Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction

Workload modeling for resource usage analysis and simulation in cloud computing

Workload modeling enables performance analysis and simulation of cloud resource management policies, which allows cloud providers to improve their systems’ Quality of Service (QoS) and researchers to evaluate new policies without deploying expensive large scale environments. However, workload modeling is challenging in the context of cloud computing due to the virtualization layer overhead, insufficient tracelogs available for analysis, and complex workloads. These factors contribute to a lack of methodologies and models to characterize applications hosted in the cloud. To tackle the above issues, we propose a web application model to capture the behavioral patterns of different user profiles and to support analysis and simulation of resources utilization in cloud environments. A model validation was performed using graphic and statistical hypothesis methods. An implementation of our model is provided as an extension of the CloudSim simulator

IoT for Development: Building a Classification Algorithm to Help Beekeepers Detect Honeybee Health Problems Early

Bees are the main pollinators of most wild plant species and are essential for the maintenance of plant ecosystems and for food production. However, in recent years they are suffering from deforestation and pesticides. Here, we propose a method to identify the health status of bee colonies. We trained, validated and tested 4 classification algorithms (Naive Bayes, k-NN, Random Forest and Neural Networks) on actual data from a beehive that was monitored for 6 months. For the generation of the classification model, we take into account data from internal sensors to the hive (temperature, relative humidity, and weight), external data (temperature, pressure, wind speed, and rainfall). We also use data from inspections performed weekly by a specialist in beekeeping. We compared the four algorithms and arrived at a high precision classification model to automatically identify the health status of bee colonies

Modeling and simulation of global and sleep states in ACPI-compliant energy-efficient cloud environments

The more large-scale data centers’ infrastructure costs increase, the more simulation-based evaluations are needed to understand better the trade-off between energy and performance, and support the development of new energy-aware resource allocation policies. Specifically in the cloud computing field, various simulators are able to predict and measure the behavior of applications on different architectures using different resource allocation policies. Yet, only a few of them have the ability to simulate energy-saving strategies, and none of them support the complete Advanced Configuration and Power Interface (ACPI) specification. ACPI defines a terminology for all possible power states of a machine and their associated power rate. The hardware industry has relied on ACPI to provide up-to-date standard interfaces for hardware discovery, configuration, power management and monitoring, enabling a better understanding of the energy consumption level of different hardware states, referred to as ACPI G-states, S-states and P-states. In this paper we improve the modeling and simulation of the ACPI G/S-states and show not only that these states offer different energy-saving levels, but also that state transitions consume energy. In addition, we model the latency to transit between two states and the effects on the turnaround time when the transitions are not performed conservatively. Furthermore, the equations provide essential information to quantify the trade-off between energy consumption and performance, and assist in the analysis/decision on which strategy fits better in the environment and how it could be refined. Our expanded energy model was implemented in CloudSim and validated with simulation-based experiments with a very high level of accuracy, with a standard deviation of at most 6%

Autonomic Context-Aware Wireless Sensor Networks

Autonomic Computing allows systems like wireless sensor networks (WSN) to self-manage computing resources in order to extend their autonomy as much as possible. In addition, contextualization tasks can fuse two or more different sensor data into a more meaningful information. Since these tasks usually run in a single centralized context server (e.g., sink node), the massive volume of data generated by the wireless sensors can lead to a huge information overload in such server. Here we propose DAIM, a distributed autonomic inference machine distributed which allows the sensor nodes to do self-management and contextualization tasks based on fuzzy logic. We have evaluated DAIM in a real sensor network taking into account other inference machines. Experimental results illustrate that DAIM is an energy-efficient contextualization method for WSN, reducing 48.8% of the number of messages sent to the context servers while saving 19.5% of the total amount of energy spent in the network

BeeNotified! A Notification System of Physical Quantities for Beehives Remote Monitoring

One of the ways to reduce inappropriate management of hives and monitor bee health is to send notifications/alerts about the data collected through sensors. This study presents  BeeNotified!, a solution for sending notifications through Telegram, e-mail, and SMS. The notifications warn about the level of temperature, humidity, sound, carbon dioxide, oxygen, hive weight and delay in data gathering. From this data, researchers and beekeepers can be informed and make changes in the locations of the hives, avoiding catastrophes and possible diseases. The results obtained with the processing time in the sending of messages showed that the messages sent via SMS and Telegram have a shorter processing time compared to the sending via e-mail. In regards to sending notifications according to user preferences, all notifications were sent correctly