The history of human civilization has shown that the major strength behind the advancement of technology has always been the hu- man’s need for medical and healthcare applications. For decades, cloud computing has dominated healthcare information systems based on everything as a service model. However, the long service delay offered by cloud applications is one of the significant limitations. During emergency situations, monitoring of patient’s health, decisions must be made quickly with limited available resources, as it directly affects the patients’ lives. The introduction of edge computing and fog computing as optimal technologies has solved these latency-related problems. This paper provides an open architectural framework for fog-based Internet of Healthcare (Fog-IoH) applications. Besides, various services and applications support in the integration of fog with the IoT health sector have been indicated. Further, the applicability of IoT in healthcare has been discussed by presenting a case study of smart gloves wearable devices.

Content retrieval in content-centric vehicular networks faces challenges that include high latency, especially when content is stored far from the requesting vehicle. On-path caching feature in the conventional vehicular named data Networks (VNDN) enables content storage that can reduce latency. However, due to the constantly changing dynamic ad hoc nature of the vehicular network, the availability of stored content for the requester vehicle cannot be guaranteed. In addition, without knowing which content will be requested, where it will be requested and when it will be requested, the content caching functionality of VNDN is underutilized. To address this issue, this manuscript proposes a content prefetching scheme for the Content-centric Internet of Vehicles (CIoV) by introducing the content Value of Popularity ( $VoP$ ) matrix. Considering vehicles requesting content of similar interests, we evaluate $VoP$ through three value update functions that follow the power law of the time elapsed since the last content requested. By multiple parameters of consumer vehicle similarity, an on-road proactive content retriever vehicle is selected. The simulation results showed that the proposed proactive on-path content prefetching mechanism significantly reduces the content delivery delay while increasing the success delivery ratio by 48% and extends the spread of content within the network by 53%.

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system.

The Internet of Medical Things (IoMT), which includes medical devices, wearable devices, sensors and apps that connect to healthcare information systems, harnesses the technology that consumers already have available to them to transform the way we keep patients safe, healthy, and connected. How to enhance human cognitive performance for the IoMT with machine learning, common sense, natural language processing, and so on, for smart healthcare are worth exploring. The aim of this special issue is to stimulate discussion on the design, use, and evaluation of cognitive models for the medical big data to leverage deeper insights from the vast amount of medical data in the IoMT for smart healthcare. Based on the reviewers’ feedback, as well as the evaluations of editors, nine papers are selected in this special issue from many submissions. The nine papers which cover broad topics are introduced briefly as follows. The paper entitled “The Behavior Guidance and Abnormality Detection for A-MCI Patients under Wireless Sensor Network” authored by Gao et al., proposes a probabilistic model checkingbased method to predict patient behaviors and detect abnormal behaviors related to mild cognitive impairment to help patients rebuild their confidence and perception under wireless sensor networks. A case study is presented to demonstrate the usability and feasibility of the proposed method. The paper entitled “Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT” authored by Ahmed et al., proposes an assistant tool for psychologists to assist them in mental health treatment and note-taking in the artificial intelligence of medical things. The experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89. The paper entitled “LesionTalk: Core Data Extraction and Multi-Class Lesion Detection in IoT based Intelligent Healthcare” authored by Guo et al., proposes a core data extraction method for multi-class lesion detection based on unlabeled medical image from the internet of things (IoT). The experimental results show that the proposed method can effectively extract the core data of multiple lesions from low-quality medical images, and improve the accuracy of the lightweight lesion detection model as well as the interpretation of detection results. The paper entitled “TSDroid: A Novel Android Malware Detection Framework based on Temporal & Spatial Metrics in IoMT” authored by Zhang et al., proposes a novel android malware detection framework based on temporal and spatial metrics in the IoMT. In this framework, the authors use TS-based clustering algorithm to obtain clustering subsets to enhance the

Hypothesis: Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early prediction of infection severity would facilitate resources for the treatment of the infection and prevent contamination to the whole field. Methodology: In this study, a hybrid prediction model was developed to predict various levels of severity of blast disease based on diseased plant images. The proposed model is a four-fold severity prediction model. The level of severity is defined based on the percentage of leaf area affected by the disease. The image dataset is derived from both primary and secondary resources. Tools: The features are first extracted with the help of the Convolutional Neural Network (CNN) approach. Then the identification and classification of the severity level of blast disease are conducted using a Support Vector Machine (SVM). Conclusion: Mendeley, Kaggle, GitHub, and UCI are the secondary resources used for dataset generation. The number of images in the dataset is 1908. The proposed hybrid model achieves 97% accuracy.

In intelligent transportation systems, the key issue of the Ride-Sharing Service (RSS) is to find proper drivers for the passengers by Intelligent Matching (IM) of two or three objects, including the positions of drivers, the travel information of passengers, and the spots where passengers and drivers meet and separate. Unfortunately, the exposure of travel plans of passengers in the IM process due to inference attacks has raised concerns about the privacy violation. To resist the inference attacks, we propose a Differentially Private Tripartite IM (DPTIM) protocol for RSS. DPTIM is based on the tripartite IM process, which intelligently finds the suitable threshold to filter out the matched objects with satisfaction scores below the threshold, so as to provide the high average satisfaction score of matched passengers. Compared to existing relevant mechanisms, DPTIM is distinguished by the feature that it leverages the inference error and differential privacy techniques to prevent the prior-information-based inference attacks and constrain the posterior information leakage, while providing satisfactory matching results. Furthermore, DPTIM meets the personalized demand of location privacy by using the passenger-specific tolerance estimation on inference errors and the personalized privacy budget. Finally, we implement DPTIM on real-world datasets, and demonstrate the satisfactory performance of DPTIM in terms of the average satisfaction score of passengers, the anti-inference-attack capability, and the passenger-specific privacy requirement.

Named-data networking (NDN) is a promising communication technology for vehicular networks because it offers mobility support and inherent en route content security. However, vanilla NDN uses pull-based communication that experiences long communication delays that make it ineligible for the cooperative driving applications requiring real-time communications, i.e., platooning. Additionally, it requires a proper content naming convention to enable content communication. Therefore, in this article, we propose a platoon-specific naming and name-based content communication scheme. The proposed protocol minimizes the communication overhead with minimum delay in platooning scenario. The simulation results show that our proposed name-based protocol offers better performance in terms of low commu-nication overhead, delay, and strongly correlated platoon dynamics compared to the vanilla NDN.

In Smart Cities' applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node's trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of [Formula: see text] and [Formula: see text] with the varying number of nodes.

Seamless connectivity between the vehicles and the infrastructures is required for the provisioning of future vehicular applications. However, the infrastructures in vehicular ad hoc networks (VANETs) such as roadside units (RSUs) are insufficient to provide ubiquitous connectivity and coverage in urban areas. Thus the services get interrupted, which results in lower network performance in VANETs. Motivated by this in this paper, we proposed a Proactive UAV Placement (PUP) technique to assist the RSUs in delivering the services in the out-of-coverage region by placing the UAVs in the appropriate positions. We first predicted the distribution of the future vehicles by utilizing the LSTM neural network. Based on the distribution of the future vehicles, the optimization problem for optimal UAV placement is formulated and solved by utilizing the Particle Swarm Optimization (PSO) algorithm. The results demonstrated that our scheme achieves better average coverage compared to two other UAV-assisted schemes.

In recent times, the advancements in IoT technology have benefited various application areas. By enabling IoT in automobile communication, vehicles are now expected to support various information and entertainment data transmission. Nevertheless, due to massive data produced by different WSN- and IoT-based multimedia and security-related applications, the management of such huge traffic by vehicular network is a challenge. This chapter utilizes Information-Centric Networking (ICN) to manage each request processing in vehicular environment. Further, to handle large data traffic during request response transmission, caching in ICN is preferred, which will improve the whole network performance by supporting fast delivery of requested content.

ue to the rapid growth of connected vehicles, many research constraints need to be addressed, e.g., reliability and latency, practical MAC and routing protocols, performance and adaptability to the changes in the environment (node density and oscillation in network topology), and validation of protocols under the umbrella of coherent assumptions using simulation methodologies. In this Feature Topic, we present 10 papers proposing very interesting solutions and architectures for futuristic and smarter connected vehicles. evaluated with supporting simulations and qualitative discussions.

Internet of Things (IoT) is an emerging trend for various applications due to its ability to connect billions of things across the globe using the Internet. The IoT-based healthcare domain always demands real-time computations in emergency situations. Due to high network traffic and sensor data from patients, most IoT-based healthcare systems often suffer from high response delay as well as from poor network resources utilization. However, the recent developments in the field of fog and cloud computing offer several benefits by providing low-cost computation as well as better storage. Further, the issues related to connectivity of billions of devices by assigning IP addresses and location-based communication is addressed by future a Internet architecture known as information-centric networking (ICN). In this paper, we propose an ICN-fog computing layer between healthcare IoT devices and the cloud. The in-network caching feature of ICN with the concept of fog by extending the cloud to the edge of the network offers real-time computations by reducing the response delay for critical scenarios.

Maritime search and rescue plays an important part in ensuring the safety of life at sea. When using wireless Sensor Network (WSN) technologies in maritime, nevertheless, it endures from situations where the measurement information is inadequate. In computing and networking for maritime applications, Wireless Sensor Network (WSNs) is a rising inflexion because of its amazing features. Simultaneously, there are some challenges faced by WSNs and node localization is one of them. Node localization is an important factor because until the location of reporting node is unknown, the data sensed by that node is totally useless. The main aim of this paper is towards gaining more improvement in localization by using swarm intelligence algorithm. To achieve this aim, a range-free and distributed method by using the application of salp swarm algorithm for moving target node in network for maritime rescue is proposed. The results are compared with existing algorithm Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA). The proposed method has approximately 10% less localization error as compared to PSO and BOA. The proposed algorithm is validated in terms of localization accuracy, localized nodes, localization errors and computing time.

Medical imaging, also known as diagnostic imaging, is growing fast due to its potential to provide accurate and detailed information regarding medical issues in cost-effective fashion. It can help detect diseases, monitor disease progression and is useful for surgical planning. The major challenge in this area is how to interpret images. Hence, doctors need tools that assist them in analysing the data and presenting them in an understandable manner to take the necessary course of action. The use of artificial intelligence (AI) in medical imaging has the potential to improve patient care, enhance collaboration between radiologists and other clinicians, and even help extend access to health services that would otherwise be impossible. One of the challenges faced in medical imaging is understanding the relationship between anatomy and the pathologic process. Hence, key areas of medical imaging that can benefit from AI are computer-aided diagnosis or interpretation, decision support systems for clinicians, and effective communication for patients. This special issue includes six articles, and each one was accepted after a blinded review process. Their major contributions are highlighted below. The first article is entitled ‘Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images.’ The authors propose a neural network-based algorithm for early diagnosis of Alzheimer's disease. The prediction model is built using MRI images. This approach provides better classification accuracy with improved performance measures. The second article is entitled ‘BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm.’ The authors focus on skin lesion detection using machine learning algorithms. A broad learning-based algorithm is used for the lesion detection and classification process. This is made with the help of incremental algorithms. The third article is entitled ‘Detecting pulmonary edema in lung resection patient through point-of-care lung ultrasonography.’ Pulmonary edema remains a serious health concern throughout the world. The authors propose an efficient algorithm for earlier detection of pulmonary edema using point-of-care lung ultrasonography. This approach provides better results when compared with traditional methods. The fourth article is entitled ‘3D brain image-based Alzheimer's disease (AD) detection techniques using fish swarm optimizer's deep convolution siamese neural network.’ The authors propose a fish swarm optimizer's deep convolution siamese neural network for earlier detection and diagnosis of Alzheimer's diseases. The prediction accuracy is comparatively better than conventional approaches, and it offers better performance. The fifth article is ‘Detecting breast cancer using novel mask R-CNN techniques.’ Breast cancer remains a serious concern throughout the world. The authors propose an R-CNN technique for efficient detection and diagnosis of breast cancer at an early stage of development. This approach provides better accuracy and precision measures. The sixth article is entitled ‘Feature optimization and identification of ovarian cancer using internet of medical thing.’ The major focus of this work is ovarian cancer. The proposed method is based on feature optimization techniques using machine learning. It provides improved optimization efficiency and better accuracy. With this approach, ovarian cancer can be detected easily at an earlier stage. Artificial intelligence (AI) has come a long way in healthcare. While it may be easy to think of AI as making machines intelligent, the more accurate definition is that it allows computers to complete tasks in ways that currently require human intelligence. At the same time, however, AI can complete many tasks that would be far too time-consuming or not possible for a human to do. This special issue has explored the significance of AI approaches for medical imaging. We hope this special issue will add significant benefits to the research community. We thank all the authors and reviewers for their timely contributions.

During the transition from traditional networks to pure Software-Defined Networks (SDNs), hybrid SDNs are derived to provide flexible services. Furthermore, developing effective failure protection algorithms is significantly urgent to improve the robustness and resilience of networks. In this paper, we propose several algorithms, including Straightforward Protection Path Selection with the Source and Destination-based Tunneling mechanism (S-PPSSDT), its simplification PPSSDT and Protection Path Selection with Load-Balancing (PPSLB) algorithms. Considering that the Protection Path Length (PPL) will affect the transmission delay of rerouted packets, S-PPSSDT leverages the source and destination-based tunneling mechanism to select optimum protection paths with the shortest length. Based on classification idea and optimization search, PPSSDT prunes unnecessary path searches in S-PPSSDT. Combining the tunneling mechanism with the priority-based Maximum Link Utilization (MLU) minimization approach, PPSLB also focuses on load balancing during failure protection to achieve potential congestion avoidance simultaneously. Besides, these algorithms redirect the traffic through various tunnels for affected paths by the non-shortest path routing capability of SDN switches. Extensive simulation results indicate that our proposed algorithms can effectively reduce the PPL and MLU of hybrid SDNs during failure protection, and have excellent performance under various network parameters compared with the state-of-the-art algorithms.

Today, the rapid development, large volume, and global access of multimedia applications have demanded the shifting of network functionality from the central server to the near user. In recent times, Edge Computing (EC) and Information Centric Networking (ICN) have been presented as arising advances for content dissemination near the end user. The EC aims to provide content locally by reducing the burden of the core networks, while ICN allows content routing and forwarding based on content names directly. The inherent in-network caching feature of ICN facilitates caching by intermediate network nodes. The combined use of EC and ICN can efficiently handle content dissemination and ultimately improves user experience. In this regards, an ICN based edge caching scheme has been proposed for handling multimedia big data traffic in IoT based smart cities, while incorporating four caching attributes in its proposed design. Firstly, a layered network architecture has been presented that offers Device-to-Device (D2D) communication and ICN support at the application layer of Base station (BS) for utilizing caching of the requested content at edge of the network. Secondly, a decision on caching of contents at network nodes in layered network architecture has been presented based on various centrality measures, which supports efficient caching. Thirdly, this work offers caching of content near the delivery path in ICN network tiers, while leveraging near path caching for fast content dissemination. Lastly, reproactive caching where proactive caching is followed by reactive caching for controlling the network traffic during peak hours has been integrated in to the design model. The performance of the proposed scheme has been evaluated by conducting simulations in Icarus- an ICN caching simulator against various caching benchmark schemes. The results received from the experiments have shown significant performance improvement of our proposed scheme for different performance metrics including cache hit ratio, content retrieval delay, content path stretch, and internal link load.

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Information-centric networking (ICN) is a paradigm shift to host-centric communication. It provides a promising solution for efficient content delivery between the subscribers and the publishers. In-network caching is of great importance as efficiency of ICN primarily depends on performance of caching strategy. Therefore, in recent years researchers proposed many caching strategies with the aim to improve ICN performance. However, due to lack of common evaluation scenario for comparison among these strategies, it is not clear which one has better performance than other. This paper compares Cache Everything Everywhere (CEE), Leave Copy Down (LCD), Random Copy One (RCO), ProbCache, Centrality-Based Caching (CBC), Greedy Caching (GrC) and Optimal Caching (OptC) in common evaluation scenario with same simulation environment. The paper also analyzes the performance of these strategies in terms of content retrieval delay (CRD) and cache hit ratio (CHR) to determine the one which best fits in every scenario.