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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]

Along with the significant growth of applications and facilities provided by the Internet of Things (IoT) in recent years, security challenges and related issues to privacy become considerable interest of researchers. On the other hand, the de facto IoT routing protocol for low-power and lossy networks called RPL is vulnerable to various types of routing attacks. Many researchers have investigated RPL security solutions focusing on effective detection of prevalent and destructive routing attacks such as blackhole attack, selective forwarding attack, rank attack and so on. Recent studies are proposing trust-based mechanisms with the aim of replacing traditional cryptography-based operations with lightweight security models in order to cover the inherent challenges of IoT devices, including energy and computational limitations. Therefore, in this paper, focusing on the problem of RPL vulnerability against well-known routing attacks, we have proposed a trust-based attack detection model, which investigates traffic behavior in different attack scenarios and detects malicious nodes relying on behavior deviation exactly at the same time as the start of any attack activity. Expected behavior is predicted by our learning model trained from the historical routing behavior pattern, using recurrent neural networks as a powerful deep learning method, which leads to attack detection with high-level accuracy and precision. Both mathematical analysis and simulation results on multiple RPL attack scenarios show clearly that the proposed trust-based defense mechanism is an effective approach capable of timely and precisely detection of routing behavior pattern deviation of malicious nodes exactly at the start time of the attack occurrence, which leads to attack detection and attacker identification based on trust scores extracted from the detected fluctuations between expected and real routing behavior patterns. [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]

The Internet of Things (IoT) is evolving rapidly, increasing demand for safeguarding data against routing attacks. While achieving complete security for RPL protocols remains an ongoing challenge, this paper introduces an innovative hybrid autoencoder–decision tree framework (HADTF) designed to detect four types of RPL attacks: decreased rank, version number, DIS flooding, and blackhole attacks. The HADTF comprises three key components: enhanced feature extraction, feature selection, and a hybrid autoencoder–decision tree classifier. The enhanced feature extraction module identifies the most pertinent features from the raw data collected, while the feature selection component carefully curates' optimal features to reduce dimensionality. The hybrid autoencoder–decision tree classifier synergizes the strengths of both techniques, resulting in high accuracy and detection rates while effectively minimizing false positives and false negatives. To assess the effectiveness of the HADTF, we conducted evaluations using a self-generated dataset. The results demonstrate impressive performance with an accuracy of 97.41%, precision of 97%, recall of 97%, and F1-score of 97%. These findings underscore the potential of the HADTF as a promising solution for detecting RPL attacks within IoT networks. [ABSTRACT FROM AUTHOR]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [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]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

One of the main sources of information dissemination is social networks. Changes in social and Internet of Things (IoT) relationships can affect the quality of service (QoS) of device-to-device (D2D) communication in fifth-generation (5G) networks. Integration of social networks and IoT networks will allow users to improve their communication abilities with proximity users. The most challenging issues are interference and channel uncertainty due to massive connectivity and random movement of users in an urban dynamic environment. In this paper, D2D-based social Internet of Things (D2D–social IoT) in a 5G network is modeled for urban regions. To address these issues, we formulate a throughput maximization problem under the maximum power and QoS constraints. The problem is divided into multiple subproblems to reduce interference and enhance throughput. First, we leverage the interference channel link for intelligent resource sharing with better QoS by the spatiotemporal method. The second one involves bisection-based power search optimization by utilizing the Mellin transformation method for latency and power minimization. Simulation results demonstrate that the proposed method significantly improves the network performance in terms of throughput, sum rate, and reduction in network latency by up to 19.91%, 26.24%, and 31.57%, respectively. It is expected that an inclusive implementation of the said method can enable resource allocation in 5G-enabled social IoT networks for the development of smart city. [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 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]

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]

For sophisticated applications, engineers should always consider multi-objective, multi-task or multi-modal problems, especially in the Internet of Things, such as the data fusion of multi-sensor systems, multiple routing problems for automation and convergence control in wireless sensor networks. Normally, utilizing traditional methods costs abundant resources and yields the homogeneous results that fails to satisfy requirements. For the multimodal situation, this paper proposes the dual-biological-community swarm intelligence algorithm based on particle swarm optimization (DBC–PSO), which is combined with the commensal evolution strategy to enhance the convergence ability. This algorithm can split tasks into two communities and guarantee regional changes in information and search accuracy for multimodal problems through a commensal strategy. Moreover, some typical parameters, including the population rate, velocity and radius influence, are considered to study the algorithm performance. The performance is evaluated on 12 well-known multimodal problems, and the simulation is compared with some algorithms that utilize a similar evolutionary approach. The results indicate that the proposed algorithm exhibits strong performance and is very promising for use in more productive work. [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 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]

Recently, security of the Internet of things on mobile phones, especially the fifth generation (5G), has attracted much attention. In this regard, state-of-the-art authentication protocols have been proposed with their focus on lightweight computations while preserving strong security. However, it has been shown that there are several attacks against these protocols, including denial of service, impersonation and desynchronization. In this paper, we present a secret disclosure attack which can be applied to many of these lightweight protocols that rely on rotation function. The complexity of our attack is only two consecutive protocol runs and with the success probability of “1”. We also implement our proposed attack on two existing protocols to show its correctness. It is obvious that with revealing secret values, all other attacks can be applied. [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 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]

In the current era of the Internet of Things (IoT), massive number of sensors are used in our daily lives. Sensors are everywhere around us. They exist in our homes, work places, streets, cars, and even ourselves. Examples include home appliances, wearable devices, and medical sensors. These sensors generate huge amount of dynamic, heterogeneous, and unstructured data that need special handling beyond the capabilities of conventional relational databases. Thus, identification of suitable data management platform to store and query this data is necessary. Despite of its popularity and efficiency in processing various types of big data, there is no single-guided study of how NoSQL data stores will behave with the Internet of Things (IoT) datasets. IoT data have its own characteristics that make it special. IoT data come from various sensors, with a wide range of formats, high velocity, and require high throughput processing with low latency. NoSQL data stores are commonly used to provide flexibility and availability for big data handling. However, there is a lack of comprehensive studies about which NoSQL data store performs the best from the two scalability aspects (scale-up and scale-out) in a distributed and parallel processing environment. This paper benchmarks the commonly used NoSQL data stores (MongoDB, Cassandra, and HBase), and compares their performance with real industrial IoT dataset. In addition, we focus on comparing the throughput, latency, and run time of the evaluated NoSQL data stores. [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]

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]

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]

Interconnection of the sensing and actuating devices providing the ability to share information across platform through a unified framework for enabling innovative applications. This is achieved by seamless ubiquitous sensing, data analytics and information representation as the unifying framework. Extending the current internet with interconnected objects and devices and their virtual representation has been a growing trend in recent years. Internet of Things (IoT) services are becoming a popular services. This will be supported challenges in a large of aspects such as smart health, green energy, smart home and personalized applications. So, the IoT plays more and more important issue in lifestyle through entertainment such as Games. As of yet, there has not been much research done on IoT environment games as a service. In this paper, we propose schemes of the design and implantation of IT convergence framework for games as a service of IoT. First of all, we discussed what to consider when design and implementation of IT convergence framework for games through contents using user’s mobile devices and various sensors in IoT environment and suggest related techniques. Then, we showed the possibility of games in the IoT environment by creating games and measuring the interactions of users in the IoT environment. [ABSTRACT FROM AUTHOR]

Recently, Internet of things (IoT) and Web of Things (WoT) lead us to the excellent era of connected everything device. However, the devices hardly show the property of the autonomous connectivity and the self-cooperation for applying in real-world environments. The purpose of this paper was to propose the adaptive IoT and WoT convergence platform that enables things to dynamically implement the smart Web without any controls from users. The adaptive IoT and WoT convergence platform, proposed in this paper, is a new type of platform which provides global inter-compatibility to help users to easily communicate with things by connecting through the webs. Through mashup of the things connected to the Web, coarsely or finely, this proposal can guarantee an efficient IoT or WoT platform management, adaptive synchronization between the things, a stable platform environment, and creating new services. The performance of our proposed platform is tested via experiments which verify that its simulations are satisfactory. [ABSTRACT FROM AUTHOR]

In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm's requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage. [ABSTRACT FROM AUTHOR]

Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions. [ABSTRACT FROM AUTHOR]

In this paper, a distributed solution is presented for the composition of complex event services in the Internet of Things (IoT) environments. The composition of objects services in the IoT environment requires scalable and adaptable methods due to the dynamics of the network. The dynamics accommodate both the large volume of objects and the tolerance of object displacement and link failure. The composition of complex event services is more scalable and reusable. The composition of these services is done in several steps. First, the user's goals are transformed into an event schema through a backward-chaining mechanism. Then, the event schema is matched with complex event services in the fog layer. Finally, this event schema is used to access the required services. Besides, heuristic functions are also provided to control network traffic, increase execution consistency, and reduce service failure rates. An extensive evaluation of various metrics has been done. The results show that the proposed solution is scalable and adaptable in a hierarchical IoT network with mobile objects. [ABSTRACT FROM AUTHOR]

With the proliferation of technology, a system of connected and interconnected devices, henceforth referred to as Internet of Things, is emerging as a viable method for automated interactions between users and environment in day-to-day life. However, such proliferation leads to an impractical task with respect to interactions among humans and devices. The major reason behind this impractical task is that domain of human's eye for interaction is limited and devices have their own obligations and prohibitions in context. Motivated by this observation, the paper has proposed four-layered framework, namely, Optimized Clustering-based Discovery Framework on Internet of Things (OCDF-IoT), that (1) automatically discovers resources and their associated services using ontology, (2) governs resources using knowledge formation and representation, (3) provides efficient procedures to index resources on the basis of maximum similarity match, and (4) delegates the selection of the near optimal resource among indexed resources. The framework's efficiency is evaluated using toll datasets that are gathered from Shambhu Toll Plaza, Panipat–Jalandhar section, Haryana, India. The obtained results support the framework's efficacy providing more accurate similarity searches, consuming less search time. It is found that framework is stable in providing accurate erred parametric resources and helps in finding the rightful resource with computation of maximum resources. The framework takes minimum CPU throughput for processing queries and increases CPU's efficiency with less load on server. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) is able to provide a prediction of linked, universal, and smart nodes that have autonomous interaction when they present services. Because of wide openness, relatively high processing power, and wide distribution of IoT things, they are ideal for attacks of the gray hole. In the gray hole attack, the attacker fakes itself as the shortest path to the destination that is a thing here. This causes the routing packets not to reach the destination. The proposed method is based on the AODV routing protocol and is presented under the MTISS-IoT name which means for the reduction of gray hole attacks using check node information. In this paper, a hybrid approach is proposed based on cryptographic authentication. The proposed approach consists of four phases, such as the verifying node trust in the IoT, testing the routes, gray hole attack discovery, and the malicious attack elimination process in MTISS-IoT. The method is evaluated here via extensive simulations carried out in the NS-3 environment. The experimental results of four scenarios demonstrated that the MTISS-IoT method can achieve a false positive rate of 14.104%, a false negative rate of 17.49%, and a detection rate of 94.5% when gray hole attack was launched. [ABSTRACT FROM AUTHOR]

The Internet has immeasurably changed all aspects of life, from work to social relationships. The Internet of things (IoT) promises to add a new dimension by making possible not only communications with and among objects but also, thereby, the vision of anytime, anywhere, anything communications. The IoT allows sensing or control of objects remotely across network infrastructures. Its application, thus, is very extensive. The principal IoT applications are infrastructure management, smart manufacturing, smart agriculture, energy management, environment monitoring, building and home automation, metropolitan-scale deployments, medicine and health care, and smart transportation. Many IoT applications entail the collection and also forwarding of event data. To realize the IoT's potential, combining it with artificial intelligence (AI) technologies is necessary. The IoT collects data, which AI processes so as to make sense of it. In order to trigger an action in the IoT and in AI applications, knowledge of the time at which an event occurs can be very useful. Time information, in fact, is an essential infrastructural component of any distributed system. Indeed, in IoT and AI applications, time information and time synchronization are among the most fundamental components. The IoT and AI thus require a scheme for data's combination with time. This paper proposes a network clock model that enables the sharing, by IoT and AI devices, of a consistent notion of time. A proposed network clock model is implemented and evaluated in an actual test platform of MICAz-compatible sensor nodes operated in TinyOS 2.0 and Arduino Uno (R3) in order to verify its feasibility. The experimental results indicate that, for any application, IoT devices are capable of maintaining standard time and serving a standard timestamp. [ABSTRACT FROM AUTHOR]

With the rapid development of sensors, wireless communication, and cloud computing, information technology today focuses on service environments created by the Internet of Things (IoT). IoT technologies have become widely used in various contexts including smart homes, building management, surveillance services, and smart farms. Some IoT applications such as Siri are popular in everyday life. IoT requires communication and interaction between various devices and services. To solve the various complex problems associated with IoT services, earlier research focused on IoT service platforms such as gateways and mobile edge computing services. However, the similarities and reusabilities of IoT services have received little attention. In this paper, we develop an IoT service classification and clustering system. We classify the operation of an IoT service into four steps that differ in their characteristics. Based on this classification, we extend the classic EM (expectation-maximization) algorithm to cluster IoT services in terms of their similarities. To validate our proposed classification and clustering system, we divide over 100 commercial IoT services into five clusters, showing that such services are well clustered by similarity and purpose. [ABSTRACT FROM AUTHOR]

With the revolution of the Internet technology, smart-sensing applications and the Internet of Things (IoT) are coupled in critical missions. Wireless sensor networks (WSNs), for example, present the main enabling technology in IoT architectures and extend the spectrum of their smart applications. However, this technology has limited resources and suffers from several vulnerabilities and security issues. Since the wireless networks used by this technology are deployed in open areas, several challenges are faced by the service provider in terms of privacy and the quality of service. Encryption can be a good solution to preserve confidentiality and privacy, but it raises serious problems concerning time latency and performance. In this paper, we propose agile framework that enables authentication, confidentiality and integrity while collecting the sensed data by using elliptic curve cryptography. [ABSTRACT FROM AUTHOR]

Provisioning services for Internet of Things (IoT) devices leads to several challenges: heterogeneity of IoT devices, varying Quality of Services requirements, and increasing availability of both Cloud and Fog resources. The last of these is most significant to cope with Cloud infrastructure providers (CIPs) limitations for latency-sensitive services. Many Fog infrastructure providers (FIPs) have recently emerged, and their number is increasing continually. Selecting a suitable provider for each service involves considering multiple factors such as the provider's available resources, geographic location, quality of service, and cost. Motivated by this, FLEX is proposed in this work as a platform for service placement in a multi-Fog and multi-Cloud environment. For each service, FLEX broadcasts service requirements to the resource managers (RMs) of the available Fog and Cloud service providers and then selects the most suitable provider for that service. FLEX is scalable and flexible as it leaves it up to the RMs to have their own policy for the placement of submitted services. Also, the problem of service placement in multi-provider environments has been formulated as an optimization problem to jointly minimize the total weighted delay and cost of services. Next, a heuristic algorithm, namely minimum cost and delay first (MCD1), is proposed to map services to FIPs and CIPs efficiently.To evaluate the performance of the proposed algorithm, extensive experiments are conducted to analyze the behavior of the algorithm under different scenarios, such as a varying number of services, providers, and the ratio of FIPs. Results show that MCD1 significantly performs better than baseline methods and genetic algorithms. In particular, the proposed algorithm can reduce the objective function value up to 26.8%. [ABSTRACT FROM AUTHOR]

In recent years, the usage of elliptic curve cryptography (ECC) in IoT applications is steadily increasing. The end nodes in IoT applications demand optimized device performance in terms of reduced power consumption and improved computing speed while not compromising on the security of the connected devices. ECC provides better security standards compared with many conventional cryptographic algorithms providing further scope to optimize the performance parameters. This work focuses on improving the key parameters like computing speed, area required for hardware implementation of ECC and demonstrates an efficient way of using the hardware resource sharing and scheduling mechanisms in elliptic curve group operations in affine coordinates which is crucial for implementation of scalar multiplication over prime field Fp. With the proposed scalar multiplication hardware architecture, we have achieved a good area-delay product and a significant reduction in cycle count when compared with other reported designs using the same affine coordinates. The proposed architecture has been implemented with 256 bits in both Xilinx Kintex-7 and Virtex-7 FPGA devices. The FPGA synthesis results show that a throughput of 68.52 kbps at a clock frequency of 124.2 MHz is achieved for F256 and the computation time is reduced around 1 ms without using any DSP slices. [ABSTRACT FROM AUTHOR]

The National Basketball Association's (NBA) tournaments are watched by people all over the world via the Internet of Things (IoT). Players who score more points are regarded as star players and are part of the focus of competition for each team. The purpose of this research is to predict the score of NBA league players based on player data using deep learning methods. Various algorithms were compared, the best model for score predictions was obtained, and a comparative analysis of the team was provided to the players. The prediction effect and average MAPE index of limit gradient boosting (XGB) are better than that of other methods. It can be seen that the XGB method has a better fit to the regression problem and high interpretability. Thus, it has the ability to accurately predict and evaluate NBA score predictions. The results can support the team's decision-making in player management. [ABSTRACT FROM AUTHOR]

Fog computing enables cloud and edge resource integration. It provides intelligent, decentralized processing of the amount of data generated by the Internet of Things (IoT) sensors for seamless integration of physical and cyber environments. This can create many benefits for society. The IoT framework uses wireless nodes to collect data and monitor the environment. Therefore, the limited power of batteries and the network lifetime are two essential problems in an IoT-based environment. Reducing energy consumption and fast transmission of collected data for reducing the response time can be improved the performance of IoT. Sending data packets by the appropriate header nodes can reduce the communication latency to the Fog. So, in the first step of the proposed method, the header nodes are selected based on many factors including the criteria of residual energy, speed of nodes, and number of neighbors in the Fog-IoT method. In the second phase, we implement a self-adapting algorithm using a control loop to solve the service location problem in the Fog-IoT approach. By considering the feature of self-adapting computing which refers to the self-management features of the distributed computing resources, the proposed method provides an optimal and centralized framework and methodology for solving the problem of deploying the Fog service based on self-adapting computing. Therefore, the real-time application requested and tasks allocation can be dynamically accepted in the shortest possible time in the Fog and the result returns to the IoT-based wireless devices. The simulation of the proposed method under the several performance metrics including the packet delivery ratio, throughput, and network lifetime has been compared with the previous works. The results show that the proposed Fog-IoT method improves all metrics in various scenarios. [ABSTRACT FROM AUTHOR]

With the rapid development in world-wide technologies affecting large-scale applications, the Internet of Things (IoT) has gained a lot of attention from such applications. The Internet of Vehicles (IoV) technology is an exploration of the IoT where radio frequency identification (RFID), one of the promising technologies, can be employed in the IoV systems. However, security and privacy are a key concern in RFID-based IoV system. In the RFID systems during wireless communication among the tag and the reader, an adversary can perform malicious attacks and compromise data privacy. Apart from privacy and security issues, the computation capability and memory of the tags are very limited as compared to the backend server and the reader; therefore, an RFID-based authentication protocol suffers from the high computational overhead. To overcome these issues, we have put forward an anonymous and secure ultralightweight RFID protocol for Internet of Vehicles (AnonSURP). In order to reduce the overhead of the RFID tags, we have exploited the cross and the left rotate operations instead of a hash function for data encryption. In particular, the proposed protocol aims at ensuring the user's location privacy, data privacy, and resisting typical known attacks. Also, the proposed protocol is ensuring the low-cost of the RFID tags and providing fast certification speed. The informal analysis ensures that the protocol AnonSURP meets the various security requirements and provides resistance from various known attacks. Finally, a comparative analysis has been performed that demonstrates the better performance of the proposed protocol from the existing protocols. [ABSTRACT FROM AUTHOR]

Fog computing not only executes applications in the vicinity of the IoT devices/users but also keeps transient data which removes the need for data transfer to the cloud on regular basis. For applications' placement, an orchestrator considers the requirement of the application and the current fog status to place the application on suitable fog nodes. The orchestrator itself may be placed on fog or cloud nodes. In a centralized approach, a fixed entity that works as an orchestrator is prone to single point failure, less mobility support, etc. Therefore, the literature advises for a decentralized approach in which a nearby fog node is selected to act as an orchestrator. Poor selection of Fog Orchestrator Nodes (FONs) may result in the performance degradation of the system. None of the earlier work proposed the selection of FON for the placement of the applications on the fog nodes. Towards this, a brief but latest survey of the works, to understand the FON selection problem, is done along with the importance of decentralized approach in the FON selection problem. Further, few performance metrics have been identified which helps in the FON selection. A lightweight FON Selection model (FONS) is proposed so that even the least powerful IoT devices can select a FON. The proposed work is validated by incorporating the FON selection algorithm in one state of art to observe a remarkable improvement in application placement. [ABSTRACT FROM AUTHOR]

As enterprise information systems grow in scale and computing resources remain limited, some computing system services run into occasional abnormalities such as degraded stability and the failure to respond in time. Fewer monitoring tools mean that system maintenance managers may not be notified when abnormal events occur. It becomes difficult to diagnose and manage the problems promptly, to decrease service interruptions and to fully grasp how computing resources are being utilized. Monitor systems that provide enterprise-wide services must perform better if they are to meet the needs of customers. Therefore, it is necessary for the system administrator to monitor the system. In this case study, we propose and develop a cloud-based monitor system that uses Java to run on the J2EE platform. We build a cloud-based performance analysis and monitoring mechanism whose system architecture has three components: a server resource performance monitor, an enterprise application systems monitor and an abnormal event notification system. The performance analysis and monitoring mechanisms are integrated and include an active diagnosis system and maintenance module to issue notifications when any system experiences abnormal operations. This results in an increase in enterprise system availability and effectively lowers the frequency of abnormal operations. System resource usage reports compiled from the data enable the optimal allocation of the enterprise's limited computing resources. This monitoring system ensures high quality and lowers the operational cost of providing information services, enhancing the provider–customer relationship. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has become a new era of communication technology for performing information exchange. With the immense increment of usage of smart devices, IoT services become more accessible. To perform secure transmission of data between IoT network and remote user, mutual authentication, and session key negotiation play a key role. In this research, we have proposed an ECC-based three-factor remote user authentication scheme that runs in the smart device and preserves privacy, and data confidentiality of the communicating user. To support our claim, multiple cryptographic attacks are analyzed and found that the proposed scheme is not vulnerable to those attacks. Finally, the computation and communication overheads of the proposed scheme are compared with other existing protocols to confirm that the proposed scheme is lightweight. A formal security analysis using AVISPA simulation tool has been done that confirms the proposed scheme is robust against relevant security threats. [ABSTRACT FROM AUTHOR]

The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user's interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate. [ABSTRACT FROM AUTHOR]

IoT is getting popular as it makes human life comfortable. The industry giants such as IBM, Microsoft, Cisco and Amazon have started offering IoT assistance in form of services. Numerous IoT applications exist today with different roles to play in day-to-day life. Because of application diversity and a good number of IoT service providers, it is difficult for IoT users to select the best one as per the requirement and expected quality of service, QoS. To address this, QoS metrics related to major IoT components, i.e., communication, computing and things, are designed to assess the alternative services. IoT users can express their requirements regarding QoS, while service providers exhibit their offerings. Because of three major IoT components, service selection is considered as multi-criteria group decision-making (MCGDM) problem. This work proposes a new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making. The proposed framework is validated by comparing it with an existing MCGDM model. A case study on IoT health-care application is provided besides the sensitivity analysis to demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Recently, the Internet of things (IoT) has attracted a lot of attention owing to its versatile applications by enabling numerous things/objects to collect and exchange data via Internet. Despite the promising role of IoT, there exists the problem of integrating many heterogeneous functions into an embedded and complex IoT system. Meanwhile, in the past decade, we have also envisioned a paradigm shift in the embedded system market toward the system on a chip (SoC) by integrating all components into a single chip. But the on-chip communications of IoT systems remain an important and challenging issue. This work proposes a new network on a chip (NoC) switching, AnyNoC, employing the shared-memory and output-queue techniques implemented using the efficient dynamic link list, particularly suitable for the IoT SoC. The proposed high-level design can achieve the optimal performance by sharing the data buffer among all ports and eliminating the head-of-line blocking problem, resulting in a virtual point-to-point characteristic without the interruption of slow devices or congestion conditions in other ports. Moreover, the proposed architecture can minimize the required memory size by virtually sharing all buffers among all ports, resulting in one queue needed for each outbound port and totally N queues are required, where N denotes the number of ports. Therefore, compared to the famous wormhole switching, the proposed NoC architecture features lower cost and higher performance, which can approach the theoretical upper bound. Moreover, for a 16×16 network, the performance gain of the throughput of the proposed switching compared to the popular wormhole switching is about 40%. [ABSTRACT FROM AUTHOR]

In recent years, various smart sensor systems have been integrated into the “Internet of Things (IOT)” with the advancement of sensing technology. A redundancy allocation is the safest, most convenient, and most economical way to increase the reliability of smart sensor systems. To solve the smart sensor systems redundancy allocation problem (RAP) in IOT, a cooperative parallel simplified swarm algorithm (pSSO) is presented in this study. This pilot study includes several innovative points. First, research is conducted to use the RAP in IOT. Second, the proposed pSSO is the first parallel algorithm to solve the RAP and the first one to parallelize the simplified swarm optimization (SSO) with the Taguchi method. A simple real-life example regarding shopping and shipping in TAOBAO is given to describe the way how to model the IOT used the RAP. As proof of the success of the proposed pSSO, detailed computational results from solving a series-parallel redundancy allocation problem with a mix of components is presented. The computational results reflect the efficiency of the pSSO proposed. [ABSTRACT FROM AUTHOR]

The Internet of Things' contribution lies in the increased value of information created by the number of interconnections among things and the subsequent transformation of processed information into knowledge for the benefit of society. The Internet of Things' sensors are deployed to monitor one or more events in an unattended environment. A large number of event data will be generated over a period of time in the Internet of Things. In the future, hundreds of billions of smart sensors and devices will interact with one another, without human intervention. Also, they will generate a large amount of data and resolutions, providing humans with information and the control of events and objects, even in remote physical environments. However, the demands of the Internet of Things cause heavy traffic or bottlenecks on particular nodes or on the paths of Internet of Things networks. Therefore, to resolve this issue, we propose an agent, Loadbot, that measures network loads and processes structural configurations by analyzing a large amount of user data and network loads. Additionally, in order to achieve efficient load balancing in the Internet of Things, we propose applying Deep Learning's Deep Belief Network method. Finally, using mathematical analysis, we address the key functions of our proposed scheme and simulate the efficiency of our proposed scheme. [ABSTRACT FROM AUTHOR]