IT has emerged as an exciting, robust field unto itself, drawing on applications, data engineering, algorithms, system software, computer systems organization, and other contemporary computer technologies as applied to business problems. IT Pro publishes articles that cross all these fields, defining the synergistic partnerships and interactions among them. All articles are readable by IT specialists employed in various industries worldwide; i.e., IT Pro articles should overcome the barriers usually created by industry-oriented vocabularies. Articles on topics across the IT spectrum are encouraged.

A sampling of topics follows: Building the corporate networking; Information resources for strategic infrastructure competitive advantage; Business intelligence applications; Internet/lntranet design and organization; Case studies applications and security; Collaborative technology; IT training issues; Conversion of legacy data; IT management and leadership; Cooperative information systems; IT strategic planning; Data distribution from multiple databases; IT tools; Data warehousing/storage issues; Intellectual property and other legal issues; Data mining; Interoperability of UNIX and NT; Decision Support Systems (DSS); Online analytical processing (OLAP); Design engineering and implementation; Online transaction processing (OLTP) of complex computer systems; Opinion pieces and analyses of trends; Developing and maintaining databases facilitated by IT; Distributed databases; Project management and prioritization of complex organizations; Systems administration; Document management systems; Systems integration; Electronic commerce applications and systems; Use and application of IT standards; Executive Information Systems (EIS); Worknow automation and systems; Y2K.

Special Issue on Computing and Communication Convergence with Edge Intelligence for Web 3.0
Submission Date: 2023-11-22

The concept of Web3.0 represents a significant advancement in the Internet’s evolution. Different from Web1.0 and Web2.0 based on “read” and “read-write” respectively, Web3.0, a distributed and user-centric internet, is proposed to empower users by enabling them to own and control their digital assets, and aiming to vest power from large corporations to individual users. As a distributed user-centric and semantic-dependent architecture, Web3.0 requires an efficient identity management and data processing system to support users’ requirements. Thus, an efficient and reliable approach to distributed network management is crucial in enhancing the service level and user experience of Web3.0. One promising technology in this regard is blockchain and distributed ledger technology, which have shown great potential for use in Web3.0 and demand further research and development.

The integration of edge computing and artificial intelligence known as Edge Intelligence has the potential to significantly enhance the management and operational capabilities of the Web3.0 architecture. By collaborating the distributed devices to achieve a system-wide resource utilization, users can enjoy a better experience and the service providers could achieve a management approach in a more load-balanced, low-cost, and high-service-quality manner. With conventional communication and network technologies, the features and value of distribution are hard to be made the most of, thus significantly restricting the communication efficiency and wasting the computing power of devices. To tackle these problems, edge computing can be applied in Web3.0 to enhance the utilization of idle edge computing resources. Furthermore, empowered by artificial intelligence, edge intelligence fosters the scheduling of task processing in the system-wide view by offloading tasks to other edge devices for acceleration and load balance, etc., which increases the efficiency, robustness, and robustness of management. Generally, mechanisms such as service offload, load balance, resource allocation, and content cache shall be optimized to refine the communication, computing, and storage of service in Web3.0. In order to further explore the combination of Web3.0 and edge intelligence and promote its progress, this feature topic solicits contributions of human-centric innovations and applications in the edge intelligence enabled Web3.0. Topics of interest include, but are not limited to:

- Incentive and consensus mechanisms based on edge intelligence for Web3.0
- Architecture and protocol design for edge computing-empowered Web3.0
- Federated learning, reinforcement learning, and other emerging technologies for intelligent Web3.0
- Design or implementation of hardware and infrastructure combing edge intelligence and Web3.0
- Fundamental studies and theoretic guidance for edge intelligence-driven Web3.0
- Semantic computing with edge intelligence in Web3.0
- Intelligent edge devices management approaches in Web3.0
- Information-Centric Networking for the edge intelligence in Web3.0
- Novel edge-based artificial intelligence application optimization in Web3.0
- Web3.0-driven edge intelligence applications
- Standardization for the edge intelligence in Web3.0
- Mobile Edge Computing Architectures and Designs to support Web 3.0 Services over 5G/6G Access Technologies

Special Issue on Revisiting Rural and Remote Connectivity Challenges in B5G and 6G Networks
Submission Date: 2024-01-17

According to the latest data released by International Telecommunication Union (ITU), the United Nations (UN) agency for Information and Communication Technologies (ICT), at present, nearly 2.9 billion people of the world are compelled to stay offline. This points to the fact that more than one third of the world population is still unconnected. This unconnected population stays in rural and remote areas that have no internet service provider. Sparse population density, low demand, lower affordability, higher cost of deployment and maintenance of both wired and wireless network infrastructures lead to a dearth of even basic internet connectivity. As a result the quality of personal and professional life of rural and remote communities is adversely impacted. Today, “affordable access to the Internet in least developed countries” is included in UN’s sustainable development goals (SDG) 2030. For many government and welfare projects globally the availability and quality of ICT infrastructure in remote and rural areas becomes a key concern.

The three use cases defined in 5G, i.e. enhanced mobile broadband(eMBB), massive machine type communication (mMTC), and ultra-reliable low latency communication (URLLC), are primarily targeted at urban population, as they require availability of advanced networking infrastructures capable of delivering high data rates with low latency. Thus, there has been a pressing need for the “fourth use case”, i.e. “global access to internet for all (GAIA)” while we define B5G and 6G network use cases.

Overcoming the aforementioned challenge demands fundamental innovation in the communication and networking technologies in the B5G and 6G networks. Some of the enabling technologies include LEO satellites, aerial platforms (HAP, MAP, LAP), TV white space, small cells and wireless backhaul. In this special issue, we solicit novel contributions from researchers engaged in industry, academia regulatory bodies to address the challenge of provisioning broadband connectivity in the remote and rural areas.

Topics of interest include, but are not limited to:
- Implementation challenges in broadband networks for Rural and Remote communities
- Innovative hardware in networks and devices to enable access to remote communities
- Envisioning the impact of 6G networks on Rural and Remote communities and their business/industry operations
- Architectures and Designs of Systems and Applications incorporating B5G and anticipated 6G networks for Rural and Remote communities
- Strategies to overcome Cybersecurity and Privacy challenges in Systems and Applications used in Rural and Remote communities
- Performance, Scalability, Usability and Reliability of Applications using B5G and upcoming 6G networks
- Intersection of Artificial Intelligence and B5G / 6G networks for Rural and Remote applications
- Engineering network convergence on the Cloud in the B5G / 6G era
- Optimizing business processes of rural and remote communities using advances in B5G and upcoming 6G networks
- Inevitability of Open Source approach in developing the strategies for upcoming 6G networks
- Role of Robotic Process Automation (RPA) in remote and poorly connected geographical regions
- Development of appropriate government policies, procedures and regulations for the upcoming 6G networks in the context of Rural and remote communities
- Drones-based applications in rural and remote communities using B5G 

Special Issue on Big Data and Artificial Intelligence for Mastering Digital Transformation in Business
Submission Date: 2024-03-13

In today’s world, Digital Transformation is requisite for all businesses, regardless of small or enterprise. Digital transformation is a broad-ranging subject, furthermore, it covers an aggregate of various sorts of initiatives such as changing the way of how a business interacts or communicates with its customers, workforce using technology, and employing modern technologies to enhance internal processes, by creating them more efficiently. There is no doubt that modern technology goes hand-in-hand with digital transformation and supports a business’s value-oriented actions. Therefore, technologies such as cloud, Internet of Things, Digital Twin, Artificial Intelligence (AI), Robotics, Machine Learning, Augmented Reality, Additive manufacturing, Mobile, and Big data are the most commonly used for businesses. These technologies enable organizations to perform countless operations fast, productively, and efficiently, such as real-time analytics, innovations, automation, flexibility, generating new insights, meeting customer demands, and decreasing downtime and cost reductions.

Technologies and tools of big data such as processing engines, storage repositories, NoSQL databases, SQL query engines, data lake and data warehouse platforms, and others are transforming the conventional processes of businesses. However, in particular, Big data and Artificial Intelligence have been emerging as a critical role in mastering digital transformation in businesses and helping to remain competitive in an increasingly evolving digital world. Big data analytics allows firms to have granular data regarding discrete or different groups of consumers. It also enables the collection of the correct data, improves operations, better consumer insights, agile supply chain management, more insightful market intelligence, data-driven innovation, real-time analytics applications, mitigating potential logistics risks, developing personalization, analyzing patterns, fraud prevention, and cybersecurity protections, and many more. AI processes are employed in natural systems in an immense variety of sectors such as IT security, customer service, decision support and business operations, accounting, and finance, human resources, tailoring promotions, anticipating future customer purchases, eliminating data issues. Therefore, AI and big data technology have been profoundly influencing business organizations for mastering digital transformation. In this context, this special issue intends to explore the significance of big data and Artificial Intelligence for mastering digital transformation in business. Topics of interest include, but are not limited to:

- Role of AI, big data, and IoT for digital transformation in business
- Influence of big data and machine learning on digital transformation in marketing
- Role of big data and AI for digital supply chain
- AI and big data for the future of digital transformation
- Business Intelligence and digital transformation in business
- AI-powered big data for business in the digital era
- Industrial applications of big data and artificial intelligence for business
- Blockchain and AI for SME’s in evolving digital era
- Innovations of AI and big data analytics for digital transformation
- AI-based business strategies for a post-covid-19 era
- Enhancing the future of digital transformation with innovative digital technologies
- AI and big data for data-driven enterprises
- Emerging challenges and risks of AI in digital transformation
- IoT and AI as a driver of digital transformation 

Special Issue on Computational Advances for Industrial Transformation Towards Smart and Sustainable Society
Submission Date: 2024-05-08

Technological innovation is the bedrock of attempts to improve strategic sustainability goals, including resource and energy efficiency. Industrialization would be impossible without technological advancement. Early acquirers of integrated and interdependent innovations in the manufacturing industries are reaping the economic advantages of increased efficiency improvements, economic output, protection, cost reductions, revenue growth, and client satisfaction and retention as they work toward Industrial Transformation for Sustainability. Among all technological advances are those associated with the Industrial Transformation for Sustainability movement, including Artificial Intelligence, deep learning, business intelligence, Internet of Things (IoT) communication, and blockchain technology. To identify a few, industrial production, power generation, utility services, automobile industries, and aerospace businesses have benefited from investment opportunities in such ground-breaking technologies. Additionally, global warming requiring additional transition to low-carbon communities necessitates a sea difference in how emerging markets and industries operate.

With regards to intelligent urban as methodologies for Industrial Transformation toward a Smart and Sustainable Society, experimental computational intelligence has been increasingly comprehended. Carbon-intensive processes that underlie the world economy could help influence a much more sustainable future across innovation, predictive analysis, computerization, electricity generation, and operational efficiencies.

Despite the numerous benefits of integrating computational intelligence methodologies into numerous Industrial Transformation for Sustainability initiatives, the effective implementation of the AI model presents numerous difficulties, including information amount and complexity, interoperability, and the precision of qualitative findings from the gathered information. Technological improvements through AI-enabled techniques, such as enhanced pattern recognition and natural language processing, have opened up numerous new research directions for evaluating the computational intelligence conceptual model by observing factual data and sensed information. Additionally, advances in computational competence methodologies like decentralized and federated learning should be used to empower the multiple edge devices and generate an optimization method under the supervision of a vital network edge.

In recent years, academics and practitioners have seen significant interest in innovative computational intelligence technologies for Industrial Transformation for Sustainability. Besides that, industry 4.0 was indeed reshaping how businesses manufacture, enhance, and distribute their consumer goods. Likewise, industrial automation integration of advanced sensing devices, integrated development environment, and robotic systems that collect information and enable more informed decision-making is critical in achieving Industrial Transformation toward a Smart and Sustainable Society. These digital technologies enable enhanced automation, preventative analysis, self-optimization of operational efficiencies, and, most importantly, a previously unattainable threshold of responsiveness and efficiency to customers. Topics of interest include, but are not limited to:

- Advanced computational using green communication for Industrial Transformation toward a Smart and Sustainable Society
- Green edge platform using caching techniques for Industrial Transformation towards Smart and Sustainable Society
- Low latency and ultra-reliable communication protocol for Industrial Transformation towards Smart and Sustainable Society
- Collaborative computational intelligence using ML/DL models for Industrial Transformation
- AI-enabled Federated/distributed learning for Industrial Transformation
- Enhanced computational intelligence progression in Industry 4.0 for Industrial Transformation towards Smart and Sustainable Society
- Blockchain-based computational intelligence for Industrial Transformation towards Smart and Sustainable Society
- Progress in Digital twin for Industrial Transformation towards Smart and Sustainable Society
- Green computing for energy-aware data centers for Industrial Transformation
- Deep learning for acquiring advanced computational intelligence for studying sustainability challenges