Missing Internet Traffic Reconstruction using Compressive Sampling

Missing traffic is a commonly problem in large-scale network. Because the traffic information is needed by network engineering task for network monitoring, there are several methods that recover the missing problem. In this paper, we proposed missing internet traffic reconstruction based on compressive sampling. The main contributions of this study are as follows: (i) explore the influence of the six missing patterns on the performance of the traffic matrix reconstruction algorithm; (ii) trace the link sensitivity; and (iii) detect the time sensitivity of the network. Using Abilene data, the simulation results show that compressive sampling can perform internet traffic monitoring such as reconstruction from missing traffic, finding link sensitivity, and detecting time sensitivity.

Underwater Data Transmission Using Frequency Shift Keying (FSK) Modulation with Bit Rate of 2400 bps

Underwater acoustic communication is a technology that uses sound or acoustic waves and water as its propagation medium. This technology has been used in various fields, such as underwater wireless sensor networks, underwater monitoring system, and surveillance systems. An acoustic modem is required to facilitate communication between nodes. In this paper, an underwater acoustic modem using Frequency Shift Keying (FSK) modulation has been designed. This modulation is widely used because of its reliability and simple design. FSK modem was designed using M=2 level or known as Binary FSK (BFSK) with 40 kHz mark frequency and 43 kHz space frequency. This study tested data packets sending and its error rate against the distance variation. Testing for 70-bit data resulted in 1% error at 100 cm distance and 37% error at 170 cm distance. When compared with the previous testing at 1200 bps which resulted in 0% and 35% error, it can be seen that error at 1200 bps is better than at 2400 bps, but the data transmission was better at 2400 bps. Addition to the number of bits sent and distance has an influence on the error value, i.e. the greater the distance and the amount of data sent, the greater the error value