As the growth of multimedia communications involving digital audio and video, it is increasingly important for the MANET (Mobile Ad Hoc Network) routing protocols to simultaneously support both real-time and non-real-time traffic. MANET energy management should offer this support because devices are equipped with limited battery power. To achieve this end, we revise the Ad hoc On-demand Distance Vector (AODV) routing protocol to provide an energy management mechanism such that both real-time and non-real-time packets can be effectively transmitted. In the proposed scheme, real-time traffic uses higher transmission power to reduce transmission delay time and selects a shortest route with the largest minimum residual energy to avoid route break. The non-real-time traffic uses normal transmission power to save energy and chooses a proper shortest route with highest average residual energy to balance node energy consumption. The simulation results show that the revised AODV obtains lower average end-to-end delay and fewer energy-exhausted nodes comparing to the conventional AODV.

L’amélioration de l’efficience alimentaire des animaux peut contribuer à un élevage plus durable par la réduction des ressources utilisées et des rejets associés. Les caractères qui déterminent l’efficience alimentaire des vaches laitières restent mal identifiés. Le projet se propose donc d’identifier les facteurs biologiques associés à la variabilité interindividuelle de l’efficience alimentaire des vaches laitières. La variation d’efficience alimentaire a été estimée avec l’ingéré résiduel, classiquement défini comme la variabilité résiduelle de l’énergie nette ingérée corrigée pour l’énergie nette du lait, l’entretien et les variations de réserves corporelles. Cet ingéré résiduel inclut par définition toutes les erreurs de mesure. Pour réduire ces erreurs, la note d’état corporel, qui classiquement se fait par notation visuelle, a été automatisée et de nombreux autres caractères candidats ont été mesurés fréquemment dans un environnement stable sur quasiment toute la lactation.La variabilité de l’ingéré résiduel ne représentait que 8% de la variabilité de l’ingéré mesuré, dont 58,9% étaient associés à de l’efficience et non de l’erreur. L’étude de la répétabilité de cet ingéré résiduel au cours de la lactation suggère d’éviter les 7 premières quinzaines au profit du milieu de lactation. Parmi tous les caractères mesurés, le comportement alimentaire, la température ruminale, la variation des réserves corporelles et l’activité expliquaient 58,9% de la variabilité de l’ingéré résiduel. Les effets de plusieurs de ces caractères semblent confondus. Leur lien de causalité av / Achieving higher feed efficiency of animals is expected to improve animal production sustainability through reduction of the used resources and of the associated emissions. The traits determining feed efficiency remain poorly understood. The present project aimed therefore at identifying the biological factors associated with feed efficiency differences in lactating dairy cows. Feed efficiency variation was estimated with the traditional residual intake, which was defined as the residual variability of net energy intake which is not explained by net energy required for lactation, maintenance and body reserves change. This residual intake includes by definition all measurement errors. To reduce these errors, body condition score, which is commonly measured visually, has been automated and several other candidate traits were measured frequently in a steady environment for almost whole lactation.Residual intake variability represented only 8% of intake variability in our study, among which only 58.9% were found to be associated with feed efficiency variability and not to errors. The repeatability analysis of the residual intake throughout the lactation suggested to avoid the 7 first lactation fortnights, and rather to focus on lactation middle. Among all measured traits, feeding behaviour, rumen temperature, body reserves change and activity explained 58.9% of residual intake variability. Many of these traits seemed confounded with others, which claimed for the need for further work to properly define their causal relationship with feed efficiency, especially focussing on di

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

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.