Connected Dominating Set Based Topology Control in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network. Moreover, Minimum-sized Connected Dominating Set (MCDS) has become a well-known approach for constructing a Virtual Backbone (VB) to alleviate the broadcasting storm for efficient routing in WSNs extensively. However, no work considers the load-balance factor of CDSsin WSNs. In this dissertation, we first propose a new concept — the Load-Balanced CDS (LBCDS) and a new problem — the Load-Balanced Allocate Dominatee (LBAD) problem. Consequently, we propose a two-phase method to solve LBCDS and LBAD one by one and a one-phase Genetic Algorithm (GA) to solve the problems simultaneously. Secondly, since there is no performance ratio analysis in previously mentioned work, three problems are investigated and analyzed later. To be specific, the MinMax Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual Backbone (LBVB) problem, and the MinMax Valid-Degree non Backbone node Allocation (MVBA) problem. Approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the dissertation. On the other hand, in the current related literature, networks are deterministic where two nodes are assumed either connected or disconnected. In most real applications, however, there are many intermittently connected wireless links called lossy links, which only provide probabilistic connectivity. For WSNs with lossy links, we propose a Stochastic Network Model (SNM). Under this model, we measure the quality of CDSs using CDS reliability. In this dissertation, we construct an MCDS while its reliability is above a preset applicationspecified threshold, called Reliable MCDS (RMCDS). We propose a novel Genetic Algorithm (GA) with immigrant schemes called RMCDS-GA to solve the RMCDS problem. Finally, we apply the constructed LBCDS to a practical application under the realistic SNM model, namely data aggregation. To be specific, a new problem, Load-Balanced Data Aggregation Tree (LBDAT), is introduced finally. Our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly

Review of Optimization Problems in Wireless Sensor Networks

International audienc

An algorithm for cost optimization of PMU and communication infrastructure in WAMS

International audiencePower system state estimation relies increasingly on PMU measurements to ef-fectively control and monitor growing and stressed transmission networks whichare also affected by transient and dynamic events. High PMU cost has moti-vated optimal PMU placement solutions but recent works have shown the effectof communication infrastructure cost in PMU configuration. In this paper,we present a new method for the design of Wide Area Measurement Systems.A topological analysis algorithm based on the Variable Neighbourhood Searchheuristic is proposed and tested in several networks, including the common IEEEtest networks and the 5804-bus Brazilian transmission system. Our results showthe flexibility, effectiveness, and scalability of the proposed methodology whencompared with recent research presented in the literature

Data Aggregation Scheduling in Wireless Networks

Data aggregation is one of the most essential data gathering operations in wireless networks. It is an efficient strategy to alleviate energy consumption and reduce medium access contention. In this dissertation, the data aggregation scheduling problem in different wireless networks is investigated. Since Wireless Sensor Networks (WSNs) are one of the most important types of wireless networks and data aggregation plays a vital role in WSNs, the minimum latency data aggregation scheduling problem for multi-regional queries in WSNs is first studied. A scheduling algorithm is proposed with comprehensive theoretical and simulation analysis regarding time efficiency. Second, with the increasing popularity of Cognitive Radio Networks (CRNs), data aggregation scheduling in CRNs is studied. Considering the precious spectrum opportunity in CRNs, a routing hierarchy, which allows a secondary user to seek a transmission opportunity among a group of receivers, is introduced. Several scheduling algorithms are proposed for both the Unit Disk Graph (UDG) interference model and the Physical Interference Model (PhIM), followed by performance evaluation through simulations. Third, the data aggregation scheduling problem in wireless networks with cognitive radio capability is investigated. Under the defined network model, besides a default working spectrum, users can access extra available spectrum through a cognitive radio. The problem is formalized as an Integer Linear Programming (ILP) problem and solved through an optimization method in the beginning. The simulation results show that the ILP based method has a good performance. However, it is difficult to evaluate the solution theoretically. A heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. Finally, we investigate how to make use of cognitive radio capability to accelerate data aggregation in probabilistic wireless networks with lossy links. A two-phase scheduling algorithm is proposed, and the effectiveness of the algorithm is verified through both theoretical analysis and numerical simulations

Improving broadcast performance in multi-radio multi-channel multi-rate wireless mesh networks.

This thesis addresses the problem of `efficient' broadcast in a multi-radio multi-channel multi-rate wireless mesh network (MR$^2$-MC WMN). In such a MR$^2$-MC WMN, nodes are equipped with multiple radio network interface cards, each tuned to an orthogonal channel, that can dynamically adjust transmission rate by choosing a modulation scheme appropriate for the channel conditions. We choose `broadcast latency', defined as the maximum delay between a packet's network-wide broadcast at the source and its eventual reception at all network nodes, as the `efficiency' metric of broadcast performance. The problem of constructing a broadcast forwarding structure having minimal broadcast latency is referred to as the `minimum-latency-broadcasting' (MLB) problem. While previous research for broadcast in single-radio single-rate wireless networks has highlighted the wireless medium's `\emph{wireless broadcast advantage}' (WBA); little is known regarding how the new features of MR$^2$-MC WMN may be exploited. We study in this thesis how the availability of multiple radio interfaces (tuned to orthogonal channels) at WMN nodes, and WMN's multi-rate transmission capability and WBA, might be exploited to improve the `broadcast latency' performance. We show the MLB problem for MR$^2$-MC WMN to be NP-hard, and resort to heuristics for its solution. We divide the overall problem into two sub-problems, which we address in two separate parts of this thesis. \emph{In the first part of this thesis}, the MLB problem is defined for the case of single-radio single-channel multi-rate WMNs where WMN nodes are equipped with a single radio tuned to a common channel. \emph{In the second part of this thesis}, the MLB problem is defined for MR$^2$-MC WMNs where WMN nodes are equipped with multiple radios tuned to multiple orthogonal channels. We demonstrate that broadcasting in multi-rate WMNs is significantly different to broadcasting in single-rate WMNs, and that broadcast performance in multi-rate WMNs can be significantly improved by exploiting the availability of multi-rate feature and multiple interfaces. We also present two alternative MLB broadcast frameworks and specific algorithms, centralized and distributed, for each framework that can exploit multiple interfaces at a WMN node, and the multi-rate feature and WBA of MR$^2$-MC WMN to return improved `broadcast latency' performance

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Integrative computational approaches for studying stem cell differentiation and complex diseases

The biological functions of the molecular components (genes, proteins, miRNAs, siRNAs,..etc) of biological cells and mutations/perturbations thereof are tightly connected with cellular malfunctions and disease pathways. Moreover, these molecular elements interact with each other forming a complex interwoven regulatory machinery that governs, on one hand, regular cellular pathways, and on the other hand, their dysregulation or malfunction in pathological processes. Therefore, revealing these critical molecular interactions in complex living systems is being considered as one of the major goals of current systems biology. In this dissertation, we introduce practical computational approaches implemented as freely available software tools to integrate heterogeneous sources of large-scale genomic data and unravel the combinatorial regulatory interactions between different molecular elements. First, we present an automated GRN pipeline that constructs the genomic regulatory machinery of a cell from expression, sequencing, and annotation datasets through three modules implemented as separated software components (plugins) and hosted by our software framework Mebitoo that aims at automation of bioinformatics workflows. Then, we extended this pipeline to a general integrative network-based approach that involves also post-transcriptional interactions and reports the computational analysis of gene and miRNA transcriptomes, DNA methylome, and somatic mutations. This workflow enables users to identify putative disease drivers and novel targets for therapeutic treatment. Regarding the incorporation of somatic mutations with other genomic data sets, a stand-alone pipeline named “SnvDMiR” was implemented to explore possible genomic proximity relationships between somatic variants and both differentially methylated CpG sites as well as differentially expressed miRNAs. Along the same lines, but targeting the effects of genomic mutations, we developed an NGS pipeline and applied it to two groups of bacterial isolates (nasal and invasive) to investigate the phylogenetic positions of the recently emerged t504 clone (Spa-type t504) in the Saarland province of Germany and to better understand the infectivity mechanism of the invasive group. Motivated by all of this, we developed TFmiR as a freely available web server for deep and integrative downstream analysis of combinatorial regulatory interactions between TFs/genes and miRNAs that are involved in the pathogenesis of human diseases. In the frame of this thesis, we employed these approaches to investigate the molecular mechanisms of cellular differentiation (namely hematopoiesis) as an example for biological processes and human breast cancer and diabetes as examples for complex diseases. In summary, the work presented in this thesis has led to the development of interesting computational approaches that have been made available as non-commercial software toolkits. The provided topological and functional analyses of our approaches as validated on cellular differentiation and complex diseases promotes them as reliable systems biology tools for researchers across the life science communities.Die Funktionsweise verschiedener molekularer Elemente (Gene, Proteine, Mutationen, miRNAs, siRNAs,... etc.) ist mit den darunterliegenden zellulären Fehlfunktionen als auch mit Krankheits-assoziierten zellulären Signalwegen verknüpft. Darüber hinaus interagieren diese molekularen Elemente auch miteinander und bilden eine komplexe ineinander verwobene regulatorische Maschinerie, die wiederum zelluläre Signalwege oder auch Krankheitsentwicklungen auf zellulärer Ebene beeinflusst. Aufgrund dessen ist heutzutage die Aufklärung dieser molekularen Interaktionen in komplexen lebenden Systemen eines der Hauptziele der Systembiologie. In dieser Dissertation stellen wir rechnerbasierte Ansätze vor welche als Software frei verfügbar sind und die Integration von großen genomischen Datensätzen als auch eine damit verbundene Aufklärung der kombinatorischen Vielfalt dieser regulatorischen Interaktionen zwischen den verschiedenen molekularen Elementen, ermöglichten. Dafür entwickelten wir anfangs eine automatisierte GRN Pipeline, welche die regulatorische Maschinerie einer Zelle auf der Grundlage von Daten zur Genexpression, über Sequenzierung als auch Annotierung von Datensätzen konstruiert. Diese Pipeline wurde in drei separate Module aufgeteilt, die alle als Software plugins verfügbar sind, und in unser Framework Mebitoo, welches bioinformatische Arbeitsabläufe automatisiert, integriert sind. Daraufhin erweiterten wir unser bisheriges Framework um einem allgemeinen und integrativen Netzwerk-basierten Ansatz, welcher post-transkriptionelle Interaktionen berücksichtigt und die rechnerbasierte Analyse von Genen als auch miRNA Transkriptomen, dem DNA Methylom und somatischen Mutationen mit einbezieht. Unser Ziel war es, dabei vermeintliche Verursacher von Krankheitsbildern als auch neue Ziele für die therapeutische Behandlung von Krankheiten zu identifizieren. Für die Integration somatischer Mutationen wurde eine eigenständige Pipeline namens „SnvDMiR“ entwickelt, welche die Analyse von möglichen genomischen Nachbarschaftsbeziehungen zwischen somatischen Mutationen und differentiell methylierten CpG Positionen als auch differentiell exprimierten miRNAs, ermöglicht. Für die Analyse von somatischen Mutationen entwickelten wir zudem eine NGS Pipeline und wendeten diese auf zwei unterschiedliche Gruppen von bakteriellen Isolaten (nasale und invasive) an, um einerseits die phylogenetische Position des kürzlich im Saarland aufgekommenen Klons t504 (Spa-type t504) zu untersuchen, aber auch um den Mechanismus, der zu einer Infektion durch invasive Stämme führt, besser zu verstehen. All dies motivierte uns dazu TFmiR als frei verfügbare Web-Applikation zu entwickeln, welche eine tief gehende integrative Analyse von den kombinatorischen regulatorischen Interaktionen zwischen TFs/Genen und miRNAs ermöglicht, die an der Krankheitsentwicklung im Menschen beteiligt sind. Die entwickelten Methoden wurden auf die zelluläre Differenzierung (Hämatopoese), als Beispiel für einen biologischen Prozess, als auch auf Brustkrebs und Diabetes, als Beispiele für komplexe Krankheiten, angewendet um deren molekulare Mechanismen zu untersuchen. Zusammenfassend hat diese Arbeit zur Entwicklung von interessanten, rechnergestützten Methoden geführt, welche als nicht-kommerzielle Software publiziert wurden. Die Validierung unserer Methoden anhand von topologischen und funktionsbasierten Analysen sowohl in zellulärer Differenzierung als auch komplexen Krankheiten, machen diese zu verlässlichen systembiologischen Werkzeugen für Wissenschaftler aus den unterschiedlichsten Naturwissenschaftsbereichen

Computer-based tools for supporting forest management. The experience and the expertise world-wide

Report of Cost Action FP 0804 Forest Management Decision Support Systems (FORSYS)Computer-based tools for supporting forest management. The experience and the expertise world-wide answers a call from both the research and the professional communities for a synthesis of current knowledge about the use of computerized tools in forest management planning. According to the aims of the Forest Management Decision Support Systems (FORSYS) (http://fp0804.emu.ee/) this synthesis is a critical success factor to develop a comprehensive quality reference for forest management decision support systems. The emphasis of the book is on identifying and assessing the support provided by computerized tools to enhance forest management planning in real-world contexts. The book thus identifies the management planning problems that prevail world-wide to discuss the architecture and the components of the tools used to address them. Of importance is the report of architecture approaches, models and methods, knowledge management and participatory planning techniques used to address specific management planning problems. We think that this synthesis may provide effective support to research and outreach activities that focus on the development of forest management decision support systems. It may contribute further to support forest managers when defining the requirements for a tool that best meets their needs. The first chapter of the book provides an introduction to the use of decision support systems in the forest sector and lays out the FORSYS framework for reporting the experience and expertise acquired in each country. Emphasis is on the FORSYS ontology to facilitate the sharing of experiences needed to characterize and evaluate the use of computerized tools when addressing forest management planning problems. The twenty six country reports share a structure designed to underline a problem-centric focus. Specifically, they all start with the identification of the management planning problems that are prevalent in the country and they move on to the characterization and assessment of the computerized tools used to address them. The reports were led by researchers with background and expertise in areas that range from ecological modeling to forest modeling, management planning and information and communication technology development. They benefited from the input provided by forest practitioners and by organizations that are responsible for developing and implementing forest management plans. A conclusions chapter highlights the success of bringing together such a wide range of disciplines and perspectives. This book benefited from voluntary contributions by 94 authors and from the involvement of several forest stakeholders from twenty six countries in Europe, North and South America, Africa and Asia over a three-year period. We, the chair of FORSYS and the editorial committee of the publication, acknowledge and thank for the valuable contributions from all authors, editors, stakeholders and FORSYS actors involved in this project

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available