The current Field-Programmable Gate Array (FPGA) implementation of cryptographic algorithms faces performance and security challenges because these algorithms were not originally designed to take FPGA features into account. One significant performance limitation arises from the iteration of a round function for a high round number, given the fixed structures like static substitution and diffusion primitives throughout the process. This paper introduces a new framework for a key-dependent, flexible one-round stream cipher scheme specifically designed to benefit from FPGA features. It is called RFCA. Security and performance analyses validate the effectiveness and robustness of the proposed solution, ensuring the desired cryptographic properties. In comparison with an AES implementation, RFCA is 34 times faster.

With the advent of the Multimedia Internet of Things (MIoT), many image compression techniques have been proposed to address the network’s considerable challenges related to performance and security. However, many MIoT devices, such as the nRF52832 SoC with 64Kb RAM or even less, have significant memory constraints, making conventional methods unsuitable. MIoT networks face considerable challenges related to performance and security due to limitations in the power, computation, and memory of MIoT devices. These limitations result in difficulties in handling high image volumes. Multimedia compression is a potential solution to reduce data size. As MIoT devices often rely on wireless connections, they are also vulnerable to diverse security attacks (passive and active). This work introduces a secure and efficient image crypto-compression technique dedicated to devices having limited memory. It also proposes using denoising and a super-resolution deep learning model to reduce the overhead of the compression process and a lightweight cipher scheme that requires a single round of simple operations to reduce the overhead of the encryption process. The proposed approach effectively addresses the mentioned challenges with minimal overhead on the MIoT device, especially in terms of computational and communication delays, and extensive experimentation underscores its suitability in both effectiveness and robustness.

The ever-growing use of digital systems has led to the accumulation of vast datasets, particularly time series, depicting the temporal evolution of variables and systems.Analysing these time series presents a tremendous challenge due to their inherent complexity and heterogeneity.Addressing an industrial need in the pharmaceutical wholesale sector, this paper introduces a new clustering method for time series: TSCAPE.The TSCAPE method uses a distance matrix calculated using dynamic time warping, followed by multidimensional scaling to project time series into a 2D Euclidean space, thus improving the last clustering stage by K-Means.Unlike conventional techniques, this approach, based on clustering of representation of distances in a Euclidean plane rather than on the curve shape, directly enhances the efficiency of the clustering process.The methodology exhibits significant potential for diverse applications, accommodating varied data types and irregular time series shapes.The research compares multiple variants and proposes metrics to assess their effectiveness on two open-access datasets.The results demonstrate the method's superiority over "only distance comparison clustering techniques", like dynamic time warping and K-Means, with future prospects aimed at predictive applications and refining the clustering process by exploring alternative, more powerful clustering algorithms.

The liberalization of the electricity market and the expansion of new forms of electricity production and consumption are paving the way for new smart digital services. These new services will certainly be relying on a new generation of smart meter (SM) that will offer, among other things, prediction of consumption and production at both household and microgrid levels.These predictions can be obtained either through a generic model trained on data collected from all SMs, or through specific models developed for each SM based on its individual data. The benefit of the generic model is that it guarantees an optimal solution. However, its implementation is not possible for security reasons. The use of specific models requires managing a large number of SMs, which poses a significant challenge.This paper presents a Federated Learning (FL) approach, a decentralized privacy-preserving paradigm that achieves com-parable performance to the generic model, considering both consumption and production scenarios.The dataset, gathered from 1153 SMs over a period of 18 months, is provided by a Swiss Distribution System Operators (DSO). Although, data are individually collected per device, the generic model is trained to holistically process this data.Our experimental results demonstrate that our FL based Long Short-Term Memory (LSTM) model performs as well as the generic model and outperforms the specific models, while preserving data privacy and security.

A ceramic monolith risks breakage during production of the exhaust systems in the automotive industry. This is due to its position at a specific angle throughout the canning phase (stuffing technique). To overcome this problem, quality control needs to be automated on each brick. This control aims to adjust, if needed, the positioning of the brick before starting the production. This paper applies image processing techniques following the Canny-Hough method and reaches more than 99% of good detection of straight lines within a tolerance of ±5 degrees, as requested by the plant. Some vision parameters (gain, exposure time and aperture range), have been tested in order to have a better visibility of the reference part. Furthermore, a repeatability test is validated in this paper, allowing the algorithm to be deployed in the plant. The dataset is accessible on the following link: https://doi.org/10.5281/zenodo.5948822

A wireless sensor network (WSN) is a set of special-ized devices that commonly monitor environmental and physical conditions. A critical aspect of applications with WSNs is their limited resources especially in multivariate sensor features when transmitting large amount of data from the nodes to the base station. The aim is then to optimize power consumption during data transmission by using data reduction methods. In this article, we study multivariate data reduction at node's level. We propose a new efficient model based on reducing collected data by aggregation and polynomial regression. We evaluate and compare our method with existing data aggregation techniques, and with the following well-known compression techniques (xz, bzip2, brotli and gzip). The simulation results show that our approach outperforms the existing methods and offers a good approximation of data quality with small approximation errors.

Due to the volume of multimedia sensed data, a network of Multimedia Internet of Things (MIoT) devices faces various challenging constraints, most notably in terms of communication overhead, power consumption, and memory usage. A set of these MIoT devices is unable to overcome the large data-size challenge via the use of the Lossy Multimedia Compression (LMC) such as JPEG and BPG since they are limited in memory and computation. Instead, in this paper, we propose to down-scale images at MIoT devices with a factor of 2, 3 or ≥ 4, which reduces the memory consumption, computation, and communicated data size and consequently the latency and energy consumption. To recuperate high-quality images, we apply a Deep Learning (DL) denoising/super-resolution model at the server-side. On the other hand, as MIoT devices use a wireless connection, there is a higher risk of transmission packets loss compared to a wired connection. Almost, packets loss are managed through costly data re-transmissions or data redundancy. However, these solutions with intrinsically voluminous data such as the multimedia one are costly, especially for limited MIoT devices. To overcome this challenge, the denoising/super-resolution model did also undergo a training model to retrieve high-quality images from down-scaled erroneous ones. The obtained results show how effective this proposed solution is, especially when it comes to the enhancement of visual quality of down-scaled and erroneous images with minimum communication, latency, and consequently resource overhead.

In the Tactile Internet-based fog computing architecture, the sensor devices represent the basic elements for sensing the surrounding environment. They gather a large amount of data due to their use in various real-world Tactile Internet applications. The huge amount of transmitted data from sensor devices to the fog gateway then to the cloud would lead to high data traffic over the network, increased consumed energy, and increased delay to provide the decision at the Fog gateway. These challenges represent a hurdle in the Tactile Internet-based fog system. This paper suggests a Data Transmission Optimization Scheme (DaTOS) in Tactile Internet-based Fog Computing Applications. The protocol works on two-level devices in the Tactile Internet-based fog computing architecture: sensor devices and fog gateway. The DaTOS implements a Lightweight Redundant Data Removing (LiReDaR) Algorithm at the sensor devices level to lower the gathered data before sending them to the fog gateway. In fog gateway, it executes a Data Set Redundancy Elimination (DaSeRE) approach to discard the repetitive data set resulting from the spatial correlation among the data readings sets of sensor nodes. To evaluate the performance of the DaTOS, it was compared to its counterpart methods in the literature like ATP, PFF and Harb. Simulation results indicate that DaTOS outperforms these methods in terms of transmitted data, energy consumption, and data accuracy.

The need to improve smart health systems to monitor the health situation of patients has grown as a result of the spread of epidemic diseases, the ageing of the population, the increase in the number of patients, and the lack of facilities to treat them. This led to an increased demand for remote healthcare systems using biosensors. These biosensors produce a large volume of sensed data that will be received by the edge of the Internet of Medical Things (IoMT) to be forwarded to the data centers of the cloud for further treatment. An edge-fog computing-enabled lossless electroencephalogram (EEG) data compression with epileptic seizure detection in IoMT networks is proposed in this article. The proposed approach achieves three functionalities. First, it reduces the amount of sent data from the edge to the fog gateway using lossless EEG data compression based on a hybrid approach of$k$-means clustering and Huffman encoding (KCHE) at the edge gateway. Second, it decides the epileptic seizure situation of the patient at the fog gateway based on the epileptic seizure detector-based Naive Bayes (ESDNB) algorithm. Third, it reduces the size of IoMT EEG data delivered to the cloud using the same lossless compression algorithm in the first step. Various measures implemented to show the effectiveness of the suggested approach and the comparison results confirm that the KCHE reduces the amount of EEG data transmitted to the fog and cloud platform and produces a suitable detection of an epileptic seizure. The average of compression power of the proposed KCHE is four times the average of compression power of other methods for all EEG records ($Z, F, N, O$, and$S$). Furthermore, the proposed ESDNB outperforms the other methods in terms of accuracy, where it provides accuracy from 99.53 % up to 99.99 % using the data set of Bonn University.

In this work, we propose three efficient variants of a message authentication encryption (MAE) algorithm, which is based on the dynamic key-dependent concept and dynamic operation mode to reach a high level of security. These variants consist of a single pass and a single round, in addition to the use of common operations for the encryption and authentication processes to reduce the required execution time and resources. Accordingly, the proposed scheme outperforms the existing solutions that are based on the static approach with multiple rounds. Furthermore, to reduce the overhead associated with the regeneration of the dynamic key and the corresponding cryptographic primitives, we propose a simple, yet effective update process. In such a scheme, even when the same plaintext is processed, it will be encrypted and authenticated using different cryptographic primitives (substitution and permutation tables in addition to round keys), which guards against the existing cryptanalysis techniques. The experimental results show that the proposed MAE variants are more efficient than the counter with cipher block chaining message authentication code (CCM), Galois message authentication code (GMAC), offset codebook mode (OCB), and the Chacha20-poly1305. The best performance is achieved with the third MAE variant that presents a high throughput with an enhancement of at least 373% compared to CCM, 90% compared to GCM, 23% compared to OCB, and 22% compared to Chacha20-poly1305.

Since the 1920's, air traffic is becoming more prevalent by the year which results in a steady increase of the number of aircrafts roaming the airspace. This requires the expansion of the air surveillance systems in order to be able to manage each one of these aircrafts. Such an accommodation is planned to be implemented using different technologies and notably the Automatic Dependent Surveillance Broadcast (ADS-B) system. The ADS-B protocol is based on the idea that aircrafts as well as air traffic controllers communicate with each other using messages. However, for practicality reasons, those messages are not encrypted thus malicious messages can be injected. Hence, these attacks need to be detected to ensure the safety of the protocol. In this paper, we evaluate deep learning architectures for the purpose of detecting anomalous/malicious ADS-B messages, especially LSTM architectures which appear to be the most promising ones.

In this paper, we propose an automatic myocardial infarction segmentation framework from Delayed Enhancement cardiac MRI (DE-MRI) using a convolutional neural network (CNN) and prior information-based post-treatments. The work was conducted on our DE-MRI dataset, which is collected from daily clinical practice. 195 cases of DE-MRI examinations constitute this dataset, including on average 7 images per case with manually drawn contours by an expert. The objective is to automatically segment myocardial infarctions on both healthy and pathological images in the dataset. In the proposed framework, a downsampling-upsampling segmentation CNN firstly generates high recall segmentations of myocardial infarction from left ventricle DE-MR images, then the proposed prior information-based post-processing method identifies and removes false-positive segmentations from the CNN's prediction. To obtain a high recall prediction, two U-NET like semantic segmentation networks are investigated: CE-NET and its backbone with Dice loss and Stochastic Gradient Descent (SGD) using a batch size of value 1. The prior information-based post-processing evaluates every single contour in the CNN's segmentations: region features in each contour are compared to criteria which are firstly estimated based on the training set images and eventually fine-tuned based on the validation set images. All non-conforming contours are removed from the predictions to improve the accuracy of the segmentation. Combining the high recall networks and prior postprocessing information, we achieve segmentation results comparable to those produced by human experts.

Nowadays, most Internet of Things (IoT) devices collect multiple features and produce multivariate time series. In an IoT application, the mining and classification of the collected data have become crucial tasks. Hybrid LSTM-fully convolutional networks (MLSTM-FCN) provide state-of-the-art classification results on multivariate time series benchmarks. This paper examines the use of the DenseNet architecture, originally proposed for computer vision applications, for the classification of multivariate time series. More precisely, this paper proposes a hybrid LSTM-DenseNet model that is able to achieve the performance of the state-of-the-art models and surpass them in many situations, based on the results obtained from various experiments on 15 benchmark datasets. Thus, this paper suggests the 1D DenseNet as a potential tool to be considered by machine learning engineers and data scientists for IoT time series classification task.

High number of rounds is needed for the existing message authentication algorithms, such as keyed hash functions like Hash-based Message Authentication Code (HMAC) or block cipher based functions like Cipher-based Message Authentication Code (CMAC) and Galois Message Authentication Code (GMAC). Moreover, the employed compression functions consist of several operations to achieve two main properties: confusion and diffusion. This large number of rounds introduces high overhead for resource-limited systems like Internet of Things (IoT) or delay-sensitive systems that have real-time requirements like Intelligent Transparent Systems. In this paper, a new lightweight message authentication algorithm is proposed to reduce the number of rounds to one. The proposed compression function is based on the RC4 stream cipher to reduce the required overhead in terms of latency and resources. Finally, the security and performance analysis shows that the proposed keyed hash function is resistant towards existing security attacks with low resources overhead.

Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and they, therefore, face an ever-increasing number of interventions, most of the time with constant resources. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters with constant resources is therefore to predict their workload, i.e., their number of interventions per hour, based on explanatory variables conditioning human activity. The purpose of this article is to show that these interventions can indeed be predicted, in a nonabsurd way, from state-of-the-art tools such as recurrent long short-term memory neural networks (LSTM). From the list of interventions in the Doubs (France), we show that it is possible to build, from scratch, a neural network capable of reasonably predicting the interventions of 2017 from those of 2012-2016. While the results could be improved, they are already promising and would allow the actions of firefighters with a constant resource to be optimized.

Computer vision applications such as object detection and recognition, allow machines to visualize and perceive their environments. Nevertheless, these applications are guided by learning-based methods that require capturing, storing and processing large amounts of images thus rendering privacy and anonymity a major concern. In return, image obfuscation techniques (i.e., pixelating, blurring, and masking) have been developed to protect the sensitive information in images. In this paper, we propose a framework to evaluate and recommend the most robust obfuscation techniques in a specific domain of application. The proposed framework reconstructs obfuscated faces via deep learning-assisted attacks and assesses the reconstructions using structural/identity-based metrics. To evaluate and validate our approach, we conduct our experiments on a publicly available celebrity faces dataset. The obfuscation techniques considered are pixelating, blurring and masking. We evaluate the faces reconstructions against five deep learning-assisted privacy attackers. The most resilient obfuscation technique is recommended with regard to structural and identity-based metrics.

Security attacks against Internet of Things (IoT) are on the rise and they lead to drastic consequences. Data confidentiality is typically based on a strong symmetric-key algorithm to guard against confidentiality attacks. However, there is a need to design an efficient lightweight cipher scheme for a number of applications for IoT systems. Recently, a set of lightweight cryptographic algorithms have been presented and they are based on the dynamic key approach, requiring a small number of rounds to minimize the computation and resource overhead, without degrading the security level. This paper follows this logic and provides a new flexible lightweight cipher, with or without chaining operation mode, with a simple round function and a dynamic key for each input message. Consequently, the proposed cipher scheme can be utilized for real-time applications and/or devices with limited resources such as Multimedia Internet of Things (MIoT) systems. The importance of the proposed solution is that it produces dynamic cryptographic primitives and it performs the mixing of selected blocks in a dynamic pseudo-random manner. Accordingly, different plaintext messages are encrypted differently, and the avalanche effect is also preserved. Finally, security and performance analysis are presented to validate the efficiency and robustness of the proposed cipher variants.

The Internet of Things (IoT) systems are vulnerable to many security threats that may have drastic impacts. Existing cryptographic solutions do not cater for the limitations of resource-constrained IoT devices, nor for real-time requirements of some IoT applications. Therefore, it is essential to design new efficient cipher schemes with low overhead in terms of delay and resource requirements. In this paper, we propose a lightweight stream cipher scheme, which is based, on one hand, on the dynamic key-dependent approach to achieve a high security level, and on the other hand, the scheme involves few simple operations to minimize the overhead. In our approach, cryptographic primitives change in a dynamic lightweight manner for each input block. Security and performance study as well as experimentation are performed to validate that the proposed cipher achieves a high level of efficiency and robustness, making it suitable for resource-constrained IoT devices.

Wireless sensor networks (WSNs) continue their ascending developement to be among the leaders of technology. Furthermore, images are of paramount importance in several applications based on WSNs. Capturing, processing and transmitting the image face several challenges, mainly because of their highly needed power consumption. The huge number of images sensed and transmitted in a Wireless Video Sensor Network (WVSN) increases the dataflow on the overall network. A WVSN consists of three different layers: the video-sensor node, the coordinator and the sink. Sending images at the same time from different sensor nodes to a coordinator causes several problems. Besides energy consumption and bandwidth usage that represent the two major challenges in WSN, the queue of images on the coordinator can cause slower detection of intrusions and thus slower reaction from the coordinator. These reasons increase the need of a mac-layer protocol to control the network. We propose a new modified communication protocol based on the S-MAC protocol. This solution consists of adding a priority bit to the S-MAC protocol. Our approach is validated by experimentation using raspberry pi 3 and by simulation in OMNET++.

Recently, Wireless Video Sensor Networks (WVSNs) have been one of the most used technologies for surveillance, event tracking, nature catastrophe and other sudden events. Those networks are composed of small embedded camera motes which help to extract the needed information for the monitored zone of interest. A WVSN is divided into 3 different layers: the video sensor-node layer, the coordinator layer and the sink. Every video sensor-node is in charge of capturing the raw data of images and videos and sending it to the coordinator for further analysis before sending the analyzed data to the sink. In a normal scenario, the load of collected images and videos from different sensor nodes on the same network is huge. Sending all the images from all the sensor nodes to the coordinator consumes a lot of energy on every sensor, and may cause a bottleneck. In this paper, some processing and analysis are added based on the similarity between frames on the sensor-node level to send only the important frames to the coordinator. Kinematic functions are defined to predict the next step of the intrusion and to schedule the monitoring system accordingly. Compared to a fully scheduling approach based on predictions, this approach minimizes the transmission on the network. Thus, it reduces the energy consumption and the possibility of any bottleneck while guaranteeing the detection of all the critical events at the sensor-node level as shown in the experiments.

Wireless Body Sensor Networks (WBSNs) are a low-cost solution allowing remote patient monitoring and continuous health assessment, thus reducing healthcare expenditure. In such networks, sensor nodes periodically collect vital signs and send them to the coordinator for fusion. However, sensor nodes have limited energy and processing resources and transmission is the most power-hungry task. In this paper, we target data reduction and energy consumption. We propose to locally adapt, in real-time, the sampling rate of a sensor node according to the variations in the vital sign being monitored and its risk. We propose to dynamically evaluate, in real-time, the risk of any vital sign given the information about the severity level of the patient's health condition and the severity level of the vital sign itself. We have tested our proposed approach on real health datasets in order to evaluate it. The results show that the percentage of detected critical events and the mean-square error (MSE) are both acceptable. In addition, the percentage of data reduction is around 50% implying a reduction of the energy consumption. Adjusting the risk of a vital sign, over time, ensures the adaptation of the sampling rate according to the overall health condition of the patient as well as the severity level of the collected measurements.

Wireless Multimedia Sensor Networks (WMSNs) are composed of small embedded video sensors that allow continuous monitoring of a given territory. They collect and analyze frames from different video sensors deployed in the area of interest. One of the most important challenges in WMSN is the big data problem affecting the energy resources of the video sensors. Cameras and video-sensors send images and videos which costs in terms of memory storage, bandwidth and energy. In this paper, we propose a technique that adapts the frame rate at the level of each video-sensor. Our aim is to reduce the number of frames sent to the coordinator without losing any important information. Our approach is based on analyzing similarity between consecutive frames for each sensor. The proposed algorithm calculates the similarity by the aggregation of color and edge similarities between frames. The results of the proposed algorithm show a reduction in terms of energy consumption and sent data while guaranteeing the detection of critical events.

Wireless Body Sensor Networks (WBSNs) are a low-cost solution for healthcare applications allowing continuous and remote monitoring. However, many challenges are addressed in WBSNs such as limited energy resources, early detection of emergencies and fusion of large amount of heterogeneous data in order to take decisions. In this paper, we propose a multisensor data fusion approach enabling one to determine the patient risk level based on vital signs scores. Consequently, a corresponding decision is taken routinely and each time an emergency is detected. This approach is based on early warning score systems, a fuzzy inference system and a technique determining the score of a vital sign given its past and current value. We evaluate our approach on real healthcare datasets.

Maintaining and improving the quality of life in ageing populations is a necessity. Hence, distant patient monitoring is a solution providing constant surveillance of vital signs and the detection of emergencies when they occur. In the past few years, wireless body sensor networks (WBSNs) emerged as a low cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measures and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a data fusion model using a decision matrix, an early warning score system and fuzzy set theory. We propose an algorithm at the coordinator level of the WBSN, aiming to take the appropriate decision when an emergency is detected.

Nowadays, Wireless Body Sensor Networks (WBSN) are emerging as a low cost solution for healthcare application to find new solutions, regarding patient monitoring which is becoming the elusive requirement. Quicker emergency detection is the main purpose to create a quicker reaction and treatment if required, such as an abnormal variation of the respiration rate, which satisfies the goal of extending life expectancy. This process can help all the chronic patients who are most of the time living alone or in nursing homes. However, the limited lifetime bio-medical sensors bring on the energy consumption challenge as one of the leading challenges in WBSN. Moreover, detecting locally an emergency is also one of the main challenges in WBSN. In this paper, we propose an adaptive sampling approach, based on fisher test theory, that estimates and adapts the sensing frequency based on previous readings and the patient criticality. The main goal is to optimize the energy consumption. Furthermore, we show how emergency alerts can be supported locally on each node of the network. To validate the effectiveness of our approach we conducted several series of simulations and built a simple energy saving comparison.

Steganography schemes are designed with the objective of minimizing a defined distortion function. In most existing state of the art approaches, this distortion function is based on image feature preservation. Since smooth regions or clean edges define image core, even a small modification in these areas largely modifies image features and is thus easily detectable. On the contrary, textures, noisy or chaotic regions are so difficult to model that the features having been modified inside these areas are similar to the initial ones. These regions are characterized by disturbed level curves. This work presents a new distortion function for steganography that is based on second order derivatives, which are mathematical tools that usually evaluate level curves. Two methods are explained to compute these partial derivatives and have been completely implemented. The first experiments show that these approaches are promising.

Summary With the increasing sophistication of image processing algorithms, and because of its low computation complexity, convolution should fully benefit from the ever‐increasing capacities of state‐of‐the‐art graphics processing units, such as Nvidia's Kepler and Maxwell family cards. Currently, it tends to be used as a preprocessing stage within more intricate image manipulations and has recently been implemented quite efficiently by several teams. However, either their implementations do not come near hardware's peak performance or are unable to process large mask sizes. Such limitations are overrun by our original parallel register‐only convolution filter implementation of two‐dimensional convolution filters that can process 32‐bit floating‐point images on a NVidia K40 card using mask sizes up to 127×127 and at the same time achieving pixel throughputs over 29GP/s, which is, as far as we know, the highest rate known to date. Such results were obtained by using registers sparingly and by designing memory access patterns that cancel both load and store replays at warp levels, along with optimizing cache use. Copyright © 2015 John Wiley & Sons, Ltd.

In wireless sensor networks (WSNs), redundant collected measures and the resulting redundant packets to sendto the sink are likely to happen repeatedly. As transmission is an expensive issue in term of energy, eliminating data redundancy and reducing communication load can minimize energy consumption and extend the whole network lifetime. In this paper, we propose an adaptive protocol composed of two phases, called aggregation and transmission protocol (ATP), that operates on each sensor node separately in order to reduce its data transmission and to save energy. We consider a cluster-based scheme in which data is sent periodically from sensor nodes to their appropriate Cluster-Heads (CHs). The proposed protocol searches, during aggregation phase, similarities between data captured during a period p in order to eliminate redundancy from raw data. While during transmission phase, sensor node searches periodic correlation of data, using one way ANOVA model and Fisher test. The proposed protocol was successfully tested on real sensor data. The obtained results show that ATP can significantly minimize energy consumption, comparing to other existing data aggregation techniques, without affecting the quality of data.

Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. This technique is usually used to reduce the energy consumed by a CPU while computing. Thus, decreasing the frequency reduces the power consumed by the CPU. However, it can also significantly affect the performance of the executed program if it is compute bound and if a low CPU frequency is selected. Therefore, the chosen scaling factor must give the best possible trade-off between energy reduction and performance. In this paper we present an algorithm that predicts the energy consumed with each frequency gear and selects the one that gives the best ratio between energy consumption reduction and performance. This algorithm works online without training or profiling and has a very small overhead. It also takes into account synchronous communications between the nodes that are executing the distributed algorithm. The algorithm has been evaluated over the SimGrid simulator while being applied to the NAS parallel benchmark programs. The results of the experiments show that it outperforms other existing scaling factor selection algorithms.

This paper presents many typical problems that are encountered when executing large scale scientific applications over distributed architectures. The causes and effects of these problems are explained and a solution for some classes of scientific applications is also proposed. This solution is the combination of the asynchronous iteration model with JACEP2P-V2 which is a fully decentralized and fault tolerant platform dedicated to executing parallel asynchronous applications over volatile distributed architectures. We explain in detail how our approach deals with each of these problems. Then we present two large scale numerical experiments that prove the efficiency and the robustness of our approach.

This paper presents a new library for the ProActive environment, called AIL-PA (asynchronous iterative library for ProActive). This new library allows to execute programs for solving large scale problems on various architectures. Two models of algorithm can be used: the synchronous iteration model which is efficient on single clusters; the asynchronous iteration model which is more efficient on distributed clusters. Both approaches are tested on both architectures, using Kernel CG of the NAS Parallel Benchmarks on the Grid'5000 platform. These tests also allow us to compare ProActive with AIL-PA and with the Jace programming environment. The results show that the asynchronous iteration model with AIL-PA is more efficient on distributed clusters than the synchronous iteration model. Moreover, these experiments also show that AIL-PA does not involve additional overhead to ProActive.

The aim of this paper is to study the behaviors of the well known conjugate gradient (CG) algorithm and the multisplitting algorithm in a grid context. We focus on the CG implementation used in the NAS benchmark and on the multisplitting approach which produces similar results (from a numerical point of view). By grid context we mean an architecture composed of several heterogeneous clusters geographically distributed and the use of a software environment able to tackle the heterogeneity of the nodes. Under these hypothesis, we performed a set of experiments on the Grid'5000 platform using the pure Java Jace V2 environment. We show that, by drastically reducing global synchronizations, the asynchronous multi-splitting method outperforms the NAS CG implementation, with distant sites, whatever the communication protocol used.

This paper introduces a parallel algorithm to solve large stiff ODE systems in a geographically distant cluster environment. This algorithm is based on the coupling of the waveform relaxation concept and the CVODE algorithm. With respect to the standard PVODE algorithm, it allows to drastically reduce the number of messages exchanged between nodes. It is a coarse grained algorithm well suited for distant grid environments connected via high latency networks. In this paper our work consists in analyzing the execution times taken by the PVODE solver and our algorithm and in explaining the benefits brought by this work.

This paper focuses on large scale experiments with Java and asynchronous iterative applications. In those applications, tasks are dependent and the use of distant clusters may be difficult, for example, because of latencies, heterogeneity, and synchronizations. Experiments have been conducted on the Grid'5000 platform using a new version of the Jace environment. We study the behavior of an application (the Poisson problem) with the following experimentation conditions: one and several sites, large number of processors (from 80 to 500), different communication protocols (RMI, sockets and NIO), synchronous and asynchronous model. The results we obtained, demonstrate both the scalability of the Jace environment and its ability to support wide-area deployments and the robustness of asynchronous iterative algorithms in a large scale context.

In this paper, first order diffusion load balancing algorithms for totally dynamic networks are investigated. Totally dynamic networks are networks in which the topology may change dynamically. Some edges or nodes can appear, disappear or move during the time. In our previous works on dynamic networks, the dynamism was limited to the edges. The main result of this study consists in proving that the load balancing algorithms reduce the unbalance on arbitrary dynamic networks. Notice that the hypotheses of our result are realistic and that for example the network does not have to be maintained connected. To study the behavior of these algorithms, we compare the load evolution by several simulations.

This paper presents CRAC, an environment dedicated to design efficient asynchronous iterative algorithms for a grid architecture. Those algorithms are particularly suited for grid architecture since they naturally allow to overlap communications by computations. Each processor computes its iterations freely without any synchronization with its neighbors. All the characteristics of CRAC are described. A real application using four distant clusters, with a total of 120 processors, shows the interest of this environment and of asynchronous algorithms.

Using Peer-to-Peer (P2P) networks is a way to federate a large amount of processors in order to solve large scale scientific problems. Those networks are decentralized, highly dynamic and composed of heterogeneous machines. The goals of our work is to compute large scale scientific iterative applications on P2P networks. We propose JaceP2P, a multi-threaded Java based library designed to build asynchronous parallel iterative applications. Using this library, it is possible to run such applications on a set of dynamic and heterogeneous machines organized in a decentralized and P2P fashion.

In a previous paper, we have shown the very high power of asynchronism for parallel iterative algorithms in a global context of grid computing. In this article, we study the interest of coupling load balancing with asynchronism in such algorithms. After proposing a noncentralized version of dynamic load balancing which is best suited to asynchronism, we verify its efficiency by some experiments on a general partial differential equation (PDE) problem. Finally, we give some general conditions for the use of load balancing to obtain good results with this kind of algorithm and discuss the choice of the residual as an efficient load estimator.

We introduce a theoretical algorithm and its practical version to perform a decentralized detection of the global convergence of parallel asynchronous iterative algorithms. We prove that, even if the algorithm is completely decentralized, the detection of global convergence is achieved on one processor under the classical conditions. The proposed algorithm is very useful in the context of grid computing in which the processors are distributed and in which detecting the convergence on a master processor may be penalizing or even impossible as in peer to peer computation frameworks. Finally, the efficiency of the practical algorithm is illustrated in a typical experiment.

Summary form only given. AIAC algorithms (Asynchronous Iterations Asynchronous Communications) are a particular class of parallel iterative algorithms. Their asynchronous nature makes them more efficient than their synchronous counterparts in numerous cases as has already been shown in previous works. The first goal is to compare several parallel programming environments in order to see if there is one of them which is best suited to efficiently implement AIAC algorithms. The main criterion for this comparison consists in the performances achieved in a global context of grid computing for two classical scientific problems. Nevertheless, we also take into account two secondary criteria, which are the ease of programming and the ease of deployment. The second goal is to extract from this comparison the important features that a parallel programming environment must have in order to be suited for the implementation of AIAC algorithms.