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Recently, many image encryption algorithms have been proposed. Part of them failed to pass the cryptanalysis test and were found to be unsecure. Some others were more secure and could resist the known cryptanalysis methods. Many of those recent schemes followed a well-known model that consists of encrypting the image's pixels using a variety of techniques and methods. Many algorithms were claimed to be secure and performant to be used in real applications. This paper investigates this claim by performing a comparative analysis between a chaotic algorithm recently proposed and the standard general purpose AES cryptosystem in a web application context. The experimental tests conducted on both algorithms in this paper proved that the pixel-wise algorithms are not suitable for this kind of applications. [ABSTRACT FROM AUTHOR]

Because of the limitations of current PCB detection methods, such as low detection accuracy and high rate of false and missed detection, a PCB defect detection algorithm based on improved RetinaNet is proposed, that is, RetinaNet-line. Firstly, the algorithm uses PVT v2B2-Hi as the backbone network of the whole model, which can not only solve the deep feature redundancy caused by the residual network in RetinaNet but also improve the feature extraction ability of the network. Then, the CARAFE up-sampling module is used to enrich the semantic features of defective targets and promote the discrimination ability of the algorithm to defective targets. Finally, HSV color space is used to process the input feature image, and the color distribution is expanded by random perturbation in H, S, and V channels, with the data enhancement completed. In this paper, experiments are conducted on the publicly shared PCB defect detection dataset from the Intelligent Open Laboratory of Peking University, and the results show that the improved algorithm achieves 94.2% in the mAP index, which is better than other networks. [ABSTRACT FROM AUTHOR]

The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user's requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users' requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users' multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC. [ABSTRACT FROM AUTHOR]

Edge computing enables portable devices to provide smart applications, and the indoor positioning technique offers accurate location-based indoor navigation and personalized smart services. To achieve the high positioning accuracy, an indoor positioning algorithm based on particle filter requires a large number of sample particles to approximate the probability density function, which leads to the additional computational cost and high fusion delay. Focusing on real-time and accurate positioning, an edge computing-enabled green multi-source fusion indoor positioning algorithm called APFP is proposed based on adaptive particle filter in this paper. APFP considers both pedestrian dead reckoning (PDR) signals in mobile terminals and the received signal strength indication (RSSI) of Bluetooth, and effectively merges the error-free accumulation of trilateral positioning and the accurate short-range positioning of PDR, which enables mobile terminals adaptively perform particle filter to reduce the computing time and power consumption while ensuring positioning accuracy simultaneously. Detailed experimental results show that, compared with the traditional particle filter algorithm and the map-constrained algorithm, the proposed APFP reduces fusion computing cost by 59.89% and 54.37%, respectively. [ABSTRACT FROM AUTHOR]

The overwhelming growth of cloud computing has introduced new challenges for the cloud infrastructure. Many cloud service providers (CSPs) have been adopting renewable energy (RE) sources to increase profitability and reduce carbon emissions. Therefore, recent literature focuses on managing cloud infrastructure with RE sources in addition to traditional non-renewable energy (NRE) sources whenever required. However, these works aim to maximize the usage of RE or minimize the cost without considering the uncertainty (UN). This paper presents three user request-based scheduling algorithms, namely UN-based future-aware best fit, UN-based round-robin, and UN-based highest available renewable first, by managing the UN of RE and NRE. These algorithms consider two types of UN, namely UN-RE and UN-NRE, concerning the user request. The proposed algorithms undergo a rigorous simulation process with 200 to 2000 user requests and 20 to 200 datacenters and are compared using overall cost, UN cost and time, and the number of used RE resources. [ABSTRACT FROM AUTHOR]

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

The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations. [ABSTRACT FROM AUTHOR]

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users' jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits. [ABSTRACT FROM AUTHOR]

Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate. [ABSTRACT FROM AUTHOR]

Cloud computing has experienced extraordinary development across a wide range of industries by giving customers the flexibility to employ computing resources as needed. The task scheduling problem is one of several major challenges in cloud computing, and it should be scheduled effectively to minimize makespan and maximize resource utilization. Therefore, this paper put forward an improved scheduling efficiency algorithm called Enhanced Marine Predator Algorithm (EMPA). Firstly, task scheduling model with makespan and resource utilization is constructed. Secondly, each individual represents a result of task scheduling, and the purpose of algorithms is to find the optimal scheduling result, therefore the operator of WOA, nonlinear inertia weight coefficient and golden sine strategy are introduced into Marine Predator Algorithm. In the simulation experiment, EMPA is compared with Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) under different number of tasks in synthetic datasets and GoCJ datasets.The experimental results show that the EMPA algorithm has more advantages in terms of makespan, degree of imbalance, and resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, we consider the matter of on-line clustering of hyperplanes within the presence of bounded noise. This is often a challenging problem that involves the segmentation of the data and the estimation of the hyperplanes parameters. The proposed algorithm consists in two successive steps. At whenever, the first step allows to assign the current data point to the most appropriate hyperplane and therefore the second step realizes an update of the parameters of the hyperplane that contains the data point. The second step springs from an Outer Bounding Ellipsoid type algorithm suitable for on-line parameters estimation within the presence of bounded noise. The performance of our algorithm is proven using synthetic and real data. [ABSTRACT FROM AUTHOR]

Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

Clustering is an important technique for data mining and machine learning. In this paper, a novel clustering algorithm, scale division and region growing (SRG), is proposed, which is based on scale division and region growing. This method uses the division-and-merge strategy, which consists of two primary phases, namely the division phase and the merging phase. In the division phases, the data is mapped to a two-dimensional space, and the clustering problem is transformed to an image processing problem. Then the image is divided into different scales to adaptively find the appropriate scale. After finding the appropriate scale, the region growing is carried out under this scale and the multiple sub-clusters are created. In the merging phases, the sub-clusters are evaluated and merged to form the final clusters in the dataset. The proposed algorithm is tested on 15 benchmark datasets including both the synthetic datasets and the real datasets, and it is found that SRG can obtain better clustering results and has higher accuracy than other typical clustering algorithms. Moreover, the experimental results on noisy datasets show that the proposed algorithm has good robustness. [ABSTRACT FROM AUTHOR]

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy. [ABSTRACT FROM AUTHOR]

As the scale and complexity of networks grow rapidly, management, maintenance, and optimization of them are becoming increasingly challenging tasks for network administrators. Software-Defined Networking (SDN) was introduced to facilitate these tasks as it offers logically centralized control, a global view of the network, and software-based traffic analysis, thus, it is widely adopted of SDN to manage large-scale networks. On the other hand, SDN is not immune to cyber attacks. In fact, its centralized architecture makes it more vulnerable to certain types of attacks, such as denial of service. Various attack mitigation strategies are proposed to strengthen the security of SDNs including statistical, threshold-based, and Machine Learning (ML) methods. Among them, Deep Learning (DL)-based models attained the best results as they were able to extract the complex relationship between input parameters and output that could not be achieved with other solutions. Hence, this paper presents a comprehensive survey of the literature on the utilization of different DL algorithms for the security of SDN. We first explain the types of attacks that SDNs are exposed to, then present papers that applied DL to detect and/or mitigate these attacks. We further discuss the public datasets used to train DL models and evaluate their advantages and disadvantages. Finally, we share insights into future research directions to improve the efficiency of DL methods for SDN security. [ABSTRACT FROM AUTHOR]

These days, cloud computing usage has been increasing with the rapid growth of Internet coverage all over the world to serve as a pay-per-use model using shared computing resources. Internet of Things (IoT) is a growing technology which is used in different applications and it needs cloud computing however the distance between cloud computing resources and the end system in IoT can cause a delay which is intolerable for delay-sensitive applications. Fog computing is a computing resource between cloud computing and end system to reduce the delay for the delay-sensitive applications in IoT. Therefore, load balancing functionality is a significant role to provide the required quality of service (QoS), quality of experience (QoE), and performance. Load balancing can be done based on response time, throughput, energy consumption, and utilization metrics. In this paper, the papers published in Elsevier, ACM, IEEE, Springer and Wiley between 2018 and 2022 have been examined to extract the load-balancing algorithms, system architecture, tools and applications, advantages and disadvantages. This review is useful for those working on load-balancing performance improvement. [ABSTRACT FROM AUTHOR]

In order to realize the real-time progressive compression of massive data and ensure the quality of compressed data, a real-time progressive compression method of massive data based on improved clustering algorithm is proposed in this paper. Through the micro clustering stage of birch method based on K-Medoids clustering, Clustering Feature Tree hierarchy is constructed and numerical clustering features are extracted; Taking this feature as the input of macro clustering order, the Clustering Feature Tree leaf nodes are clustered based on the improved K-Medoids clustering method, and the clustering data cluster set is output; The set is used as the original data of real-time progressive compression, and the data is denoised and compressed by lifting format wavelet transform. On this basis, Huffman coding is used to compress the data losslessly. The test results show that this method has good clustering effect under the optimal number of clustering centers, can complete the real-time progressive compression of a large number of data, and the availability of compressed data is more than 92%. [ABSTRACT FROM AUTHOR]

Software as a service (SaaS) provider hires resources from an Infrastructure as a Service (IaaS) provider and provides these sharable resources to user's applications on lease. However, it is becoming a more challenging issue for SaaS providers to meet user's Quality of Service (QoS) Parameter and maximize profit from cloud infrastructure. This proposed work satisfies both the user and the service provider by fulfilling service level agreement (SLA), user's QoS requirement, and increasing profit with efficient resources utilization. This paper proposes an Improved Quantum Salp Swarm Algorithm (IQSSA), which improves the Salp Swarm algorithm by incorporating the principles of Quantum computing to increase the convergence rate. Further, Quantum-inspired Salp Swarm Grey Wolf Algorithm (QSSGWA) embeds SSA with Grey Wolf Optimizer (GWO) to improve the global optimum solution, and quantum operator is used to initializing population. Proposed algorithms execute tasks under the user-defined deadline and budget constraints. Furthermore, the penalty cost is formulated and applied in the case of a deadline violation. IQSSA and QSSGWA are tested on 19 global benchmark functions, and results prove their superior performance compared to SSA, GWO, BAT, and Particle Swarm Optimization (PSO) algorithm. Furthermore, these algorithms are simulated on CloudSim, and performance matrices such as service provider's profit, makespan, SLA violation rate, task rejection rate, throughput, resource utilization, and response time are compared. The comparison analysis demonstrates that the proposed algorithms offer better performance and more efficient scheduling than existing metaheuristics. Furthermore, simulation results clearly show that QSSGWA gives the best results for all performance matrices. This proposed approach can be applied in many scientific domains, where distributed processing of data or large scale data analysis is required such as distributed and federated machine learning, serverless computing, medical applications, etc. [ABSTRACT FROM AUTHOR]

Recently, we are witnessing an enormous burst of data due to the ever-increasing number of Internet of Things (IoT) devices. The traditional cloud computing paradigm has failed to scale; to be specific, its latency and bandwidth utilization are remarkably increased and consequently, Quality of Service (QoS) is decreased. On the other hand, the data management scope in fog computing require much more considerations in terms of performance and scalability. This is because of deploying IoT applications over fog nodes considering their resource-limited and heterogeneity. However, to the best of our knowledge, there is not any literature review that systematically categorizes these issues. In this paper, we have presented a classification of data replica placement approaches considering four main categories: framework-based, graph-based, heuristic-based, and meta-heuristic-based algorithms. To sum up, the primary contribution of this study is as follows: studying articles on data replica placement in fog computing, as well as presenting their strengths and weaknesses, providing a comprehensive systematic review of current approaches and categorizing them comprehensively, discussing research challenges, and future works to improve computing and evaluation mechanisms in the fog computing environment. This paper generally provides a classification, briefly explains the reviewed techniques, and then compares these methods in the end. [ABSTRACT FROM AUTHOR]

The request-response model for constrained devices and networks has been achieved via RESTful architecture of Constrained Application Protocol (CoAP) in the Internet of Things (IoT). The latency in messages is significant in constrained networks. These latencies can be managed by introducing a mechanism for updating the client/server and network status in CoAP. This mechanism would benefit in optimizing network communication. This paper proposes a mechanism to update any latency from client/server nodes amending the existing CoAP messaging model. A few options have been proposed to the CoAP message in terms of latency-state indicator, IN/OUT timestamps, and priority, which helps implement the proposed model. These options would additionally help in improving network and client/server performance. The network performance improves as the void messages in the network would reduce. The simulation of the implemented algorithm has shown a significant improvement in terms of the network's latency, message priority, and node status. [ABSTRACT FROM AUTHOR]

Quorum algorithm has become one of the most widely adopted approaches for storing data in the failure-prone distributed storage systems. In this paper, a new approach is proposed which is able to set different levels of data consistency in key-value NoSQL databases. The proposed approach is based on the parameters set in the NoSQL-dependent structure and is applied to distributed systems, using the quorum algorithm to adjust the consistency and to determine the different grades of data. In this paper, different data writing and reading and mix reading/writing operations are implemented test-bed to study the performance of the proposed approach. In addition to its main goal for improving data consistency, the proposed approach also satisfies the accepted compromise between the pillars of the CAP and PACELC theorems. Evaluation of the results shows that the objectives for consistency and efficiency of the proposed approach have been met. [ABSTRACT FROM AUTHOR]

Image encryption algorithms based on Chaotic approach are becoming increasingly popular for remotely sensed images using parallel techniques. It has been demonstrated that the most efficient image encryption algorithms are based on Chaos. Previous research using chaos-based cryptosystems has resulted in poor performance when using a single computer, compromising privacy, security, and reliability. Furthermore, there were issues when vulnerable satellite images were processed. This paper describes a novel chaos-based encryption technique that employs an external secret key and Henon, Logistic, and Gauss iterated maps. The proposed encryption algorithm is capable of efficiently encrypting a large number of images. When the number of images increases, however, these images become very small, and the technology becomes inefficient or impractical. This paper investigates the parallel method of image encryption on a large number of remotely sensed images in Hadoop. Hadoop's file visit method has been enhanced so that it can treat the entire Tiff file as a single unit. Furthermore, the file format is being extended to be supported by Hadoop in order to support GeoTiff in Hadoop. The results of the experiments show that the proposed parallel method for encryption is effective and scalable to a large number of images when compared to other well-known methods. [ABSTRACT FROM AUTHOR]

Efficient privacy-preserving algorithms are used in internet of things (IoT) applications, virtualization and edge computing environments to decrease the high data disclosure risk. However, due to the strict security principles of current privacy-preserving algorithms, they ignore the time consumption. On the other hand, most existing privacy-preserving algorithms suffer from the overgeneralization problem, creating unnecessary information loss. Thus, two privacy-preserving algorithms are proposed in this paper for IoT applications in the virtualization and edge computing environment to address this problem and balance the information loss and disclosure risk. We first propose an overall metric to measure the performance of privacy-preserving algorithms and balance the information loss and disclosure risk. Second, the proposed algorithms can significantly decrease the time consumption by utilizing cached anonymized datasets. Moreover, the proposed algorithms also optimize the distribution of anonymized datasets and decrease the information loss by deleting the small equivalent classes. Experimental results show that our proposed algorithms are relatively fast, only consuming 34.3% operation time of current algorithms. Next, we tested the usability of anonymized datasets by putting them into an IoT application. The two datasets generate similar results, indicating that the proposed algorithms can satisfy the information loss requirements of IoT applications. [ABSTRACT FROM AUTHOR]

With the rise of 5G/6G and cloud computing, cluster management has become increasingly popular. Elastic cluster resources allow cloud clients to dynamically scale their resource requirements over time. Existing researches of cluster schedulers focus on improving resource scheduling speed, increasing cluster utilization, compacting the number of active physical machines (PMs) and time satisfaction function (TSF) within a cluster. The TSF is applied as a time to measure the parallel-VM scheduling problem. However, completing execution time (makespan) of task requests is often neglected, which results in inaccurate scheduling and unreasonable total cost computation. The total cost involves PM cost, migrate cost, and balance cost. To solve the problem of inaccurate scheduling of task requests and total cost billing in cluster management, in this paper, we propose an innovative heuristic algorithm, namely, multi-objective two-stage variable neighborhood searching (MO_STVNS), which aims at minimizing total cost while also considering TSF for active PMs. Moreover, we design a Multi-Objective FreeVM (MO-FreeVM) scheduler based on resource prediction, which incorporates a variety of algorithms to work in collaboration to provide near-optimal resource management for cluster. We evaluate MO_STVNS in different real traces and measure it through extensive experiments. The experimental results show that compared with state-of-art methods, the average total cost and average TSF of MO_STVNS are reduced by 33.75% and 60.67% respectively. [ABSTRACT FROM AUTHOR]

With the prompt development of network technology, Internet of Things (IoT), and the speedy enhancement of network performance, numerous network behaviors, including traffic management, are also growing fast. Therefore, the monitoring technology under high-speed network traffic is crucial and essential for quality of services. In fact, monitoring technology essentially helps in understanding the network behavior better; and effectively increase the abilities to monitor, control, and manage the Internet and huge amount of data. The hash technology is usually used for network monitoring and management. In this paper, we investigate how the high-performance hash technology can be used, and improved, for high-speed networks in order to meet the requirements of increasingly challenging fields of traffic detection and monitoring. Moreover, we propose an improved hash function algorithm based on the original hash function to effectively improve the performance of the hash, and enhance the technology's ability to monitor current high-speed network traffic in the field of IoT systems. The experimental results show that the performance of the improved hash technology can be increased by 35–45%, compared with the traditional hash algorithm, and this degree of improvement is undoubtedly huge for the current high-speed network traffic. Moreover, the improved hash algorithm is approximately 11.5% faster than the classical hash function. [ABSTRACT FROM AUTHOR]

Due to the limitation of computing resources and storage resources, mobile edge computing cannot cope with the massive data generated by the Industrial Internet of Things (IIoT). However, traditional mobile cloud computing has rich computing resources. Therefore, through the construction of cloud computing and edge computing collaborative system, high bandwidth and low latency network services for the Internet of things can be provided. Based on Lyapunov optimization theory, the resource allocation and power consumption in cloud-edge collaborative system are investigated in this paper. Firstly, a cloud-edge collaboration architecture is proposed, then by establishing the dynamic queue model of cloud computing server and edge computing server, and combining with the system power function to form a drift plus penalty function framework, the problem is reduced to a constrained optimization problem. Finally, the offloading algorithm based on congestion is given. The simulation results show that the proposed optimization scheme can effectively reduce the overall power consumption and congestion of cloud-edge collaborative system. [ABSTRACT FROM AUTHOR]

Containerization technology utilizes operating system level virtualization to package applications to run with required libraries and are isolated from other processes on the same host. Lightweight and quick deployment make containers popular in many data centers. Running distributed applications in data centers usually involves multiple clusters of machines. Docker Swarm is a container orchestration tool for managing a cluster of Docker containers and their hosts. However, Docker Swarm's scheduler does not consider resource utilization when placing containers in a cluster. This paper first investigated performance interference in container clusters. Our experimental study showed that distributed applications' performance can be degraded when co-located with other containers which aggressively consume resources. A new scheduler is proposed to improve performance while keeping high resource utilization. The experimental results demonstrated that the proposed prototype with machine learning based clustering algorithms could effectively improve distributed applications' performance by up to 14.5% with an average at around 12%. This work also provides theoretical bounds for the container placement problem. [ABSTRACT FROM AUTHOR]

Frequent itemset mining (FIM) is one of the prominent techniques to extract knowledge from transactional databases. Finding frequent itemsets becomes tiresome while dealing with large-scale datasets due to the enormous demand for computational resources. Cluster-based versions of FIM algorithms become a natural choice to make FIM algorithms efficient and scalable for large-scale datasets and to meet the ever-growing data needs. A variety of MapReduce-based algorithms on the Hadoop cluster has been developed to meet the expectations. Due to the iterative nature of the FIM algorithms, they are still unable to perform adequately. Bottlenecks associated with MapReduce-based FIMs include challenges originating from adapting FIM algorithms in parallel and distributed contexts, as well as reading and publishing intermediate results to HDFS, which necessitates significant disc and communication I/O. Many FIM algorithms have been redesigned on Spark to achieve efficiency for large-scale datasets by utilizing its in-memory processing capabilities. However, Spark-based FIM adaptations still face the same challenges except achieving memory efficiency. The limitations associated with basic FIM algorithms, such as repeated scanning of input data, massive candidate generation, and maintaining large conditional trees in memory, still need to be addressed. Also, tuning of Spark's shuffle behavior becomes important to control the communication overhead. In this paper, we propose a Spark-based algorithm, namely PartEclat. It uses the Eclat method in combination with the partitioning technique to gain efficiency and scalability. Vertical data format helps to avoid repeated scanning of input data to calculate individual support values. It utilizes the benefits of the partitioning technique to limit the movement of key-value pairs across the cluster nodes (shuffle overheads) during iterations. Also, it helps to deal efficiently with the memory requirements to handle large Transaction ID (TID) sets and computational overhead imposed in computing the intersection of these large TID sets. In-depth experiments with benchmark datasets have been conducted to gain insight into the efficiency and scalability performance of the algorithm. Experimental results show that the proposed scheme outperforms other Spark-based Eclat variants by reducing network and computing loads by approx. 25–40%. [ABSTRACT FROM AUTHOR]

The multiverse optimizer (MVO) is one of the most trending algorithms used nowadays. The searching space in MVO is restricted by the best solution only, leading to a poor searching domain, therefore, a long searching time. This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method solves the problem of the average positioning (AP) by dynamically enhancing the equation of updating the AP based on the best and the second-best available solutions. To evaluate The proposed IMOMVO, several datasets scenarios containing various tasks and virtual machines (Vms) were used to test the approach's capability. Standard evaluation metrics are used to validate the results of the proposed method; task execution time, throughput, and the Vms processing power. The proposed method obtained better results according to the evaluation measures than other state-of-the-art methods. The execution time achieves less time when compared to the mMVO as the proposed method achieved 186.33 s for executing 100 tasks and 934.92 for executing 600 tasks. The throughput results also achieved astonishing results as for 100 tasks, the throughput achieved 0.19, and the Vm processing power for the proposed method was 0.25 Kw for executing 100 tasks. [ABSTRACT FROM AUTHOR]

Cloud Computing provides a promising platform for executing large scale workflow applications with enormous computational resources to offer on-demand services. Tasks in a workflow may need different type of computing resources such as storage, compute and memory type. However, inappropriate selection of these resources may lead to higher makespan and resource wastage. In this paper, we propose an effective two-phase algorithm for provisioning of cloud resources for workflow applications by using its structural features to minimize makespan and resource wastage. The proposed approach considers the nature of the tasks which may be compute intensive, memory intensive or storage intensive. We assume a realistic cloud model similar to Amazon EC2 that provides virtual machines for different types of workloads. Most importantly, the workflow model used in our approach is assumed to contain limited information about the task which is applicable for real situation. The performance of the proposed work is measured using five benchmark scientific workflows. The simulation results show that the proposed approach outperforms two existing algorithms for all these workflows. [ABSTRACT FROM AUTHOR]

Nowadays, the multi-cloud environment is gaining a momentum since it provides its users with reduced costs, greater flexibility and elasticity, high availability and better fault-tolerance. Despite these advantages, these environments present many challenges, including service management and interoperability issues. Due to the diversity of cloud service providers, their heterogeneity and the exponential growth of offered services, user requirements cannot be adequately met. These problems, if not addressed effectively, can have a negative impact on the cloud environment as well as on the user experience. Thus, to answer the service management problem in multi-cloud environments, we propose in this paper a threefold contribution (i) a noval cloud federation architecture, (ii) a suitable service management system and (iii) a service publication algorithm in order to manage, store and retrieve efficiently cloud services within the federation. Our solution consists in combining several concepts including trust, clustering and , ontologies. Our aim is to automate the management process, maximise the profit and ensure a better user experience. Our experiments highlight the effectiveness of the proposed cloud federation architecture as well as the deployed management system in optimizing the storage space and answering effectively and rapidly users' requests. [ABSTRACT FROM AUTHOR]

Recently, phishing attacks have become one of the most prominent social engineering attacks faced by public internet users, governments, and businesses. In response to this threat, this paper proposes to give a complete vision to what Machine learning is, what phishers are using to trick gullible users with different types of phishing attacks techniques and based on our survey that phishing emails is the most effective on the targeted sectors and users which we are going to compare as well. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails that are growing at an alarming rate in recent years, thus will discuss the techniques of mitigation of phishing by Machine learning algorithms and technical solutions that have been proposed to mitigate the problem of phishing and valuable awareness knowledge users should be aware to detect and prevent from being duped by phishing scams. In this work, we proposed a detection model using machine learning techniques by splitting the dataset to train the detection model and validating the results using the test data , to capture inherent characteristics of the email text, and other features to be classified as phishing or non-phishing using three different data sets, After making a comparison between them, we obtained that the most number of features used the most accurate and efficient results achieved. the best ML algorithm accuracy were 0.88, 1.00, and 0.97 consecutively for boosted decision tree on the applied data sets. [ABSTRACT FROM AUTHOR]

In cloud-based recommendation system, the feature selection is implemented to reduce the large dimension of the cloud data. The feature selection increases the performance of the recommendation system without affecting the accuracy of the system. In this paper two filter model based algorithms SFS and MSFS are proposed to extract the necessary features for the recommendation system. The state of the art Naive bayes classification algorithm is used to evaluate the performance of the feature selection algorithm. The bench mark datasets Newsgroups, WebKB and Book Crossing are used for performance evaluation. The experimental results show that the proposed algorithm is superior to the existing feature selection algorithms T-Score, Information Gain and Chi squared. [ABSTRACT FROM AUTHOR]

This research paper proposes a novel approach named priority-based load balancing (PLB) for cloud computing environment. The PLB provides a resilient and adaptive task scheduling using multi-queues. Numerous strategies have already been proposed in the past researches to prioritize the tasks and mapping all the tasks to different resources available on the cloud. There is still a hindrance in the performance due to the negligible attention paid to the unused resources and tasks having low priority, eventually leading to starvation problem. To this end, the PLB algorithm has been partitioned into four sub-procedures, namely (i) Starvation-free task allocation, (ii) Inserting tasks into the dispatcher, (iii) Re-ordering tasks inside the queues and eventually, (iv) Mapping tasks onto the Virtual Machines (VMs) calculating the cost incurred for all the corresponding VMs. The sole motivation of this research work is to optimize the performance parameters by allocating all the jobs to all the available resources in the workflow model. It also consolidates the job categorization in the priority-based multi-queues, while filtering tasks from all the queues to overcome the deprivation of low priority tasks. In this paper, a test-bed setup has been deployed using CloudSim 3 and TCS WAN emulator for experimentation and results evaluation. The experimental setup imbibes different aspects such as performance measures, average response time, makespan time in order to ascertain efficiency, resource utilization ratio and bandwidth of the workflow model. The obtained results are further compared with five different approaches including- First Come First Serve, Round Robin, Min–Min, Max–Min and ACO and it was observed that the proposed strategy yielded more efficiency and accuracy in most of the cases. The experimental results have been further validated and demonstrated in order to justify the claims of the proposed approach, being able to tackle out different priority tasks and resource allocation in a stable and optimum manner. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power. [ABSTRACT FROM AUTHOR]

The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these problems. We also designed training rules for the improved particle swarm optimization to train fuzzy neural network, and the hybrid algorithm is applied to solve the path planning problem of intelligent driving vehicles. The efficiency and practicability of the algorithm are proved by experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a new version of the power of two choices, SQ(d), load balancing algorithm. This new algorithm improves the performance of the classical model based on the power of two choices randomized load balancing. This model considers jobs that arrive at a dispatcher as a Poisson stream of rate λ n , λ < 1 , at a set of n servers. Using the power of two choices, the dispatcher chooses some d constant for each job independently and uniformly from the n servers in a random way and sends the job to the server with the fewest number of jobs. This algorithm offers an advantage over the load balancing based on shortest queue discipline, because it provides good performance and reduces the overhead in the servers and the communication network. In this paper, we propose a new version, shortest queue of d with randomization and round robin policies, SQ-RR(d). This new algorithm combines randomization techniques and static local balancing based on a round-robin policy. In this new version, the dispatcher chooses the d servers as follows: one is selected using a round-robin policy, and the d - 1 servers are chosen independently and uniformly from the n servers in a random way. Then, the dispatcher sends the job to the server with the fewest number of jobs. We demonstrate with a theoretical approximation of this approach that this new version improves the performance obtained with the classical solution in all situations, including systems at 99% capacity. Furthermore, we provide simulations that demonstrate the theoretical approximation developed. [ABSTRACT FROM AUTHOR]

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

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is made up of intelligent devices that interact with each other. It allows information to be gathered and shared by these devices. In addition, IoT now has a wide range of life applications such as business, logistics, health monitoring, smart ecosystem, and information concerning individuals, social and private environments. Security-related issues involved in such a communication network are drastically increasing day by day. Therefore, existing authentication and authorization concepts must grow along with technological evolution. This paper presents an artificial intelligence (AI)-assisted Bio-inspired algorithm for secure IoT communication networks (AI-BIAS) to improve future communication. The proposed framework is categorized into two sections: First, the Bio-inspired algorithm-assisted blockchain technology for authentication and authorization in the IoT communication network is presented. Further, the Artificial Intelligence algorithm monitors the proposed IoT communication network. The experimental results show that the proposed model enhances the accuracy ratio of 98.3%, average latency of 26 ms, average cost of 25.7%, authentication time 0.04 ms, multistep prediction's MAPE, and RMSE values are 0.7645 and 0.9878 when compared to other popular methods. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) algorithm is a nature-inspired algorithm that showed its efficiency for optimizations. However, the ABC algorithm showed some imbalances between exploration and exploitation. In order to improve the exploitation and enhance the convergence speed, a multi-population ABC algorithm based on global and local optimum (namely MPGABC) is proposed in this paper. First, in MPGABC, the initial population is generated using both chaotic systems and opposition-based learning methods. The colony in MPGABC is divided into several sub-populations to increase diversity. Moreover, the solution search mechanism is modified by introducing global and local optima in the solution search equations of both employed and onlookers. The scout bees in the proposed algorithm are generated similarly to the initial population. Finally, the proposed algorithm is compared with several state-of-art ABC algorithm variants on a set of 13 classical benchmark functions. The experimental results show that MPGABC competes and outperforms other ABC algorithm variants. [ABSTRACT FROM AUTHOR]

The hybrid neural network model proposed in this paper consists of two main parts: extracting local features of text vectors by convolutional neural network, extracting global features related to text context by BiLSTM, and fusing the features extracted by the two complementary models. In this paper, the pre-processed sentences are put into the hybrid neural network for training. The trained hybrid neural network can automatically classify the sentences. When testing the algorithm proposed in this paper, the training corpus is Word2vec. The test results show that the accuracy rate of text categorization reaches 94.2%, and the number of iterations is 10. The results show that the proposed algorithm has high accuracy and good robustness when the sample size is seriously unbalanced. [ABSTRACT FROM AUTHOR]

Stream processing is a new memory computing paradigm that deals with dynamic data streams efficiently. Storm is one of the stream processing frameworks, but the default stream processing scheduler of storm also has some problems. For example, it does not consider reducing the cost in the cloud environment while ensuring the performance requirements. In this paper, a cost-efficient scheduling algorithm for Storm framework (CE-Storm) is proposed to reduce cost while satisfying deadline constrain. First, a new cost-efficient model (including resources usage cost, energy cost and communication cost) based on Storm framework is built. Then, based on the cost model, a cost-efficient scheduling algorithm which integrated resource monitoring module and communication detection module is designed. The nodes in the cluster are prioritized according to the cost-efficient information, and the nodes with higher priority are assigned tasks first to minimize the total cost of the cluster. Furthermore, this algorithm also reduces the communication cost between nodes and improves the cost effectiveness of the Storm cluster. We have performed extensive experiments on Storm clusters using Hibench's workloads in cloud environment. The result shows that the cost consumption of Storm clusters in cloud environment is reduced by 19.25% on average compared with the traditional scheduling algorithm. In others words, the proposed algorithms effectively improve the cost efficiency of Storm cluster in the cloud environment while satisfying the performance constrains. [ABSTRACT FROM AUTHOR]

The most basic and significant issue in complex network analysis is community detection, which is a branch of machine learning. Most current community detection approaches, only consider a network's topology structures, which lose the potential to use node attribute information. In attributed networks, both topological structure and node attributed are important features for community detection. In recent years, the spectral clustering algorithm has received much interest as one of the best performing algorithms in the subcategory of dimensionality reduction. This algorithm applies the eigenvalues of the affinity matrix to map data to low-dimensional space. In the present paper, a new version of the spectral cluster, named Attributed Spectral Clustering (ASC), is applied for attributed graphs that the identified communities have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the goodness of the affinity matrix, the ASC algorithm will use the Topological and Attribute Random Walk Affinity Matrix (TARWAM) as a new affinity matrix to calculate the similarity between nodes. TARWAM utilizes the biased random walk to integrate network topology and attribute information. It can improve the similarity degree among the pairs of nodes in the same density region of the attributed network, without the need for parameter tuning. The proposed approach has been compared to other primary and new attributed graph clustering algorithms based on synthetic and real datasets. The experimental results show that the proposed approach is more effective and accurate compared to other state-of-the-art attributed graph clustering techniques. [ABSTRACT FROM AUTHOR]

Internet of things has become an essential principle of human life with the widespread acceptance of intelligent environments, where everyday objects can communicate through the internet. Mobility is the most critical factor in today's internet of things devices to apply in real-world applications. Also, proper routing protocol plays a vital role in communication and reduces devices' energy consumption. The clustering approach is one of the efficient routing techniques to improve energy consumption and enhance network life. Due to the NP-Hard nature of clustering, a krill herd optimization algorithm is proposed in this paper to select the cluster head nodes and intermediate nodes required for routing. The simulation results using NS-3 confirmed that the proposed technique performs better than particle swarm optimization and cuckoo search in terms of network lifetime. The proposed technique increases the total network lifetime by at least 11.1% compared to the current clustering methods. [ABSTRACT FROM AUTHOR]

Virtual machine abnormal behavior detection is an effective way to help cloud platform administrators monitor the running status of cloud platform to improve the reliability of cloud platform, which has become one of the research hotspots in the field of cloud computing. Aiming at the problems of high computational complexity and high false alarm rate in the existing virtual machine anomaly monitoring mechanism of cloud platform, this paper proposed a two-stage virtual machine abnormal behavior-based detection mechanism. Firstly, a workload-based incremental clustering algorithm is used to monitor and analyze both the virtual machine workload information and performance index information. Then, an online anomaly detection mechanism based on the incremental local outlier factor algorithm is designed to enhance detection efficiency. By applying this two-phase detection mechanism, it can significantly reduce the computational complexity and meet the needs of real-time performance. The experimental results are verified on the mainstream Openstack cloud platform. [ABSTRACT FROM AUTHOR]

The unprecedented growth of energy consumption in data centers created critical concern in recent years for both the research community and industry. Besides its direct associated cost; high energy consumption also results in a large amount of CO2 emission and incurs extra cooling expenditure. The foremost reason for overly energy consumption is the underutilization of data center resources. In modern data centers, virtualization provides a promising approach to improve the hardware utilization level. Virtual machine placement is a process of mapping a group of virtual machines (VMs) onto a set of physical machines (PMs) in a data center with the aim of maximizing resource utilization and minimizing the total power consumption by PMs. An optimal virtual machine placement algorithm substantially contributes to cutting down the power consumption through assigning the input VMs to a minimum number of PMs and allowing the dispensable PMs to be turned off. However, VM Placement Problem is a complex combinatorial optimization problem and known to be NP-Hard problem. This paper presents an extensive review of virtual machine placement problem along with an overview of different approaches for solving virtual machine placement problem. The aim of this paper is to illuminate challenges and issues for current virtual machine placement techniques. Furthermore, we present a taxonomy of virtual machine placement based on various aspects such as methodology, number of objectives, operation mode, problem objectives, resource demand type and number of clouds. The state-of-the-art VM Placement techniques are classified in single objectives and multi-objective groups and a number of prominent works are reviewed in each group. Eventually, some open issues and future trends are discussed which serve as a platform for future research work in this domain. [ABSTRACT FROM AUTHOR]

In this paper, a discrete differential algorithm is proposed to extract data features. The strategy of regional tourism development, features extraction and the technology of retaining the subject information of industry characteristic data are analyzed to reduce the feature space dimension and achieve data dimension reduction through the key factors in the development of tourism industry in the region. In this paper, a kind of distributed feature selection system is obtained based on local fisher discriminant according to the feature selection method based on data structure and the feature of modal distribution in industrial characteristic data. The experimental results show that this feature can make the space describe the data distribution structure accurately after dimension reduction. [ABSTRACT FROM AUTHOR]

At present, the intelligent translation of computer is developing rapidly. In this paper, the overall design of interactive intelligent auxiliary translation platform based on multi-strategy is studied. In this paper, the algorithm of similarity of sentences is deeply studied. According to the needs of English–Chinese translation memory system, the similarity between sentences is calculated from the sentence syntax and semantic based on syntactic and semantic English sentence similarity calculation method. Finally, the author uses the English–Chinese translation of Navigator 7.0 manual to examine the effect of the algorithm platform developed in this paper. The experimental results show that the proposed algorithm improves the speed of translation by about 35%. [ABSTRACT FROM AUTHOR]

This paper proposes a navigation algorithm for the mobile robot which can save lots of running distances, running time and mechanical loss of the mobile robot, usually the real mobile robot is established on the embedded system and works in the indoor and dynamic environment, so this paper proposes a new algorithm to obtain the accurate moving direction of the mobile robot where the GPS and corresponding sensors are unavailable, it also proposes a navigation algorithm for the mobile robot in the dynamic environment based on the PF-SLAM algorithm which combines the Particle Filter and FAST-SLAM algorithm, they are very suitable running on the embedded system because it avoids the large complex computation, finally the simulation of the proposed algorithm is shown in the paper, the corresponding real experiment is carried out to prove the effectiveness of the algorithm for the mobile robot in our laboratory dynamic environment. [ABSTRACT FROM AUTHOR]