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The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

While traveling, people need to utilize smart tourism service systems based on the Internet of Things (IoT) to enhance their journeys' convenience. However, existing IoT-based smart tourism service systems still possess certain shortcomings. In light of this, this paper delves into the integration of IoT and machine learning (ML) technologies to improve the design of smart tourism service systems and establish a platform for intelligent tourism services. Simulated datasets are employed to evaluate the performance and functionality of the smart tourism service system. The primary contributions, as inferred from the test results, are as follows: In area 1, the model achieves a congestion recognition rate of 92.6% with an error rate of only 1.5% in congestion judgment. In area 2, congestion is accurately identified 93.5% of the time, with a misjudgment rate of just 1.2%. In area 3, the model achieves a congestion identification rate of 94.2% along with a 1.3% misjudgment rate, effectively identifying congestion situations in scenic spots. When comparing the research and design system with the system based on the support vector machine (SVM) algorithm, the results demonstrate that the smart tourism service system designed in this paper, leveraging IoT technology and ML algorithms, achieves higher prediction accuracy than the SVM-based system. These findings can serve as a valuable guide for designing future smart tourism service systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [ABSTRACT FROM AUTHOR]

The term "Internet of Things" (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of the IoT by enabling users to perform computing tasks using Internet-based services accessed through connected devices. This seamless integration of cloud technology and the IoT has become a powerful catalyst, revolutionizing the way we operate. The adoption of a distributed architecture, such as cloud computing, exposes the system to potential threats like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. To mitigate these risks, the concept of an intrusion detection system (IDS) has been introduced within the cloud environment. Various machine learning (ML) and deep learning (DL) algorithms have been proposed and implemented to effectively detect and respond to such malicious traffic in the cloud system. For dimension reduction during the training process of those algorithms, multiple independent and hybrid techniques have been proposed. This study presents an efficient ML-based real-time IDS framework with proposed hybrid feature selection techniques. Additionally, in this study, a concise comparative analysis has been conducted using five well-known public datasets. The findings presented in this paper reveal that our proposed IDS achieved a maximum accuracy of 99.98% in identifying malicious traffic. [ABSTRACT FROM AUTHOR]

The rapid growth of Internet of Things (IoT) devices has changed human interactions with the environment. IoT networks require specialized defense strategies distinct from traditional corporate contexts. Security measures such as anti-malware software, firewalls, authentication protocols, and encryption techniques are established but face limitations against evolving attack strategies. Therefore, this study proposes an intrusion detection approach for a realistic IoT environment, employing various variants of deep learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The research tested three variants for each model: DNN1, DNN2, DNN3, CNN1, CNN2, CNN3, RNN1, RNN2, RNN3. All these variants are customized and tuned differently to analyze the efficacy of the suggested methodology. Likewise, notable observation highlights the significance of aligning training and validation accuracy curves that indicate controlled overfitting, validating the model's reliability in accurately predicting intrusion and benign network traffic in IoT settings. Results reveal that RNN1 achieves the best results: accuracy of 98.61%, precision of 98.55%, recall of 98.61%, and F1-score of 98.57% compared with other DNN and CNN architectures and benchmark papers. This study advances intrusion detection within IoT networks through a comprehensive evaluation of deep learning models and inspires ongoing research to enhance intrusion detection systems' resilience in dynamic IoT environments. [ABSTRACT FROM AUTHOR]

In IoT applications, prediction models have fundamental challenges such as real-time processing, producing results with considerable/without delay, and taking action against pattern drift. While existing models can excel when data statistics remain relatively stable, real-time systems may encounter difficulties, particularly when confronted with dynamic shifts in data behavior. Analyzing data streams generated by different IoT applications and detecting complex pattern on the fly has become an open area of research. Complex event processing with adaptivity is a must to get desired features in such models. To address this issue, a comprehensive model for prediction has been proposed in this paper. It consists of two phases: (1) the basic model is constructed using historical data, (2) a fast Fourier transform-based adaptive support vector regression (FFT-ASVR) approach is proposed to predict events embedded in IoT data streams. FFT-ASVR predicts abnormal events by experiencing a change in data streams with real-time model updation. The performance of FFT-ASVR with a similar existing method SVM-RBF is presented using real-time traffic data of Madrid city. The proposed approach has significant improvement in terms of mean absolute percentage error (MAPE) for prediction, is adaptive in nature, and is also capable of handling the issue of pattern drift. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

Function-as-a-service has reduced the user burden by allowing cloud service providers to overtake operational activities such as resource allocation, service deployment, auto-scaling, and load-balancing, to name a few. The users are only responsible for developing the business logic through event-triggered functions catering to an application. Although FaaS brings about multiple user benefits, a typical challenge in this context is the time incurred in the environmental setup of the containers on which the functions execute, often referred to as the cold-start time leading to delayed execution and quality-of-service violations. This paper presents an efficient scheduling strategy FASE that uses a finite-sized warm pool to facilitate the instantaneous execution of functions on pre-warmed containers. Test-bed evaluations over AWS Lambda confirm that FASE achieves a 40% reduction in the average cold-start time and 1.29 × speedup compared to the baselines. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, Whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments demonstrate the superiority of the proposed approach over the existing one's throughput, packet delivery ratio (PDR), delay, and energy consumption efficiency is found in the result section. [ABSTRACT FROM AUTHOR]

With the proliferation of the Internet of Things and smart devices, there exists an urge to address the critical computation demands of end users for several real-time applications. Fog computing (FC) targets to address the computation requirements nearer to the end user in almost real-time. Mainly, the fog environments (FE) exhibit characteristics of resource constraint and wide heterogeneity. In addition, the complex fog environment enforces the fog resources (FR) deployment either within fixed or distributed over random geographical locations. In the literature, there exist several solutions for the optimization of FR allocation. However, the location-dependent FR allocation has not been addressed thoroughly. Hence, this paper proposes a novel hybrid clustering-based deep Q-network for location-based optimal resource allocation (HCDQN-ORA) model. Here, the first objective is to minimize the search operation for identifying suitable FR and to meet the quality of service (QoS) demand using an enhanced fog resource clustering (E-FRC) algorithm. The second objective aims to achieve optimal FR allocation among the set of location-dependent FRs using reinforcement learning techniques. Problem formulation has been modelled using Markov decision process. A deep Q-network algorithm with two variants, namely enhanced optimal resource allocation using deep Q-network (EORA-DQN) and enhanced optimal resource allocation using deep Q-network with experience replay (EORA-DQN-ER), has been incorporated. To analyse the statically located and randomly distributed FR environment, the experimental results exhibit that, in the case of clustering of FRs, E-FRC is more efficient than conventional clustering algorithms. Next, the location-dependent optimal FR allocation problem incorporates the performance analysis based on the convergence of loss function and the parametric analysis based on batch size, respectively. Results exhibit that EORA-DQN performs better only for statically allotted resources. However, the proposed EORA-DQN-ER outperforms both types of FR with a success rate of 89.6% of optimal allocation with the batch size of 200. [ABSTRACT FROM AUTHOR]

Securing confidential data against unauthorized users leads to access control policies with the rapid progression of Internet of Things (IoT) devices. Because of high mobility subscribers, the dynamic IoT environment is subjected to high signaling overhead which remains a challenging issue to guarantee data dissemination to legitimate users. The group's key management schemes are the central mechanism to deal with dynamic environments. But they are centralized concepts that cause scalability issues and suffer in handling large numbers of subscribers. Therefore, this paper proposes a modified deep reinforcement oppositional wolf learning-based group key management (MDROWL-GKM) system to monitor the data obtained in IoT properly. It does not maximize the network traffic as well as computational overhead when a group member leaves or joins. With the inclusion of an opposition-based learning gray wolf optimization algorithm, the overload issue of the modified deep reinforcement method is eliminated and the performance is enhanced. The efficacy of the proposed MDROWL-GKM system is investigated using different measures namely storage overhead, computation overhead, access response time, space complexity, re-evaluation time, policy adjustment accuracy, and communication overhead. The experimental analysis proves that the proposed MDROWL-GKM system is superior to other state-of-the-art techniques, particularly with high policy adjustment accuracy (96%), less communication overhead (8 μs) and area under curve (AUC) rate (0.982). [ABSTRACT FROM AUTHOR]

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST. [ABSTRACT FROM AUTHOR]

DDoS attacks launched by IoT botnets can be classified into low-rate and high-rate DDoS attacks, which depict two distinct scenarios of data imbalance, namely, the minimal and maximal proportion of attack traffic. Developing a detection model that can effectively address two data imbalance scenarios concurrently is crucial in safeguarding computers against DDoS attacks. This necessitates the development of a model with enhanced generalizability. However, in the examination of cross-evaluation across datasets which is closely associated with the generalizability of models, there is a limited focus on addressing the issue of data imbalance. This oversight has led to a significant decrease in model performance when compared to training and testing on a single dataset. To identify guiding principles within this context, the literature suggests employing an undersampling technique on benign instances during the preprocessing phase to eliminate redundant data. Additionally, the literature conducts simulations of various cross-evaluation scenarios and evaluates the performance of classifiers. This evaluation is done after applying representative oversamplers, undersamplers, and the method proposed in this paper. After analyzing the experiment results, it is advisable to employ oversamplers for low-rate DDoS datasets and undersamplers for high-rate DDoS datasets. Moreover, optimizing SMOTE-based algorithms for specific models will yield optimal performance. [ABSTRACT FROM AUTHOR]

Over the past few decades, the harmful effects of industrial activity and consumer society have resulted in global warming. Governments and international organizations are looking for ways to monitor activities of individuals and companies to assess their ecological impact. Unfortunately, such an approach would easily be seen as a mass surveillance tool. This is why we are proposing in this paper a novel privacy-aware global infrastructure for ecological footprint calculator based on the Internet of things and blockchain. Indeed, we take advantage of the data collection capacity of the Internet of things, the anonymization provided by public key identification and encryption, and the immutability of blockchain to implement this global system. A three-stage approach was used to validate our architecture: modeling in Petri nets to verify that the infrastructure fulfills all the required missions, implementing the three central authorities with python to record parameters such as durations, and finally modeling in queuing networks to demonstrate stability. The blockchain and Internet of things parts are used in a purely abstract manner relying on standard concepts; therefore, we have not implemented them. The experiments have produced very promising results. We have shown that for the simplest form of queue modeling, the involved servers have a utilization rate that is close to 50 % and that the overall waiting time remains below one minute. [ABSTRACT FROM AUTHOR]

Advancements in computing and communication technology have increased the number of devices connected to the Internet, leading to the emergence of the Internet of Things (IoT) technology. However, currently available network security measures are mainly designed for devices with higher computing power and cannot be directly applied to IoT devices. Client authentication is a fundamental aspect of security in IoT systems and is often achieved using digital signatures. Current IoT client authentication methods, such as RSA and ECDSA, are resource-intensive and vulnerable to attacks. Non-interactive zero-knowledge proof approaches are more suitable for IoT networks. MQTT is a widely used communication protocol in the IoT environment, but the client authentication feature of the standard MQTT protocol is poor. This paper proposes improving the elliptic curve cryptography-based non-interactive zero-knowledge proof authentication algorithm ECDSA. The proposed algorithm, T-ECDSA, uses a timestamp-based key to encrypt/decrypt the signature, valid only for a short period, making signature reuse invalid. The T-ECDSA is incorporated into the MQTT broker to avoid the requirement of a different dedicated server for authentication purpose. The experimental results demonstrate better security and improved time and execution complexity for the T-ECDSA compared to the standard ECDSA. [ABSTRACT FROM AUTHOR]

The increasing number of network attacks has led to the development of intrusion detection systems. However, these methods often face limitations such as high traffic flow data dimensions, which can reduce attack detection rates and noise sensitivity, affecting anomaly detection performance. This paper introduces a new model based on recurrent deep learning and instance-level horizontal reduction to detect anomalies and network attacks. The model uses nested sliding windows, which move with a specific step in the data and generate a different number of histogram outputs based on the type of anomaly in the data. Evaluation results on five databases show that the proposed model achieves a high accuracy of 99% in detecting different attacks, demonstrating the success of this new approach combined with deep recurrent neural networks in detecting anomalies. [ABSTRACT FROM AUTHOR]

This paper presents a review of voltammetric and related methods implemented with low-cost, mainly lab-made instrumentation. In recent years, there has been a growing interest in developing low-cost electrochemical instrumentation. Advances in electronics and software have made it easier to access high-performance components for the construction of potentiostats. Currently, lab-made instrumentation for electrochemical research can be very simple, with minimal components to perturb the system and record the response signal. It can also be very advanced, using high-performance electronic components and software development. Several authors have presented the feasibility of designing and manufacturing electrochemical instruments for voltammetry, chronoamperometry, chronopotentiometry, and electrodeposition. This review presents some available options, ranging from basic to advanced. The aim is to provide readers with the full range of possibilities for using their own instruments, especially in cases where access to commercial apparatus is difficult or where specific applications are needed, for example in the Internet of Things or artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper proposes a numerically and experimentally validated printed wideband antenna with a planar geometry for Internet of Things (IoT) applications. This design tackles the challenges associated with deploying IoT sensors in remote areas or across extensive geographical regions. The proposed design exploits a coplanar-waveguide-fed modified microstrip line monopole for excitation of circularly polarized waves radiating in the broadside direction. The primary design is based on perturbations of the microstrip line protracted from a grounded coplanar waveguide. The capacitively coupled short rectangular stubs are periodically inserted alternately and excited asymmetrically on each side of the microstrip line parallel to the direction of the electric field vector. The sequential phase excitation of the periodic stubs generates a rectangular-cascaded electric field, which suppresses the stop band at the open end. As a result, the antenna radiates in the broadside direction. The impedance bandwidth of the antenna exceeds 8 GHz in the 28 GHz mm-wave band, i.e., it ranged from 25 to 33.5 GHz. Additionally, an axial ratio below 3 dB is achieved within the operating band from 26 to 33.5 GHz with the alterations of the surface current using straightforward topological adjustments of the physical parameters. The average in-band realized gain of the antenna is 10 dBic when measured in the broadside direction. These results indicate that the proposed design has the potential to improve the connectivity between IoT devices and the constantly varying orientation of satellites by mitigating the polarization mismatch. [ABSTRACT FROM AUTHOR]

In recent years, the development of IOTA, a new type of Distributed Ledger (DL) for internet of things (IoT), has gained significant attention. IOTA DL offers key features like scalability, fast and free transactions, making it an optimal choice for IoT devices. However, a major concern with IOTA DL is its reliance on a single coordinator for transaction confirmation. This default coordinator introduces issues of single point of failure and incomplete distribution. To address these limitations, this paper proposes the Multiple Coordinator Selection (MCS) algorithm. MCS aims to overcome the problem by involving multiple coordinators in the consensus process. Four metrics, namely "trust level," "distance from input transactions," "node activity," and "transaction distribution," are defined as properties for coordinator selection. Additionally, a checklist is employed to minimize the probability of collusion within the system. Furthermore, the paper introduces a three-layered architecture based on cloud and fog computing, where the MCS algorithm is implemented. Experimental results demonstrate improved security and distribution of the system, while reducing the chances of collusion and single point of failure. [ABSTRACT FROM AUTHOR]

Trust can be considered as a multidisciplinary concept, which is strongly related to the context and it falls in different fields such as Philosophy, Psychology or Computer Science. Trust is fundamental in every relationship, because without it, an entity will not interact with other entities. This aspect is very important especially in the Internet of Things (IoT), where many entities produced by different vendors and created for different purposes have to interact among them through the internet often under uncertainty. Trust can overcome this uncertainty, creating a strong basis to ease the process of interaction among these entities. We believe that considering trust in the IoT is fundamental, and in order to implement it in any IoT entity, it is fundamental to consider it through the whole System Development Life Cycle. In this paper, we propose an analysis of different works that consider trust for the IoT. We will focus especially on the analysis of frameworks that have been developed in order to include trust in the IoT. We will make a classification of them providing a set of parameters that we believe are fundamental in order to properly consider trust in the IoT. Thus, we will identify important aspects to be taken into consideration when developing frameworks that implement trust in the IoT, finding gaps and proposing possible solutions. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and Internet of Things (IoT) technology, increasingly edge devices have entered people's daily lives. However, due to the limited performance of edge devices, complex models can affect the response speed and efficiency of the whole system. Existing research still cannot simultaneously satisfy the demand for accuracy and response speed of edge devices. This paper proposes a lightweight and highly accurate object detection model that uses the Transformer to address edge devices' limited computational capacity and storage space. Specifically, the proposed model adopts the Swin Transformer for multi-scale feature extraction to achieve better global modeling capability. In addition, we propose the Neck module with path aggregation network (PAN), which is designed with a two-feature pyramid structure capable of combining semantic and localization information in order to improve the operational performance by exploiting the underlying location features. A lightweight detection head is then developed using group convolution, fusing the two localization branches and removes the additional decoupling operation. Finally, we conduct comparative experiments on three datasets: the Retail-cabinet dataset, the Roadsign dataset, and the Pascal VOC dataset. Experimental results show that compared with the baseline model, our model achieves an 11.8% improvement in mAP on the Retail-cabinet dataset while reducing Params and FLOPs by 23.19% and 71.50%, respectively. The proposed model effectively reduces the model's computational complexity and improves detection performance, thereby possessing high practical value. This code is released on https://github.com/ydlam/ST-YOLOX. [ABSTRACT FROM AUTHOR]

Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task's accuracy. [ABSTRACT FROM AUTHOR]

Robust wireless communication using relaying system and Non-Orthogonal Multiple Access (NOMA) will be extensively used for future IoT applications. In this paper, we consider a fall detection IoT application in which elderly patients are equipped with wearable motion sensors. Patient motion data is sent to fog data servers via a NOMA-based relaying system, thereby improving the communication reliability. We analyze the average signal-to-interference-plus-noise (SINR) performance of the NOMA-based relaying system, where the source node transmits two different symbols to the relay and destination node by employing superposition coding over Rayleigh fading channels. In the amplify-and-forward (AF) based relaying, the relay re-transmits the received signal after amplification, whereas, in the decode-and-forward (DF) based relaying, the relay only re-transmits the symbol having lower NOMA power coefficient. We derive closed-form average SINR expressions for AF and DF relaying systems using NOMA. The average SINR expressions for AF and DF relaying systems are derived in terms of computationally efficient functions, namely Tricomi confluent hypergeometric and Meijer's G functions. Through simulations, it is shown that the average SINR values computed using the derived analytical expressions are in excellent agreement with the simulation-based average SINR results. [ABSTRACT FROM AUTHOR]

The Internet of Things has increased the demand for data, which has been met through the use of IoT-assisted cloud computing. However, this paradigm introduces new security complexities regarding the exchange of data between entities. In this paper, we investigate a secure strategy for managing IoT data in a cloud-assisted environment, protecting data privacy during data collection, storage, and access. Our method for mitigating the impact of IoT scalability is meticulously devised and supported by empirical evidence. Our productivity model is based on key operations, configurations, and efficiency factors. Our proposed method makes a substantial contribution to both system scalability and user data privacy, surpassing previous scale levels by a significant margin. In particular, our research investigates a secure strategy for managing IoT data in a cloud-assisted environment. We believe that our research will assist in bridging the divide between infrastructure, development, and testing teams, resulting in robust and stable productivity software. Our findings demonstrate the viability and efficacy of our proposed method, which outperforms previous models and previous research. [ABSTRACT FROM AUTHOR]

A blockchain is one distributed ledger system and keeps the high redundancy of ledger copies to make the assurance of network security. However, the continuously duplicated full copies also impose a tremendous amount of demand on some nodes for data storage. The development of blockchain technologies in the Internet of things (IoT) application scenario is hampered by the restricted storage capacities of terminal devices used in the IoT edge computing scenario, which makes it difficult to load the full copy with infinite growth. Our paper suggests a collaborative ledger storing model based on Chord Ring to address the issues with lightweight blockchains in data storage. On-chain blocks are split by Chord Ring structure and stored in various node clusters in a decentralized manner, and off-chain blocks at various levels are provided with proof-of-work consensus asynchronously and archived centrally to the cloud storage on a regular basis. The theoretical and experimental analysis indicates that this model can reduce the data storage redundancy of blockchains while ensuring the high availability of data and the high decentralization of the network. [ABSTRACT FROM AUTHOR]

In this paper, a novel blockchain-based multilevel security and authentication application is proposed to address the problem of counterfeiting in the medicine supply chain. In this blockchain-enabled application, multiple computers owned by different supply chain organizations collaborate in a distributed fashion to establish a trustworthy network. By adopting a resource-efficient Hyperledger Fabric framework, the proposed system aims to enhance transparency, integrity, and traceability in the pharmaceutical supply chain. Hyperledger Fabric blockchain also adds network security, privacy preservation, real-time tracking, and reliability features to the proposed system. Moreover, the proposed system also incorporates a blockchain-based QR code watermarking layer for authentication and verification. A validation module is designed that empowers buyers to validate the identity and history of serialized medicinal products, and buyer identity validation ensures that only legitimate buyers can access the products. Simulation outcomes and performance measurements demonstrate the effectiveness of the proposed system, including location traceability, QR code authentication, and verification. The Caliper tool has been used to measure the performance of the proposed system in terms of execution time, throughput, latency, and resource statistics for up to 100,000 transactions and 20 peers. The highest throughput achieved is 417.5 TPS (transactions per second) with 100,000 transactions and eight peers. The QR code authentication performance is tested under various noisy, cropped, and blurred attacks. We presented simulation outcomes of the implemented supply chain as well as chain code algorithms. The results indicate improved scalability, validation mechanisms, throughput, latency, and resource consumption compared to existing schemes. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) paradigm has emerged to connect the number of devices using the Internet resulting in the deployment of smart cities. Cloud computing has been applied to execute the computational demands of IoT devices by collecting data from the physical environment of a smart city. However, cloud computing could not become a proper choice for latency-sensitive applications because of remote cloud data centers. To overcome this challenge, fog computing has emerged to deal with the inherent limitations of cloud computing environment through provision of computing to the edge of a network. However, resource allocation of IoT service requests among fog nodes is considered as an NP-hard problem, which should be addressed in the fog computing environment. In this paper, an efficient optimization approach based on improved binary particle swarm optimization (IBPSO) algorithm has been provided for resource allocation of IoT requests in the hybrid fog–cloud computing environment. The proposed method aims to reduce the service request latency with ensuring load balancing among fog nodes. The performance of the proposed algorithm has been compared by the binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO)-based, and ranked-based resource allocation methods in terms of latency, missed deadline requests, run time, and load balancing. The results show that the proposed algorithm outperformed with an average of around 11 % , 22 % , 21 % , and 22 % percent in the IBPSO-based method rather than the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods, respectively. Moreover, the resource allocation based on IBPSO achieved around 11 % , 28 % , 27 % , and 25 % decline in total latency compared to the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods. Furthermore, the run time of the proposed algorithm could enhance by 45 % , 9 % , and 8 % compared to the BGA-based, BPSO-based, and BGWO-based resource allocation methods. [ABSTRACT FROM AUTHOR]

Due to dynamic changes, applications designed for the Internet of Things (IoT) require new storage resources. Storing IoT data in a fog infrastructure is a common practice to guarantee the IoT application's high performance. Multiple fog nodes spread the data replica to accommodate multiple users and reduce access latency. However, such data replicas are subjected to intentional and accidental corruption. This paper proposes a data integrity verification approach in a distributed fog environment. The proposed work uses fuzzy clustering and consensus mechanism to verify the integrity of multiple replicas stored on distributed fog nodes. Security analysis shows that the proposed approach is highly resistant to replace, replay, and forgery attacks. Performance analysis demonstrates the practicability of the proposed approach in a fog environment. [ABSTRACT FROM AUTHOR]

With the increasing user density of wireless networks, various user partitioning techniques or algorithms segregate users into smaller, more manageable clusters. The benefit of user clustering techniques in non-orthogonal multiple access (NOMA) is to optimize resource allocation and improve network performance, spectral efficiency, and user fairness in next-generation wireless networks, particularly in scenarios with a high density of users and diverse channel conditions. With increasing users, the network creates clusters before implementing non-orthogonal multiple access within these clusters. In this paper, we have organized and classified various user clustering techniques deployed from the perspective of NOMA-based communication in the current era. Furthermore, researchers have highlighted some works deploying joint resource allocation and clustering optimization based on various criteria to enhance the overall sum rate of the network. We also identify low-complexity user clustering techniques for multiple applications, e.g. the Internet of Things, unmanned aerial vehicles, and reconfigurable intelligent surfaces in the 5G and beyond communication networks. [ABSTRACT FROM AUTHOR]

Over the last decade, technological advances in smart grids have permitted the modernization of legacy electricity networks. As Internet of Things (IoT)-based smart grids are becoming an efficient response to managing changing electric demand, the heterogeneous network of equipment required to make these Cyber-Physical Systems a reality poses some security threats. This paper proposes a novel mutual authentication and key agreement scheme to ensure communications security and protect users' privacy in smart grid applications. In the proposed scheme (named EPSG), an elliptic curve cryptography (ECC) module and a physical unclonable function (PUF) are used simultaneously to provide acceptable confidentiality and integrity levels. The security analysis demonstrates that the EPSG has a robust security posture regarding transferred messages on the communication channel and physical attacks. In addition, EPSG is resistant to modeling attacks as one of the main vulnerabilities of PUF modules. Furthermore, by implementing the EPSG on an Arduino UNO microcontroller, a comparative performance evaluation (e.g., Time 156 ms, Communication cost 1408 bits, and Energy consumption 13.728 mJ) demonstrates the efficiency of the proposed EPSG. [ABSTRACT FROM AUTHOR]

In Internet-of-Things (IoT)-based healthcare systems, real-time healthcare data are gathered from patients' sensors with limited resources and transferred to end-users through gateways and healthcare service providers. Privacy of patients is a main challenge of these systems. Although privacy has already been considered in IoT-based healthcare systems, best centralized approaches yet suffer from collusion attack. Therefore, some researchers have come up with blockchain-based solutions to protect patients' privacy in IoT-based healthcare systems. However, those methods assume that parts of the entities along the end-to-end communication path from patients' sensors to the end-users are trusted or even assuming no privacy threats from internal attackers. Therefore, there is a lack of a blockchain-based approach in IoT-based healthcare systems to provide privacy for patients, assuming that all system entities are untrusted. To overcome these challenges, in this paper, we leverage a three-layered hierarchical blockchain, the zero-knowledge proof (ZKP), and the ring signature method to achieve data and location privacy of patients against both internal and external attackers. In addition, the proposed method provides anonymous authentication, authorization, and scalability, which are essential features in healthcare systems. Intuitive and formal security analyses demonstrate the resilience of our scheme against various attacks such as denial of service (DoS), modification, mining, storage, and replay attacks. The proposed method is compared to a recent blockchain-based method and also a centralized privacy-preserving scheme. Compared to the similar blockchain-based method, the computational overhead and delay of the authentication and data transfer phase are about 35% and 37% higher, respectively. Instead, the proposed method reduces memory usage of gateways by about 55% and diminishes the computational overhead and delay of information access phase by about 30% and 33% compared to the previous blockchain-based method. Therefore, the proposed method does not increase overhead and end-to-end delay considerably compared to the previous blockchain-based scheme, while some other performance metrics and security features are improved. Moreover, compared to a previous centralized method, the proposed approach shows more than 25% decrease in communication overhead and 22% improvement in memory usage of gateways, in average. Although the use of the blockchain imposes more computational overhead on service providers and may increase the latency compared to the centralized approach (depending on the type of the blockchain technology that is used), these weaknesses are negligible at the expense of increased security. [ABSTRACT FROM AUTHOR]

With the gradual entry into the era of "Internet of Things", large-scale parallel and distributed systems play a crucial role, which motivates us to explore qualitative and quantitative metric to characterize the fault tolerance and reliability of these systems. A more accurate assessment about the fault tolerance and reliability of large-scale parallel and distributed systems can be achieved by h-extra edge-connectivity. It can dynamically measure the size of the separated component under large-scale faulty links. The use of cartesian product operations to assemble large-scale interconnection networks has received extensive attention due to easy operation and scalability. Given a recursively assembled interconnection network G, the distribution of its values of h-extra edge-connectivity is non-uniform. The h-extra edge-connectivity of G, if any, is well-defined for each positive integer 1 ≤ h ≤ ⌊ | V (G) | / 2 ⌋ . This paper focuses on the concentration phenomenon about h-extra edge-connectivity of the n-th cartesian product of interconnection network K 4 (denoted by λ h (K 4 n) ): as n → ∞ , for about 64.5% of positive integers h ≤ 2 × 4 n - 1 , the h-extra edge-connectivities of the n-th cartesian product of interconnection network K 4 are concentrated on 3n numbers. That is, at least [ 3 n g - 3 g t - g (g - 1) ] 4 t links must be deleted to disconnect network K 4 n , provided that the deletion of these links does not isolate any subnetwork with at most h - 1 processors for about 64.5% of positive integers h ≤ 2 × 4 n - 1 , 0 ≤ t ≤ n - 1 and g ∈ { 1 , 2 , 3 } . It is exactly equal to the minimum cardinality of links, such that whose removal will disconnect the network K 4 n with all its resulting components having at least g × 4 t processors. Our results provide a more precise way to evaluate the reliability of interconnection networks. [ABSTRACT FROM AUTHOR]

Based on the characteristics of controllable intelligence of the Internet of Things (IoT) and the requirements of the new distribution Network for function and transmission delay, this study proposes a method of combining edge collaborative computing and distribution Network station area, and builds a distribution Network management structure model by combining the Packet Transport Network (PTN) Network structure. The multi-terminal node distribution model of distributed IoT is established. Finally, a distribution IoT management model is constructed based on the edge multi-node cooperative reasoning algorithm and collaborative computing architecture model. The purpose of this paper is to solve the problem of large reasoning delay caused by heavy computing tasks in distribution cloud servers. The final results show that the model reduces the inference delay of cloud computing when a large number of smart device terminals of distribution IoT are connected to the network. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is spreading rapidly around the world, and Message Queue Telemetry Transport (MQTT) is one of the main protocols used to explore device-to-device (D2D) communication. The industry typically requires communication systems that can transmit data continuously while optimizing both bandwidth and transmission time. Due to the vast amount of data that can be lost, companies often find that even short periods of network downtime lead to significant costs. In this paper, we propose a retransmission mechanism to allow sensor nodes to relay missing data via MQTT to a local server when it reconnects after an unexpected disconnection. To assess its performance, several tests in a digital healthcare use case scenario have been designed. Since the procedure involves transferring a considerable amount of data, our main goal is to determine the maximum payload of each message to restore the missing information, while minimizing the retransmission time without information loss. [ABSTRACT FROM AUTHOR]

The management of diabetes involves much compliance, disease awareness, and patient empowerment. Blood glucose levels in people with diabetes are abnormal, resulting in various health problems that affect their kidneys, hearts, and vision. Monitoring the patient's health has the benefit of using the Internet of Things (IoT) and diabetes patient monitoring systems. Because this system uses machine learning to classify data, its value goes beyond patient monitoring. So, this paper proposes a novel deep learning model with an effective feature selection mechanism for diabetes mellitus (DM) prediction in IoT-based healthcare environment. The proposed work starts by collecting the real-time IoT data of the patients from the Pima Indian Diabetes database. The data imbalance problem of the collected dataset is rectified by applying the synthetic minority oversampling technique. Afterward, the balanced dataset is preprocessed by applying missing value imputation and data standardization to improve the classification performance. Then, the hybrid algorithm called k-means clustering-based sailfish optimization is utilized, which performs clustering and optimization to select the essential features from the preprocessed dataset. Finally, the selected features are fed into the kaiming and switan included bidirectional long short-term memory for DM prediction. The proposed system achieves better results than the existing state-of-the-art techniques, which shows its effectiveness in predicting DM in real-time IoT datasets. [ABSTRACT FROM AUTHOR]

With the advent of internet-enabled and hybrid technologies, information is becoming increasingly accessible to the general public. Smartphones and other gadgets are used extensively by people to share and promote ideas, in a variety of ways. Human interaction and communication has become more reliable and effective through advanced computing technologies. Quantum computing is an emerging paradigm that will change the lives of individuals and the operations of organizations. Quantum computers solve problems at high speed by operating in a superposition state in which the state can be either zero or one at the same instant. Quantum sensors can be used efficiently in technological research to make accurate measurements and collect data that provide new insights into the behavior of nanomaterials. The use of quantum computing could also speed up the manufacturing process of devices with remarkable properties such as superconductivity, high strength or improved signal performance. Quantum computing has the ability to dramatically speed up the development process of various organizations and increase their efficiency and effectiveness. The security and reliability of data and communication is improved by quantum computing techniques such as key generation and entanglement dispersion. Companies use cryptographic algorithms to protect their data. However, with the advent of quantum computing, cryptographic methods that rely on numerical aspects are no longer sufficient to protect data. Quantum computing is an emerging field that is being applied to various problems that previously could not be solved using conventional methods. Quantum computing plays an important role in the field of information processing, where information is precisely analyzed. Various quantum technologies and algorithms are used to secure company data. This paper provides a systematic review of the literature on the principles of quantum computing. The SLR focuses on achieving four aims “identifying a variety of quantum IoT devices, analyzing their importance in different industries, highlighting the challenges of quantum technology, and presenting various techniques used by researchers to overcome different problems”. Quantum cryptography is identified as a key strategy for improving the security of IoT systems and ensuring the security and consistency of information. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices generate and transfer a vast volume of data in interaction with various objects. These data rely on cloud computing (CC) or fog computing (FC) for processing and calculations. Unlike CC, FC delivers task processing closer to IoT devices. Consequently, FC leads to reduced latency, increased efficiency, ease of deployment, and flexibility. The primary concern between the service requester and provider when agreeing to task offloading is trust. While several types of research are based on intelligent models for trust management, trust management in FC requires direct and indirect trust computation due to the presence of malicious nodes. To ensure security and maintain the quality of service when interacting with fog servers, trust is essential. In this paper, a new trust model for FC is proposed using a combination of the Genetic Algorithm (GA) and Learning Automaton (LA). The crossover operator in GA by LA is improved. The changes in the search space and finding optimal solutions are dependent on the crossover rate. Thus, determining an optimal value for the crossover operator impacts the problem’s solutions. The proposed methods evaluation was done in a MATLAB 2020 simulation environment. The results show that the performance of the proposed method with 200 iterations and 1000 tasks is superior in terms of reliability rate, energy consumption, and delay compared to GA and particle swarm optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and cloud computing are emerging technologies whose use has been proven in various fields of medicine and healthcare. The positive impact of these technologies on improving the performance of medical centers is also evident. Therefore, the purpose of the present study is to investigate the technological requirements of the Cloud Internet of Things (CIoT) in the healthcare industry. This was a quantitative study conducted in two rounds using the Delphi method. A non-random, purposeful, and criterion-based method was used for sampling. A minimum of one paper or research project in the IoTs, cloud computing, or CIoT fields should be completed by participants. In addition to having a master’s degree or specialized doctorate and at least 5 years of work experience, they should be faculty members. Fifteen participants in the first round of Delphi and 12 participants in the second round who completed all of the questionnaire’s questions made up the research sample. A questionnaire was used to gather information. This questionnaire was designed in four main parts with 14 axes and 48 questions. After collecting the questionnaires in the first round of Delphi, each axis was scored on a five-point Likert scale. Finally, the requirements for the implementation of CIoT in healthcare were determined by the collective agreement of experts. Functional requirements of the network layer and cloud computing, platform layer requirements, and components of quality requirements were extracted. The architectures of the application layer, cloud layer, server layer, network layer, and user layer are explored thoroughly. Furthermore, communication technologies, equipment types, analysis and management, wireless networks and sensors, secure data exchange and management, deployment details, and approaches for security management and control are the main infrastructural keys that are extracted during the Delphi rounds. The findings showed that, with a very high level of importance, the use of wireless local networks was approved more than other network layer components. Compliance with security principles emerged as the most crucial component of the platform layer. We also found the use of hybrid clouds and the necessity of software as a service to be important. All the experts emphasized the need to use application development tools, cloud services, development, and deployment on the platform as a service, data analysis, and application programming interface related to the requirements of the platform layer. Scientifically speaking, they found that the components of secure information exchange, establishing secure protocol communication, key exchange mechanisms, and maintaining information confidentiality are crucial in the field of secure data exchange. Based on the findings, it can be concluded that the implementation of this technology can improve the quality of clinical services and increase the speed of responses and patient satisfaction. It is expected that by identifying the technological requirements of CIoT, a suitable model can be designed for healthcare. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the Internet of Things and the surge in the volume of data exchanged in it, cloud computing became more significant. To face the challenges of the cloud, the idea of fog computing was formed. The heterogeneity of nodes, distribution, and limitation of their resources in fog computing in turn led to the formation of the service placement problem. In service placement, we are looking for the mapping of the requested services to the available nodes so that a set of Quality-of-Service objectives are satisfied. Since the problem is NP-hard, various methods have been proposed to solve it, each of which has its advantages and shortcomings. In this survey paper, while reviewing the most prominent state-of-the-art service placement methods by presenting a taxonomy based on their optimization strategy, the advantages, disadvantages, and applications of each category of methods are discussed. Consequently, recommendations for future works are presented. [ABSTRACT FROM AUTHOR]

Building Energy Management is one of the prominent means of devising building-specific energy strategy and most of the research studies address the approach from the demand end. While, this study presents supply side management toward the challenge of implementing supply side management, due to the uncertainty of renewable power sources which are effectively tackled in the real-time scenario. In this paper, a novel Internet of Things-based Intelligent Source Allocation System (IoT-ISAS) has been designed specifically for the implementation within commercial buildings. The primary goal of this system is to ensure consistent and reliable power supply to critical loads within the building's direct current (DC) infrastructure. By leveraging a machine learning Decision Tree algorithm, the IoT-ISAS continuously allocates power sources to critical loads, thereby optimizing efficiency and minimizing downtime. The IoT-ISAS has been successfully deployed in a real-time commercial establishment at M/s Quantanics Techserv Private Limited. The IoT-ISAS incentivizes the commercial consumers to increase their utilization of renewable power sources and thereby, alleviates the burden on the grid. Based on the obtained results, it is observed that the sources are effectively shared and the tariff is found to be reduced by 9.76% for the best source allocating scenario using IoT-ISAS. [ABSTRACT FROM AUTHOR]

In the era of highly dynamicity and uncertainty, it is of great importance to analyze the strategic decision process for companies’ technology acquisition between internal and external. In particular, the Internet of Things (IoT) has emerged as one of the crucial technologies that contribute to the resiliency and flexibility of companies willing to remain competitive in the markets. Despite the perceived advantages of IoT, it remains unclear what drive companies to adopt IoT in their businesses, due to strategic considerations about technology adoption and strategy selection. In that regard, this paper considers the relative payoffs among technology manufacturers, firms and demand markets, using game theory models and numerical analysis. Precisely by adopting a supply chain network perspective, our game theoretic representation assumes that technology manufacturers produce a technology that sell to firms, which in turn use the acquired technology to produce a commodity that is designed to be sold in the demand markets. Assuming that all the agents seek to maximize their profit functions, whereas the consumers want to satisfy their commodity requirement, in making their consumption decisions, the network structure of the problem is identified, the equilibrium conditions are derived, and the finite-dimensional variational inequality formulation is established. Finally, making use of the variational inequality theory, an existence and uniqueness theorem for the equilibrium distribution is obtained. The findings provide implications for competitive enterprises evaluating whether to adopt a new technology, identifying what conditions lead to win-win outcomes. In particular, firms must carefully consider the transaction costs both with technology manufacturers and demand markets, as key drivers of strategic considerations about the source of innovation. More precisely, when confronted with limited resources and the speed at which technology develop in the market, our findings suggest to companies to pursue a buy strategy, as it allows to gain time and benefit from lower innovation costs with respect to the alternative make strategy. [ABSTRACT FROM AUTHOR]