Mobile platform sampling for designing environmental sensor networks.

This paper proposes a method to design the deployment of sensor nodes in a new region where historical data is not available. A number of mobile platforms are simulated to build initial knowledge of the region. Further, an evolutionary algorithm is employed to find the optimum placement of a given number of sensor nodes that best represents the region of interest.

In search for a robust design of environmental sensor networks.

This paper presents an approach to the design of environmental sensor networks (ESN) which aims at providing a robust, fit-for-purpose network with minimum redundancy. A set of near optimum ESN designs is sought using an evolutionary algorithm, which incorporates redundancy and robustness as fitness functions. This work can assist the decision-making process when determining the number of sensor nodes and how the nodes are going to be deployed in the region of interest.

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration.

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN.

A Bayesian framework for the automated online assessment of sensor data quality.

Online automated quality assessment is critical to determine a sensor's fitness for purpose in real-time applications. A Dynamic Bayesian Network (DBN) framework is proposed to produce probabilistic quality assessments and represent the uncertainty of sequentially correlated sensor readings. This is a novel framework to represent the causes, quality state and observed effects of individual sensor errors without imposing any constraints upon the physical deployment or measured phenomenon.

Smart query answering for marine sensor data.

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries.

Automated data quality assessment of marine sensors.

The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available.