In recent years, wearable devices have attracted attention because of their ability to enhance the quality of life. This disruptive technology has helped healthcare professionals with intervening early in chronic diseases, especially amongst independently living patients, and has facilitated real-time monitoring of patients’ vital signs remotely. One of the major bottlenecks that hampers the adoption of wearable device is continuous power supply. Most wearable devices solely depend on battery supply. When the energy stored in the battery is depleted, the operation of wearable devices is affected. To overcome this limitation, efficient energy harvesters for wearable devices are crucial. The paper primarily aims to present a comprehensive classification of different energy sources that can be capitalised to power wearable devices. In addition, this research paper deals with the key challenges that must be considered in the development of autonomous wearable devices for telemedicine applications with a proposed system design for wearable device that uses energy harvesting technology.

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approaches to identify DDoS attacks in SDN networks between 2018 and the beginning of November 2022. To search the contemporary literature, we have extensively utilized a number of digital libraries (including IEEE, ACM, Springer, and other digital libraries) and one academic search engine (Google Scholar). We have analyzed the relevant studies and categorized the results of the SLR into five areas: (i) The different types of DDoS attack detection in ML/DL approaches; (ii) the methodologies, strengths, and weaknesses of existing ML/DL approaches for DDoS attacks detection; (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature; (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature; and (v) current research gaps and promising future directions.

Recently, there have been increasing calls for healthcare providers to provide controls for patients over their personal health records. Nevertheless, security issues concerning how different healthcare providers exchange healthcare information have caused a flop in the deployment of such systems. The ability to exchange data securely is important so that new borderless integrated healthcare services can be provided to patients. Due to its decentralized nature, blockchain technology is a suitable driver for the much-needed shift towards integrated healthcare, providing new insights and addressing some of the main challenges of many healthcare areas. Blockchain allows healthcare providers to record and manage peer-to-peer transactions through a network without central authority. In this paper, we discuss the concept of blockchain technology and hurdles in their adoption in the healthcare domain. Furthermore, a review is conducted on the latest implementations of blockchain technology in healthcare. Finally, a new case study of a blockchain-based healthcare platform is presented addressing the drawbacks of current designs, followed by recommendations for future blockchain researchers and developers.

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.

The wide proliferation of telecommunication technologies in the last decade gives rise to the number of more sophisticated security threats. Software-Defined Networking (SDN) is a new networking architecture that isolates the network control plane from the data plane that incidentally provides better features and functionalities to detect and deal with those security threats. Its elastic programmable feature permits efficient network management and provides network operators with the flexibility to monitor and fine-tune their network. However, the new technology also created many new security concerns, and the threat of Distributed Denial of Service (DDoS) attack is one of the major concerns. This paper presents a comprehensive review of state-of-the-art techniques to detect DDoS attacks on SDN controller. It first describes the SDN technology and then elaborates on the mechanism of DDoS attacks on SDN. Additionally, this paper also describes all major DDoS detection techniques and classifies them at a very high level according to the techniques or methods used. The current survey is qualitatively compared with the existing surveys using various author-defined metrics. Finally, this paper provides a guideline for future research related to detection techniques of DDoS against the SDN controller.

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.