Large-scale internet services aim to remain highly available and responsive in the presence
of unexpected failures. Providing this service often requires monitoring and analyzing tens of …

In this paper we investigate the problem of uneven energy consumption in a large class of
many-to-one sensor networks. In a many-to-one sensor network, all sensor nodes generate …

Applications in diverse domains such as astronomy, economics and industrial monitoring,
increasingly press the need for analyzing massive collections of time series data. The sheer …

In this paper, we present a method for approximating the values of sensors in a wireless
sensor network based on time series forecasting. More specifically, our approach relies on …

In a many-to-one sensor network, all sensor nodes generate CBR data and send them to a
single sink via multihop transmissions. Sensor nodes sitting around the sink need to relay …

Energy is a primary constraint in the design and deployment of wireless sensor networks
(WSNs), since sensor nodes are typically powered by batteries with a limited capacity …

Lossy temporal compression is key for energy-constrained wireless sensor networks
(WSNs), where the imperfect reconstruction of the signal is often acceptable at the data …

We propose an energy-efficient framework, called SAF, for approximate querying and
clustering of nodes in a sensor network. SAF uses simple time series forecasting models to …

Monarch is a globally-distributed in-memory time series database system in Google.
Monarch runs as a multi-tenant service and is used mostly to monitor the availability …

The explosion of time series advances the development of time series databases. To reduce
storage overhead in these systems, data compression is widely adopted. Most existing …