Clustering protocols for energy efficiency analysis in WSNS and the IOT

Throughout the development of energy efficient routing protocol for wireless sensor network clustering technique has been widely adopted approach. The Selection of cluster heads is also very important for the energy efficiency of the network. In the past, researchers have proposed multiple routing protocols; however, problems are still alive and need to be resolved because of the diversity of WSN applications. This article presents an energy-efficient routing protocol using the advanced approaches of Artificial Intelligence, the most promising field of computer science currently providing the best solutions. The proposed model uses the Deep Q-network to select the cluster head. Moreover, collected data at the cluster head is generalized as low, moderate, and high values using the fuzzy logic technique. After that, the Predictive coding theory algorithm is used for the data compression, and the lossy compression technique is applied to the data. Its compressed form also gives complete information of the data in small size and is delivered to the base station. Again, the transmitted data is reconstructed into its actual format. In the end, to justify the performance of the newly designed routing protocol, simulations are performed using the Matlab tool, and its results are evaluated in quality of service matrices and compared with well-known routing protocols

Implementasi dan Analisis Kinerja Algoritma Kompresi Data pada Jaringan Sensor Nirkabel berbasis Arduino

Jaringan sensor nirkabel merupakan sebuah jaringan yang terdiri dari beberapa sensor yang saling terhubung secara nirkabel. Jaringan sensor nirkabel umumnya digunakan untuk beberapa hal seperti sistem monitoring atau untuk mendeteksi suatu kejadian. Permasalahan yang paling sering ditemui ialah mengenai konsumsi energi dari satu node. Untuk mengirim satu bit dari satu node ke node yang lain atau ke sink, diperlukan energi yang cukup besar bila dibandingkan untuk komputasi atau akuisisi data dari sensor. Beberapa pendekatan dilakukan untuk mengurangi masalah yang dialami oleh node. Pendekatan yang akan dilakukan di tugas akhir ini yaitu menggunakan teknik kompresi data untuk mengurangi konsumsi energi saat dilakukan transmisi dan diimplementasikan ke dalam mikrokontroller yaitu Arduino. Sebelum dilakukan implementasi ke Arduino, perlu dilakukan simulasi menggunakan Matlab. Arduino sendiri akan digunakan untuk mendapatkan data pengukuran. Melalui simulasi, didapatkan bahwa metode sequential lossless entropy coding (S-LEC) merupakan metode kompresi data yang paling efisien. Hasil pengukuran juga menunjukkan bahwa metode S-LEC merupakan metode yang sangat efisien dalam mengurangi energi yang dikonsumsi dengan efisiensi sebesar 18,63%. ================================================================================================================================ Wireless sensor network is a network consisting of several sensors that are connected wirelessly to each other. Wireless sensor networks are generally used for several things such as monitoring systems or to detect an event. The most common problem is about energy consumption from one node. To send one bit from one node to another node or to the sink, it requires considerable energy compared to computing or data acquisition from the sensor. Some approaches are carried out to reduce the problems experienced by nodes. The approach to be carried out in this final project is to use data compression techniques to reduce energy consumption when carried out transmission and implemented into a microcontroller, namely Arduino. Before implementing the Arduino, a simulation using Matlab is required. Arduino itself will be used to obtain measurement data. Through simulation, it was found that the sequential lossless entropy coding (S-LEC) method was the most efficient data compression method. The measurement results also show that the S-LEC method is a very efficient method in saving energy consumed with.an efficiency of 18,63%

Energy efficient and latency aware adaptive compression in wireless sensor networks

Wireless sensor networks are composed of a few to several thousand sensors deployed over an area or on specific objects to sense data and report that data back to a sink either directly or through a series of hops across other sensor nodes. There are many applications for wireless sensor networks including environment monitoring, wildlife tracking, security, structural heath monitoring, troop tracking, and many others. The sensors communicate wirelessly and are typically very small in size and powered by batteries. Wireless sensor networks are thus often constrained in bandwidth, processor speed, and power. Also, many wireless sensor network applications have a very low tolerance for latency and need to transmit the data in real time. Data compression is a useful tool for minimizing the bandwidth and power required to transmit data from the sensor nodes to the sink; however, compression algorithms often add a significant amount of latency or require a great deal of additional processing. The following papers define and analyze multiple approaches for achieving effective compression while reducing latency and power consumption far below what would be required to process and transmit the data uncompressed. The algorithms target many different types of sensor applications from lossless compression on a single sensor to error tolerant, collaborative compression across an entire network of sensors to compression of XML data on sensors. Extensive analysis over many different real-life data sets and comparison of several existing compression methods show significant contribution to efficient wireless sensor communication --Abstract, page iv