Future Internet, Volume 14, Issue 5 (May 2022) – 38 articles

Generalized spatial modulation (GSM) technology is an extension of spatial modulation (SM) technology, and one of its main advantages is to further improve band efficiency. However, the multiple active antennas for transmission also brings the demodulation difficulties at the receiver. To solve the problem of high computational complexity of the optimal maximum likelihood (ML) detection, two sub-optimal detection algorithms are proposed through reducing the number of transmit antenna combinations (TACs) detected at the receiver. One is the maximum ratio combining detection algorithm based on repetitive sorting strategy, termed as (MRC-RS), which uses MRC repetitive sorting strategy to select the most likely TACs in detection. The other is the maximum ratio combining detection algorithm, which is based on the iterative idea of the orthogonal matching pursuit, termed the MRC-MP algorithm. The MRC-MP algorithm reduces the number of TACs through finite iterations to reduce the computational complexity. For M-QAM constellation, a hard-limited maximum likelihood (HLML) detection algorithm is introduced to calculate the modulation symbol. For the M-PSK constellation, a low-complexity maximum likelihood (LCML) algorithm is introduced to calculate the modulation symbol. The computational complexity of these two algorithms for calculating the modulation symbol are independent of modulation order. The simulation results show that for GSM systems with a large number of TACs, the proposed two algorithms not only achieve almost the same bit error rate (BER) performance as the ML algorithm, but also can greatly reduce the computational complexity. Full article

Security is a challenging issue for M2M/IoT applications due to the deployment, decentralization and heterogeneity of M2M and IoT devices. Typical security solutions may not be suitable for M2M/IoT systems regarding the difficulties encountered for their implementation on resource-constrained devices. In this paper, we discuss the architectures deployed for M2M communications and the security challenges, as well as the vulnerabilities and solutions to counter possible attacks. We present a lightweight design based on a private blockchain to secure wireless M2M communications at the device domain level. Blockchain integration provides secure storage of data while preserving integrity traceability and availability. Besides, the evaluation and experimentations under NS3 simulator of the proposed scheme show that the authentication mechanism is lightweight, and presents better performances comparatively to other protocols in terms of key parameters as communication and computational overheads, average delay and energy consumption. Full article

The domain of information-centric networking (ICN) is expanding as more devices are becoming a part of connected technologies. New methods for serving content from a producer to a consumer are being explored, and Named Data Networking (NDN) is one of them. The NDN protocol routes the content from a producer to a consumer in a network using content names, instead of IP addresses. This facility, combined with content caching, efficiently serves content for very large networks consisting of a hybrid and ad hoc topology with both wired and wireless media. This paper addresses the issue of the quality-of-service (QoS) dimension for content delivery in NDN-based networks. The Internet Engineering Task Force (IETF) classifies QoS traffic as (prompt, reliable), prompt, reliable, and regular, and assigns corresponding priorities for managing the content. QoS-linked privileged content caching (QLPCC) proposes strategies for Pending Interest Table (PIT) and content store (CS) management in dedicated QoS nodes for handling priority content. QoS nodes are intermediately resourceful NDN nodes between content producers and consumers which specifically manage QoS traffic. The results of this study are compared with EQPR, PRR probability cache, and Least Frequently Used (LFU) and Least Fresh First (LFF) schemes, and QLPCC outperformed the latter-mentioned schemes in terms of QoS-node CS size vs. hit rate (6% to 47%), response time vs, QoS-node CS size (65% to 90%), and hop count vs. QoS-node CS size (60% to 84%) from the perspectives of priority traffic and overall traffic. QLPCC performed predictably when the NDN node count was increased from 500 to 1000, showing that the strategy is scalable. Full article

The digital transformation is exciting the uptake of Internet-of-Things technologies, and raises the questions surrounding our knowledge of the positions of many of these things. A review of indoor localization technologies summarized in this paper shows that with conventional RF-based techniques, a significant challenge exists in terms of achieving good accuracy with a low power consumption at the device side. We present hybrid RF-acoustic approaches as an interesting alternative: the slow propagation speed of sound allows for accurate distance measurements, while RF can easily provide synchronization, data, and power to the devices. We explain how the combination of adequate signaling realizing a late wake-up of the devices with backscattering could position energy-neutral devices. Experiments in a real-life testbed confirmed the potential 10 cm-accuracy based on RF-harvested energy. Nonetheless, these also expose open challenges to be resolved in order to achieve accurate 3D positioning. Full article

Mobile Ad hoc Networks (MANETs) are characterized by highly dynamic phenomena and volatility. These features have a significant impact on network performance and should be present in the scenarios of experiments for the assessment of MANET-related technologies. However, the currently available experimentation approaches suffer from limitations, either employing overly abstract simulation-based models that cannot capture real-world imperfections or drawing upon “monolithic” testbeds suited only to a narrow set of predetermined technologies, operational scenarios, or environmental conditions. Toward addressing these limitations, this work proposes a versatile platform that can accommodate many of the complexities present in real-world scenarios while still remaining highly flexible and customizable to enable a wide variety of MANET-related experiments. The platform is characterized by a modular architecture with clearly defined modules for the signaling between peer mobile nodes, the tracking of each node’s location and motion, the routing protocol functionality, and the management of communication messages at each node. The relevant software runs on inexpensive Raspberry Pi-based commodity hardware, which can be readily attached to robotic devices for moving the network nodes in accordance with controlled mobility patterns. Moreover, through an appropriate tuning of certain modules, a number of important operational conditions can be precisely controlled through software, e.g., restricting the communications range (thus reducing the network density) or for emulating the mobility patterns of nodes. The effectiveness and versatility of the proposed platform are demonstrated through the realization of a series of experiments on the performance comparison of selected routing protocols under diverse network density conditions. Full article

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN. Full article

In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm’s QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme. Full article

Adaptive streaming has become a key technology for various multimedia services, such as online learning, mobile streaming, Internet TV, etc. However, because of throughput fluctuations, video quality may be dramatically varying during a streaming session. In addition, stalling events may occur when segments do not reach the user device before their playback deadlines. It is well-known that quality variations and stalling events cause negative impacts on Quality of Experience (QoE). Therefore, a main challenge in adaptive streaming is how to evaluate the QoE of streaming sessions taking into account the influences of these factors. Thus far, many models have been proposed to tackle this issue. In addition, a lot of QoE databases have been publicly available. However, there have been no extensive evaluations of existing models using various databases. To fill this gap, in this study, we conduct an extensive evaluation of thirteen models on twelve databases with different characteristics of viewing devices, codecs, and session durations. Through experiment results, important findings are provided with regard to QoE prediction of streaming sessions. In addition, some suggestions on the effective employment of QoE models are presented. The findings and suggestions are expected to be useful for researchers and service providers to make QoE assessments and improvements of streaming solutions in adaptive streaming. Full article

The adoption of remote assisted care was accelerated by the COVID-19 pandemic. This type of system acquires data from various sensors, runs analytics to understand people’s activities, behavior, and living problems, and disseminates information with healthcare stakeholders to support timely follow-up and intervention. Blockchain technology may offer good technical solutions for tackling Internet of Things monitoring, data management, interventions, and privacy concerns in ambient assisted living applications. Even though the integration of blockchain technology with assisted care is still at the beginning, it has the potential to change the health and care processes through a secure transfer of patient data, better integration of care services, or by increasing coordination and awareness across the continuum of care. The motivation of this paper is to systematically review and organize these elements according to the main problems addressed. To the best of our knowledge, there are no studies conducted that address the solutions for integrating blockchain technology with ambient assisted living systems. To conduct the review, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology with clear criteria for including and excluding papers, allowing the reader to effortlessly gain insights into the current state-of-the-art research in the field. The results highlight the advantages and open issues that would require increased attention from the research community in the coming years. As for directions for further research, we have identified data sharing and integration of care paths with blockchain, storage, and transactional costs, personalization of data disclosure paths, interoperability with legacy care systems, legal issues, and digital rights management. Full article

Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they can cause queue congestion. This paper models data interchange (EDI) messages. We sought to understand how best DevOps should model these messages for performance testing and how best to apply smart EDI content awareness that enhance the realms of Ambient Intelligence (Aml) with a Business-to business (B2B) supply chain organisation. We considered key performance indicators (KPI) for over- or under-utilisation of these queueing systems. We modelled message service and inter-arrival times, partitioned data along various axes to facilitate statistical modelling and used continuous parametric and non-parametric techniques. Our results include the best fit for parametric and non-parametric techniques. We noted that a one-size-fits-all model is inappropriate for this heavy-tailed enterprise dataset. Our results showed that parametric distribution models were suitable for modelling the distribution’s tail, whilst non-parametric kernel density estimation models were better suited for modelling the head of a distribution. Depending on how we partitioned our data along the axes, our data suffer from quantisation noise. Full article

Driver acceptance studies are vital from the manufacturer’s perspective as well as the driver’s perspective. Most empirical investigations are limited to populations in the United States and Europe. Asian communities, particularly in Southeast Asia, which make for a large proportion of global car users, are underrepresented. To better understand the user acceptance toward in-vehicle applications, additional factors need to be included in order to complement the existing constructs in the Technology Acceptance Model (TAM). Hypotheses were developed and survey items were designed to validate the constructs in the research model. A total of 308 responses were received among Malaysians via convenience sampling and analyzed using linear and non-linear regression analyses. Apart from that, a mediating effect analysis was also performed to assess the indirect effect a variable has on another associated variable. We extended the TAM by including personal characteristics, system characteristics, social influence and trust, which could influence users’ intention to use the in-vehicle applications. We found that users from Malaysia are more likely to accept in-vehicle applications when they have the information about the system and believe that the applications are reliable and give an advantage in their driving experience. Without addressing the user acceptance, the adoption of the applications may progress more slowly, with the additional unfortunate result that potentially avoidable crashes will continue to occur. Full article

The proliferation of online news, especially during the “infodemic” that emerged along with the COVID-19 pandemic, has rapidly increased the risk of and, more importantly, the volume of online misinformation. Online Social Networks (OSNs), such as Facebook, Twitter, and YouTube, serve as fertile ground for disseminating misinformation, making the need for tools for analyzing the social web and gaining insights into communities that drive misinformation online vital. We introduce the MeVer NetworkX analysis and visualization tool, which helps users delve into social media conversations, helps users gain insights about how information propagates, and provides intuition about communities formed via interactions. The contributions of our tool lie in easy navigation through a multitude of features that provide helpful insights about the account behaviors and information propagation, provide the support of Twitter, Facebook, and Telegram graphs, and provide the modularity to integrate more platforms. The tool also provides features that highlight suspicious accounts in a graph that a user should investigate further. We collected four Twitter datasets related to COVID-19 disinformation to present the tool’s functionalities and evaluate its effectiveness. Full article

From self-driving cars to detecting cancer, the applications of modern artificial intelligence (AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able to extract high-level features after the network has been trained using statistical learning. However, due to the massive amounts of parallel processing in computations, the memory wall largely affects the performance. Thus, a review of the different memory architectures applied in DNN accelerators would prove beneficial. While the existing surveys only address DNN accelerators in general, this paper investigates novel advancements in efficient memory organizations and design methodologies in the DNN accelerator. First, an overview of the various memory architectures used in DNN accelerators will be provided, followed by a discussion of memory organizations on non-ASIC DNN accelerators. Furthermore, flexible memory systems incorporating an adaptable DNN computation will be explored. Lastly, an analysis of emerging memory technologies will be conducted. The reader, through this article, will: 1—gain the ability to analyze various proposed memory architectures; 2—discern various DNN accelerators with different memory designs; 3—become familiar with the trade-offs associated with memory organizations; and 4—become familiar with proposed new memory systems for modern DNN accelerators to solve the memory wall and other mentioned current issues. Full article

Wireless Sensor Networks (WSNs) are networks of small devices with limited resources which are able to collect different information for a variety of purposes. Energy and security play a key role in these networks and MAC aspects are fundamental in their management. The classical security approaches are not suitable in WSNs given the limited resources of the nodes, which subsequently require lightweight cryptography mechanisms in order to achieve high security levels. In this paper, a security analysis is provided comparing BMAC and LMAC protocols, in order to determine, using AES, RSA, and elliptic curve techniques, the protocol with the best trade-off in terms of received packets and energy consumption. Full article

The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called ‘user profiling‘. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our online profiles could be beneficial both to customers and stores. When shopping at a traditional physical store, real time targeted “personalized” advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits. Full article

The Internet of Things (IoT) has had a tremendous impact on the evolution and adoption of information and communication technology. In the modern world, data are generated by individuals and collected automatically by physical objects that are fitted with electronics, sensors, and network connectivity. IoT sensor networks have become integral aspects of environmental monitoring systems. However, data collected from IoT sensor devices are usually incomplete due to various reasons such as sensor failures, drifts, network faults and various other operational issues. The presence of incomplete or missing values can substantially affect the calibration of on-field environmental sensors. The aim of this study is to identify efficient missing data imputation techniques that will ensure accurate calibration of sensors. To achieve this, we propose an efficient and robust imputation technique based on k-means clustering that is capable of selecting the best imputation technique for missing data imputation. We then evaluate the accuracy of our proposed technique against other techniques and test their effect on various calibration processes for data collected from on-field low-cost environmental sensors in urban air pollution monitoring stations. To test the efficiency of the imputation techniques, we simulated missing data rates at 10–40% and also considered missing values occurring over consecutive periods of time (1 day, 1 week and 1 month). Overall, our proposed BFMVI model recorded the best imputation accuracy (0.011758 RMSE for 10% missing data and 0.169418 RMSE at 40% missing data) compared to the other techniques (kNearest-Neighbour (kNN), Regression Imputation (RI), Expectation Maximization (EM) and MissForest techniques) when evaluated using different performance indicators. Moreover, the results show a trade-off between imputation accuracy and computational complexity with benchmark techniques showing a low computational complexity at the expense of accuracy when compared with our proposed technique. Full article

Users of web or chat social networks typically use emojis (e.g., smilies, memes, hearts) to convey in their textual interactions the emotions underlying the context of the communication, aiming for better interpretability, especially for short polysemous phrases. Semantic-based context recognition tools, employed in any chat or social network, can directly comprehend text-based emoticons (i.e., emojis created from a combination of symbols and characters) and translate them into audio information (e.g., text-to-speech readers for individuals with vision impairment). On the other hand, for a comprehensive understanding of the semantic context, image-based emojis require image-recognition algorithms. This study aims to explore and compare different classification methods for pictograms, applied to emojis collected from Internet sources. Each emoji is labeled according to the basic Ekman model of six emotional states. The first step involves extraction of emoji features through convolutional neural networks, which are then used to train conventional supervised machine learning classifiers for purposes of comparison. The second experimental step broadens the comparison to deep learning networks. The results reveal that both the conventional and deep learning classification approaches accomplish the goal effectively, with deep transfer learning exhibiting a highly satisfactory performance, as expected. Full article

Microblogs are one of the major social networks in people’s daily life. The increasing amount of timely microblog data brings new opportunities for enterprises to predict short-term product sales based on microblogs because the daily microblogs posted by various users can express people’s sentiments on specific products, such as movies and books. Additionally, the social influence of microblogging platforms enables the rapid spread of product information, implemented by users’ forwarding and commenting behavior. To verify the usefulness of microblogs in enhancing the prediction of short-term product sales, in this paper, we first present a new framework that adopts the sentiment and influence features of microblogs. Then, we describe the detailed feature computation methods for sentiment polarity detection and influence measurement. We also implement the Linear Regression (LR) model and the Support Vector Regression (SVR) model, selected as the representatives of linear and nonlinear regression models, to predict short-term product sales. Finally, we take movie box office predictions as an example and conduct experiments to evaluate the performance of the proposed features and models. The results show that the proposed sentiment feature and influence feature of microblogs play a positive role in improving the prediction precision. In addition, both the LR model and the SVR model can lower the MAPE metric of the prediction effectively. Full article

This work presents a Self Sovereign Identity based system proposal to show how Blockchain, Building Information Modeling, Internet of Thing devices, and Self Sovereign Identity concepts can support the process of building digitalization, guaranteeing the compliance standards and technical regulations. The proposal ensures eligibility, transparency and traceability of all information produced by stakeholders, or generated by IoT devices appropriately placed, during the entire life cycle of a building artifact. By exploiting the concepts of the Self Sovereign Identity, our proposal allows the identification of all involved stakeholders, the storage off-chain of all information, and that on-chain of the sole data necessary for the information notarization and certification, adopting multi-signature approval mechanisms where appropriate. In addition it allows the eligibility verification of the certificated information, providing also useful information for facility management. It is proposed as an innovative system and companies that adopt the Open Innovation paradigm might want to pursue it. The model proposal is designed exploiting the Veramo platform, hence the Ethereum Blockchain, and all the recommendations about Self Sovereign Identity systems given by the European Blockchain Partnership, and by the World Wide Web Consortium. Full article

Digital communication over power lines is an active field of research and most studies in this field focus on low-voltage (LV) and medium-voltage (MV) power systems. Nevertheless, as power companies are starting to provide communication services and as smart-grid technologies are being incorporated into power networks, high-voltage (HV) power-line communication has become attractive. The main constraint of conventional HV power-line carrier (PLC) systems is their unfeasibility for being migrated to wideband channels, even with a high signal-to-noise ratio (SNR). In this scenario, none of the current linear/non-linear equalizers used in single carrier schemes achieve the complete compensation of the highly dispersive conditions, which limits their operation to 4 kHz channels. In this paper, a new PLC-channel model is introduced for transmission lines incorporating the effects of the coupling equipment. In addition, the use of the single-carrier frequency-division modulation (SC-FDM) is proposed as a solution to operate PLC systems in a wide bandwidth, achieving transmission speeds above those of the conventional PLC system. The results presented in this paper demonstrate the superior performance of the SC-FDM-PLC over conventional PLC systems, obtaining a higher transmission capacity in 10 to 30 times. Full article

Distributed machine learning paradigms have benefited from the concurrent advancement of deep learning and the Internet of Things (IoT), among which federated learning is one of the most promising frameworks, where a central server collaborates with local learners to train a global model. The inherent heterogeneity of IoT devices, i.e., non-independent and identically distributed (non-i.i.d.) data, and the inconsistent communication network environment results in the bottleneck of a degraded learning performance and slow convergence. Moreover, most weight averaging-based model aggregation schemes raise learning fairness concerns. In this paper, we propose a peer-to-peer decentralized learning framework to tackle the above issues. Particularly, each local client iteratively finds a learning pair to exchange the local learning model. By doing this, multiple learning objectives are optimized to advocate for learning fairness while avoiding small-group domination. The proposed fairness-aware approach allows local clients to adaptively aggregate the received model based on the local learning performance. The experimental results demonstrate that the proposed approach is capable of significantly improving the efficacy of federated learning and outperforms the state-of-the-art schemes under real-world scenarios, including balanced-i.i.d., unbalanced-i.i.d., balanced-non.i.i.d., and unbalanced-non.i.i.d. environments. Full article

Complex systems are often designed in a decentralized and open way so that they can operate on heterogeneous entities that communicate with each other. Numerous studies consider the process of components simulation in a complex system as a proven approach to realistically predict the behavior of a complex system or to effectively manage its complexity. The simulation of different complex system components can be coupled via co-simulation to reproduce the behavior emerging from their interaction. On the other hand, multi-agent simulations have been largely implemented in complex system modeling and simulation. Each multi-agent simulator’s role is to solve one of the VRP objectives. These simulators interact within a co-simulation platform called MECSYCO, to ensure the integration of the various proposed VRP models. This paper presents the Vehicle Routing Problem (VRP) simulation results in several aspects, where the main goal is to satisfy several client demands. The experiments show the performance of the proposed VRP multi-model and carry out its improvement in terms of computational complexity. Full article

Energy management in the Internet of Vehicles (IoV) is becoming more prevalent as the usage of distributed Electric Vehicles (EV) grows. As a result, Demand Response (DR) management has been introduced to achieve efficient energy management in IoV. Through DR management, EV drivers are allowed to adjust their energy consumption and generation based on a variety of parameters, such as cost, driving patterns and driving routes. Nonetheless, research in IoV DR management is still in its early stages, and the implementation of DR schemes faces a number of significant hurdles. Blockchain is used to solve some of them (e.g., incentivization, privacy and security issues, lack of interoperability and high mobility). For instance, blockchain enables the introduction of safe, reliable and decentralized Peer-to-Peer (P2P) energy trading. The combination of blockchain and IoV is a new promising approach to further improve/overcome the aforementioned limitations. However, there is limited literature in Demand Response Management (DRM) schemes designed for IoV. Therefore, there is a need for a systematic literature review (SLR) to collect and critically analyze the existing relevant literature, in an attempt to highlight open issues. Thus, in this article, we conduct a SLR, investigating how blockchain technology assists the area of DRM in IoV. We contribute to the body of knowledge by offering a set of observations and research challenges on blockchain-based DRM in IoV. In doing so, we allow other researchers to focus their work on them, and further contribute to this area. Full article

As an emerging paradigm of service infrastructure, social internet of things (SIoT) applies the social networking aspects to the internet of things (IoT). Each object in SIoT can establish the social relationship without human intervention, which will enhance the efficiency of interaction among objects, thus boosting the service efficiency. The issue of trust is regarded as an important issue in the development of SIoT. It will influence the object to make decisions about the service delegation. In the current literature, the solutions for the trust issue are always unidirectional, that is, only consider the needs of the service requester to evaluate the trust of service providers. Moreover, the relationship between the service delegation and trust model is still ambiguous. In this paper, we present a bidirectional trust model and construct an explicit approach to address the issue of service delegation based on the trust model. We comprehensively consider the context of the SIoT services or tasks for enhancing the feasibility of our model. The subjective logic is used for trust quantification and we design two optimized operators for opinion convergence. Finally, the proposed trust model and trust-based service delegation method are validated through a series of numerical tests. Full article

Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into the models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google knowledge graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting. The embeddings-based approach measures the similarity between relevant concepts and retrieved media news entries and datasets’ metadata based on the word movers’ distance between embeddings. The semantic-based approach recourses to wikification and measures the Jaccard distance instead. The semantic-based approach leads to more diverse entries when displaying media events and more precise and diverse results regarding recommended datasets. We conclude that the explanations provided can be further improved with information regarding the purpose of potential actions that can be taken to influence demand and to provide “what-if” analysis capabilities. Full article

Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task. Full article

The multi-agent environment is now widely recognised as a key design abstraction for constructing multi-agent systems, equally important as the agents. An explicitly designed environment may have several roles, such as the inter-mediation between agents, the support for interaction, the embodiment of rules and constraints, etc. Mobility applications fit perfectly with a design in the form of a multi-agent system with an explicit environment model. Indeed, in these applications, the components of the system are autonomous and intelligent (drivers, travellers, vehicles, etc.), and the transportation network is a natural environment that they perceive and on which they act. However, the concept of the multi-agent environment may be profitably used beyond this specific geographical context. This paper discusses the relevance of the multi-agent environment in mobility applications and describes different use cases in simulation and optimisation. Full article

Dynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the Quality of Service (QoS), estimate the required resources, and modify allotted resources depending on workload, serialization, and parallelism due to resources. This article focuses on cloud QoS violation remediation using resource planning and scaling. A Resource Quantified Scaling for QoS Violation (ReSQoV) model is proposed based on the Universal Scalability Law (USL), which provides cloud service capacity for specific workloads and generates a capacity model. ReSQoV considers the system overheads while allocating resources to maintain the agreed QoS. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. The scenarios emulate QoS parameters and their respective resource utilization for ReSQoV compared to policy-based resource allocation. The results show that after USLbased Quantified resource allocation, QoS is regained, and validation of the ReSQoV is performed through the statistical test ANOVA that shows the significant difference before and after implementation. Full article

SNS24, the Portuguese National Health Contact Center, is a telephone and digital public service that provides clinical services. SNS24 plays an important role in the identification of users’ clinical situations according to their symptoms. Currently, there are a number of possible clinical algorithms defined, and selecting the appropriate clinical algorithm is very important in each telephone triage episode. Decreasing the duration of the phone calls and allowing a faster interaction between citizens and SNS24 service can further improve the performance of the telephone triage service. In this paper, we present a study using deep learning approaches to build classification models, aiming to support the nurses with the clinical algorithm’s choice. Three different deep learning architectures, namely convolutional neural network (CNN), recurrent neural network (RNN), and transformers-based approaches are applied across a total number of 269,654 call records belonging to 51 classes. The CNN, RNN, and transformers-based model each achieve an accuracy of 76.56%, 75.88%, and 78.15% over the test set in the preliminary experiments. Models using the transformers-based architecture are further fine-tuned, achieving an accuracy of 79.67% with Adam and 79.72% with SGD after learning rate fine-tuning; an accuracy of 79.96% with Adam and 79.76% with SGD after epochs fine-tuning; an accuracy of 80.57% with Adam after the batch size fine-tuning. Analysis of similar clinical symptoms is carried out using the fine-tuned neural network model. Comparisons are done over the labels predicted by the neural network model, the support vector machines model, and the original labels from SNS24. These results suggest that using deep learning is an effective and promising approach to aid the clinical triage of the SNS24 phone call services. Full article