Under a dense and large IoT network, a star topology where each device is directly connected to the Internet gateway may cause serious waste of energy and congestion issues. Grouping network devices into clusters provides a suitable architecture to reduce the energy consumption and allows an effective management of communication channels. Although several clustering approaches were proposed in the literature, most of them use the single-hop intra-clustering model. In a large network, the number of clusters increases and the energy draining remains almost the same as in un-clustered architecture. To solve the problem, several approaches use the k-hop intra-clustering to generate a reduced number of large clusters. However, k-hop proposed schemes are, generally, centralized and only assume the node direct neighbors information which lack of robustness. In this regard, the present work proposes a distributed approach for the k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2HC). The algorithm uses the two-hop neighbors connectivity to elect the appropriate set of cluster heads and strengthen the clusters connectivity. The objective is to optimize the set of representative cluster heads to minimize the number of long range communication channels and expand the network lifetime. The paper provides the convergence proof of the proposed solution. Simulation results show that our proposed protocol outperforms similar approaches available in the literature by reducing the number of generated cluster heads and achieving longer network lifetime.

The Internet of Things (IoT) represents a pervasive system that continuously demonstrates an expanded application in various domains. The energy-efficiency problem has always been a crucial issue linked to this type of network where the system lifetime strongly depends on devices’ batteries. Numerous energy-efficient networking protocols have been proposed in the literature to increase the system lifetime. However, most of the proposed approaches deal with the short-term vision of energy consumption and omit to consider the rechargeable battery degradation when evaluating the network lifetime. Indeed, the major parts of the network devices use rechargeable batteries that age and degrade over time due to several factors (temperature, voltage, charging/discharging cycle, etc.). Therefore, it is essential to promptly detect these internal and environmental degradation factors to avoid network failures. Clustering represents one of the main wireless network protocols and plays an essential role in network self organizing. In this work, we propose a novel long-term energy optimization clustering approach based on battery State of Health (SoH) prediction, called LECA_SOH. The objective is to predict the impact of cluster heads election on the rechargeable batteries SoH before applying the clustering. LECA_SOH fosters the selection of the nodes, which will less suffer from battery degradation during the future rounds, leading to extend the system lifetime. The obtained results demonstrate that the proposed clustering approach improves the network lifetime in the long term and extends the number of recharging cycles compared to the conventional energy-efficient approaches.

The Internet of Things (IoT) and the Wireless Sensor Networks (WSNs) attract attention of the research communities due to their rapid and promising development. These paradigms have been predicted to play a leading role in our daily life in the near future. The limited resources and the restricted number of communication channels in these networks create new challenges especially in collision and access conflicts. To bring appropriate solutions for these problems, several works, in the literature, focus on centralized and distributed approaches. Traditional distributed protocols assume that the MAC and Routing layers act independently. Moreover, the dynamic topology and the central point of failure restrict the development of centralized techniques. In this paper, we present a distributed weight-based TDMA-MAC scheduling algorithm (WB-DTSA). The proposed solution takes advantage of routing information to improve the communication latency and the schedule length. Simulation results show better performance, in comparison to similar distributed channel assignment approaches. The latency is improved by 47% and the schedule length is reduced by 23%.

The emergence of Wireless Sensor Networks (WSNs) attracts attention of the research communities since they cover several domains of Internet of things (IoT). The resourceconstrained sensors and the limited number of communication channels create challenges especially in collision and access conflicts. In order to bring appropriate solutions to these problems, different centralized and distributed approaches have been proposed in the literature. The major problem of centralized techniques is the central point of failure. To this end, this paper proposes a distributed TDMA scheduling algorithm. The proposed solution considers routing information to reduce the messages complexity, minimize communication latency and the schedule length. The experiment results show better performance, in comparison to referenced distributed channel allocation approaches. The latency is improved by 8%, the schedule length and the messages complexity are reduced by 24% and 22% respectively.

Mobile traffic is expected to grow by 30% by 2024, saving energy is a crucial necessity even for battery-powered devices such as smartphones for the sake of green computing. Clustering techniques were introduced to conserve the energy of network devices. However, proposed energy optimization techniques do not yield optimal battery life, they mostly consider devices with non-rechargeable batteries and deal with limited energy without considering battery aging (short-term vision). To this end, we focus on the long-term energy optimization and we introduce a dynamic clustering technique that take into consideration the state of health of devices batteries and their degradation level. The proposed scheme efficiently manages the energy resource to enhance the battery behavior which extends the network lifespan in the long term.Simulations results show that the proposed approach out-performs similar works available in the current literature. The batteries life cycle and the network lifetime are improved by 38% and 47% respectively. The average number of generated clusters is reduced by 39%.