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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 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]

With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

With the rapid convergence of the Internet of Things (IoT) and command and control communication networks, there is a swift surge in computation-intensive applications and heterogenous devices. Building an edge computing network near the protocol gateway not only facilitates network protocol conversion, but also alleviates computational load on the gateway. Nevertheless, traditional protocol gateways necessitate the inclusion of conversion methods for all communication protocols in the network for data interoperability with other devices. Consequently, constructing low-complexity protocol conversion programs, and the more efficient and equitable allocation of edge server resources, are critical challenges that need to be addressed. This paper investigates the joint problem of task offloading and heterogeneous protocol device access in command and control communication networks, deriving the benefit parameters of task offloading. We propose a real-time data unified access platform, which, by distributing configuration files, enables the IoT gateway to generate protocol conversion programs, thereby transforming all device protocols into a unified common protocol. Furthermore, to enhance the efficiency of heterogeneous device access to the network, we design a data parsing/encapsulation algorithm based on the real-time data unified access platform, supporting bidirectional mapping of two types of data. Experimental validation confirms the reasonability and effectiveness of the proposed scheme. [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]

Federated learning is a distributed machine learning approach that enables a large number of edge/end devices to perform on-device training for a single machine learning model, without having to share their own raw data. We consider in this paper a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is IoT devices selection (also called scheduling), i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we introduce DDQN-Trust, a double deep Q learning-based selection algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Finally, we integrate our solution into four federated learning aggregation approaches, namely, FedAvg, FedProx, FedShare and FedSGD. Experiments conducted using a real-world dataset show that our DDQN-Trust solution always achieves better performance compared to two main benchmarks: the DQN and random scheduling algorithms. The results also reveal that FedProx outperforms the competitor aggregation models in terms of accuracy when integrated into our DDQN-Trust solution. [ABSTRACT FROM AUTHOR]

IoT networks can provide many benefits and opportunities, although their implementation poses challenges. Cloud-only storage of IoT data would be very costly and time-consuming. In this paper, a new scheme is proposed for caching IoT content on the edge with SDN-based processing capability. The proposed scheme considers a global SDN controller, which coordinates cache decisions across the entire IoT network. The Moth-Flame Optimization-Cluster Head Selection (MFO-CHS) algorithm is used to cluster devices where the selected cluster heads send the IoT data to the edge nodes for caching. In addition, by utilizing edge caching capabilities and using MFO to select and cache the appropriate contents on edge nodes, the proposed Moth-Flame Optimization-Edge Caching (MFO-EC) algorithm can provide data with lower latency on upcoming requests. Caching can also help ensure reliability and availability since intermittent connections and power limitations affect IoT devices. Caching decisions regarding IoT characteristics are not made intelligently in the default caching scheme for maximizing device longevity and managing the possibility that content producers may become unreachable. This scheme considers several metrics, and the proposed moth-flame optimization (MFO) algorithms, MFO-CHS, and MFO-EC algorithms, which are nature-inspired paradigms for the edge caching problem called "MFO-SDN-EC", Moth-Flame Optimization-Software-defined Networking -Edge Caching, are used to select the best options regarding considered criteria and improve the QoS in the SDN based IoT environment. Based on simulations, our proposed caching method can reduce energy consumption by 45%, decrease the average response time by 49%, and also increase cache-hit rates. Furthermore, our results demonstrate our algorithm's superiority over several current approaches in terms of assessment measures. [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]

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues. [ABSTRACT FROM AUTHOR]

Today we are living in an era where everything is changing to be smart, whether it be a smart home, smart industries, smart irrigation, or a smart meter, where the word smart refers to the involvement of the Internet of Things (IoT). The increased use of IoT infrastructure in these fields has led to the failure of the nodes, increase in threats, attacks, abnormalities, and spying, which is the primary concern and an important domain of an IoT. The main objective of this paper is to use a supervised learning model to predict anomalies in the historical data which can later be incorporated into real-world scenarios to block the upcoming anomalies and attacks. This paper predicts the anomalies on the 350 K data set using the Machine Learning models and compares its performance based on the state of arts. In this paper, two different approaches are used based on the analysis done on the dataset. The classification algorithms were applied to the whole dataset in the first, and then the same classification algorithms were applied after excluding the data points having binary values (0 and 1) in the feature "value" and have achieved an average of 99.4% accuracy for the first case and 99.99% accuracy for the later. [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]

The Internet of Things (IoT) implies a new model and concept in the world of information and communication technology that the non-cooperation of some nodes in providing services to other nodes may result in the disconnection of some things with each other. Consequently, the proximity of such nodes is considerably reduced the network performance. In this paper, a multiphase method based on game theory and direct and indirect reputation has been designed to stimulate selfish and malicious nodes to cooperate in the Internet of Things, which starts with setting up nodes in the IoT network. In the first phase, nodes are grouped in clusters with cluster heads to collect data. Then, they play a dynamic and multi-person game when they promote their own data packet or others' data packet in the second phase (multi-person game phase and data packet sending). The result of the game is applied to affect between nodes in the next phase. Nodes can pick their strategy when data packet forwarding in the third phase (direct and indirect reputation update). Nodes will update the neighboring node and the reputation table if they receive a confirmation message from the destination declaring that the data packet has a direct reputation. The results show that the accuracy of selfish node detection has increased by average of 8% and the false positive rate is 7% in comparison to comparative methods. [ABSTRACT FROM AUTHOR]

With a rising incidence of product failure issues in the e-commerce sector, particularly for third-party vendors, pinpointing the root cause of these failures is critical. This paper introduces an innovative and secure architecture for product failure detection, adapted to the demands of Industry 4.0. The blockchain architecture leverages a Defensible Byzantine State Machine (DBSM) for the trustworthy authentication of Internet of Things (IoT) devices, contributing to the accurate reporting of product failures. Trust values associated with each device, reflecting its transaction history, are periodically updated. A decrease in these values triggers the extraction of novel failure features from client-generated reports, augmenting the defective product management cloud's capabilities. The architecture exhibits an authentication accuracy of 92%, a figure robust against a varying malicious attack rate (25%, 50%, 75%) in a simulated context. This study ultimately contributes to the development of advanced, secure systems for managing product failure and ensuring customer satisfaction in the e-commerce industry. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) cellular networks provide high speed services to the users in multimedia applications. Generally, a 5G network consists of large (Macro) and small (Femto/Micro) cells to provide variety of services to the User Equipments (UEs), having different requirements. Heterogeneous Networks (HetNets) contain small cells in the coverage region of macro cells. Resource allocation (RA) is a process of allocating available resources to the users according to their Quality of Service (QoS) demands. But resource allocation in 5G HetNets become challenging due to Interference among cells and Handoff. As Internet of Things (IoT) edge devices have dynamic traffic requirements, it will be difficult to allocate resources for them. In this paper, we propose an Intelligent RA Decision (IRAD) which is formulated and analyzed. In IRAD, Deep Learning based Encoder with Neural Network (DL-ENN) architecture is designed to classify the end IoT devices depending on their QoS requirements and traffic characteristics. From the output of classified results, optimum RA decisions are made to each device. In RA strategy of femtocells, the transmit power and allocated resources of each femtocell user are optimized such that minimum data rate requirements of femtocell users are satisfied and interference power from femtocells to the macro cells is reduced. The Improved Chicken Swarm Optimization (ICSO) algorithm is used for optimized RA decision to small cell users. By simulation results, we show that the proposed IRAD technique provides maximum resource utilization and throughput with reduced packet loss ratio. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as an extended version of the cloud infrastructure for providing latency-aware and scalable services for Internet of Things (IoT) devices. Adding the fog layer to the cloud computing paradigm improves the quality of service (QoS) of time-critical and delay-sensitive IoT applications. These applications consist of interconnected services that run on virtual machines (VM) or containers. Due to the resource-constrained nature of fog computing nodes and the interdependency of these VMs, the efficient placement of IoT applications has an impressive influence on the delay of IoT applications. Many IoT applications, such as industrial applications, health applications, smart cars, gaming applications, etc., are delay-sensitive. On the other hand, Distributed and heterogeneous features of fog nodes make it difficult to ensure the availability of these applications in fog environments. Unavailability in these applications (which is equivalent to infinite delay) can lead to irreparable loss. Therefore, various methods have been provided to ensure the availability of these applications. Using redundant VMs is an effective solution to ensure the availability of IoT applications. However, it is difficult to determine an efficient placement strategy for IoT applications that meet all of the required constraints of these applications in fog environments (such as resource constraints). As a result, this paper proposed a heuristic delay-efficient and availability-aware IoT application placement in fog environments called DAIP. DAIP tries to provide an application placement with low network delay to reduce applications delay. In addition, this method tries to provide an application placement with an acceptable level of availability. The DAIP is simulated using the iFogSim simulator. The simulations show that the DAIP method, in addition to ensuring the availability of applications, reduces network delay(and total delay) compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

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

Internet of Things (IoT)-enabled wireless surveillance systems are gaining more attention because of the robust development in information technology. It is due to the versatile distributed application features and improved performance of the technology, which required proper investigation. In a multimedia environment, video captured by surveillance cameras is required to be stored (uploaded) for secure preservation for the sake of futuristics analysis, especially in multimedia forensics investigation processes. While storing large raw video files over the network and extracting features as per needs, the systems' provenance with the ledger integrity, reliability, transparency, availability, immutability, and trustworthiness are the main concerns. These highlighted challenges pose a serious issue in the domain of network transmissions, where digital forensics investigation is applied. In addition, the limited use of multimedia resource constraints over the wireless network, such as computational power, network bandwidth, and storage created another type of challenging aspect throughout the investigation. However, the current lifecycle is observing the most critical in terms of consecutive duplicated video frame creation. There is a need to handle a few objects of interest, filter the frames, and investigate them before storing them on the secure digital ledger, which also relieves the pressure of network transmission at the same time. The real-time monitoring of the blind area (which is covered with dark shadows) of the video also affects the frame filtering accuracy. This impacts the forensics investigation hierarchy, investigational data management, and multimedia-enabled chain-of-custody preservation. To address such challenging issues, this paper bridges these gaps by enabling novel and secure digital forensics investigation processes using IoT-Blockchain-based collaborative technology. A blockchain hyperledger sawtooth-enabled secure and efficient digital multimedia forensics investigational distributed architecture is proposed. The process connects participating stakeholders to exchange details of the investigation, including the video surveillance pre-processed frame filtering-related chain-of-custody details by creating a private network. And so, customized consensus protocols such as proof-of-work sawtooth policies. To design and deploy such protocols, the system enables the analysis of the overall activities of scheduled multimedia transmission before preserving them on the distributed ledger. For automation, smart contracts are implemented to automate ledger redundancy verification and validation, requesting for contents of video surveillance frame filtering and maintaining immutable preservation. Substantially, it provides a secure interaction between the investigational processes because of lifecycle tuning. Through experimental results, the proposed solution provides better performance with robust capability to track and trace real-time video surveillance, frame auto-filtering redundancy, and object-of-interest investigation with an increased efficiency rate of 30% and reduces resource consumption down to 11.56%. [ABSTRACT FROM AUTHOR]

In this paper, we address the spectrum efficiency enhancement for Internet of Things (IoT) by introducing the graph-theory-based spectrum optimization reuse. During the spectrum reuse, how to ascertain the interference range and user's transmission status is critical. In this article, we model the interference range between IoT terminals by considering their interaction status which includes interaction frequency, duration and stability. The notion of interaction status is very similar to that of social distance among people in the context of pandemic control, which is a highly effective model for supporting contact tracing by analyzing the interaction frequency, duration and stability among people. Compared with other traditional dynamic spectrum optimization methods, introducing the concept of social distance can better evaluate the IoT user's interference and transmission status from a novel perspective, then enhancing the optimal spectrum allocation. By modeling the social distances among IoT terminals, we estimate the desirable interference range for IoT devices which serves as the basis for graph-theory-oriented spectrum optimization. Together with the actual physical distances between IoT devices, they form the basis for optimizing spectrum reuse patterns. Graph theory is further utilized to complete the final spectrum optimization. Furthermore, comparison simulation tests are conducted to evaluate the performances of our proposed solution in network benefits and system capacity.Kindly check and confirm the inserted city "Singapore" is correctly identified.CorrectPlease provide author biographys and photos.The biographys and photos are correct.Kindly check and confirm the corresponding affiliation processed is correctly identified.Corresponding author is both at Aff1 and Aff2. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Things (IoT), security methods need improvisation to prevent data stealing. Since IoT transfers the various heterogeneous data such as video, text, data signals, and even voices, efficient security systems must overcome the various attacks in the networks. IoT is often connected with low power devices, and these devices are vulnerable to attacks as it contains low security. Therefore, this requires a robust encryption method to protect the user data from the attacker. Traditional methods such as Data Encryption Systems (DES) and Advanced Encryption Systems (AES) are playing an essential role in ensuring the security in IoT devices, and the presence of substitution boxes (S-boxes) makes the system even more complex to counterfeit the attacks mainly side-channel attacks. Researchers claim that these traditional S-boxes need to be improvised when transmitting heterogeneous data, especially the images over the network. In this paper, A Novel dynamic substitution boxes are designed based on hybrid logistic-Henon maps with the deoxyribonucleic acid (DNA) coding systems to improve the security level of images against the side-channel attacks. The proposed structure uses the three-dimensional hybrid chaotic maps to increase the key space and uses the DNA pattern of operations to endure more security. The extensive experimentations are carried out for the proposed scheme and compared with the existing AES S-box structures. Analysis shows that the proposed structure is more resistant against side-channel attacks, as alleged. [ABSTRACT FROM AUTHOR]

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

The unrelenting growth of present-day urbanization and its consequent burden on the utility grid, and insufficient conventional energy sources encourage demand-side energy management. This needs the deployment of local microgrids that uses renewable/alternative energy sources. Also, the present-day urban scenario is equipped with a combination of heterogeneous buildings (with different generation and load profiles) formed as community microgrids. So, there is a possibility of energy sharing among these buildings to cater for their electricity needs instead of depending on the utility grid all the time, thereby can reduce the utility grid burden. However, the interoperations of such microgrids require an effective architecture, which involves a three-layer (electrical, communication, and information technology (IT)) operation. Further, each of these layers has specific challenges to be addressed. So, a continuous evolution of architecture is highly desired to fruitfully address various operational issues for the establishment of interoperable smart microgrids. In this view, this paper proposes an Internet of Things-based communication architecture for developing interoperable microgrids in a locality. To effectively use the available energy sources, an energy management scheme is proposed, which can provide energy sharing among the microgrids. Further, switchport security aspects are studied and realized a three-mode security mechanism to identify and isolate unauthorised users in the network. The implementation of the proposed architecture is done using Cisco Packet Tracer by considering a case study. From the results, it is observed that the proposed architecture fruitfully established a security-enabled energy management system that can help for the growth of interoperable smart microgrids. [ABSTRACT FROM AUTHOR]

Data analytics projects span all types of domains and applications. Researchers publish results using certain datasets and classification models. They present results with a summary of the performance metrics of their evaluated classifiers. However, readers and evaluators may not be able to compare results from the different papers for several reasons. One reason is the variations in the classification models and specific settings used in those models; a second reason is the variations of computing resources and environments used to produce those results. A third reason is a variation in the input datasets, preprocessing steps, etc. There is a need for benchmarks or baselines, where researchers can reuse the same benchmark specifications and be able to produce the same reported results. This paper aims to aggregate models from different research papers and apply them using the same classification settings, the same computing resources, and the same datasets and preprocessing steps. The goals are to show a comparable comparison and also to build a benchmark for future assessment and improvements. Four popular IoT security datasets are used in this paper. Different classification models are evaluated and are applied in the same datasets using the same settings. The classifiers results are reported and compared based on several popular performance metrics. [ABSTRACT FROM AUTHOR]

Technologies such as intelligent collection, transmission and processing of the IoT have rapidly improved logistics efficiency and significantly reduced costs, and they will eventually realize intelligent logistics management with tactics. This paper described the use of the analytic hierarchy process (AHP) method to construct key success factors for application of IoT technology in intelligent logistics. This entailed document collation, expert interviews and collation of secondary data relating to the IoT industry and intelligent logistics. The evaluation factors that determine key success factors comprise five major factors and 21 evaluations. The findings of this paper will provide a reference basis for companies seeking to develop intelligent logistics. The results show that the most important aspect of application of IoT technology to intelligent logistics is technical services. The important critical indicators are as follows: information collection capabilities; wireless communication capabilities; lower operating costs; effective market information facilitating development of more products; ensuring the integrity of data and ensuring confidentiality of information; tracking systems for cargo status; provision of e-commerce logistics services; and the determination of high-level corporate executives to promote collection of huge amounts of useful data through IoT technology. This involves big data analysis, through which effective information is generated in order to develop more products or services, and to improve business performance. [ABSTRACT FROM AUTHOR]

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

The 802.11ah standard is a new energy-efficient, wireless networking protocol which allows thousands of indoor and outdoor devices to be connected to the same access point. The Centralized Authentication Control (CAC) method, described in the standard, enables up to 8000 stations to be authenticated and associated with the same access point. A baseline implementation of the CAC method has been reported; however, it suffers from a few limitations. In this paper, an efficient methodology is proposed to minimize the CAC method's association time. The proposed methodology allows the association of a large number of stations by predicting the size of the network followed by selecting the best step size that will enable fast association between the access point and the stations of the network. The methodology consists of three stages. The first stage provides a baseline implementation of the 802.11ah standard, while the second stage eliminates the effect of too large or too small step sizes. The third stage finds the optimal step size and step repeat for each network size and predicts network size based on queue size. The performance of the proposed methodology is evaluated and compared in terms of total association time, number of total trials and percentage of ineffective trials. The methodology outperforms the baseline implementation by achieving a 30% reduction in the total association time, 30% reduction in the total number of trials and 45% reduction in the total number of ineffective trials. [ABSTRACT FROM AUTHOR]

Blockchain technology has become more important in recent years in Internet of Things (IoT) manufacturing. Many IoT manufacturing factories have successively invested in the blockchain architecture in the system to manage the data of the IoT manufacturing system for intelligence prediction. The blockchain-based system architecture can ensure the process of data transmission and preservation. However, any node must hold complete blockchain information. When there are thousands of nodes, the cost of hard disk space for storing these data will increase drastically as the number of nodes increases. In order to solve the above problems, this paper proposed a blockchain structure to reduce the space and network transmission costs. This architecture divides the traditional blockchain into two parts, which are divided into private blockchain and public blockchain according to the edge and the cloud. Each workshop will manage its own private blockchain, and the cloud will manage itself public blockchain. Under the proposed structure, each working node only needs to maintain the blockchain at its edge node and does not need to communicate with other edge node. The experimental results can effectively intelligence predict the space cost of node expansion, and it can also avoid the unnecessary network communication overhead caused by the traditional architecture. It can improve the space used of blockchain and reduce the network transfer time, and the cost of expanding nodes can be greatly reduced. [ABSTRACT FROM AUTHOR]

The Internet of Things, also called IoT for short, consists of billions of devices distributed around the world, all of which are connected through the Internet. Data is collected by things and shared with each other. Due to the nature and infrastructure of wireless IoT, they have several vulnerabilities. IoT security refers to the protection methods used to secure Internet-connected or network-based devices. In fact, the security of communication between things has become one of the open issues and challenges due to the existence of various types of attacks in IoT. One of these attacks that can disrupt the normal communication between things and destroy the network's efficiency is the wormhole attack. Thus, in this type of attack, two intruders are located in two different areas of the network and try to exchange confidential information quickly, as a result, they inform things about the fake route and things send the information to the destination from the same fake route. In fact, it is sent to the intruder's things, which leads to the deletion of data by these intruders. This attack alters the data stream and misleads the well. Therefore, to solve this problem, the proposed method of this paper (Sec-IoT) addresses two basic issues to secure communication between things against wormhole attacks in the Internet of Things. Thus, in the first phase of Sec-IoT, it examines the trust of all things in the route discovery process. The purpose of the first phase was to identify intruding nodes and remove them from the process of participating in network operations. And in the second phase, Cluster Head selection is done using TOPSIS algorithm and data transfer between things is done encrypted using Hash-based Cryptography. The goal of the second phase was to guarantee the integrity of the information sent. The simulation results showed that the proposed method is superior to HRCA and HBC methods in terms of throughput (25%, 18%), PDR (24%, 19%) and lost packet (19%, 15%). [ABSTRACT FROM AUTHOR]

Nowadays, Internet of Things (IoT) devices are ubiquitous and their number is growing rapidly. These devices produce massive amount of data which need to be efficiently processed. Since most of the IoT devices are resource constrained in terms of computational capability and power resources, they have to offload their computation jobs to more powerful computing devices. Fog–cloud computing is a promising platform for processing IoT jobs. However, due to the heterogeneity of the computing devices, how to schedule IoT jobs in this environment is a challenging issue. To tackle this issue, in this paper, we first present a system model for the job scheduling problem in fog–cloud computing with the aim of optimizing the total deadline violation time of jobs and the energy consumption of the system. Then, we propose two nature-inspired optimization techniques, grey wolf optimization and grasshopper optimization algorithm to efficiently solve the job scheduling problem in the fog–cloud environment. The performance of the proposed algorithms is evaluated against the state-of-the-art algorithms using various simulation experiments. The results demonstrate that the proposed schedulers are capable of reducing the total deadline violation time about 68% and energy consumption about 22% compared to the second-best results. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Things (IoT) and ubiquitous presence of sensor nodes, positioning technologies have become a topic of interest among researchers. While the applications of positioning systems are very vast, determining the position of moving sensor nodes, finding missing people in large areas, and object tracking are among the most popular ones. The focus of this paper is to propose a positioning system to locate missing people in theme parks. Currently, radio frequency identification (RFID) systems are used in modern theme parks to locate lost visitors. In these systems, a wristband with active RFID tag is given to each visitor and RFID readers are deployed in predetermined locations. When a visitor is in the communication range of a reader, its location can be estimated based on the location of the reader. Therefore, the accuracy of these systems is relevant to the communication range of readers. Another limitation of RFID-based systems is due to the fact that readers cannot be placed in communication range of each other as they can interference with each other. It is clear that the only way to increase the accuracy of such systems is by increasing the number of readers and decreasing the communication range of each reader. In this paper, a Bluetooth low energy (BLE)-based system is proposed to be used in theme parks for locating lost visitors. The advantage of using BLE is due to the fact that it uses frequency hopping spread spectrum (FHSS) thus readers can be placed in communication range of each other without severe interference. In the proposed method, at first, the optimal places for deploying readers are obtained using ant colony optimization (ACO). Then, a fuzzy approach is used to increase the accuracy of the system. Three different signal levels are defined to be used in our fuzzy system based on which the location of visitors can be estimated. By using three levels of signal strength, the accuracy of the system is increased compared with the similar system with the similar number of readers. The simulation results show that the accuracy of the system is improved using this method, and the cost of the system is decreased as BLE readers are much less expensive than their RFID counterparts. [ABSTRACT FROM AUTHOR]

The democratization of smart devices over the last decade has given rise what is called the Internet of Thing (IoT). In view of the multitude of functionally equivalent services that have different quality of service (QoS) levels, the services composition is one of the main challenges in the IoT environments where several devices interact with each other to perform a user's complex task. This paper proposes a QoS-aware services composition approach that exploits a novel bat algorithm (QC-NBA) to compose the best IoT services while considering user's constraints related to the QoS properties. Unlike most existing bio-inspired services composition approaches, the NBA method includes mechanisms that improve the exploration and exploitation of the composition search space. The bats habitat selection, the Doppler Effect compensation and the self-adaptive local search strategy of the NBA method speed-up the convergence and avoid the local optimum, enhancing therefore the performance of the QC-NBA algorithm in term of execution time and composition quality. The results obtained through the simulation scenarios, show that the QC-NBA approach achieves a good composition in terms of QoS utility and converges faster compared to other services composition baselines. [ABSTRACT FROM AUTHOR]

Fog computing is widely used as a mediation layer to fill the gap between Internet of Things (IoT) nodes and cloud datacenters. The fog layer can provide higher availability and better response time to IoT applications with its distributed and near-to-user nature. This paper proposes an end-to-end architecture to integrate IoT, fog, and cloud layers with higher availability, better resource utilization, and response time for time-sensitive applications. Two major flows in the proposed architecture are a) the clustering and b) the scheduling. The clustering flow involves performing dynamic and fully decentralized clustering of IoT nodes to increase the manageability of mobile IoT nodes, decrease the energy consumption in resource-constrained nodes, and improve network traffic throughput. The novel multiobjective decentralized clustering algorithm used in the clustering flow is based on fuzzy logic to allow fast execution of the algorithm on IoT nodes with limited computing power. The scheduling flow is in charge of resource scheduling in fog and cloud layers to provide time-sensitive IoT applications with an acceptable completion time by the firefly algorithm. The simulation results prove that two major flows of the proposed architecture outperform energy consumption, network throughput, success rate, and performance of IoT applications with time constraints compared to the two recent methods. [ABSTRACT FROM AUTHOR]

With increasing growth in IoT, the number of devices connected to the Internet is constantly growing. Moreover, the increase in the volume of data and their transmission through the Internet of Things, as well as the existence of inadequate bandwidth, limits cloud-based storage and data processing. Both fog and cloud computing provide the storage space, application, and data for users; however, fog is more proximate to the end user with wider geographical distribution. When bringing the computing resources closer to the required location in the fog environment, the efficiency of the system increases, and the distance at which data must be transmitted decreases. On the other hand, implementing IoT applications and satisfying the requests of end users in fog computing will create new challenges in resource allocation and dynamic resource provisioning. The flexible and usually automatic mechanisms require the determination of required virtual resources to minimize the resource consumption and service level agreement (SLA). In this paper, we introduce a framework for increasing resource management efficiency in the IoT ecosystem based on deep reinforcement learning (DRL). The proposed deep neural network (DNN) method for estimating value functions improves adaptability to different oscillating conditions, learns past sensible strategies, and as a self-learning adaptive system by replicating interactions with the fog environment. The DRL algorithm finds the best destination for implementing IoT services to compromise between minimizing average power consumption, minimizing average service latency, reducing costs, and balancing resource allocation. Finally, through simulations, we show that under different loading rates, the policy used compared to other comparable solutions is to increase utilization and reduce the rate of delay, while ensuring an acceptable level of service quality. [ABSTRACT FROM AUTHOR]

The resource-constrained nature of Internet of Things (IoT) devices and their diversity and abundancy represent a major challenge for the development of efficient security solutions that fit IoT systems and ensure end-to-end security. This paper presents an efficient IoT authentication scheme that provides mutual authentication between three different entities making up the IoT system under study; IoT devices, an IoT manufacturer server, and an authentication server. The authentication process is accomplished by the exchange of only six small messages in three successive stages: the IoT device association stage, the IoT device–AS server communication initialization stage, and the exchanged data encryption stage. The scheme employs the HMAC keyed hash function to provide mutual authentication and the AES-GCM lightweight symmetric cryptographic function to provide integrity and confidentiality for the exchanged data. The proposed scheme was simulated using the Network Simulator NS3 with C++ crypto++ library, using the various IoT enabling technology models. The obtained simulation results verified the proper functioning of the designed scheme and demonstrated the efficiency of the proposed scheme in terms of the low computational and communication overheads. The scheme needed 13.04 ms and 470 bytes to complete the mutual authentication process, including session key exchange. The low computational and communicational overhead of the proposed scheme are due to the scheme's small-sized messages, lightweight cryptographic functions, and passive discovery of IoT device. Security analysis of the scheme shows that the essential security requirements, which includes mutual authentication, confidentiality, and integrity, have been met by the proposed authentication scheme. Moreover, the scheme is inherently robust against common cyber-attacks such as the DoS, impersonation, spoofing, sniffing, and man-in-the-middle attacks. [ABSTRACT FROM AUTHOR]

The problem of Data acquisition in large distributed Wireless Sensor Networks (WSNs) scale is a hindrance in the growth of the Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted great interest from researchers. An open question of this approach is how to effectively integrate these technologies for specific tasks. The objective of this paper is two parts. First, we propose an effective deterministic clustering scheme based CS technique (EDCCS) for data collection in IoT based homogeneous and heterogeneous WSN to deal with the data acquisition problem, reduce the consumption of energy and increase the lifetime of network. Second, we propose random matching pursuit (RMP) as an effective CS reconstruction algorithm to improve the recovery process by reducing the error average at the base station (BS). The simulation results show that our proposed novel EDCCS scheme reduces at least 60% of the average power consumption and increases the network lifetime at least 1.3 times of the other schemes in homogeneous network while, it increases the network lifetime and residual energy by 1.9 times and 1.3 times respectively, compared to the other schemes in heterogeneous network. Also, our proposed RMP algorithm reduces the error average of reconstruction at least 35% compared to other reconstruction algorithms. [ABSTRACT FROM AUTHOR]

Given the ever-increasing advances in science and technology in recent years, the security and authentication issues using elliptic curve cryptography (ECC) have gained a lot of attentions especially for smart cards in a variety of networks, such as smart homes and medical-based care systems being based on the Internet of things (IoT). In systems such as health care, patient information is always crucial and no one should have access to this information. Recently, much research has been conducted in the area of security and authentication for medical care systems being based on the IoT using RFID technology. One of these schemes is the Alamr et al.'s protocol, which bears high computational cost for authentication between card and reader. In this paper, we have proposed a scheme based on ECC in order to establish a mutual authentication within RFID technology in the IoT. In the proposed scheme, computational cost, communication cost and elliptic curve point multiplication running time and data storage cost are investigated. Moreover, security requirements and various attacks have also been considered in the proposed protocols. AVISPA software has also been used for security analysis. The analyses represent that the proposed scheme has lower computational costs, lower communication costs and less elliptic curve point multiplication time compared with those of similar protocols. It has also resolved the security shortcomings of the RFID authentication protocol. [ABSTRACT FROM AUTHOR]

Thanks to the continuous development of the tourism industry, the application of Internet of things (IoT) begins to boom among modern tourists. Smart tourism utilizes Internet of things to facilitate the information dissemination and exchange, making IoT technology an indispensable part of next-generation travelling tool kits. In this paper, based on visitor selection behavior, we develop a hybrid intelligent categorization approach. Particularly, our proposed method of categorization could help tourists decide whether or not to visit a sightseeing attraction in a minutes. Cross-validation testing and performance assessment show that our designed platform can work effectively and efficiently by considering a variety of factors. [ABSTRACT FROM AUTHOR]

The number of connected smart devices enabling multimedia applications has expanded tremendously in Internet-of-Things (IoT) environments. Specifically, the requirement for a high quality of experience (QoE) for video streaming services is a crucial prerequisite for a range of use cases, including smart surveillance, smart healthcare, smart agriculture and many more. However, providing a high QoE for video streaming is challenging due to underlying dynamic network conditions. To address this issue, several adaptive bit rate (ABR) algorithms based on predetermined rules have been developed. However, they do not generalize well to a wide variety of network conditions. ABR algorithms, based on reinforcement learning (RL), have been proven to be more effective at generalizing to varying network conditions but they still have limitations, specifically, constrained exploration and high variance in value estimates. In this paper, we propose asynchronous advantage actor-critic (A3C) based actor-learner architectures for generating the adaptive bit rates for video streaming in IoT environments. To address the existing issues, we propose integrating two advanced A3C algorithms: Follow then Forage Exploration (FFE) and Averaged A3C. We demonstrate their efficacy in improving the QoE over vanilla A3C. Additionally, we also demonstrate the benefits of the proposed architecture for video streaming under different network conditions and for different variants of the QoE metric. We show that advanced A3C methods provide up to 30.70% improvement in QoE over vanilla A3C and a considerably higher QoE over other fixed-rule-based ABR algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has enhanced many industries in terms of smart decision-modeling and performance assessment. Numerous developments in industries such as national security and police forces have been revolutionized to interpret data regarding ubiquitous instances. The current paper introduces an IoT-inspired system for evaluating the competence of police officers' performance. The work presented in this study focuses on evaluating several actions of police officers to determine overall conduct using the Bayesian Classification Model. The probabilistic integrity estimate (PIE) has been quantified using efficient data processing for effective decision-making. Furthermore, the 2-level decision tree model has been presented to evaluate police personnel's efficiency. The presented model is tested on challenging data sets obtained from the online repository for validation purposes. Conspicuously, the presented methodology outperformed state-of-the-art decision methods in terms of classification efficiency, a temporal delay, prediction estimation, stability, and reliability. [ABSTRACT FROM AUTHOR]

Due to the big data exchange on the Internet of Things, proper routing and selecting the best routes for fast data transmission improve network performance. There are major challenges, like high delay, when cloud computing is used. Therefore, one solution is to use other schemes, such as fog computing. In fog computing, all data is not sent to the cloud and the fog nodes close to objects are used for data processing. This reduces the network delay. In this paper, we propose an overlapping clustering method called MFCT-IoT to select the best cluster head nodes to guarantee the fast data transfer from objects to fog nodes. The selected cluster head nodes are responsible for sending the collected data to the closest fog nodes in the network edge. Upon receiving the data, the fog nodes process it, and if a response is ready, they respond immediately to the object. Otherwise, they merge and transmit the data to the cloud servers, which are considered as the root node of the proposed hierarchical tree. After processing, the merged data is sent to the object. We compare the proposed scheme with two schemes, including ERGID and EECRP. These schemes are evaluated based on various criteria, including the response time, packet delivery ratio, end-to-end delay, network lifetime, and energy consumption. The results indicate that the proposed method outperforms others in terms of all criteria. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is a modern technology where data can be transmitted to any things (human, animal, or object) over communications networks, whether internet or intranet. Congestion occurs when the input data rate to the node was higher than the output data rate of node. Congestion control in computer network modulates traffic entry into a network in order to avoid congestive. This paper suggests a method for congestion control in the internet of things in two phases. The first phase is intra-cluster congestion control, which uses two parameters, congestion score (CS) and buffer empty space (BES), to congestion avoidance. In this phase based on these two parameters, nine states are defined to determine the congestion status of each node, and based on these 9 states the appropriate decision is made to the node. The second phase is inter-cluster congestion control. In this phase, after determining cluster head priority, the parameters of back-off timer (BFT), waiting time to receive acknowledgment (WTTR ACK) , sequence number (SEQ) and retransmission counter (RC) are used for congestion control. The proposed congestion control method is simulated by NS-2 software. A comparison between the performance of proposed method and conventional methods shows that applying proposed method results in a significant improvement in average congestion score (CS), packet lost rate, energy consumption and end-to-end delay. [ABSTRACT FROM AUTHOR]

Due to the rise of the Internet of things (IoT), the threat to information security extends from general servers to IoT devices. Possible IoT security issues include all kinds of network attacks. Distributed denial-of-service (DDoS) attacks are notoriously difficult to prevent. With the continuous evolution of DDoS attacks, from simple network packet attacks, it has become possible for hackers to hide in legitimate paths. At times, large numbers of non-legitimate attack packets can occur, which can be a major problem that enterprises cannot protect themselves against. This paper proposes a data-mining technology-based DDoS attack prediction system, designed for use in the IoT environment. The system can be divided into two major modules: 1. the DDoS attack prediction model-construction module and 2. the DDoS attack prediction defence module. In the DDoS attack prediction model-construction module, through the integration of data-mining classification technology, the SVM algorithm is used to dig out the classification basis of a possible attack. The attack prediction model is then established, and the prediction result is corrected in real time. The results of the study conducted showed that when an attacker attempts to launch an attack, the system design will predict the timing of the DDoS attack, and existing IP backtracking technology can be used to track the source of the attack, block the source of the attack in advance and achieve the purpose of defence. [ABSTRACT FROM AUTHOR]

In order to meet the needs of customers in an Internet of Things (IoT) environment, the traditional manufacturing production strategy has gradually shifted from mass production to a small number of diverse forms. In traditional industry, when the production type changes to a small number of diverse forms, the complexity of scheduling increases and the rules of adaptability between products and production lines is not easy to judge. However, in traditional production management scheduling, the adaptability of production lines is mostly planned based on past experience. If the number of orders is too large or the production schedule changes, errors will increase. This situation will cause the actual production situation to be far removed from the planned result, which will affect the schedule achievement and delivery time. The present paper reports research using association rules to explore production lines and apply logic to solve the problem of production rules between production lines and products in the car manufacturing industry. The results show that the application of data mining association rules has an accuracy above 87%. The application of data mining can provide manufacturing production rules to assist managers to make better decisions in the IoT environment and to reduce the time required for manufacturing production. [ABSTRACT FROM AUTHOR]

Fog-cloud computing is a promising distributed model for hosting ever-increasing Internet of Things (IoT) applications. IoT applications should meet different characteristics such as deadline, frequency rate, and input file size. Fog nodes are heterogeneous, resource-limited devices and cannot accommodate all the IoT applications. Due to these difficulties, designing an efficient algorithm to deploy a set of IoT applications in a fog-cloud environment is very important. In this paper, a fuzzy approach is developed to classify applications based on their characteristics then an efficient heuristic algorithm is proposed to place applications on the virtualized computing resources. The proposed policy aims to provide a high quality of service for IoT users while the profit of fog service providers is maximized by minimizing resource wastage. Extensive simulation experiments are conducted to evaluate the performance of the proposed policy. Results show that the proposed policy outperforms other approaches by improving the average response time up to 13%, the percentage of deadline satisfied requests up to 12%, and the resource wastage up to 26%. [ABSTRACT FROM AUTHOR]

In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to the network edge. This enables IoT user equipment (UE) to profit from the fog computing paradigm by mainly offloading their intensive computation tasks to fog resources. Thus, computation offloading and service placement mechanisms can overcome the resource constraints of IoT devices, and improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay. In this paper, we survey the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a kind of information carrier, which enables for all ordinary objects that can perform independent functions to realize interconnection. The standardization process of wireless local area networks (WLANs) which is an important network system of IoT is gradually advancing. IEEE 802.11ax will replace IEEE 802.11a/b/g/n/ac as the latest WLANs standard. The uplink transmission of IEEE 802.11ax adopts a hybrid access mode. The mode means that the stations (STAs) can access the network adopting either scheduling access mode or random access mode. Since the hybrid access mode supports both the scheduling and random access, how to allocate the resources among the STAs which adopt the two access modes is the first problem to be solved. In particular, IEEE 802.11ax focuses more on the network performance of the high-dense deployment scenarios. The characteristic of the interference of the overlapping basic service set (OBSS) situation is the key factor of the efficiency of resource allocation algorithm. Therefore, firstly, this paper combines capacity entropy multi-user access (CEM) with utility function, and defines a metric value to measure the utility of two kinds of users. Secondly, the simulation-assisted method is used to obtain the probability distribution characteristic of the interference power random variables from dense multi-cell scenarios of IEEE 802.11ax. Then, based on interference power random variables which generated by the probability density curve obtained by simulation, we propose an algorithm which can maximizetotal utility of scheduling and random access STAs, i.e., to maximize the total satisfaction of all STAs. The authors believe that the research can enhance the quality of experience (QoE) of users in dense multi-cell scenarios for the next generation WLANs. [ABSTRACT FROM AUTHOR]

With the progress of medical science and technology and the healthy changes in eating habits, the proportion of aged population is gradually increasing. Smart-home elderly care has thus attracted a lot of research attention in the recent past, and remains an active issue. Internet of Things (IoT) has been recognized as a key enabler for smart-home elderly care realization. In the literature, a large number of IoT services/applications and platforms have been proposed for health/elderly care. However, research reports showed that their adoption ratio is very low. This is mainly because these packet-switched IoT data communication/networking approaches are too complicated from the perspective of older people. Statistical data reflect that circuit-switched voice telephony is still their most favorable communication mechanism. In this paper, we aim to implement a circuit-switched approach to realizing smart-home elderly care IoT remote control based on our IoT platform, IoTtalk. To the best of our knowledge, our IoTtalk incorporating such capability is the first generic IoT platform in the literature that provides a telecommunication solution for smart-home elderly care. We design and implement an Android App, DTMFTalk, following the application development/execution framework of IoTtalk, to support IoT remote control via circuit-switched Dual Tone MultiFrequency (DTMF) signaling during a phone call conversation. Our real testbed deployment demonstrates that DTMFTalk can constantly and accurately recognize DTMF keys as long as the user holds the desired DTMF keys with enough periods, justifying that DTMFTalk can serve as an effective approach to IoT remote control for smart-home elderly care. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other and central nodes over a wireless network and internet. Recently, due to the crucial importance of human well-being, IoT-enabled healthcare systems have gained significant attention. On the other hand, as IoT networks are large-scaled and battery-powered, developing proper energy and resource management mechanisms for them is inevitable. On account of the large amount of data generated in IoT environments, data aggregation is vital to lower energy consumption and extend network lifespan, and many researchers have endeavored to enhance its efficiency. However, there is no optimized method for the dynamic, complex, and nonlinear nature of healthcare applications. Fuzzy logic could be effective in these scenarios because it can convert qualitative data to quantitative, implement complex nonlinear functions, and present approximate solutions for cases where there is no single optimal answer, and it changes with a slight variation in conditions. This paper proposes the F-LEACH, a Fuzzy-based data aggregation scheme for IoT-enabled healthcare applications aiming to maximize the network lifetime. According to the simulation results, the proposed method outperformed similar works by 5–20%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a network of devices (servers, sensors, nodes, and so on) used to conduct tasks like health monitoring, production monitoring, business transactions, etc. In IoT, the traditional networking paradigm in which the control and data planes are vertically integrated is utilized to link various types of networks. Software Defined Networking (SDN) is a relatively new concept that separates the control plane from the data plane, making network management and maintenance easier. In SDN, network operators prioritise the security of the overall system. The most severe attacks on systems target the Address Resolution Protocol (ARP), which then frequently act as a springboard for more complicated attacks. This paper proposes a secure SDN-based IoT architecture to manage and reduce ARP spoofing attacks by deploying a new machine near the SDN controller to handle address resolution questions. To examine address spoofing threats, we move ARP traffic to this new machine. This module works in tandem with the controller, gathering topology data and ARP requests in order to detect potential attack conditions. The ARP data is analyzed using custom methods. According to simulation results, the proposed technique increases network throughput, improves attack detection and mitigation time by 35% over existing techniques. [ABSTRACT FROM AUTHOR]