) and introduces a secure data transmission mechanism created by dynamic QAM. The security robustness given by the suggested encryption scheme is assessed, where an overall keyspace of ~10163 is achieved, which is sufficient to provide security against exhaustive attacks. The result of the scheme is verified through MATLAB simulation, where the bit error rate performance of our proposed scheme is compared with an unencrypted OFDM signal, and the performance of our proposed scheme is analyzed for an illegal user. The suggested dynamic mapping fulfills the fundamental obligations of cryptography for data security. Moreover, it enhances the level of security in OFDM‐NOMA.

Internet of Things (IoT) is considered one of the world’s ruling technologies. Billions of IoT devices connected together through IoT forming smart cities. As the concept grows, it is very challenging to design an infrastructure that is capable of handling large number of devices and process data effectively in a smart city paradigm. This paper proposed a structure for smart cities. It is implemented using a lightweight easy to implement network design and a simpler data format for information exchange that is suitable for developing countries like Pakistan. Using MQTT as network protocol, different sensor nodes were deployed for collecting data from the environment. Environmental factors like temperature, moisture, humidity, and percentage of CO 2 and methane gas were recorded and transferred to sink node for information sharing over the IoT cloud using an MQTT broker that can be accessed any time using Mosquitto client. The experiment results provide the performance analysis of the proposed network at different QoS levels for the MQTT protocol for IoT‐based smart cities. JSON structure is used to formulate the communication data structure for the proposed system.

In this article we are interested in optimizing the placement problem of security testing components for Internet of Things Architectures. Our goal is to extend existing techniques used in Fog computing to distribute application components over computational nodes. For that purpose, we identify several types of constraints, objectives functions and algorithms that can be adopted.

International audience