A Dynamic and Adaptable Service Composition Architecture in the Cloud Based on Multi-Agent Systems

Nowadays, service composition is one of the major problems in the Cloud due to the exceptional growth in the number of services deployed by providers. Recently, atomic services have been found to be unable to deal with all client requirements. Traditional service composition gives the clients a composite service without non-functional parameters. To respond to both functional and non-functional parameters, we need a service composition. Since web services cannot communicate with each other or participate dynamically to handle changes service parameters in service composition, this issue has led us to use a dynamic entity represented by an agent based on dynamic architecture. This work proposes an agent-based architecture with a new cooperation protocol that can offer an automatic and adaptable service composition by providing a composite service with the maximum quality of service. The implementation of this model has been provided in order to evaluate the authors' system. The obtained results demonstrate the effectiveness of their proposed system

SPMA-NETS: SECURITY PROTOCOL BASED MOBILE AGENT IN MANETS

The security in the communication process is an important issue since the days of homing pigeons, where the people accustomed to send encrypted messages. In nowadays, with the technologies development, this issue is considered as a research field, which take a great part of attention. The mobile ad hoc network is aspect of the evolution of communication technology; it is defined a collection of mobile nodes, with no fixed infrastructure, resource constraints, communicate with each other using the radio medium, and dynamic creation and organization. The security issue is becoming a main concern in the applications of mobile ad hoc network.In this paper, we propose a security protocol for a mobile ad hoc networks based mobile agent, where the network is consisting of a set of nodes, each node has node agent for resources estimation of the node and communicate with others agents. The network is divided into a set of clusters; each cluster has to elect a node to be the head cluster, where the monitor agent will be reside. This monitor agent controls the communication inside cluster by collecting and analysing the data from the others nodes, it creates an inspector agent, which can move from one node to another to act like a local IDS in the visited node

A new Itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption

one of the important challenges in wireless sensors networks (WSN) resides in energy consumption. In order to resolve this limitation, several solutions were proposed. Recently, the exploitation of mobile agent technologies in wireless sensor networks to optimize energy consumption attracts researchers. Despite their advantage as an ambitious solution, the itineraries followed by migrating mobile agents can surcharge the network and so have an impact on energy consumption. Many researches have dealt with itinerary planning in WSNs through the use of a single agent (SIP: Single agent Itinerary Planning) or multiple mobile agents (MIP: Multiple agents Itinerary Planning). However, the use of multi-agents causes the emergence of the data load unbalancing problem among mobile agents, where the geographical distance is the unique factor motivating to plan the itinerary of the agents. The data balancing factor has an important role especially in Wireless sensor networks multimedia that owns a considerable volume of data size. It helps to optimize the tasks duration and thus optimizes the overall answer time of the network.  In this paper, we provide a new MIP solution (GIGM-MIP) which is based not only on geographic information but also on the amount of data provided by each node to reduce the energy consumption of the network. The simulation experiments show that our approach is more efficient than other approaches in terms of task duration and the amount of energy consumption

Cross-Layer Routing Based on Semantic Web Services Discovery with Energy Evaluation and Optimization in MANET

The web services discovery process in mobile adhoc networks is considered as a very difficult challenge due to the continuous change in the topology of the network and also the lack of a fixed central directory for publishing web services. Several approaches have been proposed which are based on either keywords or identifiers representing the service to be searched or by using a specific scenario of discovery. All of those proposed solutions try to respect the constraints of ad hoc networks such as energy, bandwidth, throughput ... etc. In this paper we present our new proposed model for measuring the cost of the overall energy consumption in ad hoc networks depending on the web services discovery protocols. We also present a new optimized web services discovery protocol in MANET based on cross_layer routing techniques with the dissemination in the routing process at the same time the semantic web services information and a Discovery_Diameter parameter that we have proposed to limit the area of discovery in the network. Finally, we present simulation results of our defined approach showing a significant optimization of the energy consumption level and the average throughput

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Breast cancer classification using machine learning techniques: a comparative study

Background: The second leading deadliest disease affecting women worldwide, after  lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed.  These approaches show  their effectiveness in data classification in many fields, especially in healthcare.      Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification.  The used dataset is  Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave  the best results with an accuracy of 98% for breast cancer classification.       Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are  performant  ( good accuracy specificity and sensitivity) compared to the  other evaluated algorithms

An Improved Model for Breast Cancer Diagnosis by Combining PCA and Logistic Regression Techniques

Abstract: Breast cancer is weighed one of the most life-threatening illnesses confronting women. It happens when the multiplication of cells in breast tissue is uncontrollable. Several studies have been performed in the healthcare field for early breast cancer diagnosis. However, traditional methods can generate incomplete or misleading outcomes. To overcome these limitations, computer-aided diagnosis (CAD) systems are extensively exploited in the healthcare domain. It is designed to improve accuracy, decrease complexity, and reduce misclassification costs. The goal of this study is to present a breast cancer CAD system based on combining the Principal Component Analysis (PCA) method for feature reduction and Logistic Regression (LR) for BC tumors classification. The experiments have been conducted on Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Original Breast Cancer (WOBC) datasets from UCI repository using different training and testing subsets. Moreover, we carried out extensive comparisons of our approach with other existing approaches. Multiple metrics like precision, F1 score, recall, accuracy, and Area Under Curve (AUC) were used in this study. Experimental results indicate that the proposed approach records a remarkable performance rate with an accuracy of 1.00 and 0.98 for WDBC and WOBC respectively and outperforms the previous works by decreasing the number of features, improving the data quality, and reducing the response time.16 página

Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing

The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and realtime data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios.This work was partially supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under project FAME [RTI2018-093608-B-C33] and excellence network RCIS [RED2018-102654-T]. We also thank Carlos Llamas Jaén for his support with the setting up of the performance evaluation tests