Mohammed Haddad

Efficient distributed lifetime optimization algorithm for sensor networks

One of the main design challenges in Wireless Sensor Networks (WSN) is to prolong the system lifetime, while achieving acceptable quality of service for applications. In WSN, each sensor node is battery powered and it is not convenient to recharge or replace the batteries in many cases, especially in remote and hostile environments. Due to the limited capabilities of sensor nodes, it is usually desirable that a WSN should be deployed with high density and thus redundancy can be exploited to increase the networks lifetime. In this paper, we introduce an efficient lifetime optimization and self-stabilizing algorithm to enhance the lifetime of wireless sensor networks especially when the reliabilities of sensor nodes are expected to decrease due to use and wear-out effects. Our algorithm seeks to build resiliency by maintaining a necessary set of working nodes and replacing failed ones when needed. We provide some theoretical and simulation results, that fully demonstrate the usefulness of the proposed algorithm.

A strict strong coloring of trees

In this paper we introduce and study a new coloring parameter of a graph called the strict strong coloring (short SSColoring). A SSColoring of a graph G is a vertex proper coloring of G such that each vertex of G is adjacent to at least one not empty color class. The minimum number of colors among all SSColorings is called strict strong chromatic (short SSChromatic) number, denoted by @gss(G). In this paper we prove the NP-completeness of the problem, we discuss the @gss(G) number of trees by giving some bounds. Finally, we give an optimal polynomial algorithm for SSColoring of trees.

Robustness Study of Emerged Communities from Exchanges in Peer-to-peer Networks

Abstract The study of emerged community structure is an important challenge in networks analysis. In fact, several methods have been proposed in the literature to statistically determine the significance of discovered structures. Nevertheless, most of existing analysis models consider only the structural aspect of emerged communities. We are interested in studying the robustness of emerged communities in peer-to-peer (P2P) networks. More precisely, we consider the emerged communities in the induced graph by all the exchanges in these networks. Hence, rather than examining the robustness only on the structural properties of the graph, we will focus on the parameters that allow the emergence of community structures. In fact, perturbing these parameters might destroy most of the obtained properties at the emerged level. To the best of our knowledge, robustness of networks has never been considered from this angle before. In this paper, we study the impact of perturbing the content and the profile of nodes on the emerged communities in P2P networks. We show how these alterations affect both structure and information supported by the emerged structures.

A summary-based approach for enhancing process model matchmaking

The importance gained by the process models in modern information systems led to the increasing proliferation of process model repositories. Matching process models and assessing their similarity are critical functionalities required for the management of these collections. In this work1 we present an efficient graph-based technique for matching and evaluating the similarity of semantically annotated process models. Approximate graph matching algorithms which are used in literature (e.g., error correcting sub-graph isomorphism detection), are exponential in size of the graphs. In order to reduce the execution time and improve the applicability of the algorithm for matching and retrieval of process models, we propose a graph summarization technique which reduces the size of the graphs to be compared. Moreover, while most of the related works detect only 1–1 activity mappings, our matching approach is able to detect complex mappings (m-n) between activities based on their input/output sets. Experiments showed that the summarization technique reduces considerably the execution time, maintaining at the same time a good quality of the matching.

String Comparators Based Algorithms for Process Model Matchmaking

Retrieving matchings between process models becomes a significant challenge in many applications. Recent attempts have been done to measure similarity of process models based on graph-edit distance. This problem is known to be difficult and computational complexity of exact algorithms for graph matching is exponential. Thus, heuristics must be proposed to obtain approximations. In this paper, we propose an approach to find relevant process models based on their decomposition into paths of possible execution sequences. Then, we propose a schema to compute the similarity between two process models using the proposed decomposition. Moreover, we give particular attention to the problem of ranking a collection of process models according to a particular query.

A distributed strict strong coloring algorithm for broadcast applications in ad hoc networks

A challenging research area in computer networks is ad hoc networks. These are defined to be spontaneous and totally autonomous networks. Many communication protocols (like routing, resources sharing, ...) use broadcasting mechanism to diffuse or gather information. A strict strong coloring of a graph G is a vertex proper coloring of G such that each vertex of G is adjacent to at least one non empty color class. In this paper, we give a novel broadcasting approach based on a distributed algorithm for strict strong coloring.

A Self-stabilizing Algorithm for Edge Monitoring Problem

Self-monitoring is a simple and effective mechanism for the security of wireless sensor networks (WSNs), especially to cope against compromised nodes. A node v can monitor an edge e if both end-nodes of e are neighbors of v; i.e., e together with v forms a triangle in the graph. Moreover, some edges need more than one monitor. Finding a set of monitoring nodes satisfying all monitoring constraints is called the edge-monitoring problem. The minimum edge-monitoring problem is long known to be NP-complete. In this paper, we present a novel silent self-stabilizing algorithm for computing a minimal edge-monitoring set. Correctness and termination are proven for the unfair distributed daemon.

A Self-stabilizing Algorithm for Maximal p-Star Decomposition of General Graphs

A p-star is a complete bipartite graph K 1,p with one center node and p leaf nodes. In this paper we propose the first distributed self-stabilizing algorithm for graph decomposition into p-stars. For a graph G and an integer p ≤ 1, this decomposition provides disjoint components of G where each component forms a p-star. We prove convergence and correctness of the algorithm under an unfair distributed daemon. The stabilization time is

Self-stabilizing algorithm for maximal graph partitioning into triangles

2\lfloor \frac{n}{p+1}\rfloor +2

Dominated Colorings of Graphs

rounds.