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In order to solve the problem of how to reduce the service delay of edge computing, this paper proposes an edge cloud framework, which contains four groups under different locations between mobile edge nodes and users. A feasible cost scheme can be obtained by calculating the cost in different simulation groups. Furthermore, we give suggestions on how to deploy edge nodes at a reasonable cost for users effectively.

This paper is motivated by the IoT-Cloud framework; they are divided according to whether the nodes have templates required by users and the distance from users to distinguish various consumption levels and classify the testing result. Based on four different groups satisfying reasonable resource allocation, the cost was studied. The work focuses on the unpredictable movement within the test range. For assignment and scheduling of template tasks at each time slot, the Edge-Cloud scheme is proposed to reduce the cost.

According to the simulation results in this paper, the total cost of the four groups is lower when the closest node-set satisfies the user service directly. To improve the probability that the closest node-set can meet general requests of users and provide them with faster and more economical network services, increasing the number of edge nodes within the range of user activities is suggested.

An Edge-Cloud framework is constructed in four groups, and the conclusion of a feasible cost scheme came out.

Multiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users.

In this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on.

On examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes.

This paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.

This paper aims to inform design strategies for smart space technology to enhance libraries as environments for coworking and informal social learning. The focus is on understanding user motivations, behaviour, and activities in the library when there is no programmed agenda.

The study analyses gathered data over five months of ethnographic research at The Edge – a “bookless” library space at the State Library of Queensland in Brisbane, Australia, that is explicitly dedicated to coworking, social learning, peer collaboration, and creativity around digital culture and technology.

The results present five personas that embody people's main usage patterns as well as motivations, attitudes, and perceived barriers to social learning. It appears that most users work individually or within pre‐organised groups, but usually do not make new connections with co‐present, unacquainted users. Based on the personas, four hybrid design dimensions are suggested to improve the library as a social interface for shared learning encounters across physical and digital spaces.

The findings in this paper offer actionable knowledge for managers, decision makers, and designers of technology‐enhanced library spaces and similar collaboration and coworking spaces.

This study aims at unpacking the multiplicity of the sitting activity in public spaces through the lens of actor-network theory. In line with previous urban research focussing on outdoor activities, such empirical investigation aims to show the importance of the physical aspects of spaces, including seating, in supporting sitting activities as a way of encouraging the use of public space.

This study adopts the overlap between actor–network theory and affordances. It utilises ethnographic research involving frequent users in Dahiyat Al Hussein Park in Amman-Jordan. Data were gathered on the different seat–user relations and the translated sitting activity networks.

Analysis demonstrates different cases of alignment, misalignment and realignment between what is intended and experienced, and where these relations are maintained, disrupted or changed. These findings reveal the multiplicity of sitting activities; this is significant for understanding how they are maintained.

The research suggests a new way of conceptualising the relationship between the physical environment and users and an approach for examining sitting activities. Some studies have applied actor–network theory and/or the concept of “affordance” by highlighting relations between the object and its user and how they create sitting activities. However, only few studies have problematised the multiplicity of sitting when considering seating uses.

The intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.

We propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.

Using the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.

This paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

Wireless mesh networks (WMNs) have evolved quickly during the last several years. They are widely used in a lot of fields. Channel allocation provides basic means to guarantee mesh networks’ good performance such as efficient routing. The purpose of this paper is to study channel allocation in mesh networks.

First, the papers in channel allocation fields are surveyed, and then the limitations in existing methods noted. Graph theory is used to find a better model to represent the problem and algorithms are proposed based on this model. Simulation proved that algorithms are better than the previous conflict graph‐based approaches.

The paper analyzes the conflict graph‐based model and finds its limitations, then proposes a bipartite graph‐based model. Algorithms were devised based on this model. Simulation results illustrate that the algorithms can reduce the starvation ratio and improve the bandwidth utilization, compared with previous conflict graph‐based algorithms.

The research of this paper is based on an ideal network environment without interference or noises. It will be better if the noises are considered in future work.

To study the routing strategies of WMNs, it is not sufficient to only consider path length as routing metric since the nodes are heterogeneous. The routing metrics should include the channel bandwidths which are the results of channel allocation.

This paper presents a new bipartite graph‐based model to represent the channel allocation problem in mesh networks. This model is more efficient and includes more information compared with conflict graph model, and it also proposes channel allocation algorithms based on bipartite graph‐based model. The algorithms can reduce starvation ratio and improve the bandwidth utilization.

The purpose of this paper is to set up a new framework to enable direct modifications of volume meshes enriched with semantic information associated to multiple partitions. An instance of filleting operator is prototyped under this framework and presented in the paper.

In this paper, a generic mesh modification operator has been designed and a new instance of this operator for filleting finite element (FE) sharp edges of tetrahedral multi-partitioned meshes is also pro-posed. The filleting operator works in two main steps. The outer skin of the tetrahedral mesh is first deformed to round user-specified sharp edges while satisfying constraints relative to the shape of the so-called Virtual Group Boundaries. Then, in the filleting area, the positions of the inner nodes are relaxed to improve the aspect ratio of the mesh elements.

The classical mainstream methodology for product behaviour optimization involves the repetition of four steps: CAD modelling, meshing of CAD models, enrichment of models with FE simulation semantics and FEA. This paper highlights how this methodology could be simplified by two steps: simulation model modification and FEA. The authors set up a new framework to enable direct modifications of volume meshes enriched with semantic information associated to multiple partitions and the corresponding fillet operator is devised.

The proposed framework shows only a paradigm of direct modifications of semantic enriched meshes. It could be further more improved by adding or changing the modules inside. The fillet operator does not take into account the exact radius imposed by user. With this proposed fillet operator the mesh element density may not be enough high to obtain wished smoothness.

This paper fulfils an identified industry need to speed up the product behaviour analysis process by directly modifying the simulation semantic enriched meshes.

The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become prime choice to promote the products because of the large size of online communities. Identification of seed nodes or identifying the users who are able to maximize the spread of information over the network is the key challenge faced by organizations. It is proved as non-deterministic polynomial-time hard problem. The purpose of this paper is to design an efficient algorithm for optimal seed selection to cover the online social network as much as possible to maximize the influence. In this approach, agglomerative clustering is used to generate the initial population of seed nodes for GA.

In this paper agglomerative clustering based approach is proposed to generate the initial population of seed nodes for GA. This approach helps in creating the initial populations of Genetic algorithm from different parts of the network. Genetic algorithm evolves this population and aids in generating the best seed nodes in the network.

The performance of of proposed approach is assessed with respect to existing seed selection approaches like k-medoid, k-means, general greedy, random, discounted degree and high degree. The algorithms are compared over networks data sets with varying out-degree ratio. Experiments reveal that the proposed approach is able to improve the spread of influence by 35% as compared to contemporary techniques.

This paper is original contribution. The agglomerative clustering-based GA for optimal seed selection is developed to improve the spread of influence in online social networks. This paper is of immense importance for viral marketing and the organizations willing to promote product or services online via influential personalities.

The purpose of this paper is to present an up-to-date survey on the non-orthogonal multiple access (NOMA) technique with co-operative strategy, a fast-evolving fifth-generation (5 G) technology. NOMA is used for serving many mobile users, both in power and code domains. This paper considers the power-domain NOMA, which is now discussed as NOMA.

The first part of the paper discusses NOMA-based cooperative relay systems using different relay strategies over different channel models. In various research works, the analytical expressions of many performance metrics were derived, measured and simulated for better performance of the NOMA systems. In the second part, a brief introduction to diversity techniques is discussed. The multiple input and multiple output system merged with cooperative NOMA technology, and its future challenges were also presented in this part. In the third part, the paper surveys some new conceptions such as cognitive radio, index modulation multiple access, space-shift keying and reconfigurable intelligent surface that can be combined with NOMA systems for better performance.

The paper presents a brief survey of diverse research projects being carried out in the field of NOMA. The paper also surveyed two different relaying strategies that were implemented in cooperative NOMA over different channels and compared several performance parameters that were evaluated and derived in these implementations.

The paper provides a scope for recognizable future work and presents a brief idea of the new techniques that can be united with NOMA for better performance in wireless systems.

Social media platforms allow near‐unfettered creation and exchange of user generated content (UGC). Drawing from network science, the purpose of this paper is to examine whether high and low quality UGC differ in their connectivity structures in Wikipedia (which consists of interconnected user generated articles).

Using Featured Articles as a proxy for high quality, a network analysis was undertaken of the revision history of six different language Wikipedias, to offer a network‐centric explanation for the emergence of quality in UGC.

The network structure of interactions between articles and contributors plays an important role in the emergence of quality. Specifically the analysis reveals that high‐quality articles cluster in hubs that span structural holes.

The analysis does not capture the strength of interactions between articles and contributors. The implication of this limitation is that quality is viewed as a binary variable. Extensions to this research will relate strength of interactions to different levels of quality in UGC.

The findings help harness the “wisdom of the crowds” effectively. Organisations should nurture users and articles at the structural hubs from an early stage. This can be done through appropriate design of collaborative knowledge systems and development of organisational policies to empower hubs.

The network centric perspective on quality in UGC and the use of a dynamic modelling tool are novel. The paper is of value to researchers in the area of social computing and to practitioners implementing and maintaining such platforms in organisations.