AN IMPROVED 5G MOBILITY HANDOVER EFFICIENT BY CREATING A DIGITAL TWIN NETWORK: A REVIEW

In the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.

The very challenging requirement of WSN is energy efficiency, which depends upon the distributed design and dynamic topology of the network. This requirement can be fulfilled by the multi-hop energy efficient routing protocols. The objective of all the energy efficient protocols is to extend the network’s lifetime. Sensor nodes are battery operated, so generally concern of all routing protocols is to conserve energy. In this chapter, energy efficient protocols are categorized into two types (i) heterogeneity based energy efficient routing protocols and (ii) chain based energy efficient routing protocols. Comparisons of various energy efficient routing protocols are specified here with open issues. A systematic and comprehensive taxonomy of various energy aware schemes are discussed in depth. This chapter is focused on various energy conservation schemes and hence, discussion of various routing protocols gives the readers a new insight.

Black box information retrieval (IR) application evaluation allows practitioners to measure the quality of their IR application. Instead of evaluating specific components, e.g. solely the search engine, a complete IR application, including the user’s perspective, is evaluated. The evaluation methodology is designed to be applicable to operational IR applications. The black box evaluation methodology could be packaged into an evaluation and monitoring tool, making it usable for industry stakeholders. The tool should lead practitioners through the evaluation process and maintain the test results for the manual and automatic tests. This paper shows that the methodology is generalizable, even though the diversity of IR applications is high. The challenges in automating tests are the simulation of tasks that require intellectual effort and the handling of different visualizations of the same concept.

The black box application evaluation methodology described in this tutorial is applicable to a broad range of operational information retrieval (IR) applications. Contrary to popular, traditional IR evaluation approaches that are limited to measure the IR system performance on a test collection, the black box evaluation methodology considers an IR application in its entirety: the underlying system, the corresponding document collection, and its configuration/application layer. A comprehensive set of quality criteria is used to estimate the user’s perception of the application. Scores are assigned as a weighted average of results from tests that evaluate individual aspects. The methodology was validated in a small evaluation campaign. An analysis of this campaign shows a correlation between the testers’ perception of the applications and the evaluation scores. Moreover, functional weaknesses of the tested IR applications can be identified and then systematically targeted.

The aim of an Information Retrieval (IR) application is to support the user accessing relevant information effectively and efficiently. It is well known that system performance, in terms of finding relevant information is heavily dependent upon the IR application (i.e. the IR system exposed through the application's interface), as well as how the application is used by the user (i.e. how the user interacts with the system through the interface). Thus, a very pragmatic evaluation question that arises at the application level is: what is the effectiveness experienced by the user during the usage of the application? To be able to answer this question, we represent the usage of an application by the stream of documents the user encounters while interacting with the application. This representation enables us to monitor and track the performance over time and usage. By taking a stream-based, time-centric view of the IR process, instead of a rank-list, topic/task centric view, the evaluation can be performed on any IR based application. To illustrate the difference and the utility of this approach, we demonstrate how a new suite of usage based effectiveness measures can be applied. This work provides the conceptual foundations for measuring, monitoring and modeling the performance of any IR application which needs to be evaluated over time and in context.

This paper addresses the problem of transforming WordNet into a resource tailored to Information Retrieval (IR) applications. We address two of the major drawbacks pointed out in previous literature in relation to this semantic network. One is the fine granularity of senses defined in WordNet, which proves useless from an IR perspective. To solve this problem, we propose a set of methods that enable the automatic transformation of WordNet into a coarse grained dictionary. The other drawback is the encoding used in this resource, and the methods for accessing related words across the semantic net. Due to the high number of connections among concepts, the simple computation of a path in this net, or the generation of related concepts may become a computationally intensive process. This effect is highly undesirable in time sensitive applications such as IR applications. We propose a methodology for hierarchical encoding that enables increased efficiency in WordNet-based IR systems.

Matching between two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielded immense success in speech recognition, computer vision, and natural language processing, significantly advancing state-of-the-art of these areas. In the IR community, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fact that deep matching models have gained significant progress in these areas, there are still many challenges to be addressed when applying these models to real IR scenarios. In this workshop, we focus on the applicability of deep matching models to practical applications. We aim to discuss the issues of applying deep matching models to production systems, as well as to shed some light on the fundamental characteristics of different matching tasks in IR. website : https://wsdm2019-dapa.github.io/index.html

Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks

Terminological knowledge of the biomedical domain is important for natural language processing (NLP) and information retrieval (IR) applications, and a number of terminological knowledge sources, such as LocusLink, GeneBank, and the UMLS, already exist. However, because of the tremendous amount of research activity in the field, new terms and symbols are continually being created, many of which are published in the literature, but are not available in any of the other resources. Therefore, effective mining of the literature for new terminology is critical for furthering NLP and IR applications. Abbreviations are widely used in the biomedical domain, and the understanding of abbreviations requires a terminological knowledge base that consists of abbreviations with their associated senses. In previous work, several methods have been developed for automatic construction of abbreviation knowledge bases from parenthetical expressions. However, these methods pair abbreviations and their expansions based on manually crafted patterns or rules. In this paper, we propose an automatic method, which is not based on patterns or rules but is based on the use of collocations, to extract a set of related terms from parenthetical expressions including abbreviations associated with their expansions and other types of related terms such as synonyms, or hyponyms etc. Our method is based on the observation that terms associated with parenthetical expressions i) are usually related, and ii) are often collocations because they tend to co-occur more often than expected by chance. Our method was applied to the collection of MEDLINE abstracts. The method and the results were evaluated using two collections: Berman's handcrafted abbreviation list and the LocusLink collection.

Spell checker is a system that is used to detect and correct misspelled word. Misspelled word is a word that exists in the existing lexicon that is not correctly spelled or in shortened form. These misspelled words often result in ineffective results of the Information Retrieval (IR) application such as document retrieval. This is because IR application should be able to recognize all words in a particular language in order to be more robust. The current spell checker for the Malay language uses a dictionary that contains pair of commonly misspelled word and its correctly spelled word in detecting and correcting misspelled word. However, this type of spell checker can only correct misspelled words that exist in the existing dictionary; otherwise it requires user interaction to correct it manually. This approach works well if the spell checker is a standalone system but it is not really an effective system when the spell checker is part of another IR application such as document retrieval for weblog. This is because there will be always new misspelled words created along with the increasing number of weblog pages. Thus, the number of misspelled words will also grow extremely. In this paper, we propose a new spell checker that detects and automatically corrects misspelled words in Malay without any interaction from the user. The proposed approach automatically replaces the misspelled word if it exists in the reSpellWord dictionary. Otherwise, it will go through the process of Selangor Slang Identification or Repetitive word Identification or Opposite Word Identification whichever is suitable. If the word cannot be identified as a misspelled word, a few alternative words will be suggested and they are ranked using the Levenshtein Distance in order to choose the most likelihood word for the misspelled word. The correctly-spelled word that has the highest ranking will be chosen as a replacement for the misspelled word. This misspelled word and its correctly-spelled word are then added automatically into the dictionary in order to update the dictionary. The proposed approach is evaluated by using texts that are selected randomly from the popular Malay blog. Based on the experimental results obtained, the proposed approach is found to be effective in detecting and correcting the Malay misspelled word automatically.

A Spell checker is a system that is used to detect and correct misspelled word. Misspelled word is a word that exists in the existing lexicon that is not correctly spelled or in shortened form. These misspelled words often result in ineffective results of the Information Retrieval (IR) application such as document retrieval. This is because IR application should be able to recognize all words in a particular language in order to be more robust. The current spell checker for the Malay language uses a dictionary that contains pair of commonly misspelled word and its correctly spelled word in detecting and correcting misspelled word. However, this type of spell checker can only correct misspelled words that exist in the existing dictionary; otherwise it requires user interaction to correct it manually. This approach works well if the spell checker is a standalone system but it is not really an effective system when the spell checker is part of another IR application such as document retrieval for weblog. This is because there will be always new misspelled words created along with the increasing number of weblog pages. Thus, the number of misspelled words will also grow extremely. In this paper, we propose a new spell checker that detects and automatically corrects misspelled words in Malay without any interaction from the user. The proposed approach is evaluated by using texts that are selected randomly from the popular Malay blog. Based on the experimental results obtained, the proposed approach is found to be effective in detecting and correcting the Malay misspelled word automatically.

The interconnection of mobile devices in urban environments can open up a lot of vistas for collaboration and content-based services. This will require setting up of a network in an urban environment which not only provides the necessary services to the user but also ensures that the network is secure and energy efficient. In this paper, we propose a secure, energy efficient dynamic routing protocol for heterogeneous wireless sensor networks in urban environments. A decision is made by every node based on various parameters like longevity, distance, battery power which measure the node and link quality to decide the next hop in the route. This ensures that the total load is distributed evenly while conserving the energy of battery-constrained nodes. The protocol also maintains a trusted population for each node through Dynamic Trust Factor (DTF) which ensures secure communication in the environment by gradually isolating the malicious nodes. The results obtained show that the proposed protocol when compared with another energy efficient protocol (MMBCR) and a widely accepted protocol (DSR) gives far better results in terms of energy efficiency. Similarly, it also outdoes a secure protocol (QDV) when it comes to detecting malicious nodes in the network.

In this paper, some new indexing methodologies and applications in Information Retrieval (IR) has been presented. Some new algorithms with high coverage of IR applications has be introduced by this paper. Main strategy is introducing and evaluating Information Retrieval basic applications and modulation. Some future directions in IR methodologies and evaluations are the other subjects and focuses of this paper.

Resource and Energy Efficiency in Small and Medium Breweries

The diffusion of automated information retrieval (IR) ap plications and of applications which share many of the peculiarities of IR applications is increasing. The demand for these types of applications draws attention to the lack of methodologies available for IR data design. The data modell ing of an IR application, that is, the data modelling of the IR part of a complete information system, has not yet been approached and studied as a complete process and in a struc tured way. The first part of this paper introduces the motivations and the scope of the DIRD (Design of Information Retrieval Data) project, which is devoted to the development of an environ ment for the design of advanced applications of IR. The second part of the paper addresses the design of the IR part (or IR application) of an information system. The concep tual paradigm necessary for the design of advanced IR applica tions is investigated. The Entity Relationship (ER) approach is compared with that conceptual modelling paradigm and is examined as a candidate data model for the conceptual design of IR data. A new ER approach is then introduced: this new approach extends the constructs of the ER model to manage the complexity of IR data. Two design examples are given to present the use of the new ER approach in designing IR data.