In this paper we survey computational models for Grid scheduling problems and their resolution using heuristic and meta-heuristic approaches. Scheduling problems are at the heart of any Grid-like ...computational system. Different types of scheduling based on different criteria, such as static versus dynamic environment, multi-objectivity, adaptivity, etc., are identified. Then, heuristic and meta-heuristic methods for scheduling in Grids are presented. The paper reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristic and meta-heuristic approaches for the design of efficient Grid schedulers. We also discuss on requirements for a modular Grid scheduling and its integration with Grid architecture.
The fast development of Internet of Things (IoT) computing and technologies has prompted a decentralization of Cloud-based systems. Indeed, sending all the information from IoT devices directly to ...the Cloud is not a feasible option for many applications with demanding requirements on real-time response, low latency, energy-aware processing and security. Such decentralization has led in a few years to the proliferation of new computing layers between Cloud and IoT, known as Edge computing layer, which comprises of small computing devices (e.g. Raspberry Pi) to larger computing nodes such as Gateways, Road Side Units, Mini Clouds, MEC Servers, Fog nodes, etc. In this paper, we study the challenges of processing an IoT data stream in an Edge computing layer. By using a real life data stream set arising from a car data stream as well as a real infrastructure using Raspberry Pi and Node-Red server, we highlight the complexities of achieving real time requirements of applications based on IoT stream processing.
•Investigate challenges of an IoT data stream processing at edge computing layer.•Analyze semantic data enrichment techniques.•Use real life car data stream for semantic data enrichment and anomaly detection.•Use a real infrastructure of Raspberry Pi and Node-Red server for system deployment.•Highlight the complexity of meeting real-time requirements on IoT stream processing.
The fast development of IoT in general and wearable smart sensors in particular in the context of wellness and healthcare are demanding for definition of specific infrastructure supporting real time ...data analysis for anomaly detection, event identification, situation awareness just to mention few. The explosion in the development and adoption of these smart wearable sensors has contributed to the definition of the Internet of Medical Things (IoMT), which is revolutionizing the way healthcare is tackled worldwide. Data produced by wearable sensors continuously grow and could be spread among clinical centers, hospitals, research labs, yielding to a Big Data management problem. In this paper we propose a technological and architectural solution, based on Open Source big data technologies to perform real-time analysis of wearable sensor data streams. The proposed architecture is composed of four distinct layers: a sensing layer, a pre-processing layer (Raspberry Pi), a cluster processing layer (Kafka’s broker and Flink’s mini-cluster) and a persistence layer (Cassandra database). A performance evaluation of each layer has been carried out by considering CPU and memory usage for accomplishing a simple anomaly detection task using the REALDISP dataset.
•Presents an edge stream computing architecture.•Aims to perform real time analysis of wearable sensor data streams.•Proposes a case study in the domain of the Internet of Medical Things.
Nowadays E-health cloud systems are more and more widely employed. However the security of these systems needs more consideration for the sensitive health information of patients. Some protocols on ...how to secure the e-health cloud system have been proposed, but many of them use the traditional PKI infrastructure to implement cryptographic mechanisms, which is cumbersome for they require every user having and remembering its own public/private keys. Identity based encryption (IBE) is a cryptographic primitive which uses the identity information of the user (e.g., email address) as the public key. Hence the public key is implicitly authenticated and the certificate management is simplified. Proxy re-encryption is another cryptographic primitive which aims at transforming a ciphertext under the delegator A into another ciphertext which can be decrypted by the delegatee B. In this paper, we describe several identity related cryptographic techniques for securing E-health system, which include new IBE schemes, new identity based proxy re-encryption (IBPRE) schemes. We also prove these schemes’ security and give the performance analysis, the results show our IBPRE scheme is especially highly efficient for re-encryption, which can be used to achieve cost-effective cloud usage.
•We show how to securely integrate the IBE and IBPRE into an E-health cloud system.•We also propose a novel IBE scheme and prove its security.•Furthermore, we propose a novel IBPRE scheme. It does not follow Green’s paradigm.•We propose an E-health cloud system framework based on our IBE and IBPRE.•Our IBPRE scheme can be highly cost-effective for E-health cloud system users.
The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and ...devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system.
With the fast growth of the Internet infrastructure and the use of large-scale complex applications in industries, transport, logistics, government, health, and businesses, there is an increasing ...need to design and deploy multifeatured networking applications. Important features of such applications include the capability to be self-organized, be decentralized, integrate different types of resources (personal computers, laptops, and mobile and sensor devices), and provide global, transparent, and secure access to resources. Moreover, such applications should support not only traditional forms of reliable distributing computing and optimization of resources but also various forms of collaborative activities, such as business, online learning, and social networks in an intelligent and secure environment. In this paper, we present the Juxtapose (JXTA)-Overlay, which is a JXTA-based peer-to-peer (P2P) platform designed with the aim to leverage capabilities of Java, JXTA, and P2P technologies to support distributed and collaborative systems. The platform can be used not only for efficient and reliable distributed computing but also for collaborative activities and ubiquitous computing by integrating in the platform end devices. The design of a user interface as well as security issues are also tackled. We evaluate the proposed system by experimental study and show its usefulness for massive processing computations and e-learning applications.
With the advent of cloud computing, individuals and organizations have become interested in moving their databases from local to remote cloud servers. However, data owners and cloud service providers ...are not in the same trusted domain in practice. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective database utilization a very challenging task. To address this challenge, in this paper, we propose L-EncDB, a novel lightweight encryption mechanism for database, which (i) keeps the database structure and (ii) supports efficient SQL-based queries. To achieve this goal, a new format-preserving encryption (FPE) scheme is constructed in this paper, which can be used to encrypt all types of character strings stored in database. Extensive analysis demonstrates that the proposed L-EncDB scheme is highly efficient and provably secure under existing security model.
The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is ...concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP∖USD, EUR∖GBP, and EUR∖USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.
The fast development in the production of small, low-cost satellites is propelling an important increase in satellite mission planning and operations projects. Central to satellite mission planning ...is the resolution of scheduling problem for an optimised allocation of user requests for efficient communication between operations teams at the ground and spacecraft systems. The aim of this paper is to survey the state of the art in the satellite scheduling problem, analyse its mathematical formulations, examine its multi-objective nature and resolution through meta-heuristics methods. Finally, we consider some optimisation problems arising in spacecraft design, operation and satellite deployment systems.
Emotional learning involves the acquisition of skills to recognize and manage emotions, develop care and concern for others, make responsible decisions, establish positive relationships, and handle ...challenging situations effectively. Time is an important variable in learning context and especially in the analysis of teaching-learning processes that take place in collaborative learning, whereas time management is crucial for effective learning. The aim of this work has been to analyze the effects of emotion management on time and self-management in e-learning and identify the competencies in time and self-management that are mostly influenced when students strive to achieve effective learning. To this end, we run an experiment with a class of high school students, which showed that increasing their ability to manage emotions better and more effectively enhances their competency to manage the time allocated to the learning practice more productively, and consequently their learning performance in terms of behavioral engagement and achievement and partly, in terms of cognitive engagement and self-regulation. Teacher affective feedback was proved to be a crucial factor to enhance cognitive engagement.
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•Emotion awareness is positively related to time and self-management in learning.•Emotion management enhances students’ behavioral engagement and achievement.•Emotion management partially supports cognitive engagement and self-regulation.•Teacher affective feedback is a crucial factor to enhance cognitive engagement.•Emotional and time management can reduce student workload.
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