Secure Data Sensor In Environmental Monitoring System Using Attribute-Based Encryption With Revocation

Wireless sensor networks in internet of thing era have many applications, one of them for environment system, in environmental monitoring system everybody can access data anytime and anywhere. Information was collected using wireless sensor network, all of the data will be sent and stored in the data center. All of data in the data center can be accessed by users through HTTP protocol using a laptop, smartphone and Personal computer. The data in the data center must be secured and the data should be protected from the illegal access by the users from the environment monitoring. To secure the data from the illegal access by the user then the environment monitoring required a security with revocation aspect and encryption the data. CP-ABE (Ciphertext-Policy Attribute-Based Encryption) becomes a solution for this issue, to protect the sensor data and revoke the user. We propose a secure system using CP-ABE with user revocation for protecting the data in data center. Our system is not only encrypting the data sensor, but also revoke to the user. Our experiments system using CP-ABE showed the result for secure the sensor data and revoked the user who does not have the access rights. there are only 2 second processing time for revocation check users.

Implementasi dan Analisis Protokol Komunikasi IoT untuk Crowdsensing pada Bidang Kesehatan

Perkembangan teknologi informasi dan komunikasi telah menandai berlangsungnya era revolusi industri 4.0. Kemudahan pertukaran data antar perangkat yang bergerak menjadikan paradigma baru pada pengumpulan data terpusat yang disebut crowdsensing. Pada bidang kesehatan, crowdsensing tidak lagi mengandalkan telepon bergerak sebagai perangkat pengumpul informasi karena keterbatasan sensor tertanam pada telepon. Berbagai penelitian menggunakan crowdsensing telah mengandalkan kemampuan dari perangkat Internet of Things (IoT). Crowdsensing pada sektor kesehatan dapat membantu mengumpulkan sumber data yang substansial tentang kondisi kesehatan masyarakat secara umum. Namun, kebanyakan teknik crowdsensing hanya mengandalkan satu protokol komunikasi. Metode ini dapat menyebabkan masalah jika perangkat IoT menggunakan protokol komunikasi yang beragam. Oleh sebab itu, kami mengusulkan arsitektur gateway protokol multi-komunikasi untuk crowdsensing. Ketiga protokol komunikasi yang dijalankan pada gateway adalah MQTT, HTTP dan CoAP. Gateway ini berfungsi untuk menangkap data dari crowdsensor dan mengubah ketiga protokol ke dalam protokol yang sama dengan back-end server di cloud. Hasil pengujian menunjukkan bahwa gateway mampu menerima data dengan baik meskipun ketiga protokol dijalankan secara bersamaan. Protokol CoAP memiliki kinerja yang lebih baik daripada kedua protokol dalam pengujian throughput. Protokol MQTT memiliki performa terbaik pada pengukuran delay

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model

Adaptive Sleep Scheduling for Health Monitoring System Based on the IEEE 802.15.4 Standard

In the recent years, Wireless Sensor Networks (WSNs) have become a very popular technology for research in various fields. One of the technologies which is developed using WSN is environmental health monitoring. However, there is a problem when we want to optimize the performance of the environmental health monitoring such as the limitation of the energy. In this paper, we proposed a method for the environmental health monitoring using the fuzzy logic approach according to the environmental health conditions. We use that condition to determine the sleep time in the system based on IEEE 802.15.4 standard protocol. The main purpose of this method is to extend the life and minimize the energy consumption of the battery. We implemented this system in the real hardware test-bed using temperature, humidity, CO and CO2 sensors. We compared the performance without sleep scheduling, with sleep scheduling and adaptive sleep scheduling. The power consumption spent during the process of testing without sleep scheduling is 52%, for the sleep scheduling is 13%, while using the adaptive sleep scheduling is around 7%. The users also can monitor the health condition via mobile phone or web-based application, in real-time anywhere and anytime

Develop a User Behavior Analysis Tool in ETHOL Learning Management System

Students have different learning styles when studying online. Meanwhile, lecturers use the same method for all students who take their online lectures. These different learning styles can affect the level of understanding and the results obtained by students. By knowing student learning styles, lecturers are expected to be able to use the right way in delivering material. In this research, we developed a student behavior analysis feature on self-developed Virtual Learning Environment (VLE) called Enterprise Hybrid Online Learning (ETHOL). Students’ data collected includes data on online activities, personal data, and survey data on student learning styles. User behavior analysis was carried out by dividing into three clusters: average scores, time to collect assignments, and student learning styles. The clustering method used is the Hierarchical K-Means. The results obtained are students who have the habit of collecting assignments on time have higher scores than others. In addition, the lecturer is able to see the results of the analysis of the behavior and learning styles of each student. These results can be used as information in delivering lecture material

Implementation of Oxymetry Sensors for Cardiovascular Load Monitoring When Physical Exercise

The performance condition of an athlete must always be maintained, one way to maintain that performance is by training. Each individual has different abilities and physiological responses in receiving the portion of the exercise. Physical exercise that exceeds the body's ability can worsen the condition of the athlete itself which can result in excessive fatigue (overtraining) or can even result in injury. Therefore a system is needed to monitor the condition of the physiological response when given the intensity of the training load so that the portion of the training provided provides positive benefits for the athlete. This system was developed using an oxymetry sensor, microcontroller and wifi module ESP8266.  This system is used to collect heart rate and oxygen saturation data, then with the existing formula the heart rate value is converted to a CVL (Cardiovascular Load) value to determine the level of fatigue in athletes when given the intensity of the training load. By using a web-based application, measurement data is displayed in realtime to make it easier to see the results of monitoring. From the experimental results the system can monitor changes in the physiological condition of the athlete when given the intensity of the training load. Finally, the developed system can collect athlete's physiological data, and can store the data in a database and display it in a web application

Enhanced PEGASIS using Dynamic Programming for Data Gathering in Wireless Sensor Network

A number of routing protocol algorithms such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) have been proposed to overcome the problem of energy consumption in Wireless Sensor Network (WSN) technology. PEGASIS is a development of the LEACH protocol, where within PEGASIS all nodes are active during data transfer rounds thus limiting the lifetime of the WSN. This study aims to propose improvements from the previous PEGASIS version by giving the name Enhanced PEGASIS using Dynamic Programming (EPDP). EPDP uses the Dominating Set (DS) concept in selecting a subset of nodes to be activated and using dynamic programming based optimization in forming chains from each node. There are 2 topology nodes that we use, namely random and static. Then for the Base Station (BS), it will also be divided into several scenarios, namely the BS is placed outside the network, in the corner of the network, and in the middle of the network. Whereas to determine the performance between EPDP, PEGASIS and LEACH, an analysis of the number of die nodes, number of alive nodes, and remaining of energy were analyzed. From the experiment result, it was found that the EPDP protocol had better performance compared to the LEACH and PEGASIS protocols in terms of number of die nodes, number of alive nodes, and remaining of energy. Whereas the best BS placement is in the middle of the network and uses static node distribution topologies to save more energy

Partition-Based GTS Adjustment for Wireless Sensor Networks

The personal area network (PAN) coordinator can assign a guaranteed time slot (GTS) to allocate a particular duration for requested devices in IEEE 802.15.4 beacon-enabled mode. The main challenge in the GTS mechanism is how to let the PAN coordinator allocate time slot duration for the devices which request a GTS. If the allocated devices use the GTS partially or the traffic pattern is not suitable, wasted bandwidth will increase, which degrades the performance of the network. In order to overcome the abovementioned problem, this paper proposes the Partitioned GTS Allocation Scheme (PEGAS) for IEEE 802.15.4 networks. PEGAS aims to decide the precise moment for the starting time, the end, and the length of the GTS allocation for requested devices taking into account the values of the superframe order, superframe duration, data packet length, and arrival data packet rate. Our simulation results showed that the proposed mechanism outperforms the IEEE 802.15.4 standard in terms of the total number of transmitted packets, throughput, energy efficiency, latency, bandwidth utilization, and contention access period (CAP) length ratio

Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

Wireless Sensor Network (WSN) can be applied for Air Pollution Level Monitoring System that have been determined by the Environmental Impact Management Agency which is  PM10, SO2, O3, NO2 and CO. In WSN, node system is constrained to a limited power supply, so that the node system has a lifetime. To doing lifetime maximization, power management scheme is required and sensor nodes should use energy efficiently. This paper proposes dynamic sleep scheduling using Time Category-Fuzzy Logic (Time-Fuzzy) Scheduling as a reference for calculating time interval for sleep and activated node system to support power management scheme. This research contributed in power management design to be applied to the WSN system to reduce energy expenditure. From the test result in real hardware node system, it can be seen that Time-Fuzzy Scheduling is better in terms of using the battery and it is better in terms of energy consumption too because it is more efficient 51.85% when it is compared with Fuzzy Scheduling, it is more efficient 68.81% when it is compared with Standard Scheduling and it is more efficient 85.03% when compared with No Scheduling