ENERGY EFFICIENT SLEEP WAKEUP SCHEDULING METHOD FOR P-COVERAGE AND Q-CONNECTIVITY MODEL IN TARGET BASED WIRELESS SENSOR NETWORKS

Energy limitations are the problem that gets the most attention in the term of Wireless Sensor Networks (WSN). Sleep wakeup scheduling method is one of the most efficient techniques to increase sensor node operational time on WSN. However, in the target-based WSN environment with p-coverage and q-connectivity models, the use of wake-up scheduling has to consider the constraints on the number of connectivity on the sensor and coverage on the target. Genetic Algorithm is a solution to the problem of sleep-wake scheduling with multi-objective problems. This study proposes a new method of sleep wakeup scheduling based on Genetic Algorithm for energy efficiency in target-based WSN with p-coverage and q-connectivity models. This new method uses the sensor range, connectivity range and energy as an objective function of the fitness function in the Genetic Algorithm. With the presence of energy as a factor of the objective function can increase energy efficiency in target-based WSN with p-coverage and q-connectivity models

INSTRUMENTATION-BASED MONITORING TECHNIQUES SURVEY ON HOST, PLATFORM, AND SERVICE LEVEL IN MICROSERVICE ARCHITECTURE

Microservice is an application architecture that separates one big application into smaller ones. The architecture simplifies development, deployment, and management process. However, the architecture is quite complex thus the monitoring process becomes much more challenging. Classifications for the instrumentations that are used in the monitoring process is needed to achieve better practicality for the administrators. We surveyed the monitoring technique classification method in microservice architecture. The method is divided into three levels. They are host level, platform level, and service level. In this paper, we present the latest instruments that are being used in the monitoring process in each level. Correlation between the goals, needs, and stakeholder is also presented

Metode Penjadwalan Sleep Wakeup Dengan Efisiensi Energi Untuk Jaringan Sensor Nirkabel Berbasis Target Dengan Model P-Coverage Dan Q-Connectivity

Keterbatasan energi menjadi permasalahan yang paling banyak mendapat perhatian dalam dunia Wireless Sensor Network (WSN). Metode penjadwalan sleep wakeup merupakan salah satu teknik yang paling efisien untuk meningkatkan waktu operasional sensor node pada WSN. Namun pada lingkungan target based WSN dengan model p-coverage dan q-connenctivity, penggunaan sleep wakeup scheduling harus memperhatikan batasan jumlah konektivitas pada sensor dan coverage pada target. Genetic Algorithm menjadi solusi permasalahan dari sleep wakeup scheduling dengan permasalahan multi objectif. Penelitian ini mengusulkan sebuah metode baru sleep wakeup scheduling berbasis Genetic Algorithm untuk efisiensi energi pada target based WSN dengan model p-coverage dan q-connectivity. Metode baru ini menggunakan faktor coverage cost, connectivity cost dan sisa energi sebagai fungsi objektif pada fitness function dalam Genetic Algorithm. Dengan adanya faktor energi sebagai salah satu fungsi objektif, metode ini dapat meningkatkan efisiensi energi pada target based WSN dengan model p-coverage dan q-connectivity ================================================================================================= Energy limitations are the problem that gets the most attention in the term of Wireless Sensor Networks (WSN). Sleep wakeup scheduling method is one of the most efficient techniques to increase sensor node operational time on WSN. How-ever, in the target based WSN environment with p-coverage and q-connectivity models, the use of wake-up scheduling has to consider the constraints on the number of connectivity on the sensor and coverage on the target. Genetic Algorithm is a solution to the problem of sleep-wake scheduling with multi-objective problems. This study proposes a new method of sleep wakeup scheduling based on Genetic Algorithm for energy efficiency in target based WSN with p-coverage and q-connectivity models. This method uses coverage cost, connectivity cost and residual energy as the objective factors of the fitness function in the Genetic Algorithm. With the presence of energy as a factor of the objective function can increase energy efficiency in target based WSN with p-coverage and q-connectivity models