Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

Architecture for Automated Irrigation System

Agriculture sector provides food as well as large employment. Impact of agriculture development as traditional framing is unable to increase the crop yield. In our country , the growth of population is around 2% per year. Thus food production should increase about 2.6% per year to provide an effcient food intake. The use of water resources to be optimally connected and beneficially utilized with appropriate priorities of use. Therefore the real values of soil moisture, air humidity, temperature and water level in the soil are wirelessly transmitted using wireless technology and same is monitored for optimum production of crop production

On BER Performance of EBPSK-MODEM in AWGN Channel

In order to satisfy the higher and higher demand for communication systems, an Extended Binary Phase Shift Keying (EBPSK) system with very high spectra efficiency has been proposed. During the research, a special kind of filters was found, which can amplify the signal characteristics and remove utmost noise, i.e., at the point of the phase jumping corresponding to code “1”, produce the amplitude impulse much higher than code “0”, therefore, the aim of our study was to analyze the BER performance of the impacting filter assisted EBPSK-MODEM. Considering the receiver filtered “0” and “1”signal with Rice amplitude distribution, just having different mean values, so the BER performance of EBPSK is deduced based on the classic detection theory, and compared with the traditional BPSK modulation both in spectra efficiency and in BER performance, which lays the theoretical foundation for the feasibility of Ultra Narrow Band communications based on EBPSK modulation

Experimental study on the power consumption of timers embedded into microcontrollers

An experimental study on the current consumption of timers embedded into microcontrollers is presented in this work. The study is carried out in two commercial microcontrollers (MSP430FR5969 and ATtiny2313) and the experimental results are co mpared with the scarce data provided in their datasheets. The sensitivity (expressed in ¿A/MHz) reported in the datasheet seems to be only applicable if the frequency divider of the timer equals one. Otherwise, such a sensitivity is lower but there is a significant offset component, leading to a higher power consumption at the same operating frequency. The knowledge extracted from this work is expected to provide guidelines to better use embedded timers in low-power sensor applicationsPostprint (updated version

Adaptive sampling technique for computer network traffic parameters using a combination of fuzzy system and regression model

In order to evaluate the effectiveness of wired and wireless networks for multimedia communication, suitable mechanisms to analyse their traffic are needed. Sampling is one such mechanism that allows a subset of packets that accurately represents the overall traffic to be formed thus reducing the processing resources and time. In adaptive sampling, unlike fixed rate sampling, the sample rate changes in accordance with transmission rate or traffic behaviour and thus can be more optimal. In this study an adaptive sampling technique that combines regression modelling and a fuzzy inference system has been developed. It adjusts the sampling according to the variations in the traffic characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling gave an improved performance

Adaptive sampling for QoS traffic parameters using fuzzy system and regression model

Quality of service evaluation of wired and wireless networks for multimedia communication requires transmission parameters of packets making up the traffic through the medium to be analysed. Sampling methods play an important role in this process. Sampling provides a representative subset of the traffic thus reducing the time and resources needed for packet analysis. In an adaptive sampling, unlike fixed rate sampling, the sample rate changes over time in accordance with transmission rate or other traffic characteristics and thus could be more optimal than fixed parameter sampling. In this study an adaptive sampling technique that combined regression modelling and a fuzzy inference system was developed. The method adaptively determined the optimum number of packets to be selected by considering the changes in the traffic transmission characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling methods gave an improved performance

An Energy Effective Adaptive Spatial Sampling Algorithm for WSNs

Abstract: The objective of environmental observation with WSNs (wireless sensor networks) is to extract the synoptic structures (spatio-temporal sequence) of the phenomena of ROI (region of interest) in order to make effective predictive and analytical characterizations. Energy limitation is one of the main obstacles to the universal application of WSNs and therefore there are a large mass of researches on energy conservation for WSNs. Among them, adaptive sampling strategy is regarded as a promising method to improve energy efficiency in recent years, therefore, many researches are concerning to different kinds of energy efficient sampling scheme for WSNs. In this paper, we dedicate to investigating how to schedule sensor nodes in the spatial region domain by our adaptive sampling scheme so as to reduce energy consumption of sensor nodes. The key idea of this paper is to schedule sensor nodes to achieve the desired level of accuracy by activating sensor system only when necessary to acquire a new set of samples and then prepare to power it off immediately afterwards. By adaptively sampling the region of interest, fewer sensors are activated at the same time. Moreover, only the necessary communications are remaining with this algorithm, so as to achieve significant energy conservation than before. The algorithm proposed in this literature is named as Adaptive Spatial Sampling (ASS) algorithm in short. The simulation results verified that ASS algorithm can outperform traditional fixed sampling strategy