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Computational Intelligence (CI) is a more efficient paradigm for solving real-world problems in uncertain conditions. The traditional CI approaches are not capable to provide the complete and sufficient solutions for problems. Therefore, new techniques are necessary to efficiently solve these issues seriously. New techniques, such as Emergent Intelligence (EI), Multi-Agent System (MAS), etc., provide robust, generic, flexible, and self-organised to solve complex real-world problems. In this paper, we discuss Emergent Intelligence (EI) and its uniqueness in solving problems in an uncertain environment. We also discuss EI, Swarm Intelligence (SI) and MultiAgent System (MAS)-based problem-solving in an uncertain environment and compared their performance. We have considered two different problems: job shop scheduling using EI and MAS and route establishment for routing using MAS, SI and EI in an uncertain environment. Each problem is categorically analysed and solved step by step using MAS, SI and EI in a dynamic environment. We measure the performance of these three methods by varying the number of agents, tasks and time. Performance measures are compared and shown to demonstrate the importance of EI over MAS and SI for solving problems in an uncertain environment. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) aims to connect massive amounts of devices with higher reliability, lower latency, and faster transmission speed, which are vital for implementing e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from the physical layer, upper layer, and cross-layer perspectives on designing 5G e-health systems. We then elaborate two use cases according to our implementations (i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment). We finally envision the future research trends and challenges of 5G e-health systems. [ABSTRACT FROM AUTHOR]

The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose. [ABSTRACT FROM AUTHOR]

Summary: Software‐defined networking (SDN) is an agile, modern networking approach that facilitates innovations in the networking paradigm. The abstracted and centralized network operating system facilitates the network management and reduces operational expenditure (OPEX). The open nature and simplicity of the data‐forwarding plane dramatically reduces capital expenditure (CAPEX) by leveraging commodity servers and switches. SDN also lends itself very well to address major cloud computing issues and complement cloud services, especially in terms of network virtualization and networking as a service (NaaS). As a new technology, SDN does involve certain security challenges, which include distributed denial of service (DDoS) threats, build and run time injected malware, insider (tenant) attacks, and security holes resulting from controller misconfigurations. These are severe threats that can cripple an entire network. It is crucial to address the SDN vulnerabilities to ensure its successful deployment in private data center networks, on cloud platforms and beyond. Some security solutions leverage the built‐in features of SDN, such as its controller software component, while other solutions provide external SDN applications running above the controller. This study reviews the security solutions for the vulnerabilities of state‐of‐the‐art SDN controllers and the available countermeasures. Furthermore, an in‐depth analysis of the SDN features that support security is presented, and some unresolved research issues on SDN controllers are identified. [ABSTRACT FROM AUTHOR]

Summary: The significant increase in energy consumption by the growth of the population or by the use of new equipment has brought big challenges to the energy security as well as the environment. There is a need that consumers can track their daily use and understand consumption standards for better organizing themselves to obtain financial and energetic efficiency. With the improvement of smart networks technology for better energy supply, a smart meter is not just a simple measurement gadget anymore, but it has additional functions including smart equipment control, bidirectional communication that allows integration of users and networks, and other functionalities. Smart meters are the most fundamental components in smart power grids. Besides, the meters used with a management system can be utilized for monitoring and controlling home appliances and other gadgets according to the users' need. A solution of an integrated and single system should be more efficient and economical. Smart measurement systems allow monitoring the energy consumption of the final consumers while providing useful information about the energy quality. The information provided by these systems is used by the operators to enhance the energy supply, and different techniques can be also applied for this end, such as charge scheduling, management from the demand side, and non‐intrusive load monitoring. The Internet of Things (IoT) is becoming a great ally in the management of smart distribution and energy consumption in smart systems scenarios. To address these issues, this paper proposes and demonstrates a new smart energy meter following an IoT approach and its associated costs and benefits. The developed device incorporates several communication interfaces. In order to easily integrate with any monitoring software solution, the meter has a multi‐protocol connection. Finally, the provided solution is validated and demonstrated in real‐life environments and it is also under use. [ABSTRACT FROM AUTHOR]

Summary: Service discovery (SD) is the cornerstone of wireless and mobile systems. The discovery of effective service is the key to achieve a higher level of ubiquity, which ensures the availability of services to users and applications significantly, and the high use of services. The research in this field is limited, with a few SD protocols available for specific purposes specified in the 6LoWPAN region. This paper discusses the various issues and challenges facing the design and selection of the appropriate service discovery and cluster‐based mechanism. Moreover, attention has been drawn to the cross‐layer solution by discussing different clustering techniques used to solve the redundancy problems. Furthermore, the service discovery protocols and cluster‐based routing protocols are critically investigated considering the redundancy problem, methods, mechanisms, and architectures by categorizing them into different categories, comparing them with fundamental parameters in the WSNs and MANETs environments. In addition, a modern form is proposed to show the life cycle of the service discovery process, and a new figure has been designed to clarify to the researchers various of the open issues related to the service discovery and cluster‐based routing protocols, corresponding objectives and the techniques of solving problems. Through the discussion of the scientific papers under survey, important results and valuable recommendations were reached. The survey is concluded with a summary, open research issues, outlook, and directions for future research on the topic. [ABSTRACT FROM AUTHOR]

For the purpose of usability feature extraction and prediction, an innovative metaheuristic algorithm is introduced. Generally, the term "usability" is defined by the several researchers with respect to the hierarchical-based software usability model and it has become one of the important methods in terms of software quality. In hierarchically based software, its usability factors, attributes, and its characteristics are combined. The paper presented an algorithm, i.e., modified crow search algorithm (MCSA) mainly for extraction of usability features from hierarchical model with the optimal solution under the search for useful features. MCSA is an extension of original crow search algorithm (CSA), which is a naturally inspired algorithm. The mechanism of this algorithm is based on the process of hiding food and prevents theft and hence introduced this CSA in the field of software engineering practices as an inspiration. The algorithm generates a particular number of selected features/attributes and is applied on software development life cycles models, finding out the best among them. The results of the presented algorithm are compared with the standard binary bat algorithm (BBA), original CSA, and modified whale optimization algorithm (MWOA). The outcomes conclude that the proposed MCSA performs well than the standard BBA and original CSA as the proposed algorithms generate fewer number of feature selection equal to 17 than 18 in BBA, 23 in CSA, and 19 in MWOA. [ABSTRACT FROM AUTHOR]

Routing in Opportunistic Internet of Things networks (OppIoTs) is a challenging task because of intermittent connectivity between devices and the lack of a fixed path between the source and destination of messages. Recently, machine learning (ML) and reinforcement learning (RL) have been used with great success to automate processes in a number of different problem domains. In this paper, we seek to fully automate the OppIoT routing process by using the Policy Iteration algorithm to maximize the possibility of message delivery. Moreover, we model the OppIoT environment as a Markov decision process (MDP) replete with states, actions, rewards, and transition probabilities. The proposed routing protocol, RLProph, is able to optimize the routing process via the optimal policy obtained by solving the MDP using Policy Iteration. Through extensive simulations, we show that RLProph outperforms a number of ML-based and context-aware routing protocols on a multitude of performance criteria. [ABSTRACT FROM AUTHOR]

Among the numerous alternatives used in the world of risk balance, it highlights the provision of guarantees in the formalization of credit agreements. The objective of this paper is to compare the achievement of fuzzy sets with that of artificial neural network-based decision trees on credit scoring to predict the recovered value using a sample of 1890 borrowers. Comparing with fuzzy logic, the decision analytic approach can more easily present the outcomes of the analysis. On the other hand, fuzzy logic makes some implicit assumptions that may make it even harder for credit-grantors to follow the logical decision-making process. This paper leads an initial study of collateral as a variable in the calculation of the credit scoring. The study concludes that the two models make modelling of uncertainty in the credit scoring process possible. Although more difficult to implement, fuzzy logic is more accurate for modelling the uncertainty. However, the decision tree model is more favourable to the presentation of the problem. [ABSTRACT FROM AUTHOR]

In the current surveillance networks, a vast amount of data is generated from different sources, resulting in big data. Such data need intelligent technologies and big data analytics for its realtime analysis to provide different services. This article describes an efficient artificial intelligence and big data analytics-assisted system for real-time fire scene analysis in surveillance networks. The proposed system is based on intelligent independent and subordinate agents, where each agent has a different task to report to the fire brigade and disaster management instantly. A deep yet efficient CNN is utilized in the system for feature extraction, classification, localization, and detection of fire in video frames. When the fire is detected in the frames, a fire alert is instantly sent to the emergency department and all agents immediately start their processing for checking the severity and growth rate of the fire, recognizing the scene and all objects on fire, and evacuation monitoring. Each agent of the system instantly sends information to disaster management to stop the loss of precious human lives and minimizes other economic and ecological loss. Experimental validation shows the promising results compared to existing systems. It is believed that using such a system is the demand of the time to save humanity from massive fire disasters and can make the current surveillance networks more intelligent. [ABSTRACT FROM AUTHOR]

The increasing use of interconnected sensors to monitor patients with chronic diseases, integrated with tools for the management of shared information, can guarantee a better performance of health information systems (HISs) by performing personalized healthcare. The early diagnosis of chronic diseases such as hypertensive disorders of pregnancy represents a significant challenge in women's healthcare. Computational learning techniques are useful tools for pattern recognition in the assessment of an increasing amount of integrated data related to these diseases. Hence, in this paper, the use of machine learning (ML) techniques is proposed for the assessment of real data referred to hypertensive disorders in pregnancy. The results show that the averaged one-dependence estimator algorithm can help in the decision- making process in uncertain moments, thus improving the early detection of these chronic diseases. The best-evaluated computational learning algorithm improves the performance of HISs through its precise diagnosis. This method can be applied in electronic health (e-health) environments as a useful tool for handling uncertainty in the decision-making process related to high-risk pregnancy. [ABSTRACT FROM AUTHOR]

Indeed, optimized task scheduling is the key to achieve high performance in such computing environments. The first paper by Boveiri, Javidan, and Khayami (2020) proposes an efficient hybrid approach named ACO-CLA to solve the task-scheduling problem in the mesh-topology cluster computing environments. An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications. Designing proportional integral controller for a solar powered DC-DC converter Using PIPSO algorithm and deep learning approach in intelligent bio-medical applications. [Extracted from the article]

Recently, the Industrial Internet of Things (IIoT) is attracting growing attention from both academia and industry. Meanwhile, trust-based communication is widely utilized in various systems. In this article, studying the performance of IIoT, we investigate trust-based communication for IIoT. In particular, devoting attention to sensor- cloud, which is a paradigm of IIoT, we propose three types of trust-based communication mechanisms for sensor-cloud. Furthermore, with numerical results, we show that trust-based communication can greatly enhance the performance of sensor-cloud. Eventually, open research issues with respect to trust-based communication for sensor-cloud are discussed. [ABSTRACT FROM AUTHOR]

In vehicular delay-tolerant networks (VDTNs), an end-to-end relay path between bundle source and destination nodes may not be available. To accomplish such goal, VDTNs rely on nodes cooperation. Thus, in order to maintain network efficiency, it is very important to ensure that all network nodes follow the protocol. This is not an easy task since nodes may diverge from the protocol due to a selfish behavior or to maintain their data or resources integrity. This paper proposes a cooperative watchdog system to detect and act against misbehavior nodes in order to reduce their impact in the overall network performance. Its operation relies on a cooperative exchange of nodes reputation along the network. By detecting selfish or misbehaving nodes, it is possible to improve the overall network performance. Conducting simulation experiments considering the VDTNSim tool and the Spray-and-Wait routing protocol shows that the cooperative watchdog proposed for VDTNs is not only effective in detecting misbehaving nodes but also contributes to improve the overall network performance by increasing the bundle delivery probability and decreasing the amount of resources waste. [ABSTRACT FROM PUBLISHER]

Vehicular communications are emerging as a promising technology to enable communications using vehicles as network nodes. VDTNs appear as a novel approach to enable services and applications where it is not possible to define an end-to-end path. To allow communications in such challenging environments, VDTNs rely for their operation on cooperation between network nodes, which contributes to increasing network connectivity and improving overall network performance. To accomplish such a task, nodes may be asked to share their constrained storage, bandwidth, and energy resources with one another. However, nodes may be unwilling to cooperate in order to save resources or due to selfish behavior. This kind of node severely affects the network functionality. This article gives an overview of the field, providing motivations, challenges, and an evaluation of the impact of cooperative measures on the performance of VDTN networks. [ABSTRACT FROM AUTHOR]

Vehicular Delay-Tolerant Networks (VDTNs) are a breakthrough based DTN solution used to provide vehicular communications under challenging scenarios, characterized by long delays and sporadic connections. Using the store-carry-and-forward paradigm, this technology allows in-transit bundles to asynchronously reach the destination hop by hop over traveling vehicles equipped with short-range wireless devices. The VDTN architecture assumes out-of-band signaling with control and data planes separation and follows an IP over VDTN approach. This paper presents a real-world VDTN prototype evaluated through a safety application and a Traffic Jam Information Service. It also demonstrates the real deployment of this new vehicular communication approach. The real testbed is an important contribution since some complex issues presented in vehicular communication systems can be studied more accurately in real-world environments than in a laboratory approach. The results confirm that real deployment of VDTNs is doable and can be seen as a very promising technology for vehicular communications, although it requires appropriated technologies for outline interferences and quality of service support. [ABSTRACT FROM AUTHOR]

Vehicular communications refer to a wide range of networks proposed for environments characterized by sparse connectivity, frequent network partitioning, intermittent connectivity, long propagation delays, asymmetric data rates, and high error rates. These environments may also be characterized by a potential non-available end-to-end path. To overcome these issues and improve the overall network performance, cooperation between network nodes must be severely considered. Nodes may cooperate by sharing their storage capacity, bandwidth, or even energy resources. However, nodes may be unwilling to cooperate due to a selfish behavior or to an intent to protect the integrity of their own resources. This selfish behavior significantly affects the functionality and performance of the network. This paper overviews the most recent advances related with cooperation on vehicular networks. It also studies the impact of different cooperation levels in the performance of Vehicular Delay-Tolerant Networks (VDTNs). It was shown that scenarios with a higher number of cooperative nodes present the best results in terms of bundle delivery delay. [ABSTRACT FROM AUTHOR]

Distance learning, electronic learning, and mobile learning offer content, methods, and technologies that decrease the limitations of traditional education. Mobile learning (m-learning) is an extension of distance education, supported by mobile devices equipped with wireless technologies. It is an emerging learning model and process that requires new forms of teaching, learning, contents, and dynamics between actors. In order to ascertain the current state of knowledge and research, an extensive review of the literature in m-learning has been undertaken to identify and harness potential factors and gaps in implementation. This article provides a critical analysis of m-learning projects and related literature, presenting the findings of this aforementioned analysis. It seeks to facilitate the inquiry into the following question: "What is possible in m-learning using recent technologies?" The analysis will be divided into two main parts: applications from the recent online mobile stores and operating system standalone applications. [ABSTRACT FROM AUTHOR]

Low power over wireless personal area networks (LoWPAN), in particular wireless sensor networks, represent an emerging technology with high potential to be employed in critical situations like security surveillance, battlefields, smart-grids, and in e-health applications. The support of security services in LoWPAN is considered a challenge. First, this type of networks is usually deployed in unattended environments, making them vulnerable to security attacks. Second, the constraints inherent to LoWPAN, such as scarce resources and limited battery capacity, impose a careful planning on how and where the security services should be deployed. Besides protecting the network from some well-known threats, it is important that security mechanisms be able to withstand attacks that have not been identified before. One way of reaching this goal is to control, at the network access level, which nodes can be attached to the network and to enforce their security compliance. This paper presents a network access security framework that can be used to control the nodes that have access to the network, based on administrative approval, and to enforce security compliance to the authorized nodes. [ABSTRACT FROM AUTHOR]

The objective of this work was the proposal, design, construction and validation of a mobile health system for dietetic monitoring and assessment, called SapoFit. This application may be personalized to keep a daily personal health record of an individual's food intake and daily exercise and to share this with a social network. The initiative is a partnership with SAPO - Portugal Telecom. SapoFit uses Web services architecture, a relatively new model for distributed computing and application integration. SapoFit runs on a range of mobile platforms, and it has been implemented successfully in a range of mobile devices and has been evaluated by over 100 users. Most users strongly agree that SapoFit has an attractive design, the environment is user-friendly and intuitive, and the navigation options are clear. [ABSTRACT FROM AUTHOR]

Intelligent tutoring systems are any computer systems encompassing interactive applications with some intelligence that support and facilitate the teaching-learning process. The intelligence of these systems is the ability to adapt to each student throughout his/her learning process. This paper presents an intelligent tutoring system, called EduTutor, created for the web-based Aulanet learning management system (LMS). The system architecture and its main characteristics are described in detail. EduTutor focuses on subjects for the first cycle of studies of the Portuguese primary education system, between the first and the fourth year, Its purpose is to facilitate the perception of the learning process of each student, individually, in a virtual environment, and as a study guide. Furthermore, this intelligent tutor system was designed and its architecture was prepared for being easily integrated in higher levels of studies, different subjects, and different languages. EduTutor was validated with a large set of real eases and is being used, with success, in the Aulanet LMS platform. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are composed of thousands of smart-sensing nodes, which capture environment data for a sink node. Such networks present new challenges when compared with traditional computer networks, namely in terms of smart node hardware constraints and very limited energy resources. Ubiquitous computing can benefit from WSNs from the perspective that sensed data can be used instead of the user without explicit intervention, turning ubiquitous computing into a reality. Internet connectivity in WSNs is highly desirable, featuring sensing services at a global scale. Two main approaches are considered: proxy based or sensor node stack based. This second approach turns sensors into data-producing hosts also known as ‘The Internet of Things’. For years, the TCP/IP (Transmission Control Protocol/Internet Protocol) suite was considered inappropriate for WSNs, mainly due to the inherent complexity and protocol overhead for such limited hardware. However, recent studies made connecting WSNs to the Internet possible, namely using sensor node stack based approaches, enabling integration into the future Internet. This paper surveys the current state-of-the-art on the connection of WSNs to the Internet, presents related achievements, and provides insights on how to develop IP-based communication solutions for WSNs today. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mobile devices present the opportunity to enhance our fast-growing and globally connected society, improving user-experience through novel approaches for information dissemination through mobile communication. The research community is developing new technologies, services, and applications to enable ubiquitous environments based on mobile technology. This paper tackles several important challenges such as communication cost and device limitations for development of ubiquitous multimedia applications. And we propose a system for news delivery using a set of wireless multimedia applications. For this purpose, we have performed a case study with Apple iPhone's platform, featuring two multimedia application contexts, namely, Web and native applications. The multimedia mobile applications draw on iPhone's assets, enabling context-awareness to distribute news, improving communication efficiency and setting-up viewing optimizations, thus enhancing user-experience. The proposed system is evaluated and validated through a series of real-life experiments on real devices, with online full availability .Moreover, due to the Web application availability, the system is not restrained to Apple's iPhone platform, but can also benefit users with other devices. [ABSTRACT FROM AUTHOR]

Over the years some medical studies have tried to better understand the internal behavior of human beings. Many researchers in this domain have been striving to find relationships between intra-vaginal temperature and certain female health conditions, such as ovulation and fertile period since woman's intra-vaginal temperature is one of the body parameters most preferred in such studies. However, due to lack of a appropriate technology, medical research devoted to studying correlations of such body parameters with certain womans' body phenomena could not obtain better results. This article presents the design and implementation of a novel intra-body sensor for acquisition and monitoring of intra-vaginal temperatures. This novel intra-body sensor provides data collection that is used for studying the relation between temperature variations and female health conditions, such as anticipation and monitoring of the ovulation period, detection of pregnancy contractions, preterm labor prevention, etc.. The motivation for this work focuses on the development of this new intra-body sensor that will represent a major step in medical technology. The novel sensor was tested and validated on hospitalized women as well as normal healthy women. Finally our medical team has attested to the accuracy, usability and performance of this novel intra-body sensor. [ABSTRACT FROM AUTHOR]

The article offers the authors' insights on topics related to the IEEE Communications Society (ComSoc). Topics mentioned include the management of conferences by the organization, the role of the Conference Development Board in the strategic planning of all conferences sponsored by ComSoc, and the members of the Conference Development Board including Tom Hou, Doug Zuckerman, and Luis Correia.

Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed. [ABSTRACT FROM AUTHOR]

Summary: Low‐power wide area networks (LP‐WANs) protocols are considered options to meet the requirements of Internet of Things (IoT) solutions that need long‐range communication with low power consumption. Few studies intended to evaluate the performance of the same class of protocols coexisting in the same environment and with mobility support. This paper explores the technical characteristics of Sigfox and long‐range (LoRa) protocols and presents a performance evaluation study considering mobility scenarios. The experiments were performed in real environments. Then, two prototypes for LoRa and Sigfox equipped with Global Positioning System (GPS) were created. Quantitative and qualitative performance metrics were also proposed for this study. The results showed that the coexistence between these protocols has soft impact on the performance presented. Because of the movement, the effects of Doppler, multipath, and signal fading hardly affect the performance of both protocols. [ABSTRACT FROM AUTHOR]

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air–oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli's equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives. [ABSTRACT FROM AUTHOR]

The twelve articles in this special section focus on energy efficiency as it relates to fog or edge computing. The Internet of Things (IoT) has emerged as one of the most advanced and complex technological trends, where more than 50 billion things will be connected (e.g., mobile devices, sensors, wearable devices, and other computing nodes) to the Internet by 2020. Edge/fog computing will play an increasingly important role in handling the information flow of such large and complex networks. An unintended consequence is the impact of their operations on carbon emissions and the resulting electricity costs. Thus, there has been focus on designing energy-efficient solutions for the edge-fog environment. In this Feature Topic, state-of-the-art research advances in energy efficiency and sustainability for edge/fog deployment are presented. [ABSTRACT FROM PUBLISHER]

The emergence of the Internet of Things (IoT) and its applications has taken the attention of several researchers. In an effort to provide interoperability and IPv6 support for the IoT devices, the Internet Engineering Task Force (IETF) proposed the 6LoWPAN stack. However, the particularities and hardware limitations of networks associated with IoT devices lead to several challenges, mainly for routing protocols. On its stack proposal, IETF standardizes the RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) as the routing protocol for Low-power and Lossy Networks (LLNs). RPL is a tree-based proactive routing protocol that creates acyclic graphs among the nodes to allow data exchange. Although widely considered and used by current applications, different recent studies have shown its limitations and drawbacks. Among these, it is possible to highlight the weak support of mobility and P2P traffic, restrictions for multicast transmissions, and lousy adaption for dynamic throughput. Motivated by the presented issues, several new solutions have emerged during recent years. The approaches range from the consideration of different routing metrics to an entirely new solution inspired by other routing protocols. In this context, this work aims to present an extensive survey study about routing solutions for IoT/LLN, not limited to RPL enhancements. In the course of the paper, the routing requirements of LLNs, the initial protocols, and the most recent approaches are presented. The IoT routing enhancements are divided according to its main objectives and then studied individually to point out its most important strengths and weaknesses. Furthermore, as the main contribution, this study presents a comprehensive discussion about the considered approaches, identifying the still remaining open issues and suggesting future directions to be recognized by new proposals. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm that proposes the connection of objects to exchange information in order to reach a common objective. In IoT networks, it is expected that the nodes will exchange data between each other and with external Internet services. However, due to deployment costs, not all the network devices are able to communicate with the Internet directly. Thus, other network nodes should use Internet-connected nodes as a gateway to forward messages to Internet services. Considering the fact that main routing protocols for low-power networks are not able to reach suitable performance in the displayed IoT environment, this work presents an enhancement to the Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation (LOADng) for IoT scenarios. The proposal, named LOADng-IoT, is based on three improvements that will allow the nodes to find Internet-connected nodes autonomously and dynamically, decreasing the control message overhead required for the route construction, and reducing the loss of data messages directed to the Internet. Based on the performed assessment study, which considered several number of nodes in dense, sparse, and mobility scenarios, the proposed approach is able to present significant results in metrics related to quality-of-service, reliability, and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a region-based pricing scheme, named as RegPrice, for provisioning safety-related decisions dynamically to the end-users. Typically, heterogeneous type of sensor nodes are present in the device layer of Safe-aaS. Considering the case of safety in road transportation, we compute the fixed and variable costs incurred in procurement, deployment, and maintenance for each of these different types of sensor nodes. We introduce the concept of tariff cost, which varies with the type of road in different regions and presence of similar homogeneous sensor nodes deployed in that region. Finally, we estimate the utility of a sensor node, which is a function of the sensing area, ratio of the fixed cost to total cost incurred, responsiveness factor, and rating given by an end-user for that sensor node. The SSPs provide rent to the sensor/vehicle owners for taking their sensor nodes on lease. In order to formulate the interactions among the SSPs and sensor owners, we apply first-price, sealed-bid auction-based game theoretic approach, where SSPs act as bidders. Exhaustive simulation results depict that the proposed pricing scheme, RegPrice, is capable of reducing the expenses of a SSP by 7.51% and 9.71% compared to the existing pricing schemes  and . [ABSTRACT FROM AUTHOR]

Summary: This manuscript provides an in‐depth exploration of metaverses, charting their historical development, technological foundations, and potential multifarious applications. It critically assesses the prevailing challenges and explores potential pathways for the evolution of these expansive, virtual environments. In shedding light on the wide‐ranging implications of metaverses, it navigates societal, cultural, regulatory, and economic landscapes. It further elucidates the symbiosis between the Internet of Things (IoT) and metaverses, presenting empirical evidence derived from purpose‐built IoT frameworks for metaverse applications. A series of experiments were conducted to affirm the hypothesis that the integration of IoT into metaverse utilities, including advanced concepts such as smart buildings, the intricate task of power grid management, and the precision‐centric domain of agriculture, can drive significant progress. Quantitative findings provide compelling evidence of marked improvements in energy conservation, cost‐efficiency, and operational effectiveness arising from the incorporation of IoT into metaverse use cases. With observed mean improvements of 25% in energy conservation, 17% in cost reduction, and an impressive 22% increase in operational efficiency, specifically in the realms of smart building applications and power grid management, this study underscores the pressing need for sustained research and development initiatives in this emerging. [ABSTRACT FROM AUTHOR]

Cyber threat hunting proactively searches for cyber threats, which are undetected by the traditional defense mechanisms. It scans deep to identify malicious programs (ie, malware) that escape from detection. It is important because sophisticated cyber threats can bypass the cyber security mechanisms. The performance of the cyber threat hunting can be improved through artificial intelligence (AI), especially, explainable AI (XAI), which adds trust component to the cyber threat hunting process. Due to the inclusion of XAI, the security experts get the full explanations of the detected threats as the working of the detection model in XAI is known. Information, like, which one is a threat, how it has been detected, and why it has been detected, can be obtained very easily due to the inclusion of XAI in the cyber threat hunting. Therefore, an XAI‐envisioned mechanism for cyber threat hunting has been proposed (in short, XAISM‐CTH). The network and threat models of XAISM‐CTH are designed and discussed. The conducted security analysis proves the security of XAISM‐CTH against various potential attacks. XAISM‐CTH also performs better than the other existing schemes. At the end, a practical implementation of XAISM‐CTH has been provided to observe its impact on the performance of the system. [ABSTRACT FROM AUTHOR]

Nature presents an infinite source of inspiration for computational models and paradigms, in particular for researchers associated with the area known as natural computing. The simultaneous optimization of the architectures and weights of artificial neural networks (ANNs) through biologically inspired algorithms is an interesting approach for obtaining efficient networks with relatively good generalization capabilities. This methodology constitutes a concordance between a low structural complexity model and low training error rates. Currently, complexity and high error rates are the leading issues faced in the development of clinical decision support systems (CDSSs) for pregnancy care. Hence, in this paper the use of a biologically inspired technique, known as particle swarm optimization (PSO), is proposed for reducing the computational cost of the ANN-based method referred to as the multilayer perceptron (MLP), without reducing its precision rate. The results show that the PSO algorithm is able to improve computational model performance, showing lower validation error rates than the conventional approach. This technique can select the best parameters and provide an efficient solution for training the MLP algorithm. The proposed nature-inspired algorithm and its parameter adjustment method improve the performance and precision of CDSSs. This technique can be applied in electronic health (e-health) systems as a useful tool for handling uncertainty in the decision-making process related to high-risk pregnancy. The proposed method outperformed, on average, other approaches by 26.4% in terms of precision and 14.9% in terms of the true positive ratio (TPR), and showed a reduction of 35.4% in the false positive ratio (FPR). Furthermore, this method was superior to the MLP algorithm in terms of precision and area under the receiver operating characteristic curve by 2.3 and 10.2%, respectively, when applied to the delivery outcome for pregnant women. [ABSTRACT FROM AUTHOR]

The combination of two overlapping technologies (bigdata and cloud computing) helps easy access to the evolving applications. In this context, there is a serious requirement of ensuring the transmission of data securely in order to improve the productivity over the public channel. Since the data collected by various sources are strictly private and confidential, there is also a great requirement to deal with the privacy preservation of the bigdata. To handle this issue, a new privacy-preserving bigdata collection technique in cloud computing environment, called 2PBDC, has been designed, which allows secure communication between the bigdata gateway nodes and the cloud servers. 2PBDC is shown to be secure against various known attacks against an active/passive adversary through the formal security verification as well as informal security analysis. A detailed comparative study among 2PBDC and other existing schemes has been conducted. This study shows that 2PBDC offers a better trade-off among the security and functionality features and communication and computation overheads while these are compared with other schemes. [ABSTRACT FROM AUTHOR]

The proliferation of the greedy heterogeneous wireless network demands the ability to handle the sharply increasing resource requirements of power and spectrum. Thus, the study of novel and effective methods to allocate power and spectrum resources in order to reduce energy consumption for heterogeneous devices has become an urgent issue. In this context the article proposes a DJRA in which data fusion, combined with NOMA, SIC and beamforming techniques, are utilized to allocate power and spectrum resources to heterogeneous devices with a reduced energy consumption, where different radio access technologies are integrated into a cellular network for communication between devices. The data correlations between the devices are computed to group the devices so as to reduce data transmission. In addition, NOMA, SIC and beamforming are used in combination to reduce network interference between BSs and the heterogeneous devices. The main contribution of this article is the design of a large-scale heterogeneous wireless network paradigm to improve the percentage of resource utilization also reducing the energy consumption. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%. [ABSTRACT FROM AUTHOR]

With the widespread use of Internet of Things (IoT) in various applications and several security vulnerabilities reported in them, the security requirements have become an integral part of an IoT system. Authentication and access control are the two principal security requirements for ensuring authorized and restricted accesses to limited and essential resources in IoT. The built-in authentication mechanism in IoT devices is not reliable, because several security vulnerabilities are revealed in the firmware implementation of authentication protocols in IoT. On the other hand, the current authentication approaches for IoT that are not firmware are vulnerable to some security attacks prevalent in IoT. Moreover, the recent access control approaches for IoT have limitations in context-awareness, scalability, interoperability, and security. To mitigate these limitations, there is a need for a robust authentication and access control system to safeguard the rapidly growing number of IoT devices. Consequently, in this paper, we propose a new secure unified authentication and access control system for IoT, called SUACC-IoT. The proposed system is based around the notion of capability, where a capability is considered as a token containing the access rights for authorized entities in the network. In the proposed system, the capability token is used to ensure authorized and controlled access to limited resources in IoT. The system uses only lightweight Elliptic Curve Diffie-Hellman Ephemeral (ECDHE), symmetric key encryption/decryption, message authentication code and cryptographic hash primitives. SUACC-IoT is proved to be secure against probabilistic polynomial-time adversaries and various attacks prevalent in IoT. The experimental results demonstrate that the proposed protocol's maximum CPU usage is 29.35%, maximum memory usage is 2.79% and computational overhead is 744.5 ms which are quite acceptable. Additionally, in SUACC-IoT, a reasonable communication cost of 872 bits is incurred for the longest message exchanged. [ABSTRACT FROM AUTHOR]

The article discusses highlights of several conferences related to the Institute of Electrical and Electronics Engineers (IEEE) as of June 2019. Events reported include Communications Society (ComSoc) Distinguished Lecturer Tour of entrepreneur Fawzi Behmann in Mexico in September 2018, Distinguished Lecturer Tour in Uruguay, Chile, and Bolivia in November 2018, and the first administrative meeting and Membership Recruitment Drive of the IEEE ComSoc Nigeria Chapter for 2019.

As a paradigm of Internet of Things (IoT), social sensor cloud (SSC), which connects the social networks and the sensor networks as well as the cloud, is receiving growing attention from both the academic and industrial communities. Aiming to be a timely guidance with respect to SSC, this article investigates SSC from four perspectives: framework, greenness, issues, and outlook. Specifically, a framework for SSC is introduced first. Then, with the initiative to reduce the carbon footprint, the mechanisms that enable green SSC are discussed. With that, the open research issues for SSC are presented. Finally, an outlook for SSC is shown. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5 % detection and 0.5 % false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks. [ABSTRACT FROM AUTHOR]

Transmission and distribution systems for electricity have undergone a technological revolution in terms of operation and management using computer networks, automation, remote sensing, and information and communication technologies to improve the performance of digital electronicmeters. This work describes the integration of a wireless sensor networks (WSNs)--based communication system with an electrical energy-measurement structure, to verify the feasibility of large-scale installation of intelligent electronic meters in low-voltage consumer units. The study is based on simulations, using Castalia, considering 2 scenarios, the first in a flat network and the second in a hierarchical network of WSNs to analyze the feasibility of sending messages from intelligent electronic meters to the concessionaires through a ZigBee network. In addition, the time requirements of the IEC 61850 standard for sending and receiving manufacturing message specifications and generic object-oriented substation event type messages are verified. This work demonstrated the technical feasibility of using WSNs for different node densities by region and evaluated the location of the sink node, and adequate infrastructures for WSNs were found. This extends time checks for both vertical (usually for supervision) and horizontal (used for protection) messages. The proposed model has great potential to use a WSN infrastructure and to evaluate if this infrastructure allows data transmission of the protocols used in smart grids, mainly verifying the requirements of transmission times required by each application. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is based on interconnection of intelligent and addressable devices, allowing their autonomy and proactive behavior with Internet connectivity. Data dissemination in IoT usually depends on the application and requires context-aware routing protocols that must include auto-configuration features (which adapt the behavior of the network at runtime, based on context information). This paper proposes an approach for IoT route selection using fuzzy logic in order to attain the requirements of specific applications. In this case, fuzzy logic is used to translate in math terms the imprecise information expressed by a set of linguistic rules. For this purpose, four Objective Functions (OFs) are proposed for the Routing Protocol for Low Power and Loss Networks (RPL); such OFs are dynamically selected based on context information. The aforementioned OFs are generated from the fusion of the following metrics: Expected Transmission Count (ETX), Number of Hops (NH) and Energy Consumed (EC). The experiments performed through simulation, associated with the statistical data analysis, conclude that this proposal provides high reliability by successfully delivering nearly 100% of data packets, low delay for data delivery and increase in QoS. In addition, an 30% improvement is attained in the network life time when using one of proposed objective function, keeping the devices alive for longer duration. [ABSTRACT FROM AUTHOR]

To improve the efficiency of electricity distribution, smart grids allow communication between their devices. Pieces of legacy equipment operating in the distribution network do not communicate using any commercial protocol, such as DNP3, IEC 61850, or Modbus. Thus, herein, a middleware is proposed to allow the integration of the legacy electrical equipment into a smart grid using wireless sensor networks (WSNs). Each piece of legacy electrical equipment is connected to a sensor node, and the WSN sink node runs a middleware to enable the integration of this device with a smart grid, according to the commercial communication protocols. The middleware model is proposed to guide users in the development of a WSN-based system for integrating electrical equipment into a smart grid. The middleware was validated in a real environment, which is related to the concept of power metering. Experiments were performed using the software supervisory control and data acquisition and distributed test manager to validate the communication between the electrical equipment and the computer of the power substation control centre. [ABSTRACT FROM AUTHOR]

Agricultural industry is one of the most vital industries that has a major contribution to the economy due to its share in the Gross Domestic Product (GDP) and as a source of employment. The past few decades have seen immense change in the operation of agricultural sector with the introduction of precision farming in conjunction with Internet of Things (IoT). The application of such advancements is highly based on exchange of messages between various devices in the farming. This paper aims to study the security scenarios applicable in husbandry through the analysis of possible attacks and threats. The testbeds available for agriculture based on IoT have been studied. An architecture for smart farming is proposed which is independent of the underlying technologies that may be used and the requirements of security have been laid out based on the proposed architecture. A literature survey of security protocols for various subsectors of security in smart agriculture along with authentication protocols in smart applications provides a detailed direction of the progress in each of farming security sub-areas and identifies the dearth of existing protocols. The current progress in development of IoT-based tools and systems from industry has also been studied. [ABSTRACT FROM AUTHOR]

Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time‐ (GEOTime) based condition alongside Priority Attribute role‐based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME‐PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud. [ABSTRACT FROM AUTHOR]