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Human Activity Recognition (HAR) has seen remarkable advances in recent years, driven by the widespread use of wearable devices and the increasing demand for personalized healthcare and activity tracking. Federated Learning (FL) is a promising paradigm for HAR that enables the collaborative training of machine learning models on decentralized devices while preserving data privacy. It improves not only data privacy but also training efficiency as it utilizes the computing power and data of potentially millions of smart devices for parallel training. In addition, it helps end-user devices avoid sending users’ private data to the cloud, eliminates the need for a network connection, and saves the latency of back-and-forth communication. FL also offers significant advantages for communication by reducing the amount of data transmitted over the network, alleviating network congestion and reducing communication costs. By distributing the training process across devices, FL minimizes the need for centralized data storage and processing, leading to more scalable and resilient systems. This paper provides a comprehensive survey of the integration of FL into HAR applications. Unlike existing reviews, this paper uniquely focuses on the intersection of FL and HAR, providing an in-depth analysis of recent advances and their practical implications. We explore key advances in FL-based HAR methodologies, including model architectures, optimization techniques, and different applications. Furthermore, we highlight the major challenges and future research questions in this domain, such as model personalization and robustness, privacy concerns, concept drift, and the limited capacity of edge devices.

This paper presents a large frequency ratio antenna that covers the widely separated 5G bands. The proposed antenna consists of a 2-element rectangular DRA (rDRA)-based MIMO antenna for the sub-6 GHz band, with an embedded $1\times 4$ cylindrical DRA (cDRA) array covering the mmW band. The rDRAs are fabricated through a cost and time-efficient 3D printing process, whereas small dimensions of cDRAs are precisely machined through a CNC. The proposed antenna has a measured impedance bandwidth of 31.63% at 4.9 GHz and 21.5% at 28 GHz. The antenna has a peak gain of 6.6 dBi at the sub-6 GHz and 12 dBi at the mmW bands. Moreover, the proposed antenna has an Envelope Correlation Coefficient (ECC) of less than 0.01, whereas the simulated beam steering performance of the antenna exhibits good performance for the sector of ±50°.

Intelligent transportation systems (ITSs) have witnessed a rising interest from researchers because of their promising features. These features include lane change assistance, infotainment, and collision avoidance, among others. To effectively operate ITSs for these functions, there is a need for edge computing. One can install edge computing servers at the roadside units (RSUs). There must be seamless communication between the edge servers and the cars. Additionally, there will be some cars that experience higher delays and thus, are not preferable because they will highly degrade the performance. Therefore, in this work, we consider a vehicular network scenario and define a cost function that takes into account the latency that is determined by the car’s computing frequency, association, and resource allocation while considering fairness constraints. Our cost function is to minimize the total latency (i.e., both local computing latency and transmission latency). The cost of the optimization problem is minimized by optimizing the car’s local frequency allocation, resource allocation, and association. The problem is separable, therefore, we first compute the local frequencies of the cars using a convex optimizer. Next, we split the core problem into two separate problems: (a) the distribution of resources and (b) association, because the last defined problem (joint association and resource allocation) is NP-hard. We then suggest an iterative solution. In the end, we offer numerical findings to support the suggested solution.

Detecting two-dimensional (2D) materials in silicon chips presents a significant challenge in the field of quantum machines due to the difficulty of data collection. Specifically, among thousands of flakes, not all flakes are useful or well-annotated, resulting in noisy and hard samples within the dataset, which challenges the deep neural network (DNN) to learn. To address this problem, we propose a novel method for identifying quantum 2D flakes even when there is a high rate of missing annotations in the input images. In particular, we first propose a new mechanism for automatically detecting false negative flakes that are missing annotations. Second, we introduce an attention-based loss function to mitigate the negative impact of these unannotated flakes on the DNNs. The experimental results demonstrate that our method outperforms previous approaches.

Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens.

Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users’ activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) - the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.

Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks. Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers. In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e. mobile devices) can choose one FL task publisher over another for participation in the training of a global model. We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers. Experimental results indicate the efficacy of the proposed framework.

Gradient descent is the workhorse of deep neural networks. Gradient descent has the disadvantage of slow convergence. The famous way to overcome slow convergence is to use momentum. Momentum effectively increases the learning factor of gradient descent. Recently, many approaches have been proposed to control the momentum for better optimization towards global minima, such as Adam, diffGrad, and AdaBelief. Adam decreases the momentum by dividing it with square root of moving averages of squared past gradients or second moment. The sudden decrease in the second moment often results in the overshoot of the gradient from the minima and then settle at the closest minima. DiffGrad decreases this problem by using a friction constant based on the difference of current gradient and immediate past gradient in Adam. The friction constant further decreases the momentum and results in slow convergence. AdaBelief adapts the step size according to the belief in the current gradient direction. Another famous way of fast convergence is to increase the batch size adaptively. This paper proposes a new optimization technique named adaptive diff-batch or adadb that removes the problem of overshooting gradient in Adam, slow convergence in diffGrad, and combines the methods with adaptive batch size for further increase in convergence rate. The proposed technique uses the friction constant based on the past three differences of gradients rather than one as in diffGrad and a condition to decide the use of friction constant. The proposed technique has outperformed the Adam, diffGrad, and AdaBelief optimizers on synthetic complex non-convex functions and real-world datasets.

With the increased number of data and data-generating devices in healthcare settings, the health monitoring systems have started to experience issues, such as efficient processing and latency. Several health-monitoring systems have been designed using Wireless Sensors Networks (WSN), cloud computing, fog computing, and the Internet of Things (IoT). Most of the health monitoring systems have been designed using the cloud computing architecture. However, due to the high latency introduced by the cloud-based architecture while processing massive volumes of data, large-scale deployment of latency-sensitive healthcare applications is restricted. Fog computing that places computing servers closer to the users addresses the latency problems and increases the on-demand scaling, resource accessibility, and security dramatically. In this paper, we propose a fog-based health monitoring system architecture to minimize latency and network usage. We also present a new Load Balancing Scheme (LBS) to balance the load among fog nodes when the health monitoring system is deployed on a large scale. To validate the effectiveness of the proposed approach, we conducted extensive simulations in the iFogSim toolkit and compared the results with the cloud-only implementation, Fog Node Placement Algorithm (FNPA), and LoAd Balancing (LAB) scheme, in terms of latency and network usage. The proposed implementation of the health monitoring system significantly reduces latency and network usage compared to cloud-only, FNPA, and LAB Scheme.

Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.

Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-efficient compared to transferring all data in the case of centralized machine learning (ML). Although FL offers many advantages, it also has some challenges. A malicious aggregation server can infer device information via local model updates. Another downside of FL is the centralized aggregation server that can malfunction due to an attack or physical damage. To address these issues, we propose a novel Structured Transparency empowered cross-silo Federated Learning on the Blockchain (ST-BFL) framework. In ST-BFL, homomorphic encryption, FL-aggregators, FL-verifiers, and smart contract are employed, which satisfy various structured transparency components, such as input privacy, output privacy, output verification, and flow governance. We present the framework architecture, algorithms, and sequence diagram of our ST-BFL framework to show how different entities interact in ST-BFL for the FL process. We also present a simplified class diagram of ST-BFL’s smart contract for an FL task. Finally, we perform a simulation to analyze our framework from the perspective of aggregation time, accuracy, and storage size. The qualitative and quantitative evaluation shows that ST-BFL has the same accuracy as traditional FL. However, ST-BFL provides input privacy, output privacy, input verification, output verification, and flow governance at the expense of relatively higher computation and communication costs than traditional FL.

Retinal vessel segmentation is important for analyzing many retinal diseases, where manual segmentation of these vessels is an extensive job. Automatic segmentation of these vessels can help much in the diagnosis of these retinal diseases. Several image processing schemes that are considering the retinal vessel segmentation are lacking in segmentation performance and robustness. Laplacian of Gaussian (LoG) detectors is a popular choice method for edge detection adapted for detecting circular blob detection. Laplacian kernel approximates the second-order derivative to the image, which can be effective in combination with Gaussian smoothing in the form of LoG. The LoG detectors are famous for good border detection in noisy images. Based on their parameter, their behavior can be switched between ridge or blob detection. However, their performance generally falls off for elliptical blobs. A generalized LoG detector was proposed recently to deal with elliptical blob detection. Comparing simulation studies for the second-order ridge detector with other popular ridge detectors provides evidence of its effectiveness. The proposed ridge detector shows promise when applied to detect vessels in real-world retinal images of a publicly available database. The method’s capabilities are evaluated with a comparison of state of the art, and the performances are obtained on the database most used by researchers. The DRIVE,STARE and CHASE_DB1 databases are used for performance evaluation, and we achieved a sensitivity of 0.785, a specificity of 0.967 and an accuracy of 0.952 on the DRIVE database,a sensitivity of 0.788, a specificity of 0.966 and a precision of 0.951 and a sensitivity of 0.787, a specificity of 0.968 and a precision of 0.952 on CHASE_DB1.

Glaucoma is an incurable eye disease that leads to slow progressive degeneration of the retina. It cannot be fully cured, however, its progression can be controlled in case of early diagnosis. Unfortunately, due to the absence of clear symptoms during the early stages, early diagnosis are rare. Glaucoma must be detected at early stages since late diagnosis can lead to permanent vision loss. Glaucoma affects the retina by damaging the Optic Nerve Head (ONH). Its diagnosis is dependent on the measurements of Optic Cup (OC) and Optic Disc (OD) in the retina. Computer vision techniques have been shown to diagnose glaucoma effectively and correctly with little overhead. These techniques measure OC and OC dimensions using machine learning based classification and segmentation algorithms. This article aims to provide a comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images. Readers would be able to understand the challenges glaucoma presents from an image processing and machine learning stand-point and will be able to identify gaps in current research.

In recent years, many large-scale information systems in the Internet of Things (IoT) can be converted into interdependent sensor networks, such as smart cities, smart medical systems, and industrial Internet systems. The successful application of edge computing in the IoT will make our algorithms faster, more convenient, lower overall costs, providing better business practices, and enhance sustainability. Facial action unit (AU) detection recognizes facial expressions by analyzing cues about the movement of certain atomic muscles in the local facial area. According to the detected facial feature points, we could calculate the values of AU, and then use classification algorithms for emotion recognition. In edge devices, using optimized and custom algorithms to directly process the raw image data from each camera, the detected emotions can be more easily transmitted to the end-user. Due to the tremendous network overhead of transferring the facial action unit feature data, it poses challenges of a real-time facial expression recognition system being deployed in a distributed manner while running in production. Therefore, we designed a lightweight edge computing-based distributed system using Raspberry Pi tailed for this need, and we optimized the data transfer and components deployment. In the vicinity, the front-end and back-end processing modes are separated to reduce round-trip delay, thereby completing complex computing tasks and providing high-reliability, large-scale connection services. For IoT or smart city applications and services, they can be made into smart sensing systems that can be deployed anywhere with network connections.

Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a user-centric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

Recently fifth-generation (5G) of cellular networks appeared to enable various smart bandwidth thirsty applications. Frequency re-usability via device-to-device (D2D) communication is one of the possible ways to enhance the 5G network throughput. However, despite these technological advancements in cellular networks, users' Quality of Experience (QoE) is significantly affected by video quality adaptation according to future network conditions for video streaming. In this paper, we jointly formulate D2D pair association, video quality adaptation, and network resource allocation for scalable video streaming in a D2D communication network. The formulated problem is a mixed-integer linear programming problem and has a combinatorial nature. Thus, the formulated problem is decomposed into two subproblems: D2D pair association subproblem and resource allocation subproblem. The D2D pair association subproblem is a combinatorial optimization problem, to solve it a distributed algorithm is proposed based on two sided-matching theory. On the other hand, resource allocation subproblem is solved using conventional linear optimization techniques to get the boundary solution. Furthermore, a fully distributed quality adaptation scheme is presented using the proposed resource allocation algorithm for layered video. To validate the performance of our proposal, we perform numerical simulations for various scenarios.

Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this article, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme.

Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.

Fifth-generation (5G) and beyond networks are envisioned to provide multi-services with diverse specifications. Network slicing is identified as a key enabling technology to enable 5G networks with multi-services. Network slicing allows a transition from a network-as-an-infrastructure setup to a network-as-a-service to enable numerous 5G smart services with diverse requirements. Although several surveys and tutorials have discussed network slicing in detail, there is no comprehensive study discussing the taxonomy and requirements of network slicing. In this paper, we present and investigate key recent advances of network slicing towards enabling several Internet of Things (IoT) smart applications. A taxonomy is devised for network slicing using different parameters: key design principles, enablers, slicing resources levels, service function chaining schemes, physical infrastructures, and security. Furthermore, we discuss key requirements for network slicing to enable smart services. Finally, we present several open research challenges along with possible guidelines for network slicing.

Cloud computing provides flexibility, reliability, and scalability to its consumers. Applications that run on Cloud are now more resource hungry than ever, and the need is constantly growing. The number of Cloud applications with diverse nature is growing, expecting greater performance in low cost. Fulfilling the quality of service (QoS) demands of such diverse applications is a challenging task and requires efficient network architectures, robust scheduling schemes, powerful and adaptable routing algorithms, reliable resource management, effective data exchange policies, and QoS improvement policies. Research community endeavors to provide solutions to various challenges faced by Cloud computing. These solutions must be tested thoroughly before their implementation in a real Cloud. Simulation seems a viable choice for testing a solution in different scenarios. There are a number of simulators available, focusing on certain aspects of Cloud while neglecting others. Their generalized implementation of the Cloud ignores various critical factors, such as networking characteristics, consequently affecting the tests and results of a solution. To tackle these issues, simplify simulation process, and provide the detailed implementation of the Cloud, we present Nutshell: a novel Cloud simulator that makes it easy to model, simulate, and experiment new Cloud solutions. Salient features offered by Nutshell include 1) provision of a platform for modeling and simulating Cloud infrastructure, 2) built-in essential components and helpers to create new architectures, 3) pre-built data center architectures, 4) plug-in-based architecture, 5) communication protocols, 6) pre-built virtual machine scheduler, 7) addressing schemes, 8) user applications, 9) virtual machines, 10) Job splitting, and 11) simulation data collection and exporting.

With the advancement in technology and inception of smart vehicles and smart cities, every vehicle can communicate with the other vehicles either directly or through ad-hoc networks. Therefore, such platforms can be utilized to disseminate time-critical information. However, in an ad-hoc situation, information coverage can be restricted in situations, where no relay vehicle is available. Moreover, the critical information must be delivered within a specific period of time; therefore, timely message dissemination is extremely important. The existing data dissemination techniques in VANETs generate a large number of messages through techniques such as broadcast or partial broadcast. Thus, the techniques based on broadcast schemes can cause congestion as all the recipients re-broadcast the message and vehicles receive multiple copies of same messages. Further, re-broadcast can degrade the coverage delivery ratio due to channel congestion. Moreover, the traditional cluster-based approach cannot work efficiently. As clustering schemes add additional delays due to communication with cluster head only. In this paper, we propose a data dissemination technique using a time barrier mechanism to reduce the overhead of messages that can clutter the network. The proposed solution is based on the concept of a super-node to timely disseminate the messages. Moreover, to avoid unnecessary broadcast which can also cause the broadcast storm problem, the time barrier technique is adapted to handle this problem. Thus, only the farthest vehicle rebroadcasts the message which can cover more distance. Therefore, the message can reach the farthest node in less time and thus, improves the coverage and reduces the delay. The proposed scheme is compared with traditional probabilistic approaches. The evaluation section shows the reduction in message overhead, transmission delay, improved coverage, and packet delivery ratio.

An automatic retinal vessel segmentation method is proposed for quick and accurate segmentation of eye vessels. Such a method would play a vital role in analyzing many eye diseases. A retinal fundus image contains varying low contrasts, which undermine the performance of the segmentation process. Independent component analysis (ICA) is largely used for noise removal and consists of two architectures, designated as ICA1 and ICA2. We have validated both ICA architectures on retinal color fundus images and selected the one that provides improved contrast values. For retinal fundus, ICA2 architecture performed better than ICA1 by virtue of being more effective in compensating the low contrast values. Experiments conducted here validated the improvements over previously reported state-of-the-art methods. The impact of proposed segmentation model was assessed on publicly available databases like DRIVE and STARE. In case of the DRIVE database, the sensitivity increased 3% by (from 72% to 75%) while maintaining a segmentation accuracy of around 96%.

Evolution of social networking services has prompted a huge impact on major facets beyond the daily life of mankind. The Internet-based social media platforms are aiding us in numerous domains including healthcare, education, business, and software development. Social networking, being a communication medium, has corroborated various software development activities, especially requirement engineering. It has helped in overcoming various shortcomings of the conventional requirement engineering approaches, such as selection of stakeholders and prioritization of stakeholders, and requirements using diverse techniques based on centrality measures. However, these techniques do not address the biasedness problem while identifying and prioritizing stakeholders. To rectify this problem, specifically the in-group bias, we propose a social network-based process. It combines hybrid centrality measure and power, legitimacy, urgency technique. To validate our methodology, a controlled experiment was performed on a sample set of stakeholders. It was observed from the results of the controlled trial that the group using the proposed social network-based process not only identified the stakeholders and their requirements more efficaciously but also prioritized the stakeholders significantly better than the group that did not use our proposed process. The results also demonstrate that the group using proposed social-network-based process was less biased as compared to the group with no social network-based framework.

The evolution of high performance computing architectures has increased the essence of effective utilization of resources. The cloud computing paradigm also incorporates high performance computing architectures with costly resources to provide services to end users. Its pay per use model makes it beneficial for cloud service providers as well as end-users. Through its layered architecture, the cloud computing paradigm offers services pertaining to the infrastructure, platform, and software layers [item 1) in the Appendix],[item 2) in the Appendix]. The fog computing, similar to the cloud computing, is one of the recently evolved paradigms for which the applications and services are set to be provided in proximity to the end-users [item 3) in the Appendix]. Cloud and fog computing are the recently evolved components of Information and Communication Technologies (ICT) which has been effective in reducing energy consumption and gas emissions of the industry [item 4) in the Appendix]. However, management of heavy resources, job processing at computing nodes, and execution costs in these infrastructures require energy efficient and sustainable solutions for successful deployments.

Fog computing is a technology that brings computing and storage resources near to the end user. Being in its infancy, fog computing lacks standardization in terms of architectures and simulation platforms. There are a number of fog simulators available today, among which a few are open-source, whereas rest are commercially available. The existing fog simulators mainly focus on a number of devices that can be simulated. Generally, the existing simulators are more inclined toward sensors’ configurations, where sensors generate raw data and fog nodes are used to intelligently process the data before sending to back-end cloud or other nodes. Therefore, these simulators lack network properties and assume reliable and error-free delivery on every service request. Moreover, no simulator allows researchers to incorporate their own fog nodes management algorithms, such as scheduling. In existing work, device handover is also not supported. In this paper, we propose a new fog simulator called FogNetSim++1 that provides users with detailed configuration options to simulate a large fog network. It enables researchers to incorporate customized mobility models and fog node scheduling algorithms, and manage handover mechanisms. In our evaluation setup, a traffic management system is evaluated to demonstrate the scalability and effectiveness of proposed simulator in terms of CPU and memory usage. We have also benchmarked the network parameters, such as execution delay, packet error rate, handovers, and latency.1available at https://fognetsimpp.com

The advancements in technology have transformed the vehicles moving around us into intelligent machines. These vehicles now have the capabilities to communicate and share useful information with each other under a communication network known as Vehicular Ad hoc Network (VANETs). The aim of this vehicular digitization was to enhance the standard, ease, leisure and safety of passengers, drivers and pedestrians on the roads. Using the power of intelligent decision making and the capability of interconnectivity, VANETs has enabled vehicles to communicate and operate in different modes, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Sensor (V2S), Vehicle-to-broadband cloud (V2B) and Vehicle-to-Human (V2H). Vehicular communications in all these modes rely on vehicular characteristics, diverse road structures and resource management during the operations of VANETs. To meet the quality requirements, the vehicular characteristics and network constrains are always focused to improve VANETs performance. Vehicular communication is established by using infrastructure and devices based on the operational policies defined in the Dedicated Short Range Communications (DSRC), IEEE 802.11p and Wireless Access in Vehicular Environments (WAVE) standards. The standards addresses the VANETs key issues regarding extension of coverage areas, impact of high vehicular velocities and dynamic road conditions on communications. The standards also outline the management of a multi-path phenomena whilst cooperating with multiple ad-hoc networks and providing a suitable environment for the merger of future technologies and applications with VANETs [item 1) in the Appendix].

Recent advancements in integrated circuits, wireless communication, and MicroElectroMechanical Systems (MEMS) technology has enabled low power, nano-technology wireless sensor nodes strategically deployed on the human body to be used by different applications, such as health monitoring, assisted living, and telemedicine. This new area of research is known as Body Area Networks (BANs) [1], [2]. BAN applications cover a wide area such as sports, entertainment, military, ubiquitous health care, and many other areas. BAN has multiple opportunities of interdisciplinary research where researchers from different areas of science and technology jointly put their efforts to improve the human body monitoring and performance. Due to these joint efforts, BAN is now evolving into Body Area Nano Networks (BANNs) as well [3], [4]. The BANNs are further extended with Internet of Nano Things and Internet of Bio-Nano Things technologies.