Researchers have shown the limitations of using the single-modal data stream for emotion classification. Multi-modal data streams are therefore deemed necessary to improve the accuracy and performance of online emotion classifiers. An online decision ensemble is a widely used approach to classify emotions in real-time using multi-modal data streams. There is a plethora of online ensemble approaches; these approaches use a fixed parameter ([Formula: see text]) to adjust the weights of each classifier (called penalty) in case of wrong classification and no reward for a good performing classifier. Also, the performance of the ensemble depends on the [Formula: see text], which is set using trial and error. This paper presents a new Reward-Penalty-based Weighted Ensemble (RPWE) for real-time multi-modal emotion classification using multi-modal physiological data streams. The proposed RPWE is thoroughly tested using two prevalent benchmark data sets, DEAP and AMIGOS. The first experiment confirms the impact of the base stream classifier with RPWE for emotion classification in real-time. The RPWE is compared with different popular and widely used online ensemble approaches using multi-modal data streams in the second experiment. The average balanced accuracy, F1-score results showed the usefulness and robustness of RPWE in emotion classification in real-time from the multi-modal data stream.

The interest of Industry 4.0 in smart contracts and blockchain technologies is growing up day by day. Smart contracts have enabled new kinds of interactions whereby contractors can even fully automate processes they agree on. This technology is really appealing in Internet of Things (IoT) domain because smart devices generate events for software agents involved in a smart contract execution, making full automation possible. However, smart contracts have to comply with national and international laws and accountability of participant's actions. Soundness of a smart contract has to be verified in terms of law compliance. Here, we propose a model for verification and validation of law compliance of smart contracts in IoT environments. The main goal of this article is to propose a formal model (based on multiagent logic and ontological description of contracts) for validating law compliance of smart contracts and to determine potential responsibilities of failures.

The continuous adoption of fitness and medical smart sensors are boosting the development of Internet of Medical Things (IoMT), reshaping and revolutionizing Healthcare. This digital transformation is paving the way to new forms of care based on real-time analysis of huge amounts of data produced by sensors, which is seen as a basis for improving clinical efficiency and helping to save lives. A medical sensor typically produces several KBs of data per second so the collection and analysis of these data can be approached with Big Data technologies. The aim of this paper is to present and evaluate a hybrid architecture for real-time anomaly detection from data streams coming from sensors attached to patients. The architecture includes an edge computing data staging platform based on Raspberry Pi 3 for data logging, data transformation in RDF triple and data streaming towards a cluster computing running Apache Kafka for collecting RDFStreams, Apache Flink for running a parallel version of the Hierarchical Temporal Memory algorithm and Cassandra for data storing. The different layers of the architecture have been evaluated in terms of both CPU performance and memory usage using the REALDISP dataset.

Throughout the past decade, there has been an extensive research on scheduling the hospital resources such as the operation theatre(s) (OTs) and the experts (such as nurses, doctors etc.) inside the hospitals. With the technological growth, mainly advancement in communication media (such as smart phones, video conferencing, smart watches etc.) one may think of taking the expertise by the doctors (distributed around the globe) from outside the in-house hospitals. Earlier this interesting situation of hiring doctors from outside the hospitals has been studied from monetary (with patient having infinite budget) and non-monetary perspectives in strategic setting. In this paper, the more realistic situation is studied in terms of hiring the doctors from outside the hospital when a patient is constrained by budget. Our proposed mechanisms follow the two pass mechanism design framework each consisting of allocation rule and payment rule. Through simulations, we evaluate the performance and validate our proposed mechanisms.

Summary Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.

Special issue on advanced techniques for cloud data storage and collaborative systemsCloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality.Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems.However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others.Such issues need to

Big Data has become commonplace in most Internet-based applications, which by delivering services to planetary scale numbers of users generate very large data sets. Such data sets are considered as a valuable source of analytics information and knowledge for many purposes and domains. It is claimed each time more that Big Data and machine learning, especially data mining, are the basis for developing advanced analytics platforms for turning data into valuable assets, gaining competitive advantage and make better decisions. At the same time, however, Big Data applications are showing to be killer applications for the state of the art machine learning and data mining algorithms. Indeed, traditional data mining frameworks such as WEKA, R, etc. and those from big companies such as IBM SPSS Modeler, SAS Enterprise Miner, Oracle Data Mining, etc. are facing the challenges of 1) coping with mining large data sets within short times and 2) under high rates of data generation. The way envisaged ahead to effectively deal with such challenges is to move to Cloudbased versions of such frameworks and development of new frameworks implemented using Cloud platforms. In either case, data mining and machine learning algorithms are being fully implemented in Cloud platforms under new requirements of Big Data for efficiency and performance. In the group of newly developed frameworks there is Apache Mahout, whose goal is “to build an environmentfor quickly creating scalable performant machine learning applications". In this paper we analyse the performance of some clustering algorithms of Apache Mahout using a Twitter streaming dataset under a Hadoop MapReduce cluster infrastructure according to various evaluation criteria.

Summary Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.

Modern data science uses topological methods to find the structural features of data sets before further supervised or unsupervised analysis. Geometry and topology are very natural tools for analysing massive amounts of data since geometry can be regarded as the study of distance functions. Mathematical formalism, which has been developed for incorporating geometric and topological techniques, deals with point cloud data sets, i.e. finite sets of points. It then adapts tools from the various branches of geometry and topology for the study of point cloud data sets. The point clouds are finite samples taken from a geometric object, perhaps with noise. Topology provides a formal language for qualitative mathematics, whereas geometry is mainly quantitative. Thus, in topology, we study the relationships of proximity or nearness, without using distances. A map between topological spaces is called continuous if it preserves the nearness structures. Geometrical and topological methods are tools allowing us to analyse highly complex data. These methods create a summary or compressed representation of all of the data features to help to rapidly uncover particular patterns and relationships in data. The idea of constructing summaries of entire domains of attributes involves understanding the relationship between topological and geometric objects constructed from data using various features. A common thread in various approaches for noise removal, model reduction, feasibility reconstruction, and blind source separation, is to replace the original data with a lower dimensional approximate representation obtained via a matrix or multi-directional array factorization or decomposition. Besides those transformations, a significant challenge of feature summarization or subset selection methods for Big Data will be considered by focusing on scalable feature selection. Lower dimensional approximate representation is used for Big Data visualization. The cross-field between topology and Big Data will bring huge opportunities, as well as challenges, to Big Data communities. This survey aims at bringing together state-of-the-art research results on geometrical and topological methods for Big Data.

The papers in this special section focus on the development of algorithms and computational models for sustainable computing in cloud and data centers. These papers bring together high quality contributions covering the topics of energy efficiency cloud computing, advances in sustainable cloud and data centers, energy aware applications at various levels of the computational and data storage processes, and emerging trends in sustainable computing. New research findings, novel approaches, and algorithms are presented for energy-efficient Cloud computing, energy-aware memory management solutions for data centers and processing of large data sets, energy aware scheduling approaches for data centers and processing of large data sets, energy consumption computational models, analytics and performance evaluations, and applications as well as emerging trends and applications.

In this paper, we propose and evaluate a new fuzzy-based reliability system for Peer-to-Peer (P2P) Communications in JXTA-Overlay platform considering as a new parameter the sustained communication time. In our system, we considered four input parameters: Data Download Speed (DDS), Local Score (LS), Number of Interactions (NI) and Sustained Communication Time (SCT) to decide the Peer Reliability (PR). We evaluate the proposed system by computer simulations. The simulation results have shown that the proposed system has a good performance and can choose reliable peers to connect in JXTA-Overlay platform.

With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented the Linearly Decreasing Vmax Method (LDVM) for our WMN-PSO simulation system. In this paper, we implement a new replacement method for mesh routers called Rational Decrement of Vmax Method (RDVM). We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. From the simulation results, we found that RDVM converges faster to best solution than LDVM.

Mission planning plays an important role in satellite control systems, especially with increase of number of satellites and more complex missions to be planned. In a general setting, the satellite mission scheduling consists in allocating tasks such as observation, communication, etc. to resources (spacecrafts (SCs), satellites, ground stations). For instance, in ground station scheduling the aim is to compute an optimal planning of communications between satellites and operations teams of Ground Station (GS). Because the communication between SCs and GSs can be done during specific window times, this problem can also be seen as a window time scheduling problem. The required communication time is usually quite smaller than the window of visibility of SCs to GSs, however, clashes are produced, making the problem highly constrained. In this work we present a Web interface for solving satellite scheduling problems through various heuristic methods. The Web interface enables the users to remotely solve their problem instances through a selection of heuristic methods such as local search methods (Hill Climbing, Simulated Annealing and Tabu Search) and population-based methods (Genetic Algorithms and variants). The user can select to solve previously generated instances by the STK simulation toolkit or generate their own problem instances. The heuristic methods are easily configurable so that users can simulate a variety of scenarios, problem sizes, etc. The execution of the heuristics methods is done at a HPC Cluster infrastructure supporting efficient execution of various solvers. Additionally, the Web application allows users to keep track of their executions as well as to share problem instances with other users.

Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.

In P2P systems, each peer has to obtain information of other peers and propagate the information to other peers through neighboring peers. Thus, it is important for each peer to have some number of neighbor peers. Moreover, it is more significant to discuss if each peer has reliable neighbor peers. In reality, each peer might be faulty or might send obsolete, even incorrect information to the other peers. We have implemented a P2P platform called JXTA-Orverlay, which defines a set of protocols that standardize how different devices may communicate and collaborate among them. JXTA-Overlay provides a set of basic functionalities, primitives, intended to be as complete as possible to satisfy the needs of most JXTA-based applications. In this paper, we present two fuzzy-based systems (called FPRS1 and FPRS2) to improve the reliability of JXTA-Overlay P2P platform. We make a comparison study between the fuzzy-based reliability systems. Comparing the complexity of FPRS1 and FPRS2, the FPRS2 is more complex than FPRS1. However, it considers also the sustained communication time which makes the platform more reliable.

With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system based on Particle Swam Optimization for solving node placement problem in wireless mesh networks, called WMN-PSO. In this paper, we use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. Then, we analyze effects of weight-coefficients for SGC and NCMC. From the simulation results, we found that the best values of the weight-coefficients for SGC and NCMC are 0.7 and 0.3, respectively.

In this paper, we evaluate the performance of two Wireless Mesh Networks (WMNs) architectures considering throughput, delay, jitter, fairness index, queue and remaining energy metrics. For simulations, we used ns-3, Distributed Coordination Function (DCF) and Optimized Link State Routing (OLSR). We compare the performance for Transmission Control Protocol (TCP) Tahoe, Reno and NewReno for normal distribution of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that for both WMN architectures, the PDR values of TCP congestion-avoidance algorithms are almost the same. For I/B WMN architecture, the throughput of TCP congestionavoidance algorithms is almost the same. However, for Hybrid WMN, the throughput of TCP NewReno is higher than other algorithms. The delay and jitter of I/B WMN are lower compared with Hybrid WMN. The fairness index of Hybrid WMN is higher than I/B WMN. For both WMN architectures, the queue size is almost the same. For both WMN architectures, the energy decreases sharply, but for TCP Reno the remaining energy of Hybrid WMN is higher than I/B WMN.

Sharing a calendar of tasks and events is a cornerstone in collaborative group work. Indeed, the individual work of the members of the group as well as the group work as a whole need the calendar to guide their activity and to meet the deadlines, milestones, deliverables of a project, etc. Additionally the members of the group should be able to work both offline and online, which arises when members of the group use smartphones and can eventually run out of Internet connection from time to time, or simply want to develop some activities locally. In the former case, they should have access to the calendar locally, while in the later case they should access the calendar online, shared by all members of the group. In both cases they should be able to see eventually the same information, namely the local calendars of the members should be synchronized with the group calendar. For the case of smartphones under Android system, one solution could be using the Google calendar, however, that is not easily tailorable to collaborative group work. In this paper we present an analysis, design and implementation of group work calendar that meets several requirements such as 1) sharing among all of members of the group, 2) synchronization among local calendars of members and global group calendar, 3) conflict resolution through a voting system, 4) awareness of changes in the entries (tasks, members, events, etc.) of the calendar and 5) all these requirements under proper privacy, confidentiality and security mechanisms. Moreover, we extend the sharing of calendars among different groups, a situation which often arises in enterprises when different groups need to be aware of other projects' development, or, when some members participate in more than one project at the same time.

The papers in this special section focus on advanced techniques for reliable cloud storage. Cloud computing has emerged as a disruptive technology and new computing paradigm in which IT resources (hardware, software, applications, data storage, computing capacity, etc.) are provided as services over the Internet, through "everything-as-a-service". It builds on the foundations of distributed computing, grid computing, virtualization, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, functionality and costs. However, Cloud environment needs to manage data center virtualization, lower cost and boost reliability by consolidating systems in the cloud.

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we evaluated WMN-GA system which is based on Genetic Algorithms (GAs) to find an optimal location assignment for mesh routers. In this paper, we evaluate the performance of two different distributions of mesh clients for two WMN architectures considering throughput, delay and energy metrics. For simulations, we used ns-3 and Optimized Link State Routing (OLSR). We compare the performance for normal and uniform distributions of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that for both distributions, the throughput of Hybrid WMN is higher than I/B WMN architecture. The delay of Hybrid WMN is a lower compared with I/B WMN. The delay for Hybrid WMN is almost the same for both distributions. However for I/B WMN, the delay is lower for Uniform distribution. For Normal distribution, the energy decreases sharply, because of the high density of nodes. For Uniform distribution, the remaining energy is higher compared with Normal distribution.

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost efficient broad band connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. We already implemented 3simulation systems (WMN-HC, WMN-SA and WMN-GA) in our previous work. In this work, we present a comparative study by applying these 3 simulation systems for optimal distribution of WMN router nodes in order to provide the best network connectivity and user coverage. From the simulation results, we conclude that for small instances WMN-HC adwomen-SA have better performance than WMN-GA. However, for large instance the performance of WMN-GA simulation system is better.

The reliability of peers is very important for safe communication in Peer-to-Peer (P2P) systems. The reliability of a peer can be evaluated based on the reputation and interactions with other peers to provide different services. However, in order to decide the peer reliability there are needed many parameters, which make the problem NP-hard. In this paper, we propose and evaluate a new fuzzy-based reliability system for P2P communications in JXTA-Overlay platform. In our system, we considered three input parameters: Number of Authentic Files (NAF), Security (S) and Quality of Service (QoS) to decide the Peer Reliability (PR). We evaluate the proposed system by computer simulations. The simulation results have shown that the proposed system has a good performance and can choose reliable peers to connect in JXTA-Overlay platform.

Up to now, the problem of ensuring collaborative activities in e-Learning against dishonest students' behaviour has been mainly tackled with technological security solutions. Over the last years, technological security solutions have evolved from isolated security approaches based on specific properties, such as privacy, to holistic models based on technological security comprehensive solutions, such as public key infrastructures, biometric models and multidisciplinary approaches from different research areas. Current technological security solutions are feasible in many e-Learning scenarios but on-line assessment involves certain requirements that usually bear specific security challenges related to e-Learning design. In this context, even the most advanced and comprehensive technological security solutions cannot cope with the whole scope of e-Learning vulnerabilities. To overcome these deficiencies, our previous research aimed at incorporating information security properties and services into on-line collaborative e-Learning by a functional approach based on trustworthiness assessment and prediction. In this paper, we present a peer-to-peer on-line assessment approach carried out in a real on-line course developed in our real e-Learning context of the Open University of Catalonia. The design presented in this paper is conducted by our trustworthiness security methodology with the aim of building peer-to-peer collaborative activities, which enhances security e-Learning requirements. Eventually, peer-to-peer visualizations methods are proposed to manage security e-Learning events, as well as on-line visualization through peer-to-peer tools, intended to analyse collaborative relationship.

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost efficient broad band connectivity. In WMNs, there are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, we consider the router node placement problem in WMNs. We want to find the optimal distribution of router nodes in order to provide the best network connectivity (the maximal number of connected routers) and coverage (maximal number of covered clients). We apply our proposed WMN-SA simulation system in a realistic scenario of the distribution of mesh clients considering It oshima City, Fukuoka Prefecture, Japan. From simulation results, we found many insights that can be very important for real deployment of WMNs.

Many Internet-based applications generate huge data streams, which are known as Big Data Streams. Such applications comprise IoT-based monitoring systems, data analytics from monitoring online learning workspaces and MOOCs, global flight monitoring systems, etc. Differently from Big Data processing in which the data is available in databases, file systems, etc., before processing, in Big Data Streams the data stream is unbounded and it is to be processed as it becomes available. Besides the challenges of processing huge amount of data, the Big Data Stream processing adds further challenges of coping with scalability and high throughput to enable real time decision taking. While for Big Data processing the MapReduce framework has resulted successful, its batch mode processing shows limitations to process Big Data Streams. Therefore there have been proposed alternative frameworks such as Yahoo!S4, Twitter Storm, etc., to Big Data Stream processing. In this paper we implement and evaluate the Yahoo!S4 for Big Data Stream processing and exemplify through the Big Data Stream from global flight monitoring system.

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we implemented WMN-GA system which is based on Genetic Algorithms (GAs) and used it for node placement problem in WMNs. In this paper, we evaluate the performance of two different distributions of mesh clients for two WMN architectures considering PDR, throughput, delay and energy metrics. For simulations, we used ns-3, Optimized Link State Routing (OLSR) and TCP. We compare the performance for normal and uniform distributions of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that the PDR is almost the same for both distributions and architecture. However, the throughput is better for normal distribution and Hybrid WMN architecture. For both distributions, the delay is better for Hybrid WMN architecture. But, the energy is better for uniform distribution.

In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman test to check if we can compare Genetic Algorithm (GA) and Hill Climbing (HC). We found out that GA and HC have differences in their performance. Then, we used the implemented systems WMN-GA and WMN-HC to evaluate and compare the performance of the systems for different distributions of mesh clients in terms of Size of Giant Component (SGC) and Number of Covered Mesh Clients(NCMC). The simulation results show that for Uniform distribution the WMN-HC performs better than WMN-GA. For Normal distribution, for big radius of communication distance, the WMN-GA has the best performance. For Exponential distribution, the WMN-HC performs better than WMN-GA for all communication distances. For Weibull distribution, the WMN-HC has better performance than WMN-GA.

Finding an optimal solution is computationally hard for most combinatorial optimization problems. Therefore the use of heuristics methods aims at finding, if not optimal, near optimal solutions in reasonable amount of computation time. Due to lack of knowledge about the landscape of fitness function, searching the solution space by heuristic methods becomes very challenging. One can search the solution space through local search methods that build a path of feasible solutions. Here, the search method selects one solution at each iteration towards reaching the near optimal solution. To this group belong Hill Climbing, Simulated Annealing, Tabu Search and other algorithms. Alternatively, one can use heuristic methods that use many feasible solutions at the same time at any iteration, known as population-based heuristics. In this group there are Genetic Algorithms (GAs and its variants), Memetic Algorithms (MAs), and more generally, Evolutionary Algorithms (EAs). The research issue is, for a given combinatorial optimization problem, which of the two search methods is more effective. This is even more challenging for the case of highly constraint problems. In this paper we present a study on the effectiveness of using local search vs. Population-based search for the problem of Ground Station Scheduling problem, which is known for its high complexity and over-constraint nature.

In this paper, we propose and evaluate a new fuzzy-based reliability system for Peer-to-Peer (P2P) Communications in JXTA-Overlay platform. In our system, we considered four input parameters: Number of Authentic Files (NAF), Local Score (LS), Number of Interactions (NI) and Security (S) to decide the Peer Reliability (PR). We evaluate the proposed system by computer simulations. The simulation results have shown that the proposed system has a good performance and can choose reliable peers to connect in JXTA-Overlay platform.

With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In this paper, we used our proposed and implemented system based on Hill Climbing algorithm, called WMN-HC for mesh router node placement in WMNs. We analyze WMN-HC simulation system data for different number of nodes using Friedman test. We took into consideration 8, 16, 32, 64, 128 mesh routers and 24, 48, 96, 192, 384 mesh clients. We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics. From the analysis, for all cases, the p-value of SGC is more than 0.05. Thus, we adopt H0. On the other hand, for NCMC, we adopt H1 because the p-value is less than 0.05. Friedman test results show that there is not difference for SGC parameter. However, there is difference for NCMC parameter.

Big Data Stream processing is among the most important computing trends nowadays. The growing interest on Big Data Stream processing comes from the need of many Internet-based applications that generate huge data streams, whose processing can serve to extract useful analytics and inform for decision making systems. For instance, an IoT-based monitoring systems for a supply-chain, can provide real time data analytics for the business delivery performance. The challenges of processing Big Data Streams reside on coping with real-time processing of an unbounded stream of data, that is, the computing system should be able to compute at high throughput to accommodate the high data stream rate generation in input. Clearly, the higher the data stream rate, the higher should be the throughput to achieve consistency of the processing results (e.g. Preserving the order of events in the data stream). In this paper we show how to map the data stream processing phases (from data generation to final results) to a software chain architecture, which comprises five main components: sensor, extractor, parser, formatter and out putter. We exemplify the approach using the Yahoo!S4 for processing the Big Data Stream from Flight Radar24 global flight monitoring system.

As cloud computing becomes prevalent, many organizations outsource the expensive computing and storage into the cloud servers. This brings many benefits including relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances. Despite the tremendous benefits, the outsource storage inevitably suffers from the security, privacy, trustworthiness, and the access of outsourced data. To address these issues, there has been increased interest to propose and design new algorithms and methodologies for cloud computing and cloud services. This special issue brings together high quality contributions in these research areas. The papers of the special issue have also identified and discussed the challenges in the field that will lead to new solutions and applications for cloud computing and cloud services. The special issue consists of 12 papers organized as follows. In the first paper 1, the authors propose the guidelines of a holistic security model in online collaborative learning through an effective trustworthiness. They consider a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real time. The authors used a distributed infrastructure, such as Hadoop and cluster computing, to implement and evaluate the proposed approach for massive processing of log data. The experimental results showed the feasibility of coping with the problem of structuring and processing ill-formatted, heterogeneous, large log files to extract information on trustworthiness indicators and levels from learning groups and ultimately fill a global framework devoted to improve information security in e-learning in real time. In the second paper 2, the authors present a ciphertext-policy attribute-based proxy re-encryption scheme for data sharing in public clouds. They proposed a new single-hop unidirectional Ciphertext-Policy Attribute-Based Proxy Re-Encryptio (CP-ABPRE) scheme, which supports attribute-based re-encryption with any monotonic access structure, to tackle the open problem left by the existing CP-ABPRE schemes. The authors showed that the proposed scheme can be proved secure in the random oracle model under the decisional q-parallel bilinear Diffie–Hellman exponentiation assumption. In the third paper 3, the authors propose Ontology as a Service (OaaS), where the ontology tailoring process serves as a service in the cloud. To illustrate OaaS, in this paper, they propose sub-ontology extraction and extension, whereby multiple sub-ontologies are extracted from various source ontologies and then these extracted sub-ontologies are extended to form a complete ontology to be used by the user. The authors use a maximum extraction method to facilitate this. Unified Medical Language System (UMLS) metathesaurus ontology is used as a walk-through case study to illustrate the proposed methods. In the fourth paper 4, the authors present an auditing mechanism for the security of service in grid. They define and analyze some atomic services for grid application based on the service processes of the task. Then, some detecting methods are used for these automatic services. The authors evaluate the proposed auditing mechanism by experiments. The results show that it is feasible to ensure the security of service in grid. In the fifth paper 5, the authors show the performance of Optimized Link State Routing (OLSR) protocol for video streaming application in Vehicular Ad-hoc NETworks (VANETs) cloud computing. The simulations are conducted in urban environment in three scenarios, where roadside access points (APs) are present and not present, with and without buildings. They consider 802.11p standard and send multiple video streaming flows over User Datagram Protocol (UDP). They use different parameters for carrying out the simulation. The simulation results show that transmission for streaming is improved when roadside APs are used. In the sixth paper 6, the authors present distributed Semi-Random Low Density Parity Check (SR-LDPC) codes over multiple-access relay channel and its applications in cloud storage. They propose a novel encoding procedure to realize codes that resemble SR-LDPC codes in both structure and performance. For the case of two sources communicating with a single destination via a common relay, the two sources separately encode their own symbols of information using SR-LDPC codes. The relay then makes XOR for the two sequences received from the two sources. The destination can recover the information bits of the two sources using the sequences received directly from the two sources and the sequence received from the relay. It is shown that the sequences received by the destination are still SR-LDPC codes. Iterative decoding can be used by the destination using the sequences received from both the relay and the sources and obtain good performance. In the seventh paper 7, the authors examine the distinguishing characteristics of big data along the lines of the 3Vs (variety, volume, and velocity). Accordingly, they provide an insight into the main processing paradigms in relation to the 3Vs. It defines a lifecycle for big data processing and classifies various available tools and technologies in terms of the lifecycle phases of big data, which include data acquisition, data storage, data analysis, and data exploitation. They analyze the trends and technologies in relation to the characteristics, evolution, and processing of big data. In the eighth paper 8, the authors investigate the performance of time division broadcast (TDBC) protocol in bidirectional cloud networks in the presence of channel estimation errors (CEEs). The tight asymptotic expressions for individual and system outage probabilities are presented in closed form. It is shown that CEE causes outage probability to remain fixed even if the channel is noiseless. Then, the Symbol Error Rate (SER) performance for TDBC protocol with M-ary phase shift keying and M-ary quadrature amplitude modulation signals is analyzed. Simulation results validate the accuracy of the analytical results. Furthermore, comparison of signal-to-noise ratio gap ratio shows that TDBC protocol is less sensitive to the effect of CEE than analog network coding protocol. In the ninth paper 9, the authors deal with the development of a methodology that considers security concerns as an integral part of cloud-based systems design and implementation. They present a set of stereotypes that define a vocabulary for annotating UML-based models with information relevant for integrating the specification of security requirements into cloud architectures. This approach can be used to significantly improve productivity and overall success in the development of secure distributed cloud applications and systems. In the 10th paper 10, the authors design a secure and practical proxy re-encryption scheme, which is Chosen Ciphertext Attacks (CCA) secure under the computational Diffie–Hellman assumption in the random oracle model. They evaluate the efficiency of the proposed scheme both in theoretical comparisons with related schemes and in implementations at several security levels. The results indicate that the proposed scheme can be practical in cloud-based data sharing. In the 11th paper 11, the authors present a semantic client-side approach for web personalization of software as a service (SaaS)-based cloud services. The proposed model also provides a full prototype for the semantic client-side personalization approach and has been systematically assessed with a set of metrics. The benchmark results show good outcomes with impressive high performance and satisfied recommendation utility, which provides sufficient evidence that the proposed approach achieves significantly high benefits to encourage the further pursuit of personalization for SaaS services. Finally, in the 12th paper 12, the authors show the secure obfuscation for multiuse re-encryption and its application in cloud computing. The authors study the secure obfuscation for three kinds of new re-encryption functions: multiuse re-encryption, conditional re-encryption with keyword search, and broadcast re-encryption. They utilize the obfuscated results as tools to satisfy secure cloud computing. The authors show that the cloud computing schemes based on obfuscation have better security compared with other tools. The guest editors of this special issue wish to thank the authors for their interesting contributions to this special issue as well as for their timely collaboration in the preparation of the manuscripts. We would like to appreciate the reviewers for the careful reviewing of the papers, the useful suggestions, and feedback that helped the authors improve their manuscripts. We would like to sincerely thank Prof. Geoffrey Fox (editor-in-chief of the Concurrency and Computation: Practice and Experience) for the opportunity to edit this special issue and his guidance.

Summary This paper proposes a trustworthiness‐based approach for the design of secure learning activities in online learning groups. Although computer‐supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks that limit its potential. Among these limitations, we investigate on information security vulnerabilities in learning activities, which may be developed in online collaborative learning contexts. Although security advanced methodologies and technologies are deployed in learning management systems, many security vulnerabilities are still not satisfactorily solved. To overcome these deficiencies, we first propose the guidelines of a holistic security model in online collaborative learning through an effective trustworthiness approach. However, as learners' trustworthiness analysis involves large amount of data generated along learning activities, processing this information is computationally costly, especially if required in real time. As the main contribution of this paper, we eventually propose a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real time. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Peer‐to‐peer (P2P) computing systems have become very popular during the last years because of their ability to scale to a large number of users and efficient communication among peers. They can support complex computational processes, beyond simple file sharing, while offering advantages of decentralized distributed systems. However, such systems may suffer from availability and reliability. To increase availability and reliability, and therefore, improve the perception of peers, yielding to fast response times and rich experience, data replication techniques are the foremost means in such systems. Indeed, in many P2P applications, for example, in a groupware, documents generated along application life cycle can change over time. The need is then to efficiently replicate dynamic documents and data to support group processes and collaboration. In this paper, we propose a replication system for documents structured as XML files and evaluate it under different scenarios. The proposed system has a super‐peer architecture that provides fast consistency for late joining peers. It uses optimistic replication techniques with propagating update operations from source to destination node in push mode. The system is suitable for asynchronous collaboration in online collaborative teams accomplishing a common project in a P2P environment. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud computing moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. In this work, we study the problem of ensuring the integrity of data storage in cloud computing. To reduce the computational cost at user side during the integrity verification of their data, the notion of public verifiability has been proposed. However, the challenge is that the computational burden is too huge for the users with resource-constrained devices to compute the public authentication tags of file blocks. To tackle the challenge, we propose OPoR, a new cloud storage scheme involving a cloud storage server and a cloud audit server, where the latter is assumed to be semi-honest. In particular, we consider the task of allowing the cloud audit server, on behalf of the cloud users, to pre-process the data before uploading to the cloud storage server and later verifying the data integrity. OPoR outsources and offloads the heavy computation of the tag generation to the cloud audit server and eliminates the involvement of user in the auditing and in the pre-processing phases. Furthermore, we strengthen the proof of retrievability (PoR) model to support dynamic data operations, as well as ensure security against reset attacks launched by the cloud storage server in the upload phase.

In this paper, we used our proposed and implemented system based on Hill Climbing algorithm, called WMN-HC for mesh router node placement in WMNs. We evaluate and compare the performance of the proposed system for different movement methods. We took into consideration Normal and Uniform distributions of mesh clients. We use as evaluation metrics giant component and number of covered mesh clients. We compare the performance of WMN-HC for Normal and Uniform distribution of mesh clients and different movement methods. The simulation results show that for Normal distribution of mesh clients, the system achieves better results in terms of connectivity and user coverage for swap and radius movement method and for Uniform distribution of mesh clients, best performance is achieved for Radius method.

Data deduplication is an attractive technology to reduce storage space and upload bandwidth for increasing vast amount of duplicated and redundant data. In a cloud storage system with data deduplication, duplicate copies of data will be eliminated and only one copy will be kept in the storage. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. However, the issue of keyword search over encrypted data in deduplication storage system has to be addressed for efficient data utilization. This paper firstly proposes two constructions which support secure keyword search in this scenario. In these constructions, the integrity of the data can be realized by just checking the convergent key, without other traditional integrity auditing mechanisms. Security analysis demonstrates that our keyword search schemes are secure in terms of the definitions specified in the proposed security model.

This paper proposes a trustworthiness model for the design of secure learning assessment in on-line collaborative learning groups. Although computer supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks which limit their potential in collaborative learning activities. Among these limitations, we investigate information security requirements in on-line assessment, (e-assessment), which can be developed in collaborative learning contexts. Despite information security enhancements have been developed in recent years, to the best of our knowledge, integrated and holistic security models have not been completely carried out yet. Even when security advanced methodologies and technologies are deployed in Learning Management Systems, too many types of vulnerabilities still remain opened and unsolved. Therefore, new models such as trustworthiness approaches can overcome these lacks and support e-assessment requirements for e-Learning. To this end, a trustworthiness model is designed in order to conduct the guidelines of a holistic security model for on-line collaborative learning through effective trustworthiness approaches. In addition, since users' trustworthiness analysis involves large amounts of ill-structured data, a parallel processing paradigm is proposed to build relevant information modeling trustworthiness levels for e-Learning.

In this paper, we deal with connectivity and coverage problem in Wireless Mesh Networks (WMNs). We used Friedman test to compare Genetic Algorithm (GA) and Tabu Search (TS). We found out that GA and TS have difference in their performance. Then, we used the implemented systems WMN-GA and WMN-TS to evaluate and compare the performance of the system for different distributions of mesh clients in terms of giant component and covered mesh clients. The simulation results shows that for big radius of communication distances WMN-GA performs better than WMN-TS for Uniform, Normal and Weibull distributions of mesh clients. For Exponential distribution WMN-TS performs better than WMN-GA for all radius of communication distances.

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost-efficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. We implemented a simulation system where we consider the router node placement problem in WMNs. We want to find the optimal distribution of router nodes in order to provide the best network connectivity and provide the best coverage in a set of randomly distributed clients. We modeled 3 different realistic distribution of mesh clients (Subway, Boulevard and Stadium models). From simulation results, we found that, in the case of Subway model distribution of mesh clients, connectivity and coverage reach maximum performance. For all 3 models, when the instance size increases, the performance decreases.

Mission planning plays an important role in satellite control systems. Satellites are not autonomously operated in many cases but are controlled by tele-commands transmitted from ground stations. Therefore, mission scheduling is crucial to efficient satellite control systems, especially with increase of number of satellites and more complex missions to be planned. In a general setting, the satellite mission scheduling consists in allocating tasks such as observation, communication, etc. to resources (spacecrafts (SCs), satellites, ground stations). One common version of this problem is that of ground station scheduling, in which the aim is to compute an optimal planning of communications between satellites and operations teams of Ground Station (GS). Because the communication between SCs and GSs can be done during specific window times, this problem can also be seen as a window time scheduling problem. The required communication time is usually quite smaller than the window of visibility of SCs to GSs, however, clashes are produced, making the problem highly constrained. In this paper we present a Tabu Search (TS) algorithm for the problem, while considering several objective functions, namely, windows fitness, clashes fitness, time requirement fitness, and resource usage fitness. The proposed algorithm is evaluated by a set of problem instances of varying size and complexity generated with the STK simulation toolkit. The computational results showed the efficacy of TS for solving the problem on all considered objectives.

In a collaborative e-learning environment, students are usually involved in learning activities such as collaborative writing (e.g., an essay in Wiki form) and debates in the form of a chat or asynchronous forum. Capturing students' feelings and emotional states enclosed in such textual information is important, since sentiments can influence individual behavior and consequently group dynamics as well as individual and group performance. For this reason, emotion awareness becomes an important issue for both tutors and students themselves. Especially for tutors, if they have as accurate affective information as possible about the emotional state that every individual and group is encountered, they might be able to provide more effective feedback to the students that includes not only cognitive but also affective scaffold. A first important step to this endeavor is to develop an approach that enables labeling affective behavior and then presenting the emotional information to all the actors involved in the learning process.

In this work, we evaluate the performance of WMN-GA system for node placement in WMNs. For evaluation, we consider Exponential and Weibull distributions of mesh clients and HotSpot initial placement method. The population size is considered 64 and the number of generation 200. For evaluation, we consider the giant component and the number of covered users metrics. We also show the visualization process of mesh router placement for different number of generations. The simulation results shows that for Weibull distribution, mesh clients are concentrated at the bottom of the grid size and HotSpot method finds a better positioning of mesh clients compared with Exponential distribution.

This paper presents an approach to rebuild the benefits lost from moving from traditional Instructor-led Training to Web-based Training by using Computer-Supported Collaborative Learning and Social Networks. The innovative approach of using Learning Analytics and Educational Data Mining techniques to track and analyse the interactive behavior of on-line collaborative learning and social networks is explored to enhance and improve corporate distance training. The results of this multidisciplinary proposal addressing pedagogical, technological and business issues to support Web-based Training becomes decisive for enhancing and improving the overall distance training experience and for finding new opportunities for cost-effective ways to deliver training programs. We believe the outcomes of this research will be crucial for greatly enhance and improve corporate distance training.

As cloud computing adoption and deployment increase, the performance evaluation of the cloud environments is becoming very important. Cloud applications have different composition, configuration, and deployment requirements. Simulation and modeling techniques are suitable to quantify the performance of resource allocation policies and application scheduling algorithms in Cloud computing environments for different application and service models according to different work loads, energy performance and system size. In this paper, we give an overview of the existing distributed systems simulation and modeling tools in order to outline the main characteristics and peculiarities. We then present an outlook on new requirements to be addressed for performance evaluation of cloud applications through simulation and modeling.

In this paper, we propose and implement a medical support system considering P2P and IoT technologies. We used our implemented smart device that we call Smart Box. We have implemented in the Smart Box many functions and sensor such as body sensor, infrared sensor, chair or bed vibration control, light control, smell control and sound control. By using Smart Box the implemented system is able to control the situation of patients. We carried out experiments to evaluate the implemented system. The evaluation results show that the implemented system can detect the patient situation.

In this paper, we propose and evaluate a new fuzzy-based reliability system for Peer-to-Peer (P2P) Communications in JXTA-Overlay that decides the Peer Reliability (PR). In our system, we considered three input parameters: Local Score (LS), Number of Authentic Files (NAF) and Number of Interactions (NI). We evaluate the proposed system by computer simulations. The simulation results have shown that the proposed system has a good performance and can choose reliable peers to connect in JXTA-Overlay platform.

This paper proposes an approach to enhance information security in on-line assessment based on a normalized trustworthiness model. Among collaborative e-Learning drawbacks which are not completely solved, we have investigated information security requirements in on-line assessment (e-assessment). To the best of our knowledge, security requirements cannot be reached with technology alone, therefore, new models such as trustworthiness approaches can complete technological solutions and support e-assessment requirements for e-Learning. Although trustworthiness models can be defined and included as a service in e-assessment security frameworks, there are multiple factors related to trustworthiness which cannot be managed without normalization. Among these factors we discuss trustworthiness multiple sources, different data source formats, measure techniques and other trustworthiness factors such as rules, evolution or context. Hence, in this paper, we justify why trustworthiness normalization is needed and a normalized trustworthiness model is proposed by reviewing existing normalization procedures for trustworthy values applied to e-assessments. Eventually, we examine the potential of our normalized trustworthiness model in a real online collaborative learning course.

Personalization and the use of context is becoming ubiquitous and is being applied across a broad and diverse range of domains and systems. For example, typical domains where research has addressed the application of context and context-awareness are e-learning and e-health monitoring. Concomitant with the growing interest is context is the increasing use of Cloud-based solutions and related service models such as Software-as-a-Service. The growing use of Cloud-based solutions with the increasing interest in personalization and context is discussed and we consider Cloud-based systems and service models and propose a new service model: Context-as-a-Service. We provide an overview of the proposed Context-as-a-Service service model with an illustrative scenario based on the domain of e-health monitoring. We conclude that currently the design and implementation of context and context-aware systems is restricted to experts in the field and postulate that there is a clear requirement for a platform upon which non-expert users may utilize context-awareness in real-world applications.

In this paper, we evaluate the performance of WMN-GA system for different settings of number of mesh clients. The population size is considered 64 and the number of generation 400. We considered Uniform and Normal distributions of mesh clients. For evaluation, we consider average size of giant component (Avg. SGC) and average number of covered mesh clients (Avg. NCMC) metrics. The simulation results shows that for both distributions, with the increase of the number of mesh routers, the number of covered mesh clients is increased. With the increase of the number of mesh clients, the percentage of Avg. NCMC is increased. Comparing the simulation results for both Uniform and Normal distributions, for number of mesh routers 16 to 64 the performance of Normal distribution is better compared with Uniform distribution, but for number of mesh routers 128 for both distributions the performance is very good.

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost-efficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, we consider the router node placement problem in WMNs for different area shapes and sizes. We want to find the optimal distribution of router nodes in order to provide the best network connectivity and provide the best coverage in a set of normally distributed clients. From the simulation results, we conclude that, when we use SA, The grid area size is 512, the size of Giant Component (GC) can reach maximal and the number of covered mesh clients has the best performance. For grid area size 1024, the size of GC does not converge for all shapes and the number of covered mesh clients converges slower. For greater area sizes, both the size of GC and the number of covered mesh clients do not converge to the maximum. When the grid shape is long and narrow, connecting mesh routers with other mesh routers is difficult. Thus, the size of GC is lower compared to when the shape of grid area is square.

In this paper we evaluate the performance of WMN-HC system for node placement in WMNs. For evaluation we considered different number of mesh routers and mesh clients. We considered four different mesh client distributions. The simulation results show that our proposed and implemented WMN-HC system can find the optimal number of mesh routers for covering a given number of mesh clients deployed in a given grid area. For Exponential and Uniform distribution of mesh clients, the full coverage of mesh clients can be done with less routers compared with other distributions.

SUMMARY The analysis of the predictability of human behaviour is an emerging topic in the ubiquitous computing community. Recent endeavours in studying the human behaviour are either based on synthetic models or on real mobile user traces, but what they are mainly focusing on is location: discovering travel patterns or anticipating the whereabouts of mobile users. We extend the analysed context by studying the wireless behaviour of mobile users: interactions with both peers and network devices in academic and office environments. We propose a methodology and a set of guidelines to assess and aid in analysing wireless mobile traces. We prove that the human behaviour is predictable when the studied traced sets are convergent, complete and correct, and we obtain a surprising invariability in interacting with wireless access points, as we are able to pinpoint a wireless user to one of two locations at any specific hour. Copyright © 2013 John Wiley & Sons, Ltd.

With the fast proliferation of various computational paradigms, from Web-based to wireless and mobile computing, intelligent data processing and security have become cross-cutting features of large scale Internet-based system. On the one hand, there is data being generated everywhere, from various sources and heterogenous formats (such as document repositories, business process data, network data, mobile communications data, server logs data, etc.). On the other hand, the nature of the data, the amount, and data sensitivity, among other features, are promoting the combination of intelligent processing with security intelligence to deliver next generation secure and intelligent systems and applications. This special issue brings together high quality contributions in the interleaving research areas of intelligent data processing and intelligent techniques in security data analysis. The papers of the special issues have also identified and discussed the challenges in the field that will lead to new avenues in intelligent processing for secure and intelligent systems and applications. The special issue consists of 13 papers organized as follows. The first five papers 1-5 are devoted to intelligent secure data analysis, encryption, and authentication techniques. The next three papers 6-8 present intelligent optimization and data processing techniques, including genetic algorithms, independent component analysis, and evolutionary game theory. The following three papers 9-11 deal with data modeling and analysis in wireless, mobile, and Web-based computing systems. The final two papers 12, 13 bring intelligent techniques from image processing field. This special issue is devoted to research advances in the field of secure and intelligent data processing. The special issue follows ‘The IEEE 4th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2013)’, held at the University Politehnica of Bucharest, Romania, September 19–21, 2012, technically co-supported by IEEE Systems Man & Cybernetics (IEEE SMC). The guest editors of this special issue wish to thank the authors for their interesting contributions to this special issue as well as for their timely collaboration in the preparation of the manuscripts. We would like to appreciate the reviewers for the careful reviewing of the papers, the useful suggestions and feedback that helped the authors improve their manuscripts. We would like to sincerely thank Professor Geoffrey Fox (Editor-in-Chief of the Concurrency and Computation: Practice and Experience) for the opportunity to edit this special issue and his guidance. The first guest editor acknowledges the support from the FORMALISM research project.

Wireless Mesh Networks (WMNs) are currently attracting a lot of attention due to their ability to provide cost-efficient broadband wireless connectivity. In our previous work, we evaluated WMN-GA system that is based on Genetic Algorithms (GAs) to find an optimal location assignment for mesh routers. In this paper, we evaluate the performance of two different network topologies by simulations considering packet delivery ratio (PDR), throughput and delay metrics. For simulations, we used ns-3 simulator and Hybrid Wireless Mesh Protocol (HWMP). We evaluate the performance of the optimized network component by sending multiple constant bit rate (CBR) flows in the network. The simulation results show that when the number of generations is 5, the mesh routers are scattered in the grid area and the communication is done by multiple routes. But, when the number of generations is 200, the mesh routers are located near each other, the connectivity is maximized and less routes are used for communication. Also, there is small improvement of the throughput.

As Mobile Ad hoc Networks (MANETs) are attracting attention for their potential use in several fields such as collaborative computing and multimedia communications, there is an increasing need to minimize the overhead introduced by routing protocols in the network. In this paper, we analyze the performance of a MANET by simulations, in terms of RREQ, RREP and RERR, considering Random Waypoint Mobility (RWM) model. We consider the cases when all nodes are moving and source and destination nodes are static. We evaluate the performance by measuring the throughput and AODV control packets.

In this paper, we evaluate the performance of WMN-GA system for different settings of number of mesh clients. We consider Weibull and Exponential distribution of mesh clients, which represent hotspot scenarios. The population size is considered 64 and the number of generation 400. For evaluation, we consider average size of giant component(Avg. SGC) and average number of covered mesh clients(Avg. NCMC) metrics. The simulation results shows that for both distributions, with the increase of the number of mesh routers, the number of covered mesh clients is increased. However, with the increase of the number of mesh clients, the percentage of Avg. NCMC is almost the same. Comparing the simulation results for both Exponential and Weibull distributions, in general the performance of both distributions is almost the same, but in some cases Exponential distribution performs better.

In this paper, we investigate the performance of OLSR and AODV protocols in a VANET crossroad scenario. The mobility patterns of vehicles are generated by means of CAVENET (Cellular Automaton based Vehicular Network) and as communication protocol simulator, is used NS3(Network Simulator 3). For the simulations, we used IEEE802.11p standard, TwoRayGroundPropagationLossModel and sent multiple CBR flows over UDP between ten pairs source-destination. We use Packet Delivery Ratio (PDR), throughput and delay as evaluation metrics. We compared the performance of both protocols and the simulation results shows that for big values of transmission rates, OLSR performs better than AODV. For small transmission rates, the delay is less than 0.1sec and both protocols can be used for real time applications such as safety applications. For big values of transmission rate, the delay is higher than 1 sec and these protocols can be used for applications that tolerate this delay such as streaming and entertainment.

Ground station scheduling problem arises in spacecraft operations and aims to allocate ground stations to spacecraft to make possible the communication between operations teams and spacecraft systems. This problem consists in computing an optimal planning of communications between satellites or spacecraft (SC) and operations teams of Ground Station (GS). The information transmitted in these communications is usually basic information such as telemetry, tracking or information tasks to be performed and the time normally required for communication is usually quite smaller than the window of visibility of SCs to GSs. The problem is known for its high complexity and has been shown computationally hard to solve to optimality. Additionally, several optimization objectives can be formulated and sought for the problem, namely, windows fitness, clashes fitness, time requirement fitness, and resource usage fitness. In this paper, we present the resolution of the problem through Steady State Genetic Algorithm (SSGA), in which a few individuals are replaced during genetic evolution. We evaluated the performance of the SSGA through a suite of instances generated with the STK simulation toolkit. The Steady State could find for most instances very high quality solutions although its performance was not equally good for all considered objectives.

In this paper, we evaluate the performance of WMN-GA system for node placement problem in WMNs. For evaluation, we consider Exponential and Uniform Distribution of mesh clients and different selection and mutation operators. The population size is considered 64 and the number of generation 200. For evaluation, we consider the giant component and the number of covered users metrics. The simulation results shows that the WMN-GA system performs better for Exponential distribution of mesh clients.

Node placement problems have been long investigated in the optimization field due to numerous applications in location science (facility location, logistics, services, etc) and classification (clustering). In this paper, we propose and implement a system based on Hill Climbing algorithm, called WMN-HC for mesh router node placement in WMNs. We evaluate the performance of the proposed system by different scenarios using giant component and number of covered mesh clients metrics. We compare the performance of WMN-HC for Normal and Uniform distribution of mesh clients. The simulation results show that the system performs better for Normal distribution.

This paper explores the role and impact of emotion on learning and specifically analyzes issues related to emotion awareness and emotional feedback in e-learning. After a detailed analysis of current research work in these issues, important research questions in emotional e-learning are set and a conceptual Emotion Analysis model is proposed to answer these questions.

The interest in Mobile Ad hoc Networks (MANETs) is growing and there are potential uses in several fields such as collaborative computing and multimedia communications. Thus, there is an increasing need to support multiple communication flows in the network, while guaranteeing a good performance. In this paper, we analyze the performance of MANET by simulations, considering Random Waypoint Mobility (RWM) model and investigate the effect of AODV's HELLO packets in the network performance. We consider different mobility degrees for simulations. From results, we show that, activating the HELLO function improves the performance of MANET regarding throughput.

In this paper, we evaluate the performance of WMN-GA system for node placement problem in WMNs. For evaluation, we consider Normal and Uniform Distribution of mesh clients and different grid shapes. The simulation results shows that grid shape effects in the performance of WMN-GA. With the increasing of grid size, the mesh routers can not cover all mesh clients in the grid size and the performance of the system is decreased. The system performs better for Normal distribution of mesh clients.

On of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost-efficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, we consider the router node placement problem in WMNs and find the optimal distribution of router nodes in order to provide the best network connectivity among themselves and provide the best client coverage in a set of uniformly distributed clients. We use Simulated Annealing (SA) algorithm in our WMN-SA simulation system to calculate the size of Giant Component (GC) and number of covered users with different temperature values of SA.

In this paper, we investigate the performance of OLSR protocol for location and VoIP applications in a Manhattan grid scenario. For the simulations, we used SUMO and NS3. We considered IEEE 802.11p standard, TwoRayGroundPropagationLossModel and sent multiple CBR flows over UDP between five pairs of source-destination nodes. We use Packet Delivery Ratio (PDR), throughput, delay and average number of hops as evaluation metrics. The simulation results show that OLSR protocol can be used to send in real time location information and VoIP traffic between vehicles.

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.

In this paper, we propose a fuzzy-based reliability system for P2P Communications in JXTA-Overlay. We use four input parameters: Mutually Agreed Behaviour (MAB), Actual Behaviour Criterion (ABC), Peer disconnections (PD) and Number of Uploads (NU) and the output is Peer Reliability (PR). We evaluate the proposed system by computer simulations. The simulation results show that the proposed system have a good behaviour can be used successfully to evaluate the scale of reliability of the new peer connected in JXTA-Overlay.

The satellite scheduling and its version of ground station scheduling are increasingly attracting the attention of researchers from aerospace and optimization domain. While in the recent past satellite mission arise from large aero-spacial agencies, nowadays even smaller companies are interested in satellite missions for basic tasks such as telemetry, imaging, remote sensing, etc. The ground station scheduling problem consists in computing an optimal planning of communications between satellites or spacecraft (SC) and operations teams of Ground Station (GS). The problem is highly complex and multi-objective and in its general formulation has been shown NP-hard. Therefore, its resolution is tackled by heuristic and meta-heuristic methods. Although heuristic and meta-heuristic methods are well understood, their evaluation for specific problems, like ground station scheduling, remain a challenge. The design and development of benchmarks of instances is thus needful to evaluate such methods and also to provide the community with means to reproduce the experimental study for the same benchmark under the same or different parameter setting. In this paper, we present an XML-based benchmark of instances for the ground station scheduling generated with the STK simulation toolkit. Then we show the experimental evaluation of a Basic Genetic Algorithm using the benchmark.

A set of wireless mobile terminals, which cooperate by routing packets to each other create a Mobile Adhoc Network (MANET). MANETs are attracting attention for their potential use in several fields such as collaborative computing and disaster recovery environments. Considering mobility of the terminals, the routes change more dynamically and routing becomes a key process for operation of MANETs. In this paper, we analyze the performance of Better Approach to MANET (BATMAN) routing protocol in an outdoor bridge environment considering mobility and vertical communication. We implement two scenarios on untested and evaluate the communication in terms of bitrate and packetloss.

Wireless Mesh Networks (WMNs) are currently attracting a lot of attention from wireless research and technology community due to their importance as means for providing cost-efficient broadband wireless connectivity. WMNs are based on mesh topology, in which every node is connected to one or more nodes, enabling thus the information transmission in more than one path. In this paper, we propose a system for solving the node placement problem in WMNs and deal with the effects of size of population in GA. We evaluate the performance of the proposed system considering giant component and the number of covered users parameters. The simulation results shows that the system have a good behavior.

Over the last decades, the cooperation amongst different resources that belong to various environments has been arisen as one of the most important research topic. This is mainly because of the different requirements, in terms of jobs' preferences that have been posed by different resource providers as the most efficient way to coordinate large scale settings like grids and clouds. However, the commonality of the complexity of the architectures (e.g. in heterogeneity issues) and the targets that each paradigm aims to achieve (e.g. flexibility) remains the same. This is to efficiently orchestrate resources and user demands in a distributed computing fashion by bridging the gap among local and remote participants. At a first glance, this is directly related with the scheduling concept, which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings. In addition, the term meta-computing, hence meta-scheduling, offers additional functionalities in the area of interoperable resource management because of its great proficiency to handle sudden variations and dynamic situations in user demands by bridging the gap among local and remote participants. This work presents a review on scheduling in high performance, grid and cloud computing infrastructures. We conclude by analysing most important characteristics towards inter-cooperated infrastructures.

Simulation of Vehicular Ad hoc Networks(VANETs) is an important aspect of the analysis and design of intelligent routing and broadcasting protocols. In VANETs, the topology of the network changes very often, therefore implementation of efficient routing protocols is very important problem. The communication between vehicles is used for safety, comfort and for entertainment as well. The performance of communication depends on how better the routing takes place in the network. Routing of data depends on the routing protocols being used in network. In this paper, we have implemented two routing protocols: DSDV and DYMO and investigated the performance of the implemented routing protocols by means of CAVENET. As evaluation metrics, we use the PDR and good put. The simulation results shows that DYMO protocol has better performance than DSDV protocol.

Data replication and synchronization techniques have recently attracted a lot of attention of researchers from P2P computing community. Such techniques are fundamental to increase data availability, reliability and robustness of P2P applications. However, several issues arise, such as data consistency and designing cost-efficient solutions, due to the highly dynamic nature of P2P systems. Although there has been a lot of research work from distributed systems and database research community on replication and synchronization, P2P systems have many features that call for investigating on data replication and synchronization techniques in such systems. Recently, techniques for P2P systems are also being investigated in the context of Cloud computing system, to develop Cloud based applications that benefit from features of P2P systems. While data replication and synchronization have shown useful in the broad context of P2P systems, in this talk, we focus on data replication and synchronization for P2P super-peer collaborative systems. In such systems peers are organized in peer-groups and collaborate together synchronously and/or asynchronously to accomplish a common project by sharing documents and data (contacts, calendar information, etc.), also known as P2P groupware systems. Such systems are attractive for several application contexts such collaborative work in online teams in virtual campuses and small to medium corporates. These applications are especially interesting due to their low cost of deployment and maintenance as compared to the rather high cost centralized groupware applications, also, they provide facilities to support opportunistic collaboration by giving full control to the users. We discuss the requirements on replication techniques in view of their suitability to supporting efficient peer-group collaboration in P2P systems. Some important requirements include the dynamic nature of the P2P collaborative systems, the need for peers to work both online and offline through available consistent document replicas. We consider dynamics at both peers level, that is to assume peers leaving and joining the system, and also regarding the dynamics of the documents generated during the peer-group activity. The former requires supporting late peer joining so that a peer can update the local copies of its shared documents. In the later case, in many peer-group activities (e.g. in a software project), documents are usually partially changed over time (e.g. some parts of the document are added, revised, deleted, etc.). We discuss XML representation of documents so that changes at an original document at a peer can be efficiently propagated and updated to its replicas at other peers of the peer-group. Some computational results for optimistic replication techniques for groupware systems in multicast and unicast environments are discussed. Additionally, assuming that some peers are mobile, we require the system to be replication-aware so that mobile peers can be aware of changes in the system through real-time notifications of document changes. On the other hand, we discuss the synchronization of common data to all peers, such as contacts and calendar information, in super-peer model when several mobile peers are connected to the super-peer, which behaves as a small server for the mobile peers. The motivation for data synchronization comes from the needs of geographically distributed teams in business processes to work with intermittent connectivity and thus data synchronization becomes a basic feature. The objective is again to support collaboration of teams of mobile users by enabling anytime and anywhere access to shared contact data. The problem can be formulated as a distributed system problem. We discuss some important requirements such as no modification of server algorithms (the super-peer needs not to perform the contact synchronization task, conflict resolution can be performed at the client side.) and no complex setups at super-peer in order to achieve simple contacts and calendar information sharing. Some computational results will be discussed when using mobile peers under Android system.

Ground station scheduling problem arises in spacecraft operations and aims to allocate ground stations to spacecraft to make possible the communication between operations teams and spacecraft systems. The problem belongs to the family of satellite scheduling for the specific case of mapping communications to ground stations. Ground stations are terrestrial terminals designed for extra-planetary communications with spacecrafts. Spacecrafts are extra-planetary crafts including satellites, space stations, etc. The ground station scheduling problem is a highly constraint problem, among which, the most important is computing timing of spacecrafts communications with the ground stations. The problem can be seen as a time-window scheduling problem given that spacecrafts have their access window and visibility windows--visible time of a spacecraft to a ground station--are to be found avoiding the visibility clash among spacecrafts. The problem is indeed intractable and therefore it is unlikely to be solved in polynomial time to optimality. In this paper we evaluate the effectiveness of Genetic Algorithms (GAs) for near-optimally solving the problem. A data simulation model is used for the experimental study in order to realistically capture features of real instances and evaluate GAs for different scenarios. Computational results are given for the case of a single ground station. Through the experimental evaluation we could identify a set of parameter values that yielded the best performance of the GAs.

Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. In this paper, we investigate packet loss, routing loss and transport loss in mobile Wireless Sensor Networks (WSNs) considering AODV protocol and different radio models. We compare the simulation results for different cases using AODV protocol, Two Ray Ground and Shadowing radio models. We found that the packet loss for AODV with Two Ray Ground is lower than AODV with Shadowing when Tris lower than 50pps. When Tris larger than 50pps, the packet loss for Shadowing is decreased. The transport loss for AODV with Two Ray Ground is lower than Shadowing. The routing loss for AODV with Two Ray Ground is lower than AODV with Shadowing.

With the emergence of several new networking paradigms, optimization modeling and resolution turns out to be crucial to achieve optimized performance networks. One such networking paradigm that requires resolution of optimization problems is Wireless Mesh Networks (WMNs). In this paper, we deal with the effects of population size in GA for node placement problem in WMNs. We evaluate the performance of the proposed system for dense networks considering different distributions and considering giant component and number of covered users parameters. The simulation results show that proposed system has better performance in dense networks like hotspots for Wei bull distribution when the population size is big.

A set of wireless mobile terminals, which cooperate by routing packets to each other create a Mobile Ad hoc Network (MANET). MANETs are attracting attention for their potential use in several fields such as collaborative computing and disaster recovery environments. Considering mobility of the terminals, the routes change dynamically and routing becomes a key process for operation of MANETs. Until now we considered only the Constant Bit Rate (CBR) transmission over UDP transport protocol in our test bed. In this paper, we analyse the performance of our MANET test bed in an indoor environment considering multimedia transmissions with variable packet rate. Using linear and mesh topologies for connecting the nodes, and BATMAN routing protocol, we measure throughput, delay and packet loss metrics.

Among different emerging disruptive technologies, P2P computing has shown its usefulness in designing decentralized and scalable online learning systems. One important feature of P2P systems explored in this context is that of direct peer-to-peer communication. It has been shown in several recent research works that the direct communication between peers increases the interaction among peers and eventually the social and emotive support through peer-to-peer learning. Thus, P2P infrastructure helps creating a small social networking in online groups, in which end users (peer sat the edges of the Internet) can benefit from using aP2P infrastructure and thus overcome the limitations of reduced social interaction in web-based e-Learning systems. However, due to the dynamics of peers joining and leaving the system in an unexpected manner, such systems strive to provide availability of information. In this work we propose a replication-aware approach to increase availability of information in P2P super-peer systems. Our context is that of project-based learning in which members of a peer group work together online to accomplish a common project although our replication scheme can be used also for multimedia content distribution of learning objects.

Wireless Mesh Networks (WMNs) distinguish for they low cost nature that makes them very attractive for providing wireless Internet connectivity. Such infrastructures can be used to deploy community networks, metropolitan area networks, municipal and corporative networks, and to support applications for urban areas, medical, transport and surveillance systems. The main issues in WMNs are achievement of network connectivity and stability as well as QoS in terms of user coverage. In this paper, we propose a system for solving the node placement problem in WMNs. We evaluate the performance of WMN-GA for three different settings of population size 8, 64 and 512. For evaluation, we consider giant component and the number of covered users parameters. The simulation results shows that with the increasing of the population size, GA obtains better result. However, with the increase of the population size, the GA needs more computational time.

With the fast development in mobile technologies and ever-increasing use of smart phones in everyday activity, researchers and developers are paying attention to exploit the advantages of mobile systems to support online teams. One important advantage would-be to implement the A3 paradigm: Anytime, Anywhere, Awareness, that is notifying users about ongoing activity in their online workspace. However, due to the computational limitation of mobile devices, the extension of web-based systems to systems that support also mobile clients require specific design and implementation to make the system usable, efficient and scalable. In this work we present an event-based approach to extend web-based applications to support Anytime, Anywhere, Awareness in Online Teams. It should be noted that the A3 paradigm is meant not only as informing channel but also for social support and scaffolding. The key feature here is the definition of events linked to state changes of the objects in the common workspace. The events are then processed and send to mobile clients for consumption. We present the main requirement analysis, the building blocks of the architecture for efficient event management and the prototype implementation.

One important field of application of mobile computing systems is that of supporting online learning teams working together to accomplish a common learning goal. The traditional web-based online learning systems used to support online teams are being extended to support mobile clients and thus many of the features of collaborative learning should also be available to mobile clients. In this work, we present a prototype of a web-based application supporting mobile clients with features of coordination through a common contacts information and calendar of tasks to support different pedagogical models based on collaborative learning. The main objective is to support online mobile clients with ongoing changes in the web-based system and thus to shorten the time-to know and time-to-response among mobile clients during the team work. Additionally, our system is meant for mutual support of members as well as to support decision taking regarding learning task accomplishment. The system is based on event definition related to workspace artefacts (contacts, tasks, etc.) that observe and inform about the changes in the system. We present the main requirement analysis, the building blocks of the architecture for efficient event management and the prototype implementation that facilitates the collaborative learning activity.

Cloud computing provides an efficient and flexible means for various services to meet the diverse and escalating needs of IT end-users. It offers novel functionalities including the utilization of remote services in addition to the virtualization technology. The latter feature offers an efficient method to harness the cloud power by fragmenting a cloud physical host in small manageable virtual portions. As a norm, the virtualized parts are generated by the cloud provider administrator through the hyper visor software based on a generic need for various services. However, several obstacles arise from this generalized and static approach. In this paper, we study and propose a model for instantiating dynamically virtual machines in relation to the current job characteristics. Following, we simulate a virtualized cloud environment in order to evaluate the model's dynamic-ness by measuring the correlation of virtual machines to hosts for certain job variations. This will allow us to compute the expected average execution time of various virtual machines instantiations per job length.

Wireless networks are continuing to attract attention for their potential use in several fields such as ad-hoc networks, sensor networks, mesh networks, and vehicular networks. Vehicular Ad-hoc networks (VANETs) are expected to be massively deployed in upcoming vehicles, because their use can improve the road safety and comfort. The effective implementation of vehicular communication could improve traffic management system. This effectiveness could be achieved by designing and implementing efficient vehicular network protocols. In this paper, we have implemented two routing protocols: DSR and DYMO and investigated the performance of these routing protocols using PDR and good put metrics. The simulation results shows that DYMO protocol performs better than DSR protocol.

In P2P systems, evaluation of trustworthiness of the peers is very important for a safe communication. In order to decide the peer trustworthiness different parameters are needed. In this paper, we propose two fuzzy-based peer trustworthiness systems for JXTA-Overlay P2P platform. In the first system, we considered two parameters: Actual Behavior Criterion (ABC) and Reputation (R). In the second system, we took in consideration three parameters: Actual Behavior Criterion (ABC), Mutually Agreed Behavior (MAB) and Reputation(R). We compared the performance of the proposed systems by computer simulations. The simulation results have shown that in general the system with three parameters has better performance, but the complexity is higher.

Efficient scheduling of jobs to resources is a core service of Computational Grids (CGs). Due to the large scale, the dynamic nature and the highly heterogeneous tasks and resources, scheduling is a challenging problem in CGs. Different methods, from simple heuristics to more sophisticated optimization and artificial intelligence techniques, have been for a while now in the research and development agenda of researchers of the Grid computing community. One family of algorithms that represent interest is that of Memetic Algorithms (MAs), a variant of evolutionary algorithms that combines genetic search with local search. In this paper we present a study on the tuning of the operators in MAs for the problem of Independent Batch Scheduling in Computational Grids. The aim is to identify a combination of operators and parameters that would lead to the design of robust Grid schedulers using MA solvers that compute high quality planning in very short times. For the study, we have used both a static benchmark of instances and a Grid simulator to capture realistic features of real Computational Grids.

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we survey some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present some computational results for the case of the multi-ground stations scheduling obtained with Genetic Algorithms using the STK simulation toolkit.

Wireless sensor networks are appealing to researchers due to their wide range of application potential in areas such as target detection and tracking, environmental monitoring, and tactical systems. However, lower sensing ranges result in dense networks, which bring the necessity to achieve an effcient medium access protocl subject to power constraints. Various MAC protocols with different objectives were proposed for wireless sensor networks. In this paper, we evaluate the performance of WSNs using TwoRayGround radio model and AODV protocol considering TDMA and CSMA MAC protocols. We consider consumed energy and delay metrics to evalute the performance of WSN. The simulation results have shown that for CSMA, the consumed energy is better than TDMA. But, the delay of TDMA is better than CSMA.

Cars with petrol-driven internal combustion engines are sources of air pollution. Until alternative car engines will replace petrol-driven engines, road transportation is a major source for emissions of carbon monoxide, carbon dioxide, hydrocarbons, and many other organic compounds into the environment. There is a direct relation between the car’s emissions and its acceleration: an accelerating car will pollute more than a non-speeding car. In this paper we present a mobile system capable of guiding the driver’s decisions with the goal of reducing vehicle emissions. The system considers parameters ranging from the car’s characteristics to human reactions. In this we present results demonstrating the capability of the system to produce decisions that reduce pollution in urban traffic environment.

In Mobile Ad hoc Networks (MANETs), mobile wireless devices communicate together in an autonomous fashion. The transmission is done by multi-hop routes. Because of mobility and limited resources in MANET, it is difficult to guarantee the QoS requirements. In this work, we propose a fuzzy-based system, which improves the QoS of MANETs via data replication. We aim to support critical requirement applications based on MANETs such as disaster management scenarios in which data retrieval is an important requirement to support real time decision making. There are several techniques to optimize data replication strategy for this task. In this paper, we use fuzzy logic and build a system which has three input linguistic parameters and one output linguistic parameter. We evaluate by simulations the proposed system. The simulation results show that the proposed system has a good decision.

Much work is underway within the broad next generation technologies community on issues associated with the development of services to foster collaboration via the integration of distributed and heterogeneous data systems and technologies. In previous works, we have discussed how these could help coin and prompt future direction of their usage (integration) in various real-world scenarios such as in disaster management. This paper builds upon on our previous works and addresses the use of learning agents called learning birds in modelling the process of data collection using wireless sensor networks, Specifically, learning birds are some sort of nature-inspired learning agents collaborating to create collective behaviours. As an artificial bird flock, the swarm members collaborate in positioning while moving within a particular environment. In order to improve the diversity of the flock, each individual needs learning the how to position relatively to its neighbours. Q learning is a very famous reinforcement learning algorithm, which offers a very efficient and straightforward learning approach based-on gained experiences. Therefore, a swarm of birds collaborating and learning while exchanging information to position offers a very useful modelling approach to develop ad hoc based mobile data collection tools. To achieve this, we use a disaster management scenario.

Node placement problems have been long investigated in the optimization field due to numerous applications in facility location, logistics, services, etc. Such problems are attracting again the attention of researchers now from the networking domain, and more especially from Wireless Mesh Networks (WMNs) field. Indeed, the placement of mesh routers nodes appears to be crucial for the performance and operability of WMNs, in terms of network connectivity and stability. However, node placement problems are known for their hardness for solving them to optimality, and therefore heuristics methods are used to near-optimally solve such problems. In this work we evaluate the performance of different heuristic methods in order to judge on their suitability of solving mesh router nodes problem. We have selected methods from two different families, namely, local search methods (Hill Climbing and Simulated Annealing) and population-based methods (Genetic Algorithms). The former are known for their capability to exploit the solution space by constructing a path of visited solutions, while the later use a population of individuals aiming to largely explore the solution space. In both cases, a bi-objective optimization consisting in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage are considered. In the experimental evaluation, we have used a benchmark of instances -varying from small to large size- generated using different distributions of mesh node clients (Uniform, Normal, Exponential and Weibull).

Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. In this paper, we investigate how the sensor network performs in the case when three events move with special movement path. We compare the simulation results for three cases: stationary, random and simple 4 path. We found that the goodput of random model has the best value among the three models when Tr<; 1pps. The average packet loss of simple 4 point model is the lowest among the three models. The RE of stationary model, simple 4 point model and random model has stable values. In case of random model, the RE is better than other models.

Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In order to deal with this problem, we have proposed a power reduction algorithm for sensor networks based on fuzzy logic and number of neighbor nodes. In this paper, we propose a new fuzzy-based cluster-head selection system to improve the performance of the previous system. The proposed system in different from previous system uses 4 input linguistic parameter for cluster-head decision. We evaluate the proposed system by simulations and show that the proposed system has a good cluster-head selection.

With the emergence of wireless networking paradigm, several optimization problems are appearing in such networks. Such problem are related to optimizing network connectivity, coverage and stability. The resolution of these problems turns out to be crucial for optimized network performance. In the case of Wireless Mesh Networks, such problems include computing placement of mesh router nodes so that network performance is optimized. However, as these optimization problems are known to be computationally hard to solve, Genetic Algorithms (GAs) have been recently investigated as effective resolution methods. Mutation operator is one of the GA ingredients. Unlike crossover operators, which achieve to transmit genetic information from parents to off springs, mutation operators usually make some small local perturbation of the individuals, having thus less impact on individuals. Moreover, crossover is “a must” operator in GA and is usually applied with high probability, while mutation operators when implemented, are applied with small probability. Due to this, mutation operator is usually considered as a secondary operator. However, many studies in the literature have shown that mutation when effectively combined with selection operators can improve the performance of GAs. In this work we present the results of an experimental study on the effect of mutation and selection operators in GA for mesh router nodes placement problem. The study aims to identify the mutation and selection types that work best for instances of different characteristics.

Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an intagrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deals with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network.

We present high performing SLS algorithms for learning and inference in Markov Logic Networks (MLNs). MLNs are a state-of-the-art representation formalism that integrates first-order logic and probability. Learning MLNs structure is hard due to the combinatorial space of candidates caused by the expressive power of first-order logic. We present current work on the development of algorithms for learning MLNs, based on the Iterated Local Search (ILS) metaheuristic. Experiments in real-world domains show that the proposed approach improves accuracy and learning time over the existing state-of-the-art algorithms. Moreover, MAP and conditional inference in MLNs are hard computational tasks too. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) schema. The first algorithm performs MAP inference by performing a RoTS search within a ILS iteration. Extensive experiments show that it improves over the state-of the-art algorithm in terms of solution quality and inference times. The second algorithm combines IRoTS with simulated annealing for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.

Task scheduling and resource allocation remain still challenging problems in Computational Grids (CGs). Traditional computational models and resolution methods cannot effectively tackle the complex nature of Grid, where the resources and users belong to many administrative domains with their own access policies and users' privileges, and security and task abortion awareness are addressed as important scheduling criteria. In this paper we propose a neural network approach for supporting the security awareness of the genetic-based grid schedulers. Making a prior analysis of trust levels of the resources and security demand parameters of tasks, the neural network monitors the scheduling and task execution processes. The network learns patterns in input (tasks and machines initial characteristics) and outputs (information about resource failures and the resulting tasks and machines characteristics) data, and finally sub-optimal schedules are generated, which are then used to modify the initialization procedures of genetic scheduling algorithms. We extended the Hyper Sim-G Grid simulator framework by Neural Network module to evaluate the proposed model under the heterogeneity, the large-scale and dynamics conditions. The relative performance of GA-based and Neural Network GA-based schedulers is measured by the make span and flow time metrics. The obtained results showed the efficacy of the Neural Network approach to enhance the secure GA-based schedulers.

Call Admission Control (CAC) is one of the resource management functions, which regulates network access to ensure QoS provisioning. However, the decision for CAC is very challenging issue due to user mobility, limited radio spectrum, and multimedia traffic characteristics. In our previous work, we implemented a fuzzy based CAC system called FACS-MP, which consider many priorities. In this paper, we evaluate by simulations the performance of priority algorithm which is part of FACS-MP system. From the simulations results, we conclude that the FACS-MP make a good differentiation of different services based on different priorities.

A foremost objective in wireless networks is to facilitate the communication of mobile users and the widespread tracking and prediction of their mobility regardless of their point of attachment to the network. In indoor environments the effective users' motion prediction system and wireless localization technology play an important role in all aspects of people's daily lives, including e.g. living assistant, navigation, emergency detection, surveillance/tracking of target-of-interest, evacuation purposes, and many other location-based services. Prediction techniques that are currently used do not consider the motivation behind the movement of mobile nodes and incur huge overheads to manage and manipulate the information required to make predictions. In this paper we propose an activity-based continuous-time Markov model to define and predict the human movement patterns. Then we demonstrate the utility of Nonparametric Belief Propagation (NBP) technique in particle filtering, for both estimating the node locations and representing location uncertainties, and for prediction of the areas that would be visited and those that would not in the future. NBP method admits a wide variety of statistical models, and can represent multi-modal uncertainty. This prediction system may be used as an additional input into intelligent building automation systems.

In this paper, we evaluate the performance of WSNs for single and multi mobile events using Two Ray Ground radio model and AODV protocol. We consider good put and routing efficiency metrics to evaluate the performance of WSN. The simulation results have shown that for single mobile event, the good put is better than multi mobile events when the number of sensor nodes is higher. In case of boids model, the good put of multi mobile events is better than single mobile event. In case of random model, the RE of single mobile event is better than multi mobile events. The RE of multi mobile events is better than single mobile event in case of boids model.

Wireless Mesh Networks (WMNs) have become an important networking infrastructure for providing cost efficient broadband wireless connectivity. WMNs are showing their applicability in deployment of medical, transport and surveillance applications in urban areas, metropolitan, neighboring communities and municipal area networks. In this paper, we deal with connectivity and coverage problem of WMN. Because these problems are known to be NP-Hard, we propose and implement a system based on Genetic Algorithms (GAs). We evaluate the performance of the proposed system by different scenarios using different metrics such as client distribution, crossover rate, mutation rate, coverage area and giant component. The simulation results show that for 32 × 32 and 64 × 64 grid area, Linear Ranking is good selection operator and offers the best network connectivity and user coverage.

The design of an efficient collaborative multirobot framework that ensures the autonomy and the individual requirements of the involved robots is a very challenging task. This requires designing an efficient platform for inter-robot communication. P2P is a good approach to achieve this goal. P2P aims at making the communication ubiquitous thereby crossing the communication boundary and has many attractive features to use it as a platform for collaborative multi-robot environments. We have implemented a P2P system based on JXTA Overlay. In this paper, we use JXTA Overlay as a platform for robot collaboration and knowledge sharing. We evaluated the knowledge sharing system by many experiments. Experimental results have shown that the proposed system has a good performance and can be used successfully for knowledge sharing between robots.

A Vehicular Ad-Hoc Network, or VANET, is a form of mobile ad-hoc network, which enables communication between vehicles and nearby road-side infrastructure through a wireless sensing device installed in the vehicles. In this paper, we present a simulation system for VANET called CAVENET (Cellular Automaton based Vehicular Network). In CAVENET, the mobility patterns of nodes are generated by an 1-dimensional cellular automata. We implemented three routing protocols on CAVENET. We investigated the performance of the implemented routing protocols by CAVENET. The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.

In this paper we address the resolution of the Ground Station Scheduling problem by Genetic Algorithms. Ground Stations (GS) are required during spacecraft (S/C) operations to provide communications links between operations teams and the S/C systems. Basic communication types, such as telemetry, tele-command and tracking, are all supported by the same satellite and GS systems. The allocation of ground resources to S/C is a highly constrained problem and traditionally has been conducted manually: stations are selected for communication support with certain S/C for certain periods. The manual approach has clear limitations and new modern scheduling approaches are needed to tackle with the complexity of the problem and produce optimal solutions to scheduling operations. We propose the use of Genetic Algorithm, a well-known family of population-based methods, for solving the problem. A series of genetic operators have been designed to find the configuration that outputs the best GA solution for the problem. The proposed GA has been implemented in Matlab and experimentally studied on a set of randomly generated instances. The results of the study showed the effectiveness of the proposed GA algorithm.

There has been a lot of research recently devoted to scheduling and resource allocation in Grid systems. Research efforts have been done in particular to the use of heuristic and meta-heuristic approaches in the design of efficient Grid schedulers. In this paper we present a comprehensive study on the performance of different population-based heuristic methods, namely Genetic Algorithms, Memetic Algorithms and Cellular Memetic Algorithms for the problem. The aim is to shed light on the advantages and limitations of different population based methods as well as their hybridization with local search methods, such as Tabu Search, when solving the multi-objective version of the problem under execution time restrictions of Grid schedulers. We considered a set of scenarios that represent a high variation regarding the size of entries and static/dynamic features aiming to judge on the robustness with regard to the quality of the solutions obtained by the considered methods. These scenarios are divided into static, which provides a single set of tasks and resources for each entry, and dynamic, using a grid simulator used to observe the behavior of heuristics in Grid environments in real time.

Event detection is a major issue for applications of wireless sensor networks. In order to detect an event, a sensor network has to identify which application-specific incident has occurred based on the raw data gathered by individual sensor nodes. Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. In this paper, we propose a sensor network with mobile and static sensor nodes. We investigate how the sensor network performs in the case when the sensor nodes move. We compare the simulation results for two cases: the sensor network with stationary sensor nodes and multi mobile sensor nodes. The simulation results have shown that for the multi mobile sensors, the good put is unstable. Also, the good put of stationary sensors is better than multi mobile sensors. In case of consumed energy, the consumed energy of multi mobile sensors is better than stationary sensors. Also, the RE of multi mobile sensors is better than stationary sensors.

Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. In this paper, we propose a sensor network with mobile and static sensor nodes set-up for performing tasks like sensing a phenomenon or monitoring a region. We investigate how the sensor network performs in the case when the sensor nodes move. We compare the simulation results for two cases: the sensor network with stationary sensor nodes and multi mobile sensor nodes. The simulation results have shown that for the multi mobile sensors, the good put is unstable. The good put of stationary sensors is better than multi mobile sensors, but the consumed energy of multi mobile sensors is better than stationary sensors.

Wireless Mesh Networks (WMNs) have become an important networking infrastructure for providing cost-efficient broadband wireless connectivity. In this paper, we propose and implement a system based on Genetic Algorithms (GAs) called WMN-GA. We evaluate the performance of WMN-GA for 0.7 crossover rate and 0.3 mutation rate, Exponential Ranking and different distribution of clients considering number of covered users parameters. The simulation results show that for Normal Distribution the system has better performance. We carried out also simulations for Normal Distribution and 0.8 crossover rate and 0.2 mutation rate. The simulation results shows that the setting for 0.7 crossover rate and 0.3 mutation rate offers better user coverage.

Abstract Grid computing emerged as a paradigm for high‐performance computing and massive parallel processing. Currently, Grid systems have become an important paradigm for efficiently solving large‐scale complex problems from many fields. On the other hand, P2P paradigm originated from file‐sharing, but each time more is being used for the development of large‐scale distributed platforms. Grid and P2P systems have thus followed different trajectories pushed by different motivations, needs and research communities. In fact, both paradigms are evolving in a way that each time they are sharing more common characteristics and are mutually benefiting from their best features. Among these characteristics, we could distinguish the cooperative model for solving complex problems by exploiting their large computing capacity. As such, Grid and P2P systems have achieved notable success, in particular, for e‐Science applications, a family of complex applications arising in science and engineering that need considerable computation power. Despite important advances in the design and use of Grid and P2P systems, they remain still difficult to implement and apply to real‐life problems. The main difficulties reside in the lack of easy‐to‐use middleware for Grid and P2P, in the complexities of setting up and in the tedious task of deploying real‐world Grid/P2P platforms as well as in experimental studies which are often complex and not easy to repeat. In this paper we survey and analyze the advances in communication libraries and middleware for both Grid and P2P systems as well as their limitations when used in real Grid and P2P infrastructures. We also bring examples of real‐life applications of massive data processing that can be efficiently handled through Grid and P2P approaches. Copyright © 2010 John Wiley & Sons, Ltd.

A Mobile Ad hoc Network (MANET) is a collection of wireless mobile terminals that are able to dynamically form a temporary network without any aid from fixed infrastructure or centralized administration. In recent years, MANETs are continuing to attract the attention for their potential use in several fields such as military activities, rescue operations and time-critical applications. In this paper, we present the implementation and analysis of our implemented wireless multi-hop network testbed considering the Optimized Link State Routing (OLSR) protocol for wireless multi-hop networking. We investigate the effect of mobility and topology changing in MANET. We study the impact of best-effort traffic for non line of sight communication. In this work, we consider three models: stationary, mobility and non line of sight communication models. We assess the performance of our testbed in terms of throughput and packet loss. From the experimental results, we found that OLSR has not a good performance when the relay node is moving. Also, the performance deteriorates when the CBR is higher.

In Wireless Mesh Networks (WMNs) the meshing architecture, consisting of a grid of mesh routers, provides connectivity services to different mesh client nodes. The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. Thus, finding optimal or near-optimal mesh router nodes placement is crucial to such networks. In this work we propose and evaluate Genetic Algorithms (GAs) for near-optimally solving the problem. In our approach we seek a two-fold optimization, namely, the maximization of the size of the giant component in the network and that of user coverage. The size of the giant component is considered here as a criteria for measuring network connectivity. GAs explore the solution space by means of a population of individuals, which are evaluated, selected, crossed and mutated to reproduce new individuals of better quality. The fitness of individuals is measured with respect to network connectivity and user coverage being the former a primary objective and the later a secondary one. Several genetic operators have been considered in implementing GAs in order to find the configuration that works best for the problem. We have experimentally evaluated the proposed GAs using a benchmark of generated instances varying from small to large size. In order to evaluate the quality of achieved solutions for different possible client distributions, instances have been generated using different distributions of mesh clients (Uniform, Normal, Exponential and Weibull). The experimental results showed the efficiency of the GAs for computing high quality solutions of mesh router nodes placement in WMNs.

In this paper, we present the implementation and analysis of our implemented testbed considering the Optimized Link State Routing (OLSR) protocol for Mobile Ad hoc Networks (MANET). We investigate the effect of mobility and topology changing in the throughput of MANETs. We study the impact of best-effort traffic for Mesh Topology. Experimental time is 150 seconds. In this work, we consider 6 experimental models and we assess the performance of our testbed in terms of throughput, round trip time and packet loss. From our experiments, we found that the OLSR protocol has a good performance when the source node is moving. However, the performance is not good when the three relay nodes are moving.

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.

Grid and P2P systems have achieved a notable success in the domain of scientific and engineering applications, which commonly demand considerable amounts of computational resources. However, Grid and P2P systems remain still difficult to be used by the domain scientists and engineers due to the inherent complexity of the corresponding middleware and the lack of adequate documentation. In this paper we survey recent developments of Grid and P2P middleware in the context of scientific computing systems. The differences on the approaches taken for Grid and P2P middleware as well as the common points of both paradigms are highlighted. In addition, we discuss the corresponding programming models, languages, and applications.

Mesh router nodes placement is a central problem to Wireless Mesh Networks (WMNs). An efficient placement of mesh router nodes is indispensable for achieving network performance in terms of both network connectivity and user coverage. Unfortunately the problem is computationally hard to solve to optimality even for small deployment areas and a small number of mesh router nodes. As WMNs are becoming an important networking infrastructure for providing cost-efficient broadband wireless connectivity, researchers are paying attention to the resolution of the mesh router placement problem through heuristic approaches in order to achieve near optimal, yet high quality solutions in reasonable time. In this work we propose and evaluate a Simulated Annealing (SA) approach to placement of mesh router nodes in WMNs. The optimization model uses two maximization objectives, namely, the size of the giant component in the network and user coverage. Both objectives are important to deployment of WMNs; the former is crucial to achieve network connectivity while the later is an indicator of the QoS in WMNs. The SA approach distinguishes for its simplicity yet its policy of neighborhood exploration allows to reach promising areas of the solution space where quality solutions could be found. We have experimentally evaluated the SA algorithm through a benchmark of generated instances, varying from small to large size, and capturing different characteristics of WMNs such as topological placements of mesh clients. The experimental results showed the efficiency of the annealing approach for the placement of mesh router nodes in WMNs.

In this paper, we consider the behavior of a wireless sensor network for TwoRayGround and Shadowing radio models for the case of mobile event. By means of simulations, we analyse the performance of AODV protocol. In the previous work, we considered that the event node is stationary in the observation field. In this work, we want to investigate how the sensor network performs in the case when the event node moves. The simulation results show that the shadowing phenomena, by destroying the regularity of the network, reduce the mean distance among nodes and at the same time increase the interference level and the latency of packet transmission. We found that for mobile event, the Goodput and routing efficiency of TwoRayGround is better than Shadowing, but the Depletion of Shadowing is better than TwoRayGround.

This paper aims to explore two crucial aspects of collaborative work and learning: on the one hand, the importance of enabling collaborative learning applications to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness, feedback and support with regards to group performance and collaboration. To this end, in this paper we first propose a conceptual model for data analysis and management that identifies and classifies the many kinds of indicators that describe collaboration and learning into high-level aspects of collaboration. Then, we provide a computational platform that, at a first step, collects and classifies both the event information generated asynchronously from the users' actions and the labeled dialogues from the synchronous collaboration according to these indicators. This information is then analyzed in next steps to eventually extract and present to participants the relevant knowledge about the collaboration. The ultimate aim of this platform is to efficiently embed information and knowledge into collaborative learning applications. We eventually suggest a generalization of our approach to be used in diverse collaborative learning situations and domains.

In this paper, we present the implementation and analysis of our testbed considering the link quality window size (LQWS) parameter of optimized link state routing (OLSR) and better approach to mobile ad-hoc networking (B.A.T.M.A.N.) protocols. We investigate the effect of mobility in the throughput of a mobile ad-hoc network (MANET). The mobile nodes move toward the destination at a regular speed. When the mobile nodes arrive at the corner, they stop for about three seconds. In our experiments, we consider two cases: only one node is moving (mobile node)and two nodes (intermediate nodes) are moving at the same time. We assess the performance of our testbed in terms of throughput, round trip time, jitter and packet loss. From our experiments, we found that throughput of TCP was improved by reducing LQWS.

This work is motivated by the need to developdecentralized P2P approaches to supportlearning and teaching activity in virtualuniversities. In this paper, we present theimplementation of the JXTA-based e-LearningP2P system. We also show the design andimplementation of a smart box environment thatwill be used for stimulating the learners toincrease the learning efficiency. The proposede-learning P2P system is a useful tool formonitoring and controlling learners’ activity. Weevaluate the proposed system by experimentalresults and show that proposed system has a goodperformance. In the future, we aim to use it inreal virtual campus environments.

Modern on-line collaborative learning environments need to be continuously adapted, adjusted, and personalized to each specific target learning group. Moreover, these environments are to enable and scale the involvement of an increasing large number of single/group participants who can geographically be widely distributed, and who need transparently share a huge variety of both software and hardware distributed resources. As a result, collaborative learning applications need to be designed in a way that overcome important non-functional requirements arisen in distributed contexts, such as scalability, availability, interoperability, and integration of different, heterogeneous, and legacy collaborative learning systems. In this paper, an innovative distributed-based approach is presented for increasing the overall performance of collaborative learning systems that contributes to the effectiveness of the collaborative activities, such as online discussions. The experimental results show an outstanding effect on the learning processes and outcomes by enhancing and improving the learning experience a great deal.

Recently, performance evaluation of ad hoc network protocols is done using realistic mobility and traffic models, thus improving the validity and credibility of wireless simulation studies. In this paper we design and implement MixMobGen, a mobility generator for ad hoc vehicular applications, which takes into account mixed vehicular conditions. This is the first mobility model that takes into account, both, slow and fast vehicles. While speed for highway traffic has been modeled following a normal distribution, two studies on the mixed traffic show that speed follows a multimodal or bimodal distribution, instead. In this paper we a) design and implement MixMobGen, a realistic mobility generator that models mixed traffic conditions. b) evaluate protocol performance in 802.11 network protocols using NS 2 simulator.

This work is motivated by the need to develop decentralized P2P approaches to support learning and teaching activity in virtual universities.In this paper, we present the stimulation effects of the SmartBox for e-learning using JXTA-overlay P2P system. We also show the design and implementation of the SmartBox environment that is used for stimulating the learners motivation to increase the learning efficiency. The SmartBox is integrated with our P2P system as a useful tool for monitoring and controlling learners' activity. We found by experimental results thatthe SmartBox is an effective way to increase the learner's concentration. We also investigated the relation between learner's body movement, concentration, and amount of study. We found that when the learner is solving more exerciseshe has more body movements.This shows that when the number of body movements is hight,the learner's concentration is high. We also consider the feasibility of our P2P platform to integrate different computational end-devices.

Presently, there are many research work for sensor networks. In our previous work, we implemented a simulation system for sensor networks. But, we considered that the event node is stationary in the observation field. However, in many applications the event node may move. For example, in an ecology environment the animals can move randomly. In this work, we want to investigate how the sensor network performs in the case when the event node moves. We carried out the simulations for lattice topology and TwoRayGround radio model considering AODV and DSR protocols. We compare the simulation results for two cases: when the event node is mobile and stationary. The simulation results have shown that the routing efficiency for the case of mobile event node is better than the stationary event node using AODV protocol. Also, the goodput for the mobile event node case does not change too much compared with the stationary event case using AODV,but the goodput is not good when the number of nodes is increased.

In this work we present an implementation of Hierarchic Genetic Strategy (HGS) for Independent Job Scheduling on Computational Grids. In our formulation of the scheduling problem, makespan and flowtime parameters are simultaneously optimized. The efficient assignment of jobs to machines that optimizes both objectives is crucial for many Grid systems. The objective of this work is to examine several variations of HGS operators in order to identify a configuration of operators and parameters that works best for the problem. Differently from classical GA algorithms, which maintain only an unstructured population of individuals, HGS performs by many small populations enabling a concurrent search in the optimization domain. From the experimental study we observed that HGS implementation outperforms existing classical GA schedulers for most of considered instances of a static benchmark for the problem.

In the wake of the success of Peer-to-Peer (P2P) networking, security has arisen as one of its main concerns, becoming a key issue when evaluating a P2P system. Unfortunately, some systems' design focus targeted issues such as scalability or overall performance, but not security. As a result, security mechanisms must be provided at a later stage, after the system has already been designed and partially (or even fully) implemented, which may prove a cumbersome proposition. This work exposes how a security layer was provided under such circumstances for a specific Java based P2P framework: JXTA-Overlay.

The ad-hoc networks don't necessarily rely on infrastructure such as base stations and the network can be easily constructed autonomously and automatically. However, such network systems use wireless links, therefore the nodes cannot be always surely connected because of the limit of transmission range. Also, the node distribution is uncertain, therefore the connectivity or connectedness for the entire network cannot be guaranteed. In order to guarantee a stable connectivity of networks, in this paper we use the connectivity measure as the degree of connectedness among two or more nodes. By using Shadowing radio model, we calculate the connectivity degree and analyze the performance of ad-hoc network.

P2P systems are nowadays being used not only for file sharing but also for developing large-scale distributed applications. As an emerging paradigm for distributed computing, P2P systems are raising important issues as many novel aspects have to be dealt with in such systems. One key issue in P2P distributed computing is the efficient discovery and selection of peers, which is needed for many purposes such as efficient allocation of jobs to peers, load balancing, efficient file transfer, etc. Existing P2P distributed applications use ad hoc ways to discover and select peers, usually without any performance guarantee. In this paper we address the problem of the efficient peer selection in P2P distributed platforms. To this end, we have developed a P2P distributed platform using Sun's JXTA technology, which is endowed with resource brokerage strategies to efficiently select peers using four selection models: (a) economic scheduling model; (b) priced-based model; (c) peer-priority selection model;and, (d) random selection model. These different models are aimed to match different needs of P2P applications. Next, we have deployed the P2P JXTA platform in a real network using nodes of the PlaneLab - a planetary scale distributed infrastructure- and have experimentally evaluated the performance of the peer selection models by using a distributed application for processing large size log files of a virtual campus, which requires both efficient file transmission and processing in P2P nodes. The results of our work showed the need to develop, implement and evaluate appropriate models for efficient peer selection to match the different requirements of large-scale P2P distributed applications. Although we have used a concrete technology such as JXTA, our approach is applicable in a more general context of P2P and grid computing domain. Finally, our approach to peer selection through brokerage services is very flexible allowing extensions with other models.

In mobile cellular systems the handover is a very important process to maintain the desired quality of service (QoS). Many handover algorithms are proposed in the literature. However, to make a better handover and keep the QoS in wireless networks is very difficult. In this paper, by using random walk model and fuzzy theory we propose a new handover system. The proposed system uses 3 parameters for handoff decision: signal strength from the present base station (BS), signal strength from the neighbor BS, and the distance between Mobile Station (MS) and BS. The performance evaluation via simulations shows that proposed system can avoid ping-pong effect and has a good handover decision.

In this paper we address the issue of monitoring studentspsila and groupspsila activity in online collaborative learning environments. This issue is especially important in the collaborative e-learning context, since an efficient monitoring process can provide valuable information to online instructors who may guide and support the development of collaborative learning projects. We have developed and tested an information system model which facilitates the automatic generation of weekly monitoring reports derived from data contained in server log files. These reports provide online instructors with visual information regarding studentspsila and groupspsila activity, thus allowing for a quick and easy classification of students and groups according to their activity level. Therefore, entities with a low activity level are identified as soon as possible and just-in-time assistance can be established for them. Furthermore, instructors can use these monitoring reports to forecast potential problems -such as studentspsila dropouts or possible conflicts inside the groups due to unbalanced distribution of tasks- and take operational and tactical decisions oriented to avoid them.

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and during the last few years enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In our previous work, we implemented a e-learning system that is able to increase the learning efficiency by stimulating learners motivation. In this work, we designed and implemented new functions to improve the system performance.

Performance is a key feature of large-scale computing systems. However, the achieved performance when a certain program is executed is significantly lower than the maximal theoretical performance of the large-scale computing system. The model-based performance evaluation may be used to support the performance-oriented program development for large-scale computing systems. In this paper we present a hybrid approach for performance modeling and prediction of parallel and distributed computing systems, which combines mathematical modeling and discrete-event simulation. We use mathematical modeling to develop parameterized performance models for components of the system. Thereafter, we use discrete-event simulation to describe the structure of system and the interaction among its components. As a result, we obtain a high-level performance model, which combines the evaluation speed of mathematical models with the structure awareness and fidelity of the simulation model. We evaluate empirically our approach with a real-world material science program that comprises more than 15,000 lines of code.

File sharing is among the most important features of the today's Internet-based applications. Most of such applications are server-based approaches inheriting thus the disadvantages of centralized systems. Advances in P2P systems are allowing to share huge quantities of data and files in a distributed way. In this paper, we present extensions of JXTA protocols to support filesharing in P2P systems with the aim to overcome limitations of server-mediated approaches. Our proposal is validated in practice by deploying a P2P file sharing system in a real P2P network. The empirical study revealed the benefits and drawbacks of using JXTA protocols for P2P file sharing systems.

The efficient file searching is an essential feature in P2P systems. While many current approaches use brute force techniques to search files by meta information (file names, extensions or user-provided tags), the interest is in implementing techniques that allow content-based search in P2P systems. Recently, clustering techniques have been used for searching text documents to increase the efficiency of document discovery and retrieval. Integrating such techniques into P2P systems is important toenhance searching in P2P file sharing systems. While some effort has been done for content-based searching for text documents in P2P systems, there has been few research work for applying these techniques for multimedia content in P2P systems. In this paper we introduce two P2P content-based clustering techniques for multimedia documents. These techniques are an adaptation of the existing Class-based Semantic Search (CSS) algorithm for text documents. The proposed algorithms have been integrated into a JXTA-based overlay P2P platform, and some initial evaluation results are provided. The JXTA-Overlay together with the considered clustering techniques is thus very useful for developing P2P multimedia applications requiring efficient searching of multimedia contents in peer nodes.

Wireless networks have become increasingly popular and advances in wireless communications and electronics have enabled the development of different kind of networks such as mobile ad-hoc networks (MANETs), wireless sensor networks (WSNs) and wireless sensor-actor networks (WSANs). These networks have different kind of characteristics, therefore new protocols that fit their features should be developed. We have developed a simulation system to test MANETs, WSNs and WSANs. In this paper, we consider the performance behavior of two protocols: AODV and DSR using TwoRayGround model and Shadowing model for lattice and random topologies. We study the routing efficiency and compare the performance of two protocols for different scenarios. By computer simulations, we found that for large number of nodes when we used TwoRayGround model and random topology, the DSR protocol has a better performance. However, when the transmission rate is higher, the routing efficiency parameter is unstable.

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and during the last few years enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In our previous work, we implemented an e-learning system that is able to increase the learning efficiency by stimulating learners' motivation. In this work, we designed and implemented new functions such as: interface changing function, new ranking function and learner's learning situation checking function to improve the system performance.

In this paper we present the design and implementation of a Web interface for HyperSim-G Grid simulation package. The Web interface aims to facilitate the work of users with the Grid simulator package. Thus, through the Web interface, users can remotely run their own simulations, trace the state of the simulations, export the simulation results as well as graphically represent them for a better understanding. Also, the interface facilitates the configuration of many parameters of the simulator as opposed to the straightway command line running of the simulator. Moreover, all the simulation results are stored at the server side and can be consulted and extracted at any time either in pdf or spreadsheet form. The Web application is based on Condor queueing system running on a cluster to allow a large number of users run the simulator simultaneously.

The efficient allocation of jobs to grid resources is indispensable for high performance grid-based applications. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, we present in this paper a new tabu search (TS) algorithm for the problem of batch job scheduling on computational grids. We consider the job scheduling as a bi-objective optimization problem consisting of the minimization of the makespan and flowtime. The bi-objectivity is tackled through a hierarchic approach in which makespan is considered a primary objective and flowtime a secondary one. An extensive experimental study has been first conducted in order to fine-tune the parameters of our TS algorithm. Then, our tuned TS is compared versus two well known TS algorithms in the literature (one of them is hybridized with an ant colony optimization algorithm) for the problem. The computational results show that our TS implementation clearly outperforms the compared algorithms. Finally, we evaluated the performance of our TS algorithm on a new set of instances that better fits with the concept of computational grid. These instances are composed of a higher number of -heterogeneous- machines (up to 256) and emulate the dynamic behavior of these systems.

This work is motivated by the need to develop decentralized P2P approaches in order to control the end-devices in a wide-area network without changing the network security policy. In this paper, we present the implementation of the JXTA-based P2P system called JXTA-Overlay. We use JXTA-Overlay for the control of end-devices such as: a smart box, robot, car and room lightning. We evaluate the proposed system by many experiments and have shown that the proposed system has a good performance and can be used successfully for the control of end-devices.

With the emerging paradigm of grid computing and the development of grid infrastructures, grid-based applications are becoming a common approach for solving many complex, large-scale problems in science and engineering. In order to benefit from the large computing power of grid systems, efficient allocation of jobs to resources is necessary. In this work, we consider the allocation problem in immediate mode, in which jobs are allocated as soon as they arrive in the system. We implemented several methods and measured four parameters of the system: makespan, flow- time, resource utilization and matching proximity. The immediate methods are especially interesting when good quality allocations are necessary in very short time. The considered methods have been tested using the most difficult benchmark in the literature for the problem. The computational results allowed us to identify which of considered methods perform better for makespan, flowtime, resource utilization and matching proximity. Also, we were able to evaluate the usefulness of such methods if we knew in advance certain grid characteristics such as degree of consistency of computing, heterogeneity of jobs and resources.

In order to support multimedia communication, it is necessary to develop routing algorithms which use for routing more than one QoS parameters. This is because new services such as video on demand and remote meeting systems require better QoS. Also, for admission control of multimedia applications different QoS parameters should be considered. In our previous work, we proposed an intelligent routing and CAC strategy using cooperative agents. In this paper, we propose and evaluate the performance of SSRA algorithm and a GA-based InterD agent. Performace evaluation shows that proposed agents have a good behavior

User modelling in on-line distance learning is an important research field focusing on two important aspects: describing and predicting students' actions and intentions as well as adapting the learning process to students' features, habits, preferences, and so on. The aim is to greatly stimulate and improve the learning experience. Indeed, on the one hand, students' intentions may change during the realization of learning activities and thus their actions evolve accordingly as the learning process moves forward. On the other hand, adaptive systems can effectively plan and design appropriate learning tasks according to students' features, habits and interests with the aim of facilitating the achievement of the learning goal. In this context, user modelling implies a continuous processing and analysis of user interaction data during long-term learning activities, which produces large and considerably complex information. As a consequence, processing this information is costly and could require computational capacity beyond that of a single computer. In this paper, we show how a Grid approach can considerably decrease the processing time of log data of on-line distance educational web-based systems. Our prototype is based on the masterworker paradigm and is implemented using a peer-topeer platform running on the Planetlab nodes. The results of our study show the feasibility of using Grid middleware to speed and scale up the processing of log data and thus achieve an efficient and dynamic user modeling in on-line distance learning.

Broadband satellite constellation networks will be required to carry all types of IP traffic, real time interactive traffic as well as non-real time traffic, warranting the need for appropriate QoS for these different traffic flows. In this paper we investigate the need for MPLS traffic engineering in GEO/MEO/LEO satellite networks to address QoS issues. We compare the service received by TCP and UDP flows when they share a link and when they are routed on explicit MPLS traffic trunks. Since MPLS traffic trunks allow non-shortest path links also to be used, the total network throughput goes up with proper traffic engineering. If UDP and TCP flows are mixed in a trunk, TCP flows receive reduced service as the UDP flows increase their rates. Also, we found that with MPLS traffic engineering we can protect real time traffic and VoIP traffic from packet loss and excessive jitter by separating them from other congestion unresponsive flows

Problem determination in today's computing environments consumes between 30 and 70% of an organization's IT resources and represents from one third to one half of their total cost of ownership. The first step to cutting down costs and to enable autonomic computing systems is to have all parts of the system report status in a common log data format and semantics. The generic log adapter (GLA) is a generic parsing engine shipped with the IBM's Autonomic Computing Toolkit that has been conceived to convert proprietary log data into a standard log data event-based format in real time. However, in order to provide generic support for parsing the majority of today's unstructured log data formats the GLA makes heavy use of regular expressions that incur in performance limitations. Current approaches proposed to increase GLA's performance have revolved around fine-tuning the set of regular expressions used to configure the GLA for a particular log data format or writing specific parsing code. In this work we propose a very new approach consisting in transparently parallelizing the GLA by taking advantage of its internal architecture and the fact that structuring log data is a task that lends itself very well to parallelization. We present a master-worker strategy that "gridifies" the GLA efficiently in a completely transparent way for the user

In this work, we consider the behavior of a wireless ad- hoc network under TwoRayGround and Shadowing radio models. By means of simulations, we analyze the performance of three routing protocols: AODV, DSR, and DSDV, and study the global energy depletion inside the network. We found that the energy consumption due to AODV and DSR can be very different with respect to the radio model. In particular, we found a maximum relative difference of 70%. On the other hand, for the proactive DSDV routing protocol the energy consumption rate seems to be independent of the radio model, at least for moderate size of the network (256 nodes). Therefore, for small networks, although proactive protocols offer a great advantage in term of delay, the energy consumption is independent against link failures due to the irregularity of the radio medium.

Computational grids are an important emerging paradigm for large-scale distributed computing. As grid systems become more wide-spread, techniques for efficiently exploiting the large amount of grid computing resources become increasingly indispensable. A key aspect in order to benefit from these resources is the scheduling of jobs to grid resources. Due to the complex nature of grid systems, the design of efficient grid schedulers becomes challenging since such schedulers have to be able to optimize many conflicting criteria in very short periods of time. In this work we exploit the capabilities of cellular memetic algorithms (cMAs) for obtaining efficient batch schedulers for grid systems. A careful design of the cMA methods and operators for the problem yielded to an efficient and robust implementation. Our experimental study, based on a known static benchmark for the problem, shows that this heuristic approach is able to deliver very high quality planning of jobs to grid nodes and thus it can be used to design efficient dynamic schedulers for real grid systems. Such dynamic schedulers can be obtained by running the cMA-based scheduler in batch mode for a very short time to schedule jobs arriving to the system since the last activation of the cMA scheduler.

The goal of this paper is to explore the possibility of using CSCL component-based software under a Grid infrastructure. The merge of these technologies represents an attractive, but probably quite laborious enterprise if we consider not only the benefits but also the barriers that we have to overcome. This work presents an attempt toward this direction by developing a generic platform of CSCL components and discussing the advantages that we could obtain if we adapted it to the Grid. We then propose a means that could make this adjustment possible due to the high degree of genericity that our library component is endowed with by being based on the generic programming paradigm. Finally, an application of our library is proposed both for validating the adequacy of the platform which it is based on and for indicating the possibilities gained by using it under the Grid.

We present two generic parallel skeletons for the tabu search method-a well known meta-heuristic for approximately solving combinatorial optimization problems. The first skeleton is based on independent runs while the second in the classical master-slave model. Our starting point is the design and implementation of a sequential skeleton that is used later as basis for the two parallel skeletons. Both skeletons provide the user with the following: a permit to obtain parallel implementations of the tabu search method for concrete combinatorial optimization problems from existing sequential implementations; there is no need for the user to know either parallel programming or communication libraries; and the parallel implementation of tabu search for a concrete problem is obtained automatically from a sequential implementation of tabu search for the problem. The skeletons, however, require from the user a sequential instantiation of the tabu search method for the problem at hand. The skeletons are implemented in C++ using MPI as the communication library and offer genericity, flexibility, component reuse, robustness and time savings. We have instantiated the two skeletons for the 0-1 multidimensional knapsack problem, among others, for which we report computational results.

In this paper we deal with the parallel approximability of a special class of Quadratic Programming (QP), called Smooth Positive Quadratic Programming. This subclass of QP is obtained by imposing restrictions on the coefficients of the QP instance. The Smoothness condition restricts the magnitudes of the coefficients while the positiveness requires that all the coefficients be non-negative. Interestingly, even with these restrictions several combinatorial problems can be modeled by Smooth QP. We show NC Approximation Schemes for the instances of Smooth Positive QP. This is done by reducing the instance of QP to an instance of Positive Linear Programming, finding in NC an approximate fractional solution to the obtained program, and then rounding the fractional solution to an integer approximate solution for the original problem. Then we show how to extend the result for positive instances of bounded degree to Smooth Integer Programming problems. Finally, we formulate several important combinatorial problems as Positive Quadratic Programs (or Positive Integer Programs) in packing/covering form and show that the techniques presented can be used to obtain NC Approximation Schemes for "dense" instances of such problems.