Danielo G. Gomes

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.

WSNs clustering based on semantic neighborhood relationships

We propose a semantic clustering model based on a fuzzy inference system to find out the semantic neighborhood relationships in wireless sensor networks in order to both reduce energy consumption and improve the data accuracy. As a case study we describe a structural health monitoring application which was used to illustrate and assess the proposed model. We conduct experiments in order to evaluate the proposal in two different scenarios of damage with different data aggregation methods. We also compared our proposal, using the same data set, with a deterministic clustering method and with the LEACH algorithm. The results indicate that our approach is an energy-efficient clustering method for WSNs, outperforming both the deterministic clustering and LEACH algorithms in about 70% and 47% of energy savings respectively. The energy saving comes from the fact that we have a more efficient in-network data aggregation process since by exploiting the semantic relation between sensor nodes we can potentially aggregate more similar data and consequently, decrease the data redundancy (thus minimizing transmissions). Nodes that are semantically unrelated can operate in low-duty cycle, further reducing the energy consumption. Moreover, our proposal has the potential to improve the data accuracy provided for the application where accuracy is a QoS requirement in typical WSN applications.

A semantic middleware for autonomic wireless sensor networks

In this paper we present a proposal that combines the benefits of autonomic and semantic sensor networks to build a semantic middleware for autonomic wireless sensor networks. The key feature of the proposed middleware is a rule-based reasoning engine based on ontology and fuzzy logic. We also propose a semantic-aware topology control based on computing semantic neighborhoods relationships. The middleware was tailored to provide support for Structural Health Monitoring applications. However, it has a flexible architecture and it can be extensible to several other application domains such as ambient intelligence, habitat monitoring and fire detection. We use the oil platform structural health monitoring domain as a case study. The paper presents the middleware architecture and the proposed ontologies.

CloudReports: An Extensible Simulation Tool for Energy-Aware Cloud Computing Environments

The cloud computing paradigm integrates several technological models to provide services to a large number of clients distributed around the world. It involves the management of large data centers that represent very complex scenarios and demand sophisticated techniques for optimization of resource utilization and power consumption. Since the utilization of real testbeds to validate such optimization techniques requires large investments, simulation tools often represent the most viable way to conduct experimentation in this field. This chapter presents CloudReports, an extensible simulation tool for energy-aware cloud computing environments to enable researchers to model multiple complex simulation scenarios through an easy-to-use graphical user interface. It provides report generation features and a simple API (Application Programming Interface) that makes possible the development of extensions that are added to the system as plugins. CloudReports is an open-source project composed of five mandatory modules and an optional extensions module. This chapter describes all these modules, their integration with the CloudSim toolkit, and a case study that demonstrates an evaluation of power consumption of data centers with a power model that is created as a CloudReports extension.

FairCPU: Architecture for Allocation of Virtual Machines Using Processing Features

This paper proposes an architecture to handle the allocation of virtual machines based on the processing power for heterogeneous Clouds, where there is a wide variety of CPU types. Our major contribution is a novel representation of the processing capacity in terms of the Processing Unit (PU) and the CPU usage limitation in order to isolate the processing capability from the Physical Machine (PM) where the Virtual Machine (VM) is allocated. The efficiency of the proposed architecture is validated by extensive replications of five experiments using a real private cloud. The results show that it is possible to use the proposed idea to define a PU, supported by the CPU usage limitation, to enable the VMs processing power remain at the same level regardless of the PM.

Application of wireless sensor networks for beehive monitoring and in-hive thermal patterns detection

A beehive monitoring able of detecting stress condition of the bees in high temperature.Overheating can lead to absconding.Monitoring and analysis of the microclimate on hives alert of overheating.Use of clustering for similarities to detect thermal patterns that indicate overheating.Due to the patterning of the collected thermal data, we avoid sending redundant data, reducing the energy cost. As cold-blooded animals, bees seek to control the environment thermal variation to live and work in their hives. In semi-arid regions, such as in Northeast Brazil, bees lead a natural thermoregulation mechanism inside their hives so that they can deal with high temperatures. However, when thermoregulation is not fully accomplished, all bees can leave the nest in a process known as colony absconding. In such a process, absconding is due to a thermal stress stimulus. In this context, here we propose a proactive monitoring of hives using a wireless sensor network which detects atypical heating. Through thermal patterns obtained on a daily basis, we developed a mechanism for detecting the temperature rise inside the hive (microclimate). Our results show various thermal patterns related to hive conditions, and highlight the temperature as a key factor to detect potential absconding conditions.

Multiple linear regression to improve prediction accuracy in WSN data reduction

Simple linear regression is usually used for WSN data reduction. The mechanism is concerned about energy consumption, but neglects the prediction accuracy. The prediction error from it is often ignored and inconsistencies are forwarded to the user application. This paper proposes to use a method based on multiple linear regression to improve prediction accuracy. The improvement is achieved by multivariate correlation of readings gathered by sensor nodes in field. Tests show that our solution outperforms some current solutions adopted in the literature.

A predictive algorithm for mitigate swarming bees through proactive monitoring via wireless sensor networks

Swarming is the massive outflow of the bees in a hive, whose most common causes are high temperatures, lack of food, stress and humidity changes. Among the types of swarming, one in which the complete abandonment of the hive occurs, has created large losses to Brazilian beekeepers, especially the Northeast. In an attempt to mitigate this problem, we propose in this paper a system for monitoring hive, via a wireless sensors network capable of identifying the preswarming colony behavior. Through a pattern of collections obtained from the cyclical behavior daily temperatures, we developed a predictive algorithm based on pattern recognition techniques, able to detect the increase in temperature in the hive (microclimate) responsible for the typical stress of bees that culminates in swarming. This mechanism is also able to recognize and avoid sending redundant information over the network in order to reduce radio communication, thereby reducing costs of data transmission and energy.

An Autonomic Computing-based architecture for cloud computing elasticity

Elasticity is an important feature of cloud computing, and can be understood as how a computational cloud fits to variations in their workload by provisioning and deprovisioning resources. Autonomic Computing brings many concepts quite useful in the construction of elastic cloud computing solutions, such as control loops and thresholds-based rules. This paper proposes an elastic architecture for cloud computing based on concepts of Autonomic Computing. For its validation, we designed two experiments using microbenchmarks, applied in both private and hybrid clouds. Results shown cloud computing and Autonomic Computing may work well together in the elasticity provisioning.

Semantic Clustering in Wireless Sensor Networks

Wireless Sensor Networks have critical resource constraints and minimizing resources usage is crucial to extend the network lifetime. Energy saving in WSNs can be achieved through several techniques, such as topology control and clustering, to provide a longer lifetime and scalability to the network. In this paper we propose a semantic clustering model based on a fuzzy inference system to find out the semantic neighborhood relationships in the network. As a case study we describe the structural health monitoring domain application which has been used to illustrate and verify the proposed model.