In Wireless Sensor Networks, low latency, energy efficiency, and coverage problems are considered as three key issues in designing routing protocols. In this paper we present a new protocol called Low Energy Adaptive Tier Clustering Hierarchy (LEATCH), which offers a good compromise between delay and energy consumption and resolves some coverage problems. For our purpose, a two level hierarchical approach has been proposed to organize a sensor network into a set of clusters, every cluster divided into small clusters that are called Mini Clusters. As the way the clusters are organized, for each mini cluster we define a Mini ClusterHead (MCH). Every MCH communicates with the cluster-head directly, it aggregates its mini-cluster information. In addition, we have made some changes in the procedure of cluster head and mini cluster head election. LEATCH promises better performances than the conventional LEACH protocol which is one of the most known hierarchical routing protocols using the probabilistic model to manage the energy consumption in WSNs. Simulation results show that LEATCH performs better than LEACH in term of energy, delay, coverage and scalability.

Vehicular ad hoc network (VANET) nodes are characterized by their high mobility and by exhibiting different mobility patterns. Therefore, VANET clustering schemes are required to account for the mobility parameters among neighboring nodes to produce relatively stable clustering schemes. In this article, we propose a novel cluster-head (CH) selection scheme for VANETs. This scheme is based on a fuzzy logic-powered, k-hop distributed clustering algorithm. It deals efficiently with scalability and stability issues of VANETs and is able to achieve highly stable clustering topologies as compared with other schemes. Our proposed clustering scheme strives to maintain a safe intervehicle distance as a one prime metric for CH selection. Moreover, a major contribution of our work is the proposal of a novel strategy for constructing fuzzy logic-based clustering algorithms useful for VANETs. This proposed solution is useful in an Internet of things-based setting that involves controlled vehicle-to-vehicle communication. We first derive mathematically, a new average distance estimation formula that is used as a metric for selecting CHs, leading to safer clusters that avoid collisions with front and rear vehicles. Furthermore, the new proposed scheme creates stable clusters by reducing reclustering overhead and prolonging clusters' lifetimes.

Many applications introduced by Vehicular Ad-Hoc Networks (VANETs), such as intelligent transportation and roadside advertisement, make VANETs become an important component of metropolitan area networks. In VANETs, mobile nodes are vehicles which are equipped with wireless antennas; and they can communicate with each other by wireless communication on ad-hoc mode or infrastructure mode. Clustering vehicles into different groups can introduce many advantages for VANETs as it can facilitate resource reuse and increase system capacity. The main contribution of our work is a new strategy for clustering a VANET and improvements in many classical clustering metrics. One of the main ideas is the definition of a new optimized selection metric for the clustering of vehicular nodes, in the framework of Next Generation Vehicular ad-hoc Network. These metrics should select clusterheads which provide safe clusters and avoid collisions with adjacent vehicle nodes and intend to create stable clusters by reducing reclustering overhead and prolonging cluster lifetime

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While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.