Mouloud Koudil

Using artificial bees to solve partitioning and scheduling problems in codesign

Partitioning and scheduling are two central issues in the design of embedded systems since they can widely influence the characteristics of the system under design. The numerous constraints imposed by the environment and/or the underlying target architecture of mixed systems (containing hardware and software parts) make these two problems hard to solve. This paper introduces an automatic approach that integrates simultaneously partitioning and scheduling. It is inspired by the collective behavior of social insects such as bees in order to find a feasible solution to partitioning, using scheduling to find the shortest execution time to the system under design.

A new weighted shortest path tree for convergecast traffic routing in WSN

Tree topologies are widely used in WSN in order to route convergecast traffic to the sink. We consider in this paper the Shortest Path routing Tree (SPT) problem in WSN under different metrics; we show that the basic SPT based strategies are unsuitable for the many-to-one WSN when considering some metrics to compute link costs. Indeed, existing SPT approaches aim to construct a tree rooted at the sink such that the cost of the path from any node to the sink is minimal, while the cost of a given path is computed as summation of the costs of links that compose this path. However, in many-to-one WSN, links which are close to the sink are more critical than other links when using some metrics. We propose in this paper a new weighted path cost function, and we show that our cost function is more suitable for WSN. Based on this cost function, we propose a simple and efficient weighted shortest path tree construction which does not introduce new overheads. We consider, then, the particular case of energy-aware routing in WSN when we apply our new solution in order to construct more suitable energy-aware SPT. We conduct extensive simulations which show that our approach allows to enhance the network lifetime up to 17% compared to the basic one.

AntPart : an algorithm for the unsupervised classification problem using ants

Unsupervised classification is one of the tasks of data-mining. In this paper, a method named AntPart for the resolution of exclusive unsupervised classification is introduced. It is inspired by the behavior of a particular species of ants called Pachycondyla apicalis. The performances of this method are compared with those of three other ones, also inspired by the social behavior of ants: AntClass, AntTree and AntClust.

Image segmentation using quantum genetic algorithms

On one hand, image segmentation is a low-level processing task which consists in partitioning an image into homogeneous regions. It can be seen as being a combinatorial optimization problem. In fact, considering the huge amount of information that an image carries, it is impossible to find the best segmentation. On the other hand, quantum genetic algorithms are characterized by their high diversity, and by a good balance between global and local search. In this paper, we present a quantum genetic algorithm for image segmentation

Solving partitioning problem in codesign with ant colonies

Partitioning problem in codesign is of great importance since it can widely influence the characteristics of the system under design. The numerous constraints imposed by the environment and/or the underlying target architecture, in addition to its NP-Completeness makes the problem hard to solve. This paper introduces an automatic partitioning approach inspired by the collective behavior of social insects such as ants, which are able to find the shortest path from their nest to a food source.

ISA An algorithm for image segmentation using ants

On one hand, there is the problem to solve which is the image segmentation. It is a low-level processing task which consists in partitioning an image into homogeneous regions. Segmentation can be seen as a combinatorial optimization problem. In fact, considering the huge amount of information that an image carries, it is impossible to find the best segmentation. On the other hand, the reduced individual properties of ants as well as the simplicity of their behaviours led to the design of several methods of optimization by ants such as: optimization by colony of ants (ant colony optimization) and classification by co-operating ants (AntClass, AntTree, AntClust...). In this paper, we present an algorithm for the resolution of the segmentation problem. This algorithm, named ISA (image segmentation using ants), is based on the behavior of ants while cleaning their nest. The image to be segmented represents the environment of the ants. Initially, ants are generated and are positioned randomly on the image. Then they start moving. During its move (searching for a label to the current pixel) an ant can perform a number of tasks such as labeling a pixel, or improving the homogeneity of regions by correcting possible errors (changing the label of a pixel).

Bird Flocking Congestion Control for CoAP/RPL/6LoWPAN Networks

This paper proposes a congestion control solution for CoAP/RPL/6LoWPAN networks. CoAP/RPL/6LoWPAN has been considered as one of the most appropriate protocol stack for industrial applications of the Internet of Things (IoT). Since IoT nodes may have constrained resources, mechanisms used to control congestion must operate effectively with minimal consumption. In this paper, a method for congestion control in an IoT environment governed by the CoAP/RPL/6LoWPAN protocol stack is proposed. Our solution is based on the concept of bird flocking to guide packets in order to avoid congestion. Simulation shows that our proposal guarantees better results compared to congestion control mechanisms of the protocol stack in terms of data latency, packet loss ratio and packet delivery ratio.

TAS-IoT: Trust-Based Adaptive Security in the IoT

Providing efficient security services in dynamic low-power environments as the Internet of Things (IoT) is a challenging task. The deployment of static security services will consume the energy even if it is not required in some situations, so this induces a waste of resources. In this paper, we introduce an efficient model for adaptive security in the IoT based on trust management. Most of existing adaptive security approaches lack of practical means to evaluate threats. On the other hand, trust management systems are designed to deal with selfish behaviors or internal attacks and not to assist cryptographic measures. Our solution evaluates the trust level related to the presence of security threats among nodes, and adapt consequently cryptographic measures. The obtained simulation results show that our solution reduces considerably energy consumption and remains yet secure.

NoC routing protocols – objective-based classification

Abstract NoCs (Network on Chips) are the most popular interconnection mechanism used for systems that require flexibility, extensibility and low power consumption. However, communication performance is strongly related to the routing algorithm that is used in the NoC. The most important issues in the routing process are: deadlock, livelock, congestion and faults. In this paper, a classification of NoC routing protocols is proposed according to the problems they address. Two main families emerge: mono objective and multi objectives. A discussion of the advantages and the drawbacks of each protocols family is given. A summary of the most used practices in this field and the less used ones is provided. This survey shows that it is hard to satisfy the four objectives at the same time with classical methods, highlighting the strengths of multi-objectives approaches.

Adaptive failure detection in low power lossy wireless sensor networks

In this paper, we investigate the use of failure detectors (FD) in Wireless Sensor Networks (WSN). We provide a classification of FD with respect to some WSN criteria. The focus will be on energy depletion and lossy links. We then propose a new general FD model tailored to WSN constraints, called Adaptive Neighborhood Failure Detector for Low-power lossy WSN (AFDEL). AFDEL provides adaptive local failure detection robust against packet loss (intermittent failures) and saves the use of energy, bandwidth and memory storage. Furthermore, we introduce in AFDEL model an adaptive timer strategy. This strategy offers the possibility to customize the dynamic timer pattern with respect to application requirements in terms of completeness and accuracy. We illustrate the use of this strategy by proposing three failure detection techniques based on AFDEL general model. As a part of this work, we give a stochastic based approach for timer adaptation. We evaluate all techniques using implementation on MiXim/Omnet++ framework. The overall experiments show that AFDEL achieves better trade-off between accuracy/completeness and detection/recovery period compared to static timer approach FaT2D (Benhamida and Challal, 2010) and HeartBeat timer-free technique (Aguilera et al., 1997). Moreover, using adaptive timer strategy in local interaction/detection makes it possible to reduce energy consumption and the overall exchanged data overhead.