Design and Analysis of Soft Computing Based Improved Routing Protocol in WSN for Energy Efficiency and Lifetime Enhancement

Mobile wireless sensor networks have been developed as a result of recent advancements in wireless technologies. Sensors in the network are low-cost and have a short battery life, in addition to their mobility. They are more applicable in terms of the essential properties of these networks. These networks have a variety of uses, including search and rescue operations, health and environmental monitoring, and intelligent traffic management systems, among others. According to the application requirements, mobile wireless sensor nodes are energy limited equipment, so energy conservation is one of the most significant considerations in the design of these networks. Aside from the issues posed by sensor node mobility, we should also consider routing and dynamic clustering. According to studies, cluster models with configurable parameters have a substantial impact on reducing energy usage and extending the network's lifetime. As a result, the primary goal of this study is to describe and select a smart method for clustering in mobile wireless sensor networks utilizing evolutionary algorithms in order to extend the network's lifetime and ensure packet delivery accuracy. For grouping sensor nodes in this work, the Genetic Algorithm is applied initially, followed by Bacterial Conjugation. The simulation's results show a significant increase in clustering speed acceleration. The speed of the nodes is taken into account in the suggested approach for calibrating mobile wireless sensor nodes

Mathematical model of interactions immune system with Micobacterium tuberculosis

Tuberculosis (TB) remains a public health problem in the world, because of the increasing prevalence and treatment outcomes are less satisfactory. About 3 million people die each year and an estimated one third of the world's population infected with Mycobacterium Tuberculosis (M.tb) is latent. This is apparently related to incomplete understanding of the immune system in infection M.tb. When this has been known that immune responses that play a role in controlling the development of M.tb is Macrophages, T Lymphocytes and Cytokines as mediators. However, how the interaction between the two populations and a variety of cytokines in suppressing the growth of Mycobacterium tuberculosis germ is still unclear. To be able to better understand the dynamics of infection with M tuberculosis host immune response is required of a model.One interesting study on the interaction of the immune system with M.tb mulalui mathematical model approach. Mathematical model is a good tool in understanding the dynamic behavior of a system. With the mediation of mathematical models are expected to know what variables are most responsible for suppressing the growth of Mycobacterium tuberculosis germ that can be a more appropriate approach to treatment and prevention target is to develop a vaccine. This research aims to create dynamic models of interaction between macrophages (Macrophages resting, macrophages activated and macrophages infected), T lymphocytes (CD4 + T cells and T cells CD8 +) and cytokine (IL-2, IL-4, IL-10,IL-12,IFN-dan TNF-) on TB infection in the lung. To see the changes in each variable used parameter values derived from experimental literature. With the understanding that the variable most responsible for defense against Mycobacterium tuberculosis germs, it can be used as the basis for the development of a vaccine or drug delivery targeted so hopefully will improve the management of patients with tuberculosis. Mathematical models used in building Ordinary Differential Equations (ODE) in the form of differential equation systems Non-linear first order, the equation contains the functions used in biological systems such as the Hill function, Monod function, Menten- Kinetic Function. To validate the system used 4th order Runge Kutta method with the help of software in making the program Matlab or Maple to view the behavior and the quantity of cells of each population