Elephant Herding Optimization for Service Selection in QoS-Aware Web Service Composition

Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence

Secure and Efficient Sharing Aggregation Scheme for Data Protection in WSNs

International audienceWireless sensor networks (WSNs) are omnipresent in a multitude of applications. One of the important common requirements of these applications is the data security. Indeed, the exchanged data in WSNs are often considered as a preferred target, which can be a subject of several threats, such as eavesdropping , replay, falsification, alteration, etc. Another important common requirement of WSNs applications is data aggregation. Indeed, the limitations of such networks in terms of energy, bandwidth and storage accentuate the need of data aggregation. In this paper, we address these two issues. We propose a new efficient approach for data integrity and credibility protection for WSNs, while ensuring the data aggregation. We consider a cluster-based network architecture, where sensor nodes are equally distributed in clusters. Each sensor node is in charge to deliver one bit of the sensed data and at the same time observe the remaining parts through a parity control based encryption approach. In this manner, the sensed data could be effectively and securely controlled with a low overhead compared to the classical aggregation approaches, where all the nodes transmit individually the sensed data. To validate the proposed protocol we have simulated it using the simulator CupCarbon and in order to evaluate its efficiency in terms of energy, we have developed a prototype with the TelosB platform, where the obtained results show that our method is less energy consuming

B-alphaWSP Selection Algorithm: a Load Balancing Convergecast forWSNs

Many-to-one is a common traffic pattern in wireless sensor networks (WSNs), where nodes periodically forward data hop by hop to a single sink. Unfortunately, such communication pattern leads to lose nodes prematurely, especially those located in the sink neighborhood. That phenomenon is essentially due to the high load transit on these nodes, causing a spuriously activation rate comparing to other nodes therefore more energy waste. This is called the funneling effect, which causes also other issues including high delay and packet loss due to the congestion, and collisions. The objective of this work is to reduce the impact of this phenomenon by employing a load-balancing technique in the route selection process. Therefore, we propose the B-éWSP (Balanced-éWeighted Shortest Path) routing algorithm. Simulation results show that our algorithm can effectively reduce the impact of the funneling effect on the nodes activity. Consequently, it reduces the energy consumption and maintains a satisfactory Packet Reception Rate (PRR) comparing to the éWSP algorithm

A Metric for Evaluating the Privacy Level of a Business Process Logic in a Multi-Cloud Deployment

International audienceSome companies are willing to execute their business processes (BP) in the cloud for enjoying its benefits. However, they are also reluctant because of the new security risks that using cloud resources introduces. Security risk includes many dimensions , but this work focus on preserving the privacy of the logic of a BP deployed in a multi-cloud context by preventing a coalition of malicious clouds to reconstruct important information from this logic. More precisely, the paper presents a BP logic privacy metric directly supporting the evaluation of the risk a company has its logic hacked in a particular multi-cloud configuration Index terms— BP modelling, BP deployment in the cloud, Security risk, BP logic privac

InterRC: An Inter-Resources Collaboration Heuristic for Scheduling Independent Tasks on Heterogeneous Distributed Environments

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms

Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition

The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics

Impact of Duty-Cycling: Towards Mostly-Off Sensor Networks

International audienceWireless Sensor Networks (WSNs) became omnipresent in our daily life. As a result, they have emerged as a fruitful research topic, because of their advantages, especially their low cost and easy deployment. However, these attractive merits imply that available resources, especially energy, in each sensor node have to be wisely used through different network dynamics. Besides other techniques, duty-cycling (DC) is the first widely used one to save energy in WSNs. However, due to the continuous changes, mainly in the energy availability, the nodes have to operate in a very low DC which is a required strategy in many applications in order to keep the network operational. This article presents a detailed survey that provides an interesting view of different DC schemes that are proposed to tackle the specific WSN challenges, and it also gives a novel classification of DC schemes that includes the most recent techniques. The last part aims to investigate the impact of the low DC on both the network and the application layer

UNCERTAINTY IN THE PERT’S CRITICAL PATH

In this paper, the problem of scheduling is addressed. Due to difficulties in scheduling projects, researchers and professionals have proposed a tremendous number of works aiming at finding the best method to accomplish this phase of any project, especially if the decision maker is facing the challenge of uncertain estimations. One of the most used families of techniques is discussed in this paper, namely the Fuzzy Program Evaluation and Review Technique techniques. This family of techniques is based mainly on using the classical Program Evaluation and Review Technique and the fuzzy set theory. This work presents a comparison between two interesting techniques used to tackle the problem of uncertainty, namely the Model for Project Scheduling with Fuzzy Precedence Links and the Centroid techniques. The first technique is based on the relationship strength between each two activities in order to resolve the problem of the critical path. The second technique is based on a very simple mathematical concept and arithmetic of fuzzy numbers to tackle the same problem. Based on the results of a numerical example, we noticed that the simplicity and inexpensiveness of the Centroid method beat the complicated and expensive characteristics of the Model for Project Scheduling with Fuzzy Precedence Links

Gene Selection and Classification of Microarray Data Method Based on Mutual Information and Moth Flame Algorithm

International audienc