Quality-aware sensor data collection

Quality-aware sensor data collection

Many sensor applications often require to collect raw sensed values from many sensor nodes to one centralized server. Sensor data collection typically comes with various quality requirements, e.g., the level of precision requested for temperature values, the time constraints for getting the data, or the percentage of data that is needed. This paper presents a quality-aware sensing framework whe...

Quality-Aware Sensor Data Management

In this chapter, we provide a data management perspective on large-scale sensor environments applications posing non-functional requirements to meet the underlying timeliness, reliability and accuracy needs in addition to the functional needs of data collection. Due to the large-scale regional spread, we need methods that will allow scaling of today’s systems to large-scale deployments. Our dat...

What does quality-aware data collection really achieve in energy harvesting wireless sensor networks?

Energy is the major bottleneck for wireless sensor networks. Although energy harvesting sensor nodes have emerged as a solution, new challenges in energy management arise due to the intermittently available energy. We also observe that many applications can tolerate certain levels of data inaccuracies. Here, we exploit the application’s tolerance for data inaccuracy to achieve an equilibrium be...

Efficient Data Collection in Interference-Aware Wireless Sensor Networks

In this paper, we study the Minimum Latency Collection Scheduling (MLCS) problem in Wireless Sensor Networks (WSNs) adopting the two interference models: the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the MLCS problem is to compute the minimum latency schedule, that is, to compute a schedule with the minim...

Quality of Information Aware Data Delivery in Wireless Sensor Networks