Service Discovery Research Articles

A Holistic Analysis of Internet of Things (IoT) Security: Principles, Practices, and New Perspectives

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.

Re-positioning university libraries for relevance in a dynamic learning environment

During the COVID-19 pandemic, university libraries are characterised as highly responsive and receptive to technological innovation, and emergent teaching and learning paradigms in the educational landscape. The exploratory design was adopted to gain insight from library leaders/directors in Ghana. A purposive sampling method was used to sample library directors from four universities in Ghana. Our paper found that libraries in Ghana provide virtual services including libguides, e-resources, online chat systems, etc. to support the changing user needs in the information environment. However, major innovations instituted during the pandemic are discovery services, web 3.0-based, multimedia technology services and research data management. Our paper revealed that, major shifts in pedagogy and technology have occurred in education, demanding effective communication, collaboration, and teamwork by library staff in meeting universities' teaching, learning, and research mandate. Consequently, providing innovative library services and facilities aligned with university pedagogy must be strongly emphasised by the library leaders.

Venture Capital's Role in Advancing Plastic Surgery.

Abstract Plastic surgery sector has experienced significant growth, driven by an aging population's demand for minimally invasive procedures and technological innovation. Despite this, the role of venture capital (VC) in driving innovation within this sector remains underexplored. This study aimed to analyze the trends in VC investments in plastic surgery over the last 20 years, providing insights into the financial landscape of the sector. A retrospective cross-sectional analysis was conducted on VC investments in plastic and aesthetic surgery companies worldwide from January 1, 2003 to December 31, 2023, utilizing PitchBook (Seattle, WA). Companies were categorized into therapeutic and thematic areas, with investment trends analyzed by deal and company values, average investment size, and total capital invested. The study found 543 VC firms made 402 investments in 163 companies, totaling $1.98 billion, increasing by 15,143% over the study period, and focused on general plastic surgery and facial cosmetic procedures. Significant investments were also made in surgical software (25.3%), biotechnology and drug discovery (22.8%), and clinic and outpatient services (20.3%). Geographically, most investments were made in companies registered in Asia (45%) and North America (33.2%). VC investments have contributed to the growth and innovation in the plastic surgery sector, particularly in minimally invasive devices. The focus on general and facial cosmetic surgery aligns with market demand trends for aesthetic procedures. These findings underscore the importance of VC in shaping the future of plastic surgery and suggest the potential for strategic investments to further drive innovation. Level of Evidence:5 (Diagnostic)

A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations.

I Can’t Get No Satis-Searching: Reassessing Discovery Layers in Academic Libraries Journal of Web Librarianship

Much has been written about discovery layer strengths and weaknesses in academic libraries. Many academic libraries have a discovery layer, using products, such as those from OCLC, EBSCO, ExLibris, Summon, or open-source products, such as VuFind. But after over 10 years of academic libraries adopting discovery layers as part of their discovery ecosystem, it may be time to take a step back and revisit the question posed by the speakers at the American Libraries Live “Making the Discovery Decision” January 2014 online discussion, namely: What is a discovery layer and its purpose? Are discovery layers accomplishing what they purport to accomplish for the end user? Through examining three particular discovery layers—OCLC WorldCat Discovery, ExLibris Primo VE, and EBSCO EDS—in regards to their conformance to the National Information Standards Organization (NISO)'s Open Discovery Initiative (ODI) “Checklist for Discovery Service Providers,” relevancy ranking algorithms, and mapping tables with facets, this article argues that the goals of discovery layers need to be reconsidered. Specifically, rather than the discovery layers’ goal to be Google-like and provide a self-sufficient user experience, discovery layers might be better promoted by libraries as a tool to be used most effectively with assistance from librarians. Discovery Layer vendors could focus their design efforts more on accurate faceted searching, citation tools, and saved search features, and less on esthetics and patron self-sufficiency.

A Trust-Aware and Authentication-Based Collaborative Method for Resource Management of Cloud-Edge Computing in Social Internet of Things

The Social Internet of Things (S-IoT) paradigm is focused on topic of the Internet of Things (IoT), which accelerates the object issues by working with the concept of social networks. Searching and finding a new object in the community are considered to manage the number of friends and complex relationships between them and affect the ability to navigate at the cloud-edge layer, and resources, such as battery lifetime of S-IoT devices and energy resources, are important challenges in this field. In the processing of social messages of remote devices, increasing the battery life of devices that require such requirements plays the most important role. In this research, a collaboration scenario is presented to consider object attributes, friend’s functions and intelligent friend selection among objects for group messaging. First, a general reference model is designed and presented to select a friend to access group message remote processing services and minimize cloud-edge resources. The simulation results show that, for the correct communication of friends at the edge of the network and in each service discovery, according to the length of the path in the network, it is possible to establish stable communication and make better service with the least possible. The results show that if we want to develop a method for friendship between objects in communication in cloud computing, the proposed method can greatly improve the effectiveness of providing reliable message processing types.

Capability Reconciliation for a CSP Approach to Virtual Device Composition

The dynamic composition of systems of networked appliances, or virtual devices, enables users to generate complex, strong, specific systems. Leading mobile ad hoc network (MANET)-based composition schemes currently make use of service discovery mechanisms that depend on periodic service advertising by controlled broadcast. The result is the unnecessary depletion of node resources such as battery and processing power. The dynamic and ad hoc nature of the discovery and composition of services is also not addressed by current schemes; this inevitably leads to capability differences that need to be reconciled when the input of one service is not compatible with the output of another. Our approach addresses the distributed constraint satisfaction problem in virtual device composition in MANETs; our simulation shows its effectiveness and efficiency.

Discovery of 6G Services and Resources in Edge-Cloud-Continuum

Discovery of 6G Services and Resources in Edge-Cloud-Continuum

Q-KGSWS: Querying the Hybrid Framework of Knowledge Graph and Semantic Web Services for Service Discovery

Q-KGSWS: Querying the Hybrid Framework of Knowledge Graph and Semantic Web Services for Service Discovery

An Intelligent Web Service Discovery Framework based on Improved Biterm Topic Model

An Intelligent Web Service Discovery Framework based on Improved Biterm Topic Model

Security of Topology Discovery Service in SDN: Vulnerabilities and Countermeasures

Security of Topology Discovery Service in SDN: Vulnerabilities and Countermeasures

Fully Decentralized Horizontal Autoscaling for Burst of Load in Fog Computing

With the increasing number of Web of Things devices, the network and processing delays in the cloud have also increased. As a solution, fog computing has emerged, placing computational resources closer to the user to lower the communication overhead and congestion in the cloud. In fog computing systems, microservices are deployed as containers, which require an orchestration tool like Kubernetes to support service discovery, placement, and recovery. A key challenge in the orchestration of microservices is automatically scaling the microservices in case of an unpredictable burst of load. In cloud computing, a centralized autoscaler can monitor the deployed microservice instances and make scaling actions based on the monitored metric values. However, monitoring an increasing number of microservices in fog computing can cause excessive network overhead and thereby delay the time to scaling action. We propose DESA, a fully DEcentralized Self-adaptive Autoscaler through which microservice instances make their own scaling decisions, cloning or terminating themselves through self-monitoring. We evaluate DESA in a simulated fog computing environment with different numbers of fog nodes. Furthermore, we conduct a case study with the 1998 World Cup website access log, examining DESA’s performance in a realistic scenario. The results show that DESA successfully reduces the scaling reaction time in large-scale fog computing systems compared to the centralized approach. Moreover, DESA resulted in a similar maximum number of instances and lower average CPU utilization during bursts of load.

Using Qualtrics XM to Create a Point-of-Use Survey to Assess the Usability of a Local Implementation of Primo

In 2020, Libraries and Cultural Resources (LCR) at the University of Calgary used Qualtrics XM to design and pilot a point-of-use survey to collect user feedback on the usability of our implementation of Primo, Ex Libris's web-scale discovery service. Over a two-week period, users were presented with the pop-up survey while searching and asked to provide feedback. This article summarizes how we designed and implemented this point-of-use survey and the lessons learned from this project.

Unlocking potential: Harnessing the power of metadata for discoverability and accessibility

Metadata plays a crucial role in organizing, discovering, and accessing publications and products. This paper explores the complex metadata ecosystem and its impact on downstream functions. Focusing on author-provided metadata, it addresses the challenges faced by authors in terms of discoverability, and offers best practices for optimizing metadata. The paper also discusses publication-level metadata standards and related initiatives. It highlights the importance of KBART files for linking subscription products and explores the three types of linking in discovery services. Additionally, it emphasizes the role of metadata in search engine optimization and social media. The paper explores how metadata can enhance accessibility and emphasizes the need for collaboration across various teams and systems. Overall, it underscores the ongoing efforts to build robust metadata pipelines and improve the user experience in information discovery and interchange.

MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog–cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog’s ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog’s ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

Unearthing historical insights: semantic organization and application of historical newspapers from a fine-grained knowledge element perspective

PurposeThis paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.Design/methodology/approachAccording to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.FindingsThis article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.Originality/valueHistorical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

Addressing Body Image Disturbance through Metaverse-Related Technologies: A Systematic Review

There is an increasing awareness about body image disturbance and eating disorders which calls for a multidisciplinary approach. The usefulness of new technologies for improving body image distortion has been addressed in the scientific literature, but has not included current strategies such as Metaverse-related technologies. Seemingly, this is the first systematic review which focuses on the efficacy of Metaverse-related technologies in reducing body image disturbance related to eating disorders like Anorexia nervosa and Bulimia nervosa. The main objective of this study was to review the scientific studies published in the last decade to answer the following three questions: (1) Are Metaverse-related technologies useful in mitigating body image disturbance in patients diagnosed with Anorexia nervosa and Bulimia nervosa? (2) What are the advantages and disadvantages? (3) Are the patients satisfied after using this kind of technology? The results obtained were that 80% of the included studies found metaverse-related technologies useful for the improvement in body image disturbance, in addition to various advantages, such as a decrease in eating disorder symptomatology. Whereas patient satisfaction was only evaluated in 20% of the included studies, with the majority of patients evaluating the use of metaverse-related technologies positively. We will conduct a systematic review of the peer-reviewed literature from EBSCO Discovery Service, and Web of Science (WOS), following the PRISMA statements. Only Journal articles published from 2013 to the present, written in the English language, will be reviewed. The findings are expected to offer valuable insights for the development of novel approaches for the improvement in body image disturbance in healthcare settings.

A Meta-Review to Guide Military Screening andTreatment ofGambling Problems.

Excessive gambling can cause substantial biopsychosocial problems (e.g., difficulties with finances, relationships, mental, and physical health). For military Service Members, it can also result in security clearance denial or revocation, failure to achieve promotions, and premature career termination. Recent congressional mandates have obligated the U.S. Department of Defense to screen for problematic gambling, the predictive values of which are a function of (i) problem prevalence and (ii) tool sensitivity and specificity. This meta-review (i.e., systematic review of systematic reviews) on the screening properties of gambling assessment tools and the effectiveness of treatments for gambling disorder is to inform military services on responding to Service Members' gambling problems. EBSCO Discovery Service, PubMed, PsycINFO, Ovid Medline, Social Care Online, Epistemonikos, International Health Technology Assessment, and the Cochrane Central Register of Controlled Trials electronic databases were searched up to December 2022 for systematic reviews and meta-analyses on measurements of adult subclinical or gambling, and interventions targeting individuals with GD. Three and four studies were included in each section of the current meta-review (i.e., assessment tools and treatment). For review 1, the estimated risk of bias was assessed using the Risk of Bias in Systematic Reviews. Thirty-one tools were identified through the three systematic reviews. All had modest sensitivities and specificities; combined with low prevalences in the general SM population, positive results would be incorrect 64-99% of the time. However, if screening were conducted with SMs referred for alcohol problems, a positive result on the best screening tools would be correct 76% of the time. Several commonly used treatment approaches had demonstrated efficacy for GD. The combination of low prevalence of GD and subclinical gambling problems in the general population, coupled with modest sensitivity and specificity, makes screening unfeasible in the general SM population. However, dual-phase screening in higher-prevalence subpopulations (i.e., SMs already identified with substance-abuse or mental-health problems) would be viable. Regarding treatment, several interventions-already used in military healthcare-with extensive empirical track records have been successfully used to treat adults with GD.

Influence of Libraries in the Discovery Services Process

Influence of Libraries in the Discovery Services Process

Grey Wolf Optimizer-based Decentralized Service Discovery in the Internet of Things Applications

Introduction: The Internet of Things (IoT) has emerged as a significant technology in recent years, wherein each object is equipped with sensors and applications that provide functionality through services. Due to the increasing benefits of heterogeneous objects with constrained resources in high environments, traditional service discovery approaches become impractical for dynamic IoT networks. Therefore, service discovery poses a considerable challenge for the Internet of Things. Methods: This paper introduces a novel decentralized discovery algorithm based on the Grey Wolf Optimizer (GWO) for IoT services. GWO is a recent metaheuristic in swarm intelligence designed to solve combinatorial optimization problems. Results: simulation results indicate that GWO achieves high discovery success with minimal steps required for service discovery. Conclusion: Our approach maintains its performance and exhibits good scalability as the number of objects increases in the decentralized approach for IoT.