Optical intelligent reflecting surface-assisted NOMA for VLC system: Fundamentals, potentials, open research issues, and future trends

Cloud manufacturing (CMfg) is a new manufacturing paradigm over computer networks aiming at using distributed resources in the form of manufacturing capabilities, hardware, and software. Some modern technologies such as cloud computing, Internet of Things (IoT), service-oriented, and radio-frequency identification (RFID) play a key role in CMfg. In CMfg, all resources needed for manufacturing such as hardware, software, and manufacturing capabilities are virtualized; the services are provided by manufacturing resources. In this paper, the key characteristics, concepts, challenges, open issues, and future trends of cloud manufacturing are presented to direct the future researches. Accordingly, five directions of advances in CMfg are introduced and the articles in five categories are reviewed and analyzed: (1) studies focused on the architecture and platform design of CMfg; (2) studies concentrated on resource description and encapsulation; (3) studies focused on service selection and composition; (4) studies aimed at resource allocation and service scheduling; and (5) studies aimed at service searching and matching. The article also aims at providing a development diagram in the area of CMfg as a roadmap for future research opportunities and practice.

Wireless Multimedia Sensor Networks (WMSNs) have emerged and shifted the focus from the typical scalar wireless sensor networks to networks with multimedia devices that are capable to retrieve video, audio, images, as well as scalar sensor data. WMSNs are able to deliver multimedia content due to the availability of inexpensive CMOS cameras and microphones coupled with the significant progress in distributed signal processing and multimedia source coding techniques. These mentioned characteristics, challenges, and requirements of designing WMSNs open many research issues and future research directions to develop protocols, algorithms, architectures, devices, and testbeds to maximize the network lifetime while satisfying the quality of service requirements of the various applications. In this thesis dissertation, we outline the design challenges of WMSNs and we give a comprehensive discussion of the proposed architectures and protocols for the different layers of the communication protocol stack for WMSNs along with their open research issues. Also, we conduct a comparison among the existing WMSN hardware and testbeds based on their specifications and features along with complete classification based on their functionalities and capabilities. In addition, we introduce our complete classification for content security and contextual privacy in WSNs. Our focus in this field, after conducting a complete survey in WMSNs and event privacy in sensor networks, and earning the necessary knowledge of programming sensor motes such as Micaz and Stargate and running simulation using NS2, is to design suitable protocols meet the challenging requirements of WMSNs targeting especially the routing and MAC layers, secure the wirelessly exchange of data against external attacks using proper security algorithms: key management and secure routing, defend the network from internal attacks by using a light-weight intrusion detection technique, protect the contextual information from being leaked to unauthorized parties by adapting an event unobservability scheme, and evaluate the performance efficiency and energy consumption of employing the security algorithms over WMSNs.

The seminar on Digital Evidence and Forensic Readiness provided the space for interdisciplinary discussions on clearly defined critical aspects of engineering issues, evaluation and processes for secure digital evidence and forensic readiness. A large gap exists between the state-of-the-art in IT security and best-practice procedures for digital evidence. Experts from IT and law used this seminar to develop a common view on what exactly can be considered secure and admissible digital evidence. In addition to sessions with all participants, a separation of participants for discussing was arranged. The outcome of these working sessions was used in the general discussion to work on a common understanding of the topic. The results of the seminar will lead to new technological developments as well as to new legal views to this points and to a change of organizational measures using ICT. Finally, various open issues and research topics have been identified. In addition to this report, open research issues will also be published in the form of a manifesto on digital evidence. One possible definition for Secure Digital Evidence was proposed by Rudolph et al. at the Eighth Annual IFIP WG 11.9 International Conference on Digital Forensics 2012. It states that a data record can be considered secure if it was created authentically by a device for which the following holds: - The device is physically protected to ensure at least tamper-evidence. - The data record is securely bound to the identity and status of the device (including running software and configuration) and to all other relevant parameters (such as time, temperature, location, users involved, etc.) - The data record has not been changed after creation. Digital Evidence according to this definition comprises the measured value and additional information on the state of the measurement device. This additional information on the state of the measurement device aims to document the operation environment providing evidence that can help lay the foundation for admissibility. This definition provided one basis of discussion at the seminar and was compared to other approaches to forensic readiness. Additional relevant aspects occur in the forensic readiness of mobile device, cloud computing and services. Such scenarios are already very frequent but will come to full force in the near future. The interdisciplinary Dagstuhl seminar on digital evidence and forensic readiness has provided valuable input to the discussion on the future of various types of evidence and it has build the basis for acceptable and sound rules for the assessment of digital evidences. Furthermore, it has established new links between experts from four continents and thus has set the foundations for new interdisciplinary and international co-operations.

Cancelable and hybrid biometric cryptosystems are two techniques used to offer protection against the security and privacy challenges faced by users of biometric authentication systems. The main objective of this paper is to present a critical review of current and emerging trends as well as open research issues in cancellable and hybrid biometric systems. The study examines cancelable biometrics under two main categories, namely non-invertible transformation and biometric salting. It also explores hybrid cryptosystems as means of providing improved template security and user privacy. The review focusses on the modes of operation, performance accuracy, security and privacy of various types of cancellable and hybrid biometric cryptosystems. It also provides a more comprehensive survey of latest research works in cancellable and hybrid biometric cryptosystems than existing review papers in these fields. The paper will provide readers with up-to-date information on current directions and open research issues in cancelable and hybrid biometric cryptosystems.

In this paper, we present the classification and review of security schemes in mobile computing system. We classify these schemes based on types the infrastructure used in the mobile computing system-Mobile Ad Hoc Networks (MANET) and Mobile Agent model. Mobile Ad Hoc Networks are pervasive, ubiquitous and without any centralized authority. These unique characteristics, combined with ever-increasing security threats, demand solutions in securing ad hoc networks prior to their deployment in commercial and military applications. This paper reviews the prevailing mobile ad hoc network security threats, the existing solution schemes, their limitations and open research issues. We also explain the Intrusion detection and response technique as an alternate method to protect the MANET based mobile computing systems and their approaches. A literature review of important existing Intrusion Detection approaches and Intrusion Response Approaches for MANET is also presented. This paper also presents the limitations of existing Intrusion Detection and Response Approaches for MANET and open research issues in providing MANET security. With respect to Mobile Agent based mobile computing system, we have presented the classification of various types of security attacks in Mobile Agent based model and presented the security solutions for those type of attacks proposed by the various schemes and the open research issues in providing security for Mobile Agent based mobile computing system. Such classification enhances the understanding of the proposed security schemes in the mobile computing system, assists in the development and enhancement of schemes in the future and helps in choosing an appropriate scheme while implementing a mobile computing system.

Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms (HIOAs). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms (HIOAs) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

A huge repository of terabytes of data is generated each day from modern information systems and digital technolo-gies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues.

Internet of Things and cloud computing, a huge repository of terabytes of data is generated each day from modern information systems and digital technologies. For decision making, analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Based on the challenges and open research issues, it opens a new horizon for researchers to develop the solution additionally. Keywords: Big data analytics; Hadoop; Massive data; Structured data; Unstructured Data

Body area network (BAN) technology has emerged in recent years as a subcategory of wireless sensor network technology targeted at monitoring physiological and ambient conditions surrounding human beings and animals. However, BAN technology also introduces a number of challenges seldom seen before due to the scarcity of hardware and radio communication resources and the special properties of the radio environment under which they operate. In this chapter, we review the foundations of BANs along with the most relevant aspects relating to their design and deployment. We introduce current, state-of-the-art applications of BAN, as well as the most challenging aspects concerning their adoption and gradual deployment. We also discuss issues pertaining to sensor node communications, trade-offs, and interfacing with external infrastructure, in addition to important aspects relating to wearable sensor technology, enabling software and hardware, as well as future trends and open research issues in BANs.

Intelligence in the Internet of Medical Things era: A systematic review of current and future trends

Chapter 3 - An OpenSim guided tour in machine learning for e-health applications

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI’s flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

The rapid proliferation of fifth-generation (5G) wireless networks and the conceptualization of sixth-generation (6G) networks have revolutionized global connectivity, enabling ultra-high-speed data transmission, ultra-low latency, and massive device connectivity for applications such as the Internet of Things (IoT), autonomous vehicles, smart cities, and immersive augmented reality. However, these advancements come at a significant environmental cost, with the information and increased power consumption and carbon emissions. Green communication, which focuses on energy-efficient, sustainable network design, has emerged as a critical research area to address these challenges. This research paper provides an exhaustive analysis of green communication strategies in 5G and next-generation networks, covering energy-efficient technologies, resource management, renewable energy integration, security challenges, and experimental results. Through extensive simulations and real-world experiments, we evaluate key techniques such as small cell sleep modes, massive MIMO optimization, device-to-device (D2D) communication, and AI-driven resource allocation. The results demonstrate substantial energy savings while maintaining quality of service (QoS). The paper also explores standardization efforts, future trends, and open research issues, offering a roadmap for sustainable wireless communication systems. With over 7,000 words, detailed tables, graphs, and interpretations, this study aims to guide researchers, policymakers, and industry professionals toward achieving carbon-neutral networks by 2030.

In this chapter, the authors explore the open challenges and research issues related to XAI in modern smart cities. They begin by providing an overview of XAI and its importance in smart cities. They then discuss the key challenges of developing XAI systems for smart cities, including the need for transparency, interpretability, and trustworthiness. They also examine the challenges of integrating XAI systems with existing infrastructure and data sources in smart cities. Finally, they explore the potential research issues and future research directions for XAI in smart cities, including the development of new XAI techniques and the exploration of ethical and societal implications of XAI in smart cities. Overall, this chapter provides a comprehensive overview of the open challenges and research issues related to XAI in modern smart cities, which can serve as a roadmap for researchers, policymakers, and practitioners working in this field.

With the increasing demands for cloud computing services, the development of technologies based on virtualization in data centers was noticed. In the virtualized data center, the efficient mapping of virtual machines to physical machines is done using the consolidation technique. Due to the advantages of the server consolidation technique, a large body of research has been done in this field. A comprehensive study on the different server consolidation solutions has not been done yet, though. In this study, a systematic review has been done on a set of researches related to server consolidation. After investigating the considered researches, their proposed solutions were categorized into three groups based on the type of decision making for running the consolidation process. Groups involve static method, dynamic method (including threshold-based and periodic-based adaptation) and prediction-based dynamic method. Thereafter, we discussed handling the challenges presented in each research by investigating the proposed approach for developing consolidation technique. Then, the open issues in each study were expressed. Finally, the objectives, evaluation parameters, optimization methods and the affecting parameters of server consolidation in all studies were investigated and analyzed.