Soil Energy Harvester for Batteryless Wireless Sensor Network Node using Redox Method

The Wireless Sensor Network technologies has great advantage that provide us with cheap solution to deal with telecommunication infrastructure problem that don’t exist in extreme and isolated area. However, the biggest problem exist within wireless sensor network was WSN node limited power. In this paper we try to provide battery less power sources for Wireless Sensor Network Node using Redox method. Using 9 combinations of electrodes circuits, it can provide 6.53 volt and turn on Arduino Mini Pro microcontroller. However, the second it turns on Arduino Mini Pro the voltage drops to 1.73 Volts. Hence this energy harvester can provide power to the Arduino Mini Pro microcontroller with unstable power supply

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept

SENSOR SELECTION COMPARISON BETWEEN FUZZY TOPSIS ALGORITHM AND SIMPLE ADDITIVE WEIGHTING ALGORITHM IN AUTOMATIC INFUSE MONITORING SYSTEM APPLICATION

One of the critical equipment to support a patient in the hospital would be an infuse system. One of the main problems with the infuse system was manual monitoring. Few researchers try to build a low cost infuse system using a low-cost sensor and microcontroller. This paper proposes a fuzzy Topsis algorithm and Simple Additive Weighting (SAW) algorithm to choose the best sensor for a low cost to the infuse system, which is one of the Multiple Criteria Decision Making (MCDM) problems. Several simulations using three sensors, such as LDR (photoresistor), phototransistor, and photodiode, are performed. By using these two algorithms, it can be shown that the phototransistor emerges as the best sensor with value 1, even though it has the price six times higher from the LDR sensor and three times higher from the photodiode

RANCANG BANGUN SMART CHICKEN COOP BERBASIS WEMOS

Abstrak -- Smart Chicken Coop merupakan suatu alat pemberi pakan, pengatur suhu serta penanganan kotoran secara otomatis. Saat ini peternak dalam memberikan pakan menggunakan sistem konvensional, yaitu menaburkan pakan pada tempatnya sepanjang kandang dan berpindah dari kandang yang satu ke kandang yang lain, juga dalam hal memberikan penerangan dilakukan secara manual dengan menghidupmatikan saklar. Pada peternakan pemberian pakan ternak secara manual akan menghabiskan banyak waktu dan tenaga. Oleh karena itu, dibutuhkan suatu sistem otomasi untuk membantu dan mendukung peternak dalam pemberian pakan hewan ternak.Pada Penelitian ini akan akan dirancang suatu alat Smart Chicken Coop berbasis mikrokontroler WEMOS dengan memanfaatkan sensor DHT11 dan RTC DS3231. Sensor DHT11 digunakan sebagai pendeteksi suhu kandang. Sedangkan untuk RTC DS3231 digunakan sebagai pengelolaan waktu untuk proses pemberian pakan dan pembuangan kotoran pada prototipe alat ini. Dari hasil pengujian tingkat pembacaan error Sensor DHT11 adalah rata – rata 1,39%. Hal ini dikarenakan adanya pengaruh suhu ruangan pada saat diletakkan miniatur kandang ayam.  Untuk hasil pengujian lama waktu kerja heater untuk menaikkan suhu 1 derajat membutuhkan waktu rata – rata 1 menit 28 detik.  Sedangkan untuk cooler lamanya waktu cooler untuk menurunkan suhu 1 derajat membutuhkan waktu rata – rata 1 menit 43 detik. Sedangkan untuk pengujian sistem pengaturan suhu kandang, sistem pemberian pakan dan pebuangan kotoran dapat berfungsi dan bekerja sesuai dengan rancangan awal. Kata Kunci: WEMOS D1, Arduino Uno, sensor DHT11 dan RTC DS323

Sistem fuzzy : panduan lengkap aplikatif

xvi, 144 halaman.: 23 c

Benchmarking In Microcontroller Development Board Power Consumption For Low Power Iot Wsn Application

One of the advantages of Wireless Sensor Network would be its ability to reduce cost of communication system using node to node communication. However Wireless Sensor Network also had a disadvantage which is has limited energy which is include this as low power application. This small energy capacity has limit WSN node capability to operate for a long time. In this paper, we compare power consumption for 3 popular microcontroller development platforms that use for fast development and prototyping Wireless Sensor Network node. The power consumption was including active mode (using most energy) and deep sleep mode (using least energy) operation. From benchmarking we can see that lolin ESP32 as a microcontroller development platform has the most efficient in power consumption which is only 40 mA in active and 0.05 in deep sleep mode, compare with arduino pro mini 8 mA in active and 0.3 mA in deep sleep mode, and wemos D1 mini 74 mA in active and 0.13 mA in deep sleep mode. This low power consumption in deep sleep mode has resulting in longer operational time which is almost 48 Month for lolin ESP3

Comparison in Quality of service Performance For Wireless Sensor Network Routing between Fuzzy Topsis and SAW Algorithm

one of the advantages of Wireless Sensor Network would be its ability to reduce cost of communication system using node to node communication. Because of many things data transfer is Wireless Sensor Network operation sometimes has disturbance. A routing algorithm is a network coding that intends to enhance network performance for better operation with or without any disturbance. Fuzzy TOPSIS and SAW as MCDM algorithm is proposed for routing algorithm in Wireless Sensor Network operation. From our simulation both SAW  and Fuzzy Topsis algorithm can be used in network coding (routing) to provide better QOS for Wireless Sensor Network compare with shortest path routing. For delay it perform better at about 2/3 (shortest path routing 50 millisecond, both SAW and Fuzzy Topsis algorithm 33 millisecond), and for packet loss at about 3/4 (shortest path routing 21 bit loss, both SAW and Fuzzy Topsis algorithm 16 bit loss). From our simulation both SAW and Fuzzy Topsis algorithm algorithm has benefit which is lower delay and packet loss but at higher cost which is more hopping for communication channel (shortest path routing 3 hopping, both SAW and Fuzzy Topsis algorithm 5 hopping

Self-Organized Wireless Sensor Network (SOWSN) for dense jungle applications

To facilitate wireless sensor networks deployment in dense jungle environments, the challenges of unreliable wireless communication links used for routing data between nodes and the gateway, and the limited battery energy available from the nodes must be overcome. In this paper, we introduce the SelfOrganized Wireless Sensor Network (SOWSN) to overcome these challenges. To develop the traits needed for such SOWSN nodes, three types of computational intelligence mechanisms have been featured in the design. The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. The second feature is the introduction of the fuzzy logic ANFIS-optimized Near Ground Propagation Model to predict the wireless transmission link quality and power transfer between transmitters. The third feature is the introduction of the (Levenberg Marquardt artificial neural network (LM-ANN) for Adaptive Dynamic Power Control to further optimize the transmitter power levels, radio modulation, Spreading Factor configurations, and settings of the employed SOWSN LoRaWAN nodes based on predicted wireless transmission link quality parameters. The introduced features were extensively evaluated and analyzed using simulation and empirical measurements. Using clustering, near-ground propagation, and adaptive transmission power control features, a robust wireless data transmission system was built while simultaneously providing power conservation in SOWSN operation. The payload loss can be improved using SAW clustering from 1275 bytes to 5100 bytes. The result of power conservation can be seen from the reduction of transmission power in SOWSN nodes with the increase of transmission time (TOA) as its side effect. With the original power transmission at 20 dBm, original TOA time at 96.832 milliseconds for all nodes, and SNR 3 as input, transmission power was reduced to 12.76 dBm and the TOA increased to 346.78 milliseconds for all nodes