Redes de Sensores Sem Fio são um tipo especial de rede ad-hoc que são posicionadas em uma região para monitorar fenômenos físicos. Considerando que os sensores dessas redes são independentes e possuem um raio de cobertura pequeno, é comum a utilização de um grande número de sensores para monitorar uma área grande. Um problema nesses tipos de redes é garantir que o máximo de dados capturados por esses sensores sejam coletados e transmitidos até uma estação base para que possam ser analisados por usuários. Uma abordagem para resolver esse problema é por meio da utilização de sensores especiais chamados cluster heads. Esses sensores são posicionados estrategicamente para coletar a informação de um grupo de sensores e transmiti-la para a estação base. Assim surge a necessidade de agrupar esses sensores. Nesse trabalho é proposta uma técnica híbrida baseada no algoritmo de agrupamento de dados K-Médias e em detecção comunidades em redes complexas. Esse algoritmo, chamado de QK-Médias, tenta aproveitar as vantagens das duas abordagens em duas etapas. Primeiro a rede é quebrada em comunidades usando uma técnica de detecção de comunidades. Em seguida essas comunidades são quebradas em subcomunidades de tal forma que os cluster heads consigam gerenciar. Os resultados obtidos a partir do agrupamento de sensores utilizando o QK-Médias mostram que é possível diminuir o número de mensagens perdidas na rede utilizando menos cluster heads que algoritmos tradicionais de agrupamento em redes de sensores sem fio / Wireless Sensor Networks are a special kind of ad-hoc network that are deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes and small radio coverage, it is common to use a large number of sensors. A common problem in this sort of network is to guarantee that the highst number of captured data was sucessfull broadcast to the base station. One approach to solve this problem use special sensors called cluster heads. These sensors are responsible for collecting data from a group of common sensors and broadcast it to a base station. Thus, it is necessary to cluster these sensors. Here we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. This new algorithm is composed by two steps. First, the network is broken into communities and then broken into subcommuinties that the cluster heads can deal with. Simulation results show that QK-Means can decrease the rate of lost messages in the network using less cluster heads than tradicional clustering algorithms

Redes de Sensores Sem Fio são um tipo especial de rede ad-hoc que são posicionadas em uma região para monitorar fenômenos físicos. Considerando que os sensores dessas redes são independentes e possuem um raio de cobertura pequeno, é comum a utilização de um grande número de sensores para monitorar uma área grande. Um problema nesses tipos de redes é garantir que o máximo de dados capturados por esses sensores sejam coletados e transmitidos até uma estação base para que possam ser analisados por usuários. Uma abordagem para resolver esse problema é por meio da utilização de sensores especiais chamados cluster heads. Esses sensores são posicionados estrategicamente para coletar a informação de um grupo de sensores e transmiti-la para a estação base. Assim surge a necessidade de agrupar esses sensores. Nesse trabalho é proposta uma técnica híbrida baseada no algoritmo de agrupamento de dados K-Médias e em detecção comunidades em redes complexas. Esse algoritmo, chamado de QK-Médias, tenta aproveitar as vantagens das duas abordagens em duas etapas. Primeiro a rede é quebrada em comunidades usando uma técnica de detecção de comunidades. Em seguida essas comunidades são quebradas em subcomunidades de tal forma que os cluster heads consigam gerenciar. Os resultados obtidos a partir do agrupamento de sensores utilizando o QK-Médias mostram que é possível diminuir o número de mensagens perdidas na rede utilizando menos cluster heads que algoritmos tradicionais de agrupamento em redes de sensores sem fio / Wireless Sensor Networks are a special kind of ad-hoc network that are deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes and small radio coverage, it is common to use a large number of sensors. A common problem in this sort of network is to guarantee that the highst number of captured data was sucessfull broadcast to the base station. One approach to solve this problem use special sensors called cluster heads. These sensors are responsible for collecting data from a group of common sensors and broadcast it to a base station. Thus, it is necessary to cluster these sensors. Here we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. This new algorithm is composed by two steps. First, the network is broken into communities and then broken into subcommuinties that the cluster heads can deal with. Simulation results show that QK-Means can decrease the rate of lost messages in the network using less cluster heads than tradicional clustering algorithms

Pour augmenter la durée de vie des réseaux de capteurs sans fil, une solution est d'améliorer l'efficacité énergétique des protocoles de communication. Le regroupement des nœuds du réseau de capteurs sans fil en cluster est l'une des meilleures méthodes. Cette thèse présente propose plusieurs améliorations en modifiant les paramètres du protocole de référence LEACH. Pour améliorer la distribution énergétique des "cluster-heads", nous proposons deux protocoles de clustering centralisés k-LEACH et sa version optimisée k-LEACH-VAR. Un algorithme distribué, appelé e-LEACH, est également proposé pour réduire l'échange d'information périodique entre les nœuds et la station de base lors de l'élection des "cluster-heads". Par ailleurs, le concept l'équilibrage énergétique est introduit dans les métriques d'élection pour éviter les surcharges des nœuds. Ensuite, nous présentons une version décentralisée de k-LEACH qui, en plus des objectifs précédents, intègre la consommation d'énergie globale du réseau. Ce protocole, appelé, k-LEACH-C2D, vise également à favoriser la scalabilité du réseau. Pour renforcer ce dernier et l'autonomie des réseaux, les deux protocoles de routage "multi-hop" probabiliste, dénotés FRSM et CB-RSM construisent des chemins élémentaires entre les "cluster-heads" et la station de base. Le protocole CB-RSM forme une hiérarchie des "cluster-heads" pendant la phase de formation des clusters, en mettant un accent sur l'auto-ordonnancement et l'auto-organisation entre les "cluster-heads" pour rendre les réseaux le plus "scalable". Ces différents protocoles reposent sur l'idée de base que les nœuds ayant l'énergie résiduelle la plus élevée et la plus faible variance de consommation de l'énergie deviennent "cluster-head". Nous constantans le rôle central de la consommation du nœud dans nos différentes propositions. Ce point fera l'objet de la dernière partie de cette thèse. Nous proposons une méthodologie pour caractériser expérimentalement la consommation d'un nœud. Les objectifs visent à mieux appréhender la consommation pour différentes séquences d'état du nœud. Enfin, nous proposons un modèle global de la consommation du nœud. / To increase the lifetime of wireless sensor networks, a solution is to improve the energy efficiency of the communication's protocol. The grouping of nodes in the wireless sensor network clustering is one of the best methods. This thesis proposes several improvements by changing the settings of the reference protocol LEACH. To improve the energy distribution of "cluster-heads", we propose two centralized clustering protocols LEACH and k-optimized version k-LEACH-VAR. A distributed algorithm, called e-LEACH, is proposed to reduce the periodic exchange of information between the nodes and the base station during the election of "cluster-heads". Moreover, the concept of energy balance is introduced in metric election to avoid overloading nodes. Then we presented a decentralized version of k-LEACH, which in addition to the previous objectives, integrates the overall energy consumption of the network. This protocol, called k-LEACH-C2D, also aims to promote the scalability of the network. To reinforce the autonomy and networks, both routing protocols "multi-hop" probability, denoted CB-RSM and FRSM build elementary paths between the "cluster-heads" and elected the base station. The protocol, CB-RSM, forms a hierarchy of "cluster-heads" during the training phase clusters, with an emphasis on self-scheduling and self-organization between "cluster-heads" to make the networks more scalable. These protocols are based on the basic idea that the nodes have the highest residual energy and lower variance of energy consumption become "cluster-head". We see the central role of consumption of the node in our proposals. This point will be the last part of this thesis. We propose a methodology to characterize experimentally the consumption of a node. The objectives are to better understand the consumption for different sequences of the node status. In the end, we propose a global model of the consumption of the node.

There has been a huge surge in the Internet of Things (IoT) applications in recent years. The sensor nodes in the IoT network generate data continuously that directly affects the longevity of the network. Even though the potential of IoT applications are immense, there are numerous challenges like security, privacy, load balancing, storage, heterogeneity of devices, and energy optimization that have to be addressed. Of those, the energy utilization of the network is of importance and has to be optimized. Several factors like residual energy, temperature, the load of Cluster Head (CH), number of alive nodes, and cost function affect the energy consumption of sensor nodes. In this paper, a hybrid Whale Optimization Algorithm-Moth Flame Optimization (MFO) is designed to select optimal CH, which in turn optimizes the aforementioned factors. The performance of the proposed work is then evaluated with existing algorithms with respect to the energy-specific factors. The results obtained prove that the proposed method outperforms existing approaches.