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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]

The Internet of Things (IoT) technology has led to the development of intelligent hardware devices that can interact with each other through the internet, enabling the interconnection of everything. However, this interconnectivity also increases vulnerability to illegal operations by attackers. Therefore, ensuring the security of the IoT system is critical. In this paper, we present an efficient intrusion detection ensemble system for IoT security using metaheuristic (MH) optimization algorithm and machine learning. The system comprises data preprocessing, feature selection, and ensemble classification. Firstly, we propose an enhanced variant of the artificial hummingbird algorithm (EAHA) with three key improvements over the original AHA. The hunger weight is introduced into AHA’s guided foraging behavior to better balance exploration and exploitation. Second, alternating and cooperative foraging strategy is applied in territorial foraging behavior to improve exploitation ability and prevent the algorithm from getting trapped in local optima. Finally, we incorporate greedy Cauchy mutation, using the hummingbirds’ position information as the mutation operator to further strengthen the global search capability of the algorithm. The algorithm is tested on 10 classical benchmark functions and statistically analyzed to demonstrate the feasibility of the improvement. Furthermore, we implement a binary variant of the EAHA (BEAHA) for feature selection. The BEAHA is combined with machine learning classifiers to determine the most optimal feature set. To improve the performance of the proposed network intrusion detection system (NIDS), we construct an ensemble model based on multiple classifiers. We evaluate the feature selection ability of BEAHA and the performance of the developed system using three intrusion detection datasets NSL-KDD, CIC-IDS2017, and CSE-CIC-IDS2018. The experimental results demonstrate that BEAHA selects an average of 7.29, 9.03, and 4.22 features for fitness function evaluation using three classifiers on three datasets, achieving average accuracies of 99.74%, 99.59%, and 98.51%, respectively. By optimizing the ensemble system for intrusion detection with BEAHA, we reduce the number of features by at least 69% on all datasets while maintaining or surpassing the classification accuracy achieved with the full feature set. These results highlight the competitiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

With the advancement of Internet of Things (IoT) technology, IoT systems have been widely infiltrating and deployed on a large scale globally. Consequently, network attacks on IoT devices and the intermediary communication media have increased significantly, making the focus on IoT network security particularly important. This paper introduces a novel IoT Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs leverage the topological structure of graph-based data to build correlations between traffic flows. The data can be represented in a flow-based form, where such data can construct a graph’s network structure representation based on source and destination addresses. This paper proposes a new improved GNN, incorporating global attention mechanisms and local gating mechanisms into the edge-based graph neural network model GraphSAGE, improving the sampling strategy of graph embeddings. Then, graph contrastive learning is applied to enhance feature representation, capturing edge features and topological information of the graph more comprehensively, achieving IoT network intrusion detection. We call this method E-GRACL (Edge-based GraphSAGE with residual connections, global attention, gating mechanisms, and contrastive learning). Our method applies GNN to the problem of IoT network intrusion detection using flow-based data. We conducted extensive experimental evaluations on three recent benchmark datasets, and the results demonstrate that our method has certain advantages in both binary and multi-class classification metrics, proving the great potential of GNN in IoT intrusion detection. [ABSTRACT FROM AUTHOR]

Low-Power Wide Area Networks (LPWANs) represent a category of wireless technologies hailed for their efficiency in facilitating communication for Internet of Things (IoT) applications. This efficacy is attributed to their characteristics of low power consumption, extensive wireless transmission range, and cost-effectiveness. Despite these notable advantages, LPWANs exhibit drawbacks such as limited processing power, modest transmission rates, and notably constrained payload sizes, posing challenges for encryption techniques. The inadequacy of existing cipher-chaining encryption methods for LPWANs is underscored by their dependency on high computing power and payload capacity. To address this issue, this paper introduces an innovative chaining encryption approach tailored for LPWAN IoT technology. The proposed method incorporates a key synchronization mechanism based on deep learning algorithms. The effectiveness of this approach has been rigorously assessed through case studies and experiments. The experimental results demonstrate the commendable performance of the proposed approach: i) The proposed method achieves an average effort of 1.0202 for key synchronization after 10 lost packets, compared to 1.0215 for the best existing method; ii) It attains a 98.45% success rate in key synchronization after 10 lost packets, while the best existing method achieves 98.19%; iii) The proposed approach ensures 98.27% accuracy in receiving messages correctly under conditions of completely random data receipt, compared to 98.22% for the best existing method. These results establish the proposed method as a highly competitive solution among encryption approaches for LPWANs. [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]

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]

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]

The Internet of Things (IoT) has emerged as an inevitable part of human life, that includes online learning, smart homes, smart cars, smart grids, smart cities, agriculture, and e-healthcare. It allows us to operate them 24/7 from anywhere. These smart IoT devices streamline our daily lives by automating everything around us. Several security issues have arisen with the continuous growth of non-secure IoT devices. Distributed Denial of Service (DDoS) attack is one of the most prominent security threats to Internet-based services and IoT platforms. It has the potential to break down the victim's server or network by transferring an immense amount of irrelevant traffic from the pool of compromised IoT devices. In this article, we present: (i) A comprehensive cyberattacks taxonomy for IoT platforms, (ii) Systematically demonstrate IoT technology: evolution, applications, and challenges, (iv) Systematic review of existing machine learning (ML) and deep learning (DL)-based detection approaches for large-scale IoT traffic-based DDoS attacks, (v) Characterize publicly available IoT-traffic-specific datasets, and (vi) Discuss various open research issues with possible solutions for detecting IoT traffic-based DDoS attacks, including future directions. [ABSTRACT FROM AUTHOR]

There is a significant surge in unmanned aerial vehicles' (UAVs) popularity with an ongoing increase in demand due to their multi-functional uses. The capacity of the UAVs to respond to society and industry needs account for their pervasiveness. UAVs can be strategically deployed to enhance network efficiency and support critical missions, including vital tasks like infrastructure monitoring operations. To be effective, a UAV must interact securely with its network's entities, such as ground control stations, other UAVs, air traffic control systems, and navigation satellite systems. UAVs are exposed to a dangerous and costly world of cyber dangers as a result of cyber technology and connections. The UAV and the Ground Control Station (GCS) exchange information using communication lines, which are vulnerable to cyber-attacks. As a result, detective, defensive, and preventative countermeasures are critical. The Micro Air Vehicle Link (MAVLink) protocol is a widely used lightweight communication protocol for enabling communication between UAVs and GCSs. It outlines communication exchanges between a GCS and a UAV. It carries information about the UAV's condition as well as commands for control from the GCS. Although widely used, the MAVLink protocol lacks sufficient security measures and is susceptible to various types of attacks, which can pose significant risks to public safety. UAV data should be encrypted because they are very sensitive. To give a high level of security, complex operation encryption techniques are developed. These mathematical operations bring forth issues with efficiency and power usage, though. One of these methods, the ChaCha cipher, recently gained popularity after Google used it in several applications. In the current study, a new stream cipher method with low-duty cycles is proposed for protecting data in UAVs and is compared with existing security algorithms on the basis of various factors. The research findings show that by including the suggested method in MAVLink, it is possible to maintain both message secrecy and battery life for a resource-constrained UAV. This can be done while using comparable amounts of memory and CPU and without affecting MAVLink's performance. [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 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]

Cardiovascular disease (CVD) is among one of the notable menaces to society worldwide. CVD causes the highest number of deaths each year making it one of the most life-threatening diseases across the globe. Most deaths from CVD are sudden therefore patients do not have a chance to get medical assistance in time. Consequently, an immense need for a smart real-time system arises that can be used to monitor heart patients' activities affecting their cardiac health. This system acts as a life-saving tool during serious health emergencies. Data analysis in real-time will proves to be a substantial enhancement in innovative healthcare practices, by which in the near future we can develop an effective, faster, and smarter diagnosis system for doctors. If we talk about real-time data monitoring possibilities, Internet of Things (IoT) empowered systems can provide one of the better solutions. IoT-enabled intelligent healthcare system include a variety of applications, such as Blood Pressure (BP) check, Heart Rate (HR) monitoring, Electrocardiography (ECG) observation, etc. This paper recommends an IoT-enabled ECG monitoring system for data generation (with the help of Node MCU ESP32 and heart rate sensor AD8232) and an intelligent hybrid classification model for data classification. The dataset used has two classes where class 1 represents healthy patients and class 2 represents cardiac ill patients. A comparison among state-of-the-art algorithms and recommended hybrid models has been carried out to establish the accurateness and suitableness of our recommended model. The recommended model attains the highest accuracy of 99.7% under different validation criteria among all the state-of-the-art algorithms, i.e. Adaboost (91.88%), Bagging (92.40%), random forest (92.48%), K-Nearest Neighbor (92.38%), and support vector machine (91.98%). The recommended hybrid model not only handles the complexities of class imbalance for electrocardiogram datasets but will also help in building intelligent and accurate IoT-enabled healthcare systems. [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]

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency. [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]

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

Smart transportation systems have been the focus of research due to the development of smart cities. However, existing vehicular networks are not sufficient enough to fulfill the vision of futuristic smart cities due to limited flexibility, scalability, poor connection, and insufficient intelligence. These technological hurdles make the role of Software-Defined Networking (SDN) very important to improve the overall performance of the existing vehicular networks considering the unique properties of SDN such as Decoupling of network planes and Real-time network programming. This leads to the development of Software-Defined Vehicular Networks (SDVNs). SDVNs help to realize the development of smart transportation systems which further helps to optimize the vision of truly smart cities. However, the security remains a consistent concern due to the increased mobility, larger attack surface, and improvised future attack vector. This work includes the different design components, and offers a detailed survey to understand different security issues including the architectural and functional ones. Additionally, multiple security solutions are discussed including Service-based, Infrastructure-based, and Application-based solutions. Furthermore, the work also covers the possible challenges in the development of SDVNs based on Improved Architectural Development, Holistic Integration, Effective Orchestration, Environmental Volatility Handling, Global Network Management, Efficient Components/Technologies Integration, Diverse Security Offerings, and Design Issues' Maintenance. Lastly, the work highlights the resultant opportunities based on Application, Open Research, Network Management, Device Configuration, Traffic Management, QoS, and Efficient Routing. [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]

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 has become a prominent paradigm in providing shared resources to serve different applications near the edge. Similar to other computing paradigms such as cloud and grid, in fog computing, service-level agreements (SLAs) are essential between fog providers and end-users to guarantee the quality of service (QoS). However, due to the unique characteristics of fog resources, such as being highly distributed and heterogeneous, with their dynamic nature having nonrestrictive provider participation, SLA management techniques and frameworks, which are available for Clouds and Grids, are not directly applicable. The availability of the resources in the cloud is much more controllable and predictable compared to fog. Moreover, due to the multiple ownership of fog infrastructure and unrestricted environment, autonomous end-devices are allowed to participate with different SLAs to serve the applications near the edge as a result is a lack of trust exists between the entities and managing and enforcing SLAs according to the application QoS in this environment is a complex task. Thus, the SLA management must be undertaken in a more trustworthy manner to ensure that agreement. To fill this gap, this paper proposes an automated SLA management framework for fog computing that utilizes Smart contracts and blockchain technology to monitor and enforce SLAs in a more trustworthy manner. The results obtained from the experiments, which were conducted in the blockchain private network, show that the framework can ensure precise and efficient SLAs enforcement in the fog. The performance of the proposed framework is better than existing work in terms of transaction cost and time. [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]

Fog-integrated cloud (FiC) contains a fair amount of heterogeneity, leading to uncertainty in the resource provisioning. An admission control manager (ACM) is proposed, using a modified fuzzy inference system (FiS), to place a request based on the request's parameters, e.g., CPU, memory, storage, and few categorical parameters, e.g., job priority and time sensitivity. The ACM considers the extended three-layer architecture of FiC. FiC nodes are classified into three computing nodes: fog node, aggregated fog node, and cloud node using modified FiS model. For performance study, extensive simulation experiments have been carried out on real Google trace. Different batches on the number of relevant rules are created and compared on metrics of job execution time, memory overhead, accuracy, and hit ratio with the modified rules. The proposed work has also been compared with the state of the art. The results have been encouraging and exhibit the benefits of the proposed model apart from being it lightweight with reduced number of rules, especially suited for the FiC. [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]

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]

Despite that IoT technology provides a promising future for human life, some significant challenges such as routing, security, low-cost equipment, energy consumption, privacy, and reliability can considerably affect its performance. In recent studies, routing has been considered as one of the most critical challenges in IoT due to many existing IoT devices in a network. Selecting a non-optimal path increases collision probability and packet latency which sharply reduces the network performance. Hence, a novel flexible, scalable, and efficient routing protocol named LOADng-AT has been introduced in this study to overcome this challenge. Using LOADng-AT, firstly, taking advantage of the hello message, information about link quality parameters up to two hops is exchanged among nodes. This information includes various parameters such as bi-/unidirectional links, ETX, number of neighbors, link stability, received and residual energy, and SINR, gathered by hello message. Secondly, considering those parameters simultaneously, LOADng-AT efficiently chooses the best possible route based on the hybrid AHP-TOPSIS algorithm. The proposed protocol considerably decreases the RREQ broadcast storm. Moreover, it supports an error recovery path without rerunning a new routing process that noticeably decreases the network routing delay. LOADng-AT can be easily adapted to any QoS parameter with very low complexity which is very important for a delay-sensitive IoT-based network. Finally, in case of noisy condition, the proposed protocol can be efficiently used in low-quality links. Simulation results based on several scenarios in the OMNET ++ simulator show that end-to-end delay (EED) and packet delivery ratio (PDR) parameters significantly improved in the proposed method compared to other similar methods. In other words, using LOADng-AT, the mean values of PDR parameter were 98.64 and 82 percent for network sizes 400 × 200 and 800 × 400 m2, respectively, while the mean values of EED were 0.001 and 0.018 for network sizes 400 × 200 and 800 × 400 m2, respectively. The overall improvement for PDR and EED parameters in all 60 considered scenarios compared to similar methods were 82.26 and 16.6 percent, respectively. [ABSTRACT FROM AUTHOR]

Under the complex environmental of the front-end manufacturing process of semiconductors, on-site managers are concerned about the production performance of the bottleneck machine, hoping to predict the future production volume as a reference for future production decisions in Internet of Things (IoT). In this study, we proposed neural network methods were used to predict production performance by using genetic programming (GP) and backward propagation neural network (BPN). GP is applied to numerical prediction to calculate the difference between the actual output value and the predicted output value. BPN can be divided into an input layer processing unit, a hidden layer processing unit, and an output layer processing unit, which can be divided into three different functional layers. This study uses the production data of semiconductor manufacture factory to compare the results of bottleneck machine production performance predictions carried out by GP and BPN methods. The result show that the average prediction accuracy of the two methods are reaching the high level of practical requirements and indicates BPN is better than GP method. Both methods can provide a reference for manager practical to make decisions to improve production performance in semiconductor industry. [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]

This research aims to further promote the popularization and application of agricultural Internet of Things (IoT) and solve the practical problems of low transmission efficiency in agricultural data transmission. The targeted study is conducted on the video coding in agricultural IoT system to further promote the development of agricultural IoT system and improve the application effect of video surveillance technology. Firstly, an intelligent decision management platform is designed based on agricultural IoT system, consisting of the remote control module and video surveillance module. Meanwhile, a three-dimensional High Efficiency Video Coding (3D-HEVC) fast algorithm based on Bayesian Decision Theory is designed aiming to optimize the 3D-HEVC technology. Experiments are also performed to investigate the performance of intelligent management platform and 3D-HEVC fast algorithm based on Bayesian Decision Theory. The experimental results show that the intelligent management platform has good application effects in the database performance test, website access concurrency test, and data reception performance test. Compared to the traditional 3D-HEVC algorithm, the proposed 3D-HEVC fast algorithm reduces the coding time of depth map and total coding time by 46.5% and 22.52%, respectively, in addition to a better application effect. Meanwhile, with the support of the 3d-HEVC fast algorithm, the agricultural intelligent management platform shows better video transmission performance and application effect meeting the actual needs at this stage. The experiment results have certain practical value, providing scientific and effective reference data for subsequent research. [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]

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]

Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial–temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model. [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 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]

With the popularization of Internet of Things (IoT) and Social Internet of Things (SIoT), innumerable smart objects are allowed to establish associations among each other. Among numerous existing smart object recommendation methods, none have explored the failures of smart objects in smart social networks. As smart objects are subject to failure, we strongly believe that incorporation of smart objects' failure in addition to other contextual factors (such as geographical, social, manufacturer, and economic influence) play a crucial role in smart object recommendation. However, heterogeneous nature of these contextual factors makes it challenging for estimating user and smart object representations in the Smart object-Based Social Network (SBSN). The key contribution of this work is to propose a novel smart object recommendation architecture called SORec for SBSN. The aforementioned contextual influences are incorporated into SORec by deriving transition probabilities among two nodes in SBSN which help in attaining more precise representation of users and objects. The latent representations of smart objects and users are estimated by employing Node2Vec graph embedding technique which help in improved training of the model and superior recommendation performance. Extensive experiments have been conducted on the real-world dataset to evaluate the performance of the SORec. Experimental results clearly indicate that recommendations generated by SORec are more precise, when compared to its variants and baseline methods such as PMF (41.03%), KGE (19.59%), DeepWalk (25.49%), and LINE (18.11%). [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]

Rise of Big Data techniques has led to the requirement for low latency analysis of high-velocity continuous data streams in real time. Several solutions, including Stream Processing Systems (SPSs), have been developed to enable real-time distributed stream processing. However, emerging application scenarios such as smart cities and wearable assistance that involve highly variable data rates keep on posing new challenges to the established stream processing engines for maintaining cost-effective executions. To cater to such scenarios, many modern SPSs have been proposed that leverage Cloud environment. The run-time scalability incorporated in these SPSs is in their early adaptations and are based on fixed scale sizes. Moreover, these scaling approaches do not adequately consider both the structure of the hosted streaming applications and the characteristic features of the underlying Cloud environment. Achieving true cost benefits of orchestrating streaming applications on Cloud-based pay-as-you-go model while maintaining the desired QoS, necessitates that both these issues are accounted in making the scaling decisions. This work presents a heterogeneity-aware, efficient auto-scaling strategy StreamScale-H which addresses both these issues. Simulation experiments, on representative stream applications, indicate that the proposed StreamScale-H auto-scaling algorithm exhibits much better performance in comparison with the state-of-the-art algorithms. [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]

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]

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]

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]

To enable the Internet of things, the semiconductor manufacturing process has progressed from the micron to the deep submicron level. Quality improvement is one of the great challenges in wafer fabrication. Computer-integrated manufacturing (CIM) has arisen as a means by which to reduce wafer rework and continuously improve the semiconductor production process. This study uses statistical process control (SPC) and data mining technology to analyze the collected semiconductor process data. A real-time feedback algorithm is employed to ensure that each product lot is manufactured using optimized process parameters. This maximizes production capability, increases the semiconductor yield rate and reduces the cost of manufacturing. This paper focuses on wafer fabrication facilities (often called "fabs," or "foundries"). The data mining architecture is implemented between CIM and the manufacturing execution system. Association rules and the k-means clustering algorithm are used together with real-time feedback control analysis to extract and analyze each parameter that affects the semiconductor production yield. This combination of real-time feedback and SPC using historical process data allows the system to predict the optimum process parameters for the next lot. The system compensates dynamically to accommodate differences among various machines and products, giving each machine a level of flexibility in manufacturing the product. The proposed semiconductor system can be applied to traditional manufacturing industry process analysis. Our results show how our system can improve the semiconductor manufacturing process in terms of processing capability, yield rate, stability and flexibility, while reducing costs, thus creating a competitive advantage for the wafer fab. [ABSTRACT FROM AUTHOR]