Optimising concreting equipment operations in India: An artificial intelligence and reliability-based approach

• Monitors moisture in concrete via IoT sensors for 21 days. • Forecasts moisture using Holt-Winters’ method with high accuracy. • Reduces construction time and ensures durable, reliable concrete. • Practical application with embedded sensors and predictive analytics. The quality control of concrete is a significant barometer for ensuring a timely and robust construction program. IoT provides exciting possibilities of ensuring real time data is collected and analyzed to ensure actionable insights and facilitate decision-making in the construction industry. This study sheds some light on exactly how this can be done using wireless sensors embedded in a concrete sample to provide a real-time moisture profile of the concrete sample as it cures and attains strength over a period of 21 days. Time series forecasting using Holt-Winters’ method is then used to forecast the future moisture profile of the concrete sample with a high degree of accuracy. This has significant implications in reducing construction time while also ensuring durable concrete with high reliability. The real-time monitoring and prediction of water content in concrete members through time series modeling provides a valuable tool for construction professionals to ensure the quality and longevity of their buildings. The proposed method can be applied in practice by using embedded sensors to gather data and analyzing it using predictive analytics.

Although recent advancements in the information and communication technologies have facilitated the deployment of IoT-enabled telecare medical information systems (TMISs), security concerns have greatly hindered their acceptability. A good deal of session key generation protocols (SKGPs) has been proposed recently to bring a secure medium for medical data transfer in the TMISs. Nevertheless, careful investigation indicates that there are two critical gaps in current schemes. First and the foremost, most lightweight schemes are insecure against advanced threats and relatively-secure schemes have a delay in each shared key generation. Second, most schemes are designed using the RSA or ECC cryptosystem, making them insecure against quantum attacks. To fill these gaps, this paper proposes a low-latency high-reliability SKGP for resource-limited IoT devices using only symmetric encryption/decryption and hash functions. Since the proposed scheme is only based on the AES-256 and is free from the RSA or ECC cryptosystem, it can also resist quantum attacks. To support the security and efficiency claims, we have provided extensive formal security analyses and all-inclusive literature review. Our comparative results indicate that the proposed protocol is the best compared to even top 10 protocols considering both execution and communication costs. Our implementation results also indicate that the protocol execution on the selected IoT device only takes almost 80:μs and it is scalable enough so that servers can service thousands of devices.

Fog computing (FC) has emerged as a promising platform for processing delay-sensitive Internet of Things (IoT) tasks. Fog nodes (FNs) are prone to failure, which necessitates the introduction of a mechanism to increase FC dependability. Leveraging a failure-aware task scheduling approach is one way to make FC a dependable platform for IoT task execution. However, the dynamicity, heterogeneity, and execution uncertainty inherent in FC make designing such a system difficult. To this end, we propose a learning-based dynamic fault-tolerant scheduling approach that considers the three intrinsic characteristics of the IoT-Fog environment to improve fog service reliability for time-constrained IoT tasks. The proposed approach provides a hybrid fault-tolerance solution based on proactive, reactive and replication failure handling approaches. It also integrates a dynamic task runtime and energy-awareness in the decision-making to address the dynamicity, uncertainties, and energy consumption challenges in the FC environment. To validate the performance of the proposed fault-tolerant task scheduling approach, we carried out extensive experimental analysis and compared it with several baseline approaches. The proposed task scheduling approach improves fog service reliability, energy efficiency, and resource utilisation compared to baseline methods.

The distance vector hop algorithm is commonly used for sensor node localization. However, its high localization accuracy error and stability issues make it unsuitable for many applications. To overcome these concerns, this article proposes a new function and binary distance vector hop (FBDV-Hop) algorithm with binary controllers and function correction methods while considering the application requirement. In FBDV-Hop, binary controllers are designed to analyze the optimization effect of the module fully and make it adaptable to diverse scenarios. The correction strategies were based on average hop distance measurement, estimated distance, equation composition method, and localization after supplementary correction, which were divided into four modules in accordance with the module error sources of different design correction functions. The simulation experiment was designed to analyze the principle of the role of each module in depth and achieve the optimal optimization effect. The experimental results show that the localization error optimization rate under the FBDV-Hop algorithm was more than 70%, and the optimization rate, stability, effectiveness, and adaptability of the algorithm were considerably greater than the baseline algorithms.

The fog-based IoT (IoT-Fog) network, which combines Internet of Things (IoTs) and fog computing, has quickly become a key enabler of emerging applications such as smart transportation, smart homes, and smart grids. It has, however, introduced an IoT device cloning attack, which allows adversaries to mount a range of attacks on IoT networks. IoT devices have a built-in security system, making them an easy target for hackers. Therefore, it is critical to reliably identify and isolate cloned IoT devices to protect IoT networks from adversaries taking control of the network. Existing approaches do not address the problems of compromised device and repeated cloned device simultaneously without requiring the device’s exact locations. To this end, we propose a new low complexity IoT device cloning detection approach called Maximum Distance Separable (MDS) which is appropriate for IoT-Fog architecture. We validated the efficiency of MDS analytically and evaluated its performance by comparing it to state-of-the-art approaches in terms of detection rate, communication overhead, memory overhead, and computation overhead. The results indicate that the proposed approach has a very high detection rate, negligible communication and memory overhead and promising detection time.

The Industrial Internet of Things (IIoT) has developed into a promising technology that raises the level of productivity and automation for smart manufacturing. Due to the growing importance of cross-domain (e.g., factory) collaboration in manufacturing, it is now commonplace for IIoT devices from different domains to communicate with one another, raising significant privacy and security risks. Recent studies have utilized blockchain in their authentication scheme to build trust across multiple domains. This integration, however, resulted in substantial communication, computation, and storage overheads, as well as vulnerability against Distributed Denial-of-Service (DDoS) attacks. To overcome these issues, in this article, we propose an efficient and highly secure blockchain-assisted authentication scheme using a combined off-chain and on-chain approach. The suggested protocol just employs one domain server for authentication of both local and foreign domain devices. The distinctive security features and applicability of the proposed protocol are demonstrated by formal security proof and performance analysis as well as comparisons with leading research works.

Q-rung orthopair hesitant fuzzy set (q-ROHFS) is a potent and effective technique for dealing with more general and complex uncertainty. Multiple attribute decision-making (MADM) under complex uncertainty has been a key research issue. However in the existing MADM approaches, the fuzzy entropies involve much higher hesitancy degree loss and the fuzzy measure of attributes can not be determined objectively. Also these existing MADM methods under complex uncertainty have high data redundancy and low computational efficiency. In order to solve these problems, this paper proposes a novel q-rung orthopair hesitant fuzzy information MADM method based on the Choquet integral. Firstly, we give the axiomatic definition of q-rung orthopair hesitant fuzzy entropy (q-ROHFE) by extending dual hesitant fuzzy information entropy and derive the fuzzy entropy construction theorem and the two related q-ROHFE formulas, which greatly reduces the loss of hesitancy degree resulting from the existing fuzzy entropy. Secondly, combined with λfuzzy measure and proposed q-ROHFE, a constrained nonlinear fuzzy measure optimization model for q-rung orthopair hesitant fuzzy decision making is presented, which addresses the difficulty that existing research cannot determine the fuzzy measure of attributes under fuzzy MADM. Thirdly, an improved Choquet integral-based VIKOR approach based on the fuzzy measure computed by the model is developed. Finally, two real-life cases are shown to fully illustrate the suggested approach. Experiment results demonstrate that the proposed fuzzy entropy has much less hesitancy degree loss and the proposed approach significantly increases computational efficiency while reducing data redundancy. And our method has strong adaptability and scalability.

The Internet of Things (IoT) has advanced smart grid (SG) infrastructure by providing smart meters (SMs) with enhanced capabilities such as the ability to leverage the Internet platform for bidirectional information exchange. Cryptographic keys are necessary for securely exchanging sensitive information between SMs and energy providers. To manage these keys, a secure key management protocol (KMP) with little overhead and influence on the SG’s overall performance is necessary. Although various KMPs are available for IoT-enabled SG environments, exiting solutions have several flaws in terms of certificate revocation, security requirements, and overall SG performance. To address these challenges, this paper proposes a blockchain-based computationally-efficient and highly-secure KMP for IoT-enabled SG environments. We show that, compared to existing solutions, the proposed KMP has better SM side efficiency with improved security and more properties such as perfect forward secrecy, conditional anonymity, and simple SM revocation.

In the Internet of Vehicles, low service delay and fast response are two essential factors to ensure the safety and smooth operation of vehicle networking. As core technologies, the 5G and edge computing networks play a fundamental role in reducing the pressure on the backbone network of vehicle networking and decreasing the service exchange delay. The previous edge server placement strategy can not be directly applied to deploying vehicle networking services, resulting in the degradation of system performance and user quality of experience. To solve the above problem, we propose a reliable edge server deployment algorithm called CFD based on Deep Deterministic Policy Gradient (DDPG). Firstly, the Canopy algorithm is used to cluster the location information of roadside units, and the initial cluster number is obtained. Then, the fuzzy C clustering algorithm (FCM) is leveraged to remove the "noise" and acquire the roadside units’ initial division and priority matrix. Finally, based on the DDPG algorithm, the optimal division of roadside units is obtained, and the cluster center is utilized as the deployment location of the edge server. Many experiments have been conducted, and the results show that, compared with the benchmark algorithm, the CFD algorithm improves the load balancing degree by 25%.

Many organizations leverage Distributed Stream processing systems (DPSs) to get insights from the data generated by different users/devices, e.g., the Internet of Things (IoT) devices or user clicks on a website, on geographically distributed datacenters. The worker nodes in such environments are connected through Wide Area Network (WAN) links with various delays and bandwidth. Therefore, minimizing the execution latency of a task on the worker nodes while using the links with enough bandwidth and lower cost to steer the traffic of the applications is a challenging task. In this paper, we formulate the worker node placement for a geo-distributed DSPs network as a multi-criteria decision-making problem. Then, we propose an additive weighting-based approach to solve it. The users can prioritize the worker node placement according to the network-relevant parameters. We also propose a framework that can be integrated with the current DPSs to execute the tasks. We test our placement approach on three widely used stream processing systems, i.e., Apache Spark, Apache Storm, and Apache Flink, on three custom graphs adopted from the real cloud providers. We run the streaming query of the Yahoo! streaming benchmark on these three DPSs. The experimental results show that our approach improves the performance of Spark up to 2.2x–7.2x, Storm up to 1.2x–3.4x, and Flink up to 1.4x–3.3x compared with other placement approaches, which makes our framework useful for use in practical environments.

Critical infrastructures across many industries, such as smart water treatment and distribution networks (SWTDNs) and power generation and public transport networks, depend on the supervisory control and data acquisition (SCADA) system. However, being the core component of the critical infrastructures, it has made the SCADA-based SWTDN system an attractive target for cyberattacks. A successful attack on the SCADA will have a devastating impact on an SWTDN in terms of proper operations; therefore, safeguarding the SCADA from cyberattacks is of paramount. With the increasing cyberattacks on SWTDN, both in number and sophistication, the need to detect these attacks early has become a subject of great interest among practitioners and researchers. To this end, we propose a novel strategy, based on a semisupervised approach. Two semisupervised approaches, including unsupervised learning and deep learning-based approaches, have been proposed. The proposed approaches can involve learning dynamic cyberattack patterns from unlabeled data in an SWTDN. We validate the proposed semisupervised approach experimentally using an operational water treatment plant testbed. The proposed approach achieved almost 100% accuracy and substantially outperforms the existing baseline approaches used in this article. The outcome of the experiment is encouraging and demonstrates the potential use of the semisupervised approach for security control in smart water distribution.

In a typical formulation of decision-making under uncertainty, a decision-maker must choose a single-optimal option among many possible options. However, the problem of selecting a unique and optimal choice has remained a significant challenge to solve. In this article, we propose a new interval-valued intuitionistic fuzzy soft set (IVIFSS) based decision-making approach to address this problem. The proposed approach is based on the choice value and score value of membership/nonmembership degrees. Furthermore, three parameter reduction algorithms are proposed. We apply the proposed approaches on a real application to demonstrate their working and effectiveness. We also compare the proposed approach against the adjustable IVIFSSs approach and show that the proposed approach has lower computation overhead and enable a decision-maker to choose top options to make a proper decision.

The Industrial Internet of Things (IoT) typically consists of several thousands of heterogeneous devices, such as sensors, actuators, access points, machinery, end-users' handheld equipment, and supply chain. In such an industrial environment, a multitude of data is generated from massive IoT devices, e.g., sensors for monitoring the environment, reading temperature, and gauging pressure. Most of the data are from delay-sensitive and computation-intensive applications, such as real-time manufacturing and automated diagnostics, which require big data analytics with low latency. Machine learning (ML) has been witnessed as an efficient solution for big data analytics. The majority of such ML algorithms are centralized methods, meaning that they first gather data from different users for use as a training dataset, which is placed on the ML server, and then build a model to classify the new data samples by applying the ML algorithms to this training dataset. However, the access to these datasets in the centralized ML methods raises concerns about data privacy for users. Federated learning (FL) was designed to protect data privacy to address a part of these issues. In FL, each participant uses a global training model without uploading their private data to a third-party server. Compared with the conventional ML, FL can preserve data security, especially in terms of participant data during the learning process. In particular, FL can also help in updating server-side data for the global model, and the participant is not required to provide their data. However, in FL, individual computing units may show abnormal actions, such as faulty software, hardware invasions, unreliable communication channels, and malicious samples deliberately crafting the model. To mitigate these challenges, we require robust policies to control the learning phases in FL. Motivated by the abovementioned issues, this special section solicits original research and practical contributions that advance the security and privacy of the FL solutions for industrial IoT applications as follows.

Differential evolution (DE) algorithm can be used in edge/cloud cyberspace to find an optimal solution due to its effectiveness and robustness. With the rapid increase of the mobile traffic data and resources in a cybertwin-driven 6G network, the DE algorithm faces some problems such as premature convergence and search stagnation. To deal with the problems mentioned above, in this article, an improved DE algorithm based on hierarchical multistrategy in a cybertwin-driven 6G network (denoted by DEHM) is proposed. Based on the fitness value of the population, DEHM classifies the population into three sub-population. Regarding each sub-population, DEHM adopts different mutation strategies to achieve a tradeoff between convergence speed and population diversity. In addition, a new selection strategy is presented to ensure that the potential individual with good genes is not lost. Experimental results suggest that the DEHM algorithm surpasses other benchmark algorithms in the field of convergence speed and accuracy. The proposed DEHM is expected to be leveraged in edge/cloud cyberspace, aiming at reducing energy costs and improving resource utilization.

Fog computing has become a platform of choice for executing emerging applications with low latency requirements. Since the devices in fog computing tend to be resource constraint and highly distributed, how fog computing resources can be effectively utilized for executing delay-sensitive tasks is a fundamental challenge. To address this problem, we propose and evaluate a new task scheduling algorithm with the aim of reducing the total system makespan and energy consumption for fog computing platform. The proposed approach consists of two key components: 1) a new bio-inspired optimization approach called Ant Mating Optimization (AMO) and 2) optimized distribution of a set of tasks among the fog nodes within proximity. The objective is to find an optimal trade-off between the system makespan and the consumed energy required by the fog computing services, established by end-user devices. Our empirical performance evaluation results demonstrate that the proposed approach outperforms the bee life algorithm, traditional particle swarm optimization and genetic algorithm in terms of makespan and consumed energy.

With Cloud computing becoming mainstream for the execution of various applications, the multi-objective scheduling algorithms for providing the most suitable services to users have gained much attention. As provisioning Cloud services that satisfy end-users quality of service (QoS) requirements is complex and challenging, scheduling algorithms for cloud computing tend to focus on optimizing the execution cost or the execution time within user-defined deadline constraints. This paper addresses the problem of efficiently allocating Cloud services among competing jobs to achieve multiple end-users QoS. We design and develop a framework called Autonomic Resource Provisioning and Scheduling (ARPS) framework. ARPS framework has the decision-making capability to schedule the jobs at the best resources within the deadline and optimizes both the execution time and the cost simultaneously. The ARPS framework is also integrated with the spider monkey optimization (SMO) algorithm based scheduling mechanism. Our proposed mechanism is intended to solve a multi-objective optimization problem, including minimizing processing time, cost, and energy consumption. We study the effectiveness of the proposed scheduling mechanism through extensive simulation analysis using Cloudsim To assess the relative performance of our method, we compare it against four existing mechanisms. Experimental results show that the proposed mechanism outperforms its counterparts.

Virtualized networked datacenters (VNDCs) are gaining considerable attention for stochastic task execution under real-time constraints. However, the problem of efficiently minimizing the high energy consumption while ensuring high quality of service (QoS) in VNDCs has not been fully addressed. Although many solutions have been proposed to address this challenge, they are not efficient and only consider one or two of the energy consuming resources of VNDCs. To this end, an adaptive energy-aware algorithm,MCEC, that efficiently reduces the energy consumption of VNDCs while ensuring high QoS is proposed. Different from the existing approaches, the MCEC algorithm considers energy consumed by computing resources, virtual machine (VM) reconfiguration, communication resources and storage media resources while meeting user QoS requirements defined in the service level agreement (SLA). To validate the effectiveness of our algorithm, we carried out extensive experiments and compared the performance of our algorithm with existing baseline algorithms. The results of the experiments show that our algorithm substantially outperforms the baseline algorithms with respect to reducing energy consumption while respecting the service level agreement.

Emerging applications such as those running on the Internet of Things (IoT) devices produce constant data streams that need to be processed in real-time. Distributed stream processing systems (DSPs), with geographically distributed cluster networks interconnected via wide area network (WAN) links, have recently gained interest in handling these applications. How-ever, these applications have stringent requirements such as low-latency and high bandwidth that must be guaranteed to ensure the quality of service (QoS). These application requirements raise fundamental DSPs resource management and scheduling challenge. In this paper, we formulate the problem of placement of worker nodes on a geo-distributed DSPs cluster network as a multi-criteria decision-making problem and propose an additive weighting-based approach to solve it. The proposed solution finds the trade-off among different network parameters and allows executing the tasks according to the desired performance metrics. We evaluated the proposed approach using the Yahoo! streaming benchmark on a testbed and compare it against mechanisms deployed in Apache Spark, Apache Storm, and Apache Flink. The results of the evaluation show that our approach improves the performance of Spark up to 2.2x-7.2x, Storm up to 1.2x-3.4x, and Flink up to 1.4x-3.3x compared to other approaches, which makes our approach useful for use in practical environments.

Summary Many emerging applications such as social networks have prompted remarkable attention in graph data analysis. Graph data is typically high‐dimensional in nature, and dimensionality reduction is critical regarding storage, analysis, and querying of such data efficiently. Although there are many dimensionality reduction methods, it is not clear to what extent the performances of the various dimensionality reduction techniques differ. In this article, we review some of the well‐known linear dimensionality reduction methods and perform an empirical analysis of these approaches using large multidimensional graph datasets. Our results show that in linear unsupervised learning methods, the principal component analysis, singular value decomposition, and neighborhood preserving embedding methods achieve better retrieval data performance than other methods of the statistical information category, dictionary methods, and embedding methods, respectively. Regarding supervised learning methods, the experimental results demonstrate that linear discriminant analysis and partial least squares presented almost similar results.

The rapid development of Internet of Things (IoT) platforms provides the industrial domain with many critical solutions, such as joint venture virtual production systems. However, the extensive interconnection of industrial systems with corporate systems in industrial Internet of Things (IIoT) networks exposes the industrial domain to severe cyber risks. Because of many proprietary multilevel protocols, limited upgrade opportunities, heterogeneous communication infrastructures, and a very large trust boundary, conventional IT security fails to prevent cyberattacks against IIoT networks. Recent secure protocols, such as secure distributed network protocol (DNP 3.0), are limited to weak hash functions for critical response time requirements. As a complementary, we propose an adaptive trust boundary protection for IIoT networks using a deep-learning, feature-extraction-based semisupervised model. Our proposed approach is novel in that it is compatible with multilevel protocols of IIoT. The proposed approach does not require any manual effort to update the attack databases and can learn the rapidly changing natures of unknown attack models using unsupervised learnings and unlabeled data from the wild. Therefore, the proposed approach is resilient to emerging cyberattacks and their dynamic nature. The proposed approach has been verified using a real IIoT testbed. Extensive experimental analysis of the attack models and results shows that the proposed approach significantly improves the identification of attacks over conventional security control techniques.

With many applications generating a large streaming data set, there is a critical need for scalable computing frameworks for processing it with low latency. Although there are various big data stream processing frameworks, they lack adequate virtualization and optimization mechanisms aimed at enhancing the process of big data stream processing. To address this problem, we propose a cloud-based framework that couples virtual machines and software-defined networks to enhance Cloud resource allocations to the applications with streaming data processing requirements. A novel Cloud resource allocation algorithm based on the proposed framework is also proposed. We validated the proposed resource allocation algorithm using CloudSIM and compared the proposed algorithm with baseline algorithms to determine its effectiveness. The results indicate that by using a virtual machine and an SDN controller, the virtual machine is able to handle 2000 requests at a maximum of 136 seconds.

One of the major challenges for safeguarding the security of big data in the cloud is how to detect and prevent malicious software (malware). Despite of the fact that security and privacy are critical issues in big data, more research needs to be done in this area. As malware can affect the reliability of the data and subsequently the reputation of the system, it is critical to detect and remove malware from a system as early as possible. Recently, ensembles that combine a set of classifiers have been proposed as an efficient approach for malware detection. Unfortunately, the size, meHA85-C0002-A008mory and processing requirements as well as the high cost of data transfer during training and operation make large ensemble classifiers unsuitable for big data in the cloud. To address this problem, we propose a new advanced ensemble pruning method, Hybrid Consensus Pruning (HCP), which is the first pruning algorithm that employs a fast consensus function to combine several classifier classes into one scheme. To test the effectiveness of the HCP method, we conducted experiments comparing its performance with Ensemble Pruning via Individual Contribution ordering (EPIC), Directed Hill Climbing Ensemble Pruning (DHCEP) and K-Means Pruning approaches for pruning very large ensemble classifiers for malware detection. The results of the experiments show that HCP achieved better results by producing better ensemble classifiers as compared to those created by EPIC, DHCEP and K-Means Pruning.

This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.

A clear trend in the evolution of network-based services is the ever-increasing amount of multimedia data involved. This trend towards big-data multimedia processing finds its natural placement together with the adoption of the cloud computing paradigm, that seems the best solution to cope with the demands of a highly fluctuating workload that characterizes this type of services. However, as cloud data centers become more and more powerful, energy consumption becomes a major challenge both for environmental concerns and for economic reasons. An effective approach to improve energy efficiency in cloud data centers is to rely on traffic engineering techniques to dynamically adapt the number of active servers to the current workload. Towards this aim, we propose a joint computing-plus-communication optimization framework exploiting virtualization technologies, called MMGreen. Our proposal specifically addresses the typical scenario of multimedia data processing with computationally intensive tasks and exchange of a big volume of data. The proposed framework not only ensures users the Quality of Service (through Service Level Agreements), but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies. To evaluate the actual effectiveness of the proposed framework, we conduct experiments with MMGreen under real-world and synthetic workload traces. The results of the experiments show that MMGreen may significantly reduce the energy cost for computing, communication and reconfiguration with respect to the previous resource provisioning strategies, respecting the SLA constraints.

An accurate prediction of breast cancer risk can enable physicians to detect the cancer at an early stage. This paper is focused on the problem of predicting breast cancer risk based on a subset of genes. We developed a breast cancer risk prediction model based on an ensemble of Deep Neural Network (DNN) and Support Vector Machine (SVM) approaches. The proposed model was evaluated based on microarray gene expression dataset using accuracy, precision, and recall matrices and compared it with existing work. The outcomes of the experiment show that the proposed approach can predict breast cancer much more accurately based on genes as compared to the existing models.

Malicious software (malware) pose serious challenges for security of big data. The number and complexity of malware targeting Android devices have been exponentially increasing with the ever growing popularity of Android devices. To address this problem, multi-classifier fusion systems have long been used to increase the accuracy of malware detection for personal computers. However, previously developed systems are quite large and they cannot be transferred to Android platform. To this end, we propose Iterative Classifier Fusion System (ICFS), which is a system of minimum size, since it applies a smallest possible number of classifiers. The system applies classifiers iteratively in fusion with new iterative feature selection (IFS) procedure. We carry out extensive empirical study to determine the best options to be employed in ICFS and to compare the effectiveness of ICFS with several other traditional classifiers. The experiments show that the best outcomes for Android malware detection have been obtained by the ICFS procedure using LibSVM with polynomial kernel, combined with Multilayer Perceptron and NBtree classifier and applying IFS feature selection based on Wrapper Subset Evaluator with Particle Swarm Optimization.

Electronic trading (e-trading) provides a virtual marketplace (e-Marketplace) where buyers and sellers can engage in business activities through electronic media rather than direct physical contact. Although negotiation is a fundamental component of e-trading, the critical risks of missing out on top utility offers that expire before client's negotiation deadline has not been addressed. In order to address these problems, we propose a mobile-agent based secure one-to-many bilateral e-trade negotiation framework that efficiently manages the risk of losing top utility offers and maximizes client's utility taking into account various temporal constraints. Theoretical and empirical analysis of the proposed approach is performed. We evaluated the performance of the proposed strategy in terms of client's utility and negotiation time and compared it with two baseline negotiation strategies. The experimental analysis shows that the proposed strategy maximizes client's utility, shortens negotiation time, and ensures adequate market search. Proofs of validity of the proposed utility function are presented. The security protocol is formally verified and the verification shows that the protocol is free of security flaws and hence, negotiation data are secured.

With the volume and variety of network attacks increasing, efficient approaches to detect and stop network attacks before they damage the system or steal data is paramount to users and network administrators. Although many different detection mechanisms have been proposed, exiting detection methods generally tend to successfully detect attacks only after the attacks have finished and caused damage to the system. As recent attacks employ polymorphism technology and complicated attack techniques, it has become even more difficult for these approaches to detect attacks in a timely manner. In this paper, we propose an efficient network attack detection algorithm called seed expanding (SE) that detects attacks before they damage the system. SE employs the Two-Seed-Expanding network traffic clustering scheme, which clusters attack traffic into different attack phases. First we pre-process the networks traffic, including constructing the network flow, changing continuous-valued attributes into nominal attributes by adopting the discretization method, and further turning into binary features. Then based on these features, SE computes a weight for each flow and iteratively selects seeds to expand until all flows are divided into clusters. To investigate the effectiveness of the proposed approach, we undertook extensive experimental analyses. The results of the experiment show that the pre-procession greatly improves clustering performance, and the Two-Seed-Expanding Algorithm is better than K-Means and other kinds of Seed-Expanding in attack-flow clustering. These cluster results can be further used in attack detection.

Applications and techniques

Summary The cyber security threats from phishing emails have been growing buoyed by the capacity of their distributors to fine‐tune their trickery and defeat previously known filtering techniques. The detection of novel phishing emails that had not appeared previously, also known as zero‐day phishing emails, remains a particular challenge. This paper proposes a multilayer hybrid strategy (MHS) for zero‐day filtering of phishing emails that appear during a separate time span by using training data collected previously during another time span. This strategy creates a large ensemble of classifiers and then applies a novel method for pruning the ensemble. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization‐based pruning. This paper introduces and investigates a multilayer hybrid pruning. Its application in MHS combines all three approaches in one scheme: ranking, clustering, and optimization. Furthermore, we carry out thorough empirical study of the performance of the MHS for the filtering of phishing emails. Our empirical study compares the performance of MHS strategy with other machine learning classifiers. The results of our empirical study demonstrate that MHS achieved the best outcomes and multilayer hybrid pruning performed better than other pruning techniques. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti‐virus engines employ a signature‐template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti‐virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression‐based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper‐based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute‐force search strategies and order of (m‐1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m‐1). The proposed approach uses multi‐linear regression and thep‐value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi‐collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine‐based template approach with complete data and provides a better statistical fitness. Copyright © 2016 John Wiley & Sons, Ltd.

Evolutionary algorithms (EAs) have recently been suggested as a candidate for solving big data optimization problems that involve a very large number of variables and need to be analyzed in a short period of time. However, EAs face a scalability issue when dealing with big data problems. Moreover, the performance of EAs critically hinges on the utilized parameter values and operator types, thus it is impossible to design a single EA that can outperform all others in every problem instance. To address these challenges, we propose a heterogeneous framework that integrates a cooperative co-evolution method with various types of memetic algorithms. We use the cooperative co-evolution method to split the big problem into subproblems in order to increase the efficiency of the solving process. The subproblems are then solved using various heterogeneous memetic algorithms. The proposed heterogeneous framework adaptively assigns, for each solution, different operators, parameter values and a local search algorithm to efficiently explore and exploit the search space of the given problem instance. The performance of the proposed algorithm is assessed using the Big Data 2015 competition benchmark problems that contain data with and without noise. Experimental results demonstrate that the proposed algorithm, with the cooperative co-evolution method, performs better than without the cooperative co-evolution method. Furthermore, it obtained very competitive results for all tested instances, if not better, when compared to other algorithms using lower computational times.

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to a serious consequences including patient death. Therefore, an approach for congestion estimation and control is imperative to enhance the network quality of service (QoS). In this paper, we propose a new fuzzy based congestion detection and control protocol for a WBAN application that monitors patient ECG signals remotely. The proposed system is able to detect link congestion by considering local information such as available bandwidth (BW) and end to end delay. The proposed protocol changes the rate of transferred ECG information and assigns priorities to patients based on their level of urgency. As a result, the proposed approach provides a better QoS for transmitting highly important ECG signs.

Due to recent technological advancements in wireless communications and low-power sensor devices, wireless body area networks (WBANs) has become increasingly popular in pervasive healthcare monitoring. However, continuously collecting patients' physiological signs will result in large amounts of monitored data that require a scalable architecture for storage and analysis. This fact motivates the integration of WBANs with Cloud technology to manage and store humongous data effectively. In this paper, we propose a Cloud-based WBAN framework for real-time health monitoring of patients. The significance of the proposed framework is the use of mobile technology and Cloud computing to provide services for the ill and elderly people in their independent living. This paper describes the general design of the proposed approach as well as a case study and implementation setup for the real-time monitoring and analysis process of Electromyography (EMG) healthcare system.

Summary There has been a growing interest in sharing and mining social network data for a wide variety of applications. In this paper, we address the problem of privacy disclosure risks that arise from publishing social network data. Specifically, we look at the vertex re‐identification attack that aims to link specific vertex in social network data to specific individual in the real world. We show that even when identifiable attributes such as names are removed from released social network data, re‐identification attack is still possible by manipulating abstract information. We present a new type of vertex re‐identification attack model called neighbourhood‐pair attack. This attack utilizes the information about the local communities of two connected vertices to identify the target individual. We show both theoretically and empirically that the proposed attack provides higher re‐identification rate compared with the existing re‐identification attacks that also manipulate network structure properties. The experiments conducted also show that the proposed attack is still possible even on anonymised social network data. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols.

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Workflow applications require workflow processing in which workflow tasks are processed based on their dependencies. With the emergency of complex distributed systems such as grids and clouds, efficient workflow scheduling (WFS) algorithms have become the core components of the workflow management systems (WfMS). Thus, WFS that allocates each task in the workflow to a relevant resource with the aim of improving system performance and end user satisfaction is fundamentally important. In this paper, we propose a new workflow scheduling algorithm called Layered Workflow Scheduling Algorithm (LWFS) for scheduling workflow applications. We studied the efficacy of the LWFS scheduling experimentally and compared its performance with approaches including Improved Critical Path using Descendant Prediction (ICPDP), Highest Level First with Estimated Time (HLFET), Modified Critical Path (MCP) and Earliest Time First (ETF). The results of the experiments show that the proposed approach outperforms other approaches.

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

Performance analysis of two-hop decode-amplify-forward relayed system in different fading conditions

Operating systems and programmes are more protected these days and attackers have shifted their attention to human elements to break into the organisation's information systems. As the number and frequency of cyber-attacks designed to take advantage of unsuspecting personnel are increasing, the significance of the human factor in information security management cannot be understated. In order to counter cyber-attacks designed to exploit human factors in information security chain, information security awareness with an objective to reduce information security risks that occur due to human related vulnerabilities is paramount. This paper discusses and evaluates the effects of various information security awareness delivery methods used in improving end-users’ information security awareness and behaviour. There are a wide range of information security awareness delivery methods such as web-based training materials, contextual training and embedded training. In spite of efforts to increase information security awareness, research is scant regarding effective information security awareness delivery methods. To this end, this study focuses on determining the security awareness delivery method that is most successful in providing information security awareness and which delivery method is preferred by users. We conducted information security awareness using text-based, game-based and video-based delivery methods with the aim of determining user preferences. Our study suggests that a combined delivery methods are better than individual security awareness delivery method.

Purpose – The purpose of this paper is to mitigate vulnerabilities in web applications, security detection and prevention are the most important mechanisms for security. However, most existing research focuses on how to prevent an attack at the web application layer, with less work dedicated to setting up a response action if a possible attack happened. Design/methodology/approach – A combination of a Signature-based Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS), namely, the Intelligent Intrusion Detection and Prevention System (IIDPS). Findings – After evaluating the new system, a better result was generated in line with detection efficiency and the false alarm rate. This demonstrates the value of direct response action in an intrusion detection system. Research limitations/implications – Data limitation. Originality/value – The contributions of this paper are to first address the problem of web application vulnerabilities. Second, to propose a combination of an SIDS and an AIDS, namely, the IIDPS. Third, this paper presents a novel approach by connecting the IIDPS with a response action using fuzzy logic. Fourth, use the risk assessment to determine an appropriate response action against each attack event. Combining the system provides a better performance for the Intrusion Detection System, and makes the detection and prevention more effective.

Data grids have been adopted by many scientific communities that need to share, access, transport, process, and manage geographically distributed large data collections. Data replication is one of the main mechanisms used in data grids whereby identical copies of data are generated and stored at various distributed sites to either improve data access performance or reliability or both. However, when data updates are allowed, it is a great challenge to simultaneously improve performance and reliability while ensuring data consistency of such huge and widely distributed data. In this paper, we address this problem. We propose a new quorum-based data replication protocol with the objectives of minimizing the data update cost, providing high availability and data consistency. We compare the proposed approach with two existing approaches using response time, data consistency, data availability, and communication costs. The results show that the proposed approach performs substantially better than the benchmark approaches.

There has been a rapid growth of interest in developing approaches that are capable of dealing with imprecision and uncertainty. To this end, an interval-valued fuzzy soft set (IVFSS) that combines soft set theory with interval-valued fuzzy set theory has been proposed to handle imprecision and uncertainty in applications such as decision-making problems. However, there has been little focus on parameter reduction of the interval-valued fuzzy soft sets, which is significant in decision-making problems. In this paper, we introduce four different definitions of parameter reduction in interval-valued fuzzy soft sets to satisfy different the needs of decision makers. We propose four heuristic algorithms of parameter reduction. Finally, the algorithms are compared and summarized from the aspects of easy degree of finding reduction, applicability, reduction result, exact level for reduction, multiusability, applied situation, and computational complexity. The results of the experiment show that the methods reduce the redundant parameters while preserving certain decision abilities.

Congestion in wireless sensor networks (WSNs) is a crucial issue. That is due to the relatively high node density and source-to-sink communication pattern. Congestion not only causes packet loss, but also leads to excessive energy consumption as well as delay. Therefore, in order to prolong network lifetime and improve fairness and provide better quality of service, developing a novel solution for congestion estimation and control is important to be considered. To address this problem, we propose a type-2 fuzzy logic based algorithm to detect and control congestion level in WSNs. The proposed algorithm considers local information such as packet loss rate and delay to control congestion in the network. Simulation results show that our protocol performs better than a recently developed protocol in prolonging network lifetime as well as decreasing packet loss.

The Radio frequency identification (RFID) system is a new technology which uses the open air to transmit information. RFID technology is one of the most promising technologies in the field of ubiquitous computing which is revolutionizing the supply chain. It has already been applied by many major retail chains such as Target, Wal-Mart, etc. The networked RFID system such as supply chain has very unique and special business needs which lead to special sets of RFID security requirements and security models. However, very little work has been done to analyze RFID security parameters in relation to networked RFID systems business needs. This paper presents a critical analysis of the networked application's security requirements in relation to their business needs. It then presents a comparative study of existing literature and the ability of various models to protect the security of the supply chain in a RFID deployment.

The continuously rising Internet attacks pose severe challenges to develop an effective Intrusion Detection System (IDS) to detect known and unknown malicious attack. In order to address the problem of detecting known, unknown attacks and identify an attack grouped, the authors provide a new multi stage rules for detecting anomalies in multi-stage rules. The authors used the RIPPER for rule generation, which is capable to create rule sets more quickly and can determine the attack types with smaller numbers of rules. These rules would be efficient to apply for Signature Intrusion Detection System (SIDS) and Anomaly Intrusion Detection System (AIDS).

Resource provisiomng is an important and challenging problem in the large-scale distributed systems such as Cloud computing environments. Resource management issues such as Quality of Service (QoS) further exacerbate the resource provisioning problem. Furthermore, with the increasing functionality and complexity of Cloud computing, resource failures are inevitable. Therefore, the question we address in this paper is how to provision resources to applications in the presence of resource failures in a hybrid Cloud computing environment. To this end, we propose three Cloud resource provisioning policies where we utilize workflow applications to drive the system workload. The proposed strategies take into account the workload model and the failure correlations to redirect requests to appropriate Cloud providers. Using real failure traces and workload models, we evaluated the performance and monetary cost of the proposed policies. The results of our experiments show that we can decrease the deadline violation rate of users' requests to as low as 20% with a limited cost on Amazon public Cloud.

The issue of virtual property theft in virtual worlds is a serious problem which has ramifications in both the real and virtual world. Virtual world users invest a considerable amount of time, effort and often money to collect virtual property items, only to have them stolen by thieves. Many virtual property thefts go undetected, with thieves often stealing virtual property items without resistance, leaving victims to discover the theft only after it has occurred. This paper presents the design of a detection framework that uses an algorithm for identifying virtual property theft at two key stages: account intrusion and unauthorized virtual property trades. Initial tests of this framework on a synthetic data set show an 80% detection rate with no false positives. This framework can allow virtual world developers to tailor and extend it to suit their specific virtual world software and provide an effective way of detecting virtual property theft while being a low maintenance, user friendly and cost effective.

This paper addresses a resource selection problem for applications that update data in enterprise grid systems. The problem is insufficiently addressed as most of the existing resource selection approaches in grid environments primarily deal with read-only job. We propose a simple yet efficient algorithm that deals with the complexity of resource selection problem in enterprise grid systems. The problem is formulated as a Multi Criteria Decision Making (MCDM) problem. Our proposed algorithm hides the complexity of resource selection process without neglecting important components that affect job response time. The difficulty on estimating job response time is captured by representing them in terms of different QoS criteria levels at each resource. Our experiments show that the proposed algorithm achieves very good results with good system performance as compared to existing algorithms.

Establishing trust for resource sharing and collaboration has become an important issue in distributed computing environment. In this paper, we investigate the problem of establishing trust in hybrid cloud computing environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. We present a fully distributed framework that enable trust-based cloud customer and cloud service provider interactions. The framework aids a service consumer in assigning an appropriate weight to the feedback of different raters regarding a prospective service provider. Based on the framework, we developed a mechanism for controlling falsified feedback ratings from iteratively exerting trust level contamination due to falsified feedback ratings. The experimental analysis shows that the proposed framework successfully dilutes the effects of falsified feedback ratings, thereby facilitating accurate and fair assessment of the service reputations.

In this paper we address the problem of securing networked RFID applications. We develop and present a RFID security protocol that allows mutual authentication between the reader and tag as well as secure communication of tag data. The protocol presented uses a hybrid method to provide strong security while ensuring the resource requirements are low. To this end it employs a mix of simple one way hashing and low- cost bitwise operations. Our protocol ensures the confidentiality and integrity of all data being communicated and allows for reliable mutual authentication between tags and readers. The protocol presented is also resistant to a large number of common attacks.

Phishing emails are more dynamic and cause high risk of significant data, brand and financial loss to average computer user and organizations. To address this problem, we propose a hybrid feature selection approach based on combination of content-based and behavior-based. Our proposed hybrid features selections are able to achieve 93% accuracy rate as compared to other approaches. In addition, we successfully tested the quality of our proposed behavior-based feature using the Information Gain, Gain Ratio and Symmetrical Uncertainty.

This paper addresses the problem of privacy- preserving data publishing for social network. Research on protecting the privacy of individuals and the confidentiality of data in social network has recently been receiving increasing attention. Privacy is an important issue when one wants to make use of data that involves individuals' sensitive information, especially in a time when data collection is becoming easier and sophisticated data mining techniques are becoming more efficient. In this paper, we discuss various privacy attack vectors on social networks. We present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. This study provides a summary of the current state-of-the-art, based on which we expect to see advances in social networks data publishing for years to come.

In this paper, we propose a service-oriented content adaptation framework and an approach to the Content Adaptation Service Selection (CASS) problem. In particular, the problem is how to assign adaptation tasks (e.g., transcoding, video summarization, etc) together with respective content segments to appropriate adaptation services. Current systems tend to be mostly centralized suffering from single point failures. The proposed algorithm consists of a greedy and single objective assignment function that is constructed on top of an adaptation path tree. The performance of the proposed service selection framework is studied in terms of efficiency of service selection execution under various conditions. The results indicate that the proposed policy performs substantially better than the baseline approach.

RFID technology promises to revolutionize supply chains and usher in a new era of cost savings, efficiency and business intelligence. The use of low cost RFID devices in supply chain management systems has been increasing dramatically. While a lot of research has been carried out in trying to make a completely RFID enabled global supply chain a reality there still remains a number of hurdles to be surmounted before that vision can be realized. In this paper, we analyze the specific requirements of a RFID enabled global supply chain. Then we develop and present a RFID architecture that is optimized for developing global supply chain applications but is also fully compatible with the currently used RFID architectures. Finally we do a comparative analysis of our framework with the current RFID architecture standard showing our framework has a number of significant advantages over it.

In its current form, RFID system are susceptible to a range of malevolent attacks. With the rich business intelligence that RFID infrastructure could possibly carry, security is of paramount importance. In this paper, we formalise various threat models due tag cloning on the RFID system. We also present a simple but efficient and cost effect technique that strengthens the resistance of RFID tags to cloning attacks. Our techniques can even strengthen tags against cloning in environments with untrusted reading devices.

Grid users and their jobs need credentials to access grid resources and services. It is important to minimize the exposure of credentials to adversaries. A practical solution is needed that works with existing software and is easy to deploy, administer, and maintain. Thus, credential management services are the wave of the future for virtual organizations such as Grid computing. This paper describes architecture of a scalable, secure and reliable on-line credential management service called SafeBox for InterGrid computing platform. SafeBox provides InterGrid users with secure mechanism for storing one or multiple credentials and access them based on need at anytime from anywhere.

Content adaptation is an attractive solution for the ever growing desktop based Web content delivered to the user via heterogeneous devices, in order to provide acceptable experience while surfing the Web. Bridging the mismatch between the rich content and the user device's resources (display, processing, navigation, network bandwidth, media support) without user intervention requires a proactive behavior. While content adaptation poses multitude of benefits, without proper strategies, adaptation will not be truly optimized. There have been many projects focused on content adaptation that have been designed with different goals and approaches. In this paper, we introduce a comprehensive classification for content adaptation system. The classification is used to group the approaches applied in the implementation of existing content adaptation system. Survey on some content adaptation systems also been provided. We also present the research spectrum in content adaptation and discuss the challenges.

Abstract The trends in parallel processing system design and deployment have been toward networked distributed systems such as cluster computing systems. Since the overall performance of such distributed systems often depends on the efficiency of their communication networks, performance analysis of the interconnection networks for such distributed systems is paramount. In this paper, we develop an analytical model, under non‐uniform traffic and in the presence of communication locality, for the m‐port n‐tree family interconnection networks commonly employed in large‐scale cluster computing systems. We use the proposed model to study two widely used interconnection networks flow control mechanism namely the wormhole and store&forward. The proposed analytical model is validated through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions. Copyright © 2007 John Wiley & Sons, Ltd.

Parallel I/O Scheduling in the Presence of Data Duplication on Multiprogrammed Cluster Computing Systems

\n\t\t\t\t\tMobile computing has enabled users to seamlessly access databases even when they are on the move. However, in the absence of readily available high-quality communication, users are often forced to operate disconnected from the network. As a result, software applications have to be redesigned to take advantage of this environment while accommodating the new challenges posed by mobility. In particular, there is a need for replication and synchronization services in order to guarantee availability of data and functionality, (including updates) in disconnected mode. To this end we propose a scalable and highly available data replication and management service. The proposed replication technique is compared with a baseline replication technique and shown to exhibit high availability, fault tolerance and minimal access times of the data and services, which are very important in an environment with low-quality communication links. \n\t\t\t\t

The study of the communication networks for distributed systems is very important, since the overall performance of these systems is often depends on the effectiveness of its communication network. In this paper, we address the problem of networks modeling for heterogeneous large-scale cluster systems. We consider the large-scale cluster systems as a typical cluster of clusters system. Since the heterogeneity is becoming common in such systems, we take into account network as well as cluster size heterogeneity to propose the model. To this end, we present an analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions

Data replication is one of the key components in data grid architecture as it enhances data access and reliability and minimises the cost of data transmission. In this paper, we address the problem of reducing the overheads of the replication mechanisms that drive the data management components of a data grid. We propose an approach that extends the resource broker with policies that factor in user quality of service as well as service costs when replicating and transferring data. A realistic model of the data grid was created to simulate and explore the performance of the proposed policy. The policy displayed an effective means of improving the performance of the grid network traffic and is indicated by the improvement of speed and cost of transfers by brokers.

Summary form only given. This paper proposes a context-based meta-search engine. The goals of the proposed meta-search engine are to help ease and guide the searching efforts of a novice Web user toward their desired objectives. The context-based meta-search engine benefits the user the most when the user does not know what exact document he or she is looking for. Comparison of the context-based meta-search engine with both Google and Guided Google shows that the results returned by context-based meta-search engine is much more intuitive and accurate than the results returned by both Google and Guided Google.

In data-intensive distributed systems, replication is the most widely used approach to offer high data availability, low bandwidth consumption, increased fault-tolerance and improved scalability of the overall system. Replication-based systems implement replica control protocols that enforce a specified semantics of accessing the data. Also, the performance depends on a host of factors chief of which is the protocol used to maintain consistency among object replica. In this paper, we propose a new low-cost and high data availability protocol for maintaining replicated data on networked distributed computing systems. We show that the proposed approach provides high data availability, low bandwidth consumption, increased fault-tolerance and improved scalability of the overall system as compared to standard replica control protocols.

MAPFS is a parallel file system integrated with a multiagent system responsible for the information retrieval. One of the fields where the agents can be very useful is precisely in the development of information recovery systems. The usage of a multiagent system implies coordination among the agents that belong to such system. The main goal of the agent cooperation is the interaction among them for achieving a common objective in a distributed system. Thus, a communication framework must be provided. This paper shows the MAPFS cooperation model and its communication framework, emphasizing its relation with the whole system.

Summary form only given. With the momentum gaining for the grid computing systems, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. Unfortunately, fault-tolerance has not been factored into the design of most existing grid scheduling strategies. To this end, we propose a fault-tolerant scheduling policy that loosely couples job scheduling with job replication scheme such that jobs are efficiently and reliably executed. Performance evaluation of the proposed fault-tolerant scheduler against a nonfault-tolerant scheduling policy is presented and shown that the proposed policy performs reasonably in the presence of various types of failures.

As computation and communication hardware performance continue to rapidly increase, I/O represents a growing fraction of application execution time. This gap between the I/O subsystem and others is expected to increase in future since I/O performance is limited by physical motion. Therefore, it is imperative that novel techniques for improving I/O performance be developed. Parallel I/O is a promising approach to alleviating this bottleneck. However, very little work exist with respect to scheduling parallel I/O operations explicitly. In this paper, we address the problem of effective management of parallel I/O in cluster computing systems by using appropriate I/O scheduling strategies. We propose two new I/O scheduling algorithms and compare them with two existing scheduling Approaches. The preliminary results show that the proposed policies outperform existing policies substantially.

Cluster computing has come to prominence as a cost-effective parallel processing tool for solving many complex computational problems. The key to making cluster computing work well is the middleware technologies that can manage the policies, protocols, networks, and job scheduling across the interconnected set of computing resources. The research question addressed in this paper is the on-line job scheduling problem for multi-cluster systems. To this end, we propose an on-line dynamic scheduling policy that manages multiple job streams across both single and multiple cluster computing systems with the objectives of improving the mean response time and system utilization. The performance of the proposed scheduling policy is compared against a space-sharing policy and a time-sharing policy. The results of the experiments show that the proposed policy produces significantly better response times than the other two policies.

In high throughput computing environments, an opportunistic scheduling policy is used for placement of batch jobs on idle workstations for execution. In this paper, we propose a new opportunistic scheduling policy with the aim of exploiting the idle resources provided by systems such as Condor in an opportunistic manner. We compared the performance of the proposed scheduling policy with three policies namely the scheduling policy used in Condor, job rotate and round robin policies through simulation. The results show that the proposed scheduling policy offers substantial performance improvements over the exiting approaches. Furthermore, this improved performance is achieved without loss of throughput and it results in a more interactive nature of the system thus increasing its appeal.

A framework for the design of a coupled knowledge/data base (KB/DB) medical information systems is presented. The framework is based on a knowledge model of semantic network of concepts and relations. We present the implication of coupling, but keeping the knowledge base from the database separate on updating, searching, and browsing of both the knowledge base and the database component of the system. A prototype implementation of this framework using an object-oriented paradigm is discussed.>