Malika Ioualalen

Detecting DoS attacks in WSN based on clustering technique

WIth the facility of deployment, Wireless Sensor Networks becomes very popular but have special characteristics such as limited battery, limited processing power, and limited storage that makes the energy consumption saving a real challenge. Add to this and due to their distributed deployment, these networks are exposed to denial of service attacks such as jamming and greedy attacks. In all cases these attacks tackle the energy consumption in order to degrade the overall Quality of Service (QoS). In this paper, we propose an energy-preserving solution to detect compromised nodes in WSNs. The proposed method is based on hierarchical clustering technique which elect Controlled nodes (Cnode) that analyze the traffic inside a cluster and to send warnings to the cluster-head (CH) whenever an abnormal behavior is detected. The proposed method is dynamic as the Cnodes are periodically elected among ordinary nodes on each atomic cluster. Such a solution results in a better energy balance while maintaining good detection coverage as it is based on the distance between nodes, the output throughput and delay between packets transmission.

An Approach for Performance Modelling and Analysis of Multi-tiers Autonomic Systems

The need of a modern system to be more autonomous with regard to its environment make autonomic systems very attractive, providing them with self-management capabilities. Usually, an autonomic system reconfigures itself to overcome some problem or optimize itself. However, these reconfigurations may result in a loss of performance or even a service degradation. To avoid such situations, this paper proposes a performance prediction approach for autonomic systems, based on formal modelling. Our goal is to forecast the most appropriate configuration for an autonomic system, particularly, a multi-tier system, in order to get an efficient use of resources. The main idea is to model resource allocation triggered by the autonomic loop for achieving the best configuration. Then, we show how to analyse the whole system and evaluate the autonomic loop impact on system performances. The method is based on Stochastic Petri Nets (SPN) modelling. The resource availability, the workload intensity and the autonomic features are targeted. A typical example of a multiers system with one server is presented, analysed with the GreatSPN tool, to show the effectiveness of the proposed approach.

A Queuing Model for Business Processes Elasticity Evaluation

Cloud Computing is a recent trend in information technology, it refers to a model for enabling ubiquitous, convenient, on demand network access to a shared pool of computing resources that can be rapidly provisioned and released with minimal effort. One of the characteristics of the Cloud is elasticity. Elasticity is the ability of Cloud service to rapidly change the amount of its allocated resources in order to meet the demands variation while ensuring a given QoS. Cloud environments are being increasingly used for deploying and executing business processes and particularly Service-based Business Processes (SBPs). Provisioning of elastic infrastruc-tures and platforms is not sufficient to provide elasticity of SBPs. Therefore, SBPs must be provided with elasticity so that they will be able to be adapted to the variations of workload, while ensuring their functional and non-functional properties. In this paper, we are interested in evaluating SBP selasticity. To do that, we propose to model SBPs elasticity using queuing model system with variable number of services copies to represent SBPs adaptation to demands variation. Afterwards, we propose to use this queuing model to evaluate SBPs elasticity.

A clustering method for wireless sensors networks

Clustering algorithms have been widely used in many domains so as to partition a set of elements into several subsets, each subset (or “cluster”) grouping elements which share some similarities. These algorithms are particularly useful in wireless sensor networks (WSNs), where they allow data aggregation and energy cuts. By forming clusters and electing cluster heads responsible for forwarding their packets, the small devices that compose WSNs have not to reach directly the base station (BS) of the network. They spare energy and they can lead further in time their measuring task, so as to detect forest fires or water pollution for example. In this paper, we will apply a new and general clustering algorithm, based on classificability and ultrametric properties, to a WSN. Our goal is to get clusters with a low computational complexity, but with an optimal structure regarding energy consumption.

Towards Efficient Component Performance Analysis in Component Based Architectures

The desire to bring better quality and higher efficiency in software design has led to the development of Component Based Systems. This kind of development has several benefits, however, at the performance level, no guarantees ensure software correctness and good performance properties. To help application designers to meet desired performance of their applications, this paper proposes a modular analysis process that allows to assess independently and efficiently component performances and its impact on a component based architecture. This process is achieved through a modelling phase, based on Stochastic Well-formed Nets (SWN), a high level model of Stochastic Petri nets, and a compositional structured performance evaluation method. It starts from the system definition given in a suitable Architecture Description Language, the targeted component implementation and an ”abstract view” of other components, then provides efficiently system performance indexes. The process is illustrated through an application example.

Clustering wireless sensors networks with FFUCA

Clustering algorithms play an important role in design and deployment of in wireless sensor networks (WSNs). We apply here a general clustering algorithm namely FFUCA (Fast and Flexible Unsupervised Clustering Algorithm) on WSNs. This application shows that FFUCA provides rapidly a strong organizational structure of nodes. We compare its built structures with those of the common algorithm LEACH to validate our approach. We aim to provide an optimal structure regarding energy consumption but with a low computational complexity.

Structured performance analysis for component-based systems

The component-based system (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. This paper proposes a compositional method for modelling and structured performance analysis of CBS. Modelling is based on stochastic well-formed nets (SWNs), a high level model of stochastic Petri nets, widely used for dependability analysis of concurrent systems. Starting from the definition of the system given in a suitable architecture description language, and from the definition of the elementary components, we build an SWN of the global system together with a set of SWNs modelling the components of the CBS and their connections. From these models, we derive performances of the system thanks to a structured analysis induced by the structure of the CBS. We describe the application of our method through an example designed in the framework of the CORBA component model.

Performance evaluation of Fractal component-based systems

Component-based system development is now a well accepted design approach in software engineering. Numerous component models have been proposed, and for most of them, specific software tools allow building component-based systems (CBS). Although these tools perform several checks on the built system, few of them provide formal verification of behavioural properties nor performance evaluation of the resulting system. In this context, we have developed a general method associating to a CBS, a formal model, based on stochastic well formed nets, a class of high-level Petri nets, allowing qualitative behavioural analysis together with performance evaluation of this CBS. The definition of the model heavily depends on the (run time) component model used to describe the CBS. In this paper, we instantiate our method to Fractal CBS and its reference Java implementation Julia. The method starts from the Fractal architectural description of a system and defines rules to systematically generate element models of the CBS and their interactions. We then apply a structured method for both qualitative and performance analysis, taking into account the given implementation of the Fractal model. The main interest of our method is to take advantage of the compositional definition of such systems to carry out an efficient analysis. The paper concentrates on performance evaluation and presents our method step by step with an illustrative example.

Using Formal Model for Evaluation of Business Processes Elasticity in the Cloud

As it has been the case with other technologies, the availability of Service-based Business Processes (SBPs) in the Cloud allows imagining new usage scenarios. Typically, these scenarios include the execution of thousands of processes during a very short period of time requiring temporarily a very important amount of resources. Novel and innovative approaches for modeling of business processes should be developed to allow supporting these scenarios and others in a safer and cost-effective way. For instance, it is necessary to define strategies to scale resource consumed by business processes up and down to ensure their adaptation to the workload changes. In this paper, we focus on how to model and evaluate SBPs elasticity strategies. We propose an analytical model based on queuing model with variable number of servers to represent SBPs adaptation to demands’ variation. We consider a queuing model as Markov chain to evaluate elasticity strategies in the steady state, and to calculate the indices of performance. Our analytical model allows Cloud providers to evaluate and decide about the elasticity strategy to consider before implementing it in real environments.

Preventing Denial of Service attacks in Wireless Sensor Networks

Sensor networks are tiny independent devices which are characterized by limiting battery, processing power and storage memory, that makes saving consumption energy as real challenge power. Morever, there are many techniques used used to conserve energy in Wireless Sensor Networks (WSNs) the clustering technique is one of them. In terms of security, WSNs are more vulnerable to attacks than wired networks. However, radio frequencies used in WSNs are open, making the eavesdropping fairly easy. By considering energy consumption and in order to prevent from Denial of Service (DoS) attacks, present study introducing a preventing DoS attacks approach, which is based on using clustering techniques.