Access-centric in-network storage optimization in distributed sensing networks

Abstract

Distributed sensing networks are getting increasingly complex these days. The main reason are the changing demands of the users and application scenarios, which require multipurpose systems. Enabled by continuously improving computational and storage capacities of sensors, this development leads to an increasing number of different algorithms which run concurrently in a sensing network.Thereby, they enable sensor-actuator platforms to perform various kinds of analysis and actions in parallel.Within such a sensor network a variety of algorithms is performed simultaneously. When developing distributed vision and control algorithms, developers focus mainly on the consecutive processing stages. Such a process typically begins with perceiving raw sensor data and terminates with delivering high-level event data to responsible entities. Thereby, different stages may be performed at varying locations within the underlying network. Although the researchers may apply custom optimizations to their data flows, these are highly specific. During design time, it is impossible to anticipate each system environment or predict their algorithms’ possible interactions and synergies with other data flows. We propose a generic storage architecture which separates algorithms from data storage and retrieval. By making use of the fact that most data in sensing networks refers to geographic areas, our architecture takes care of the data flow and its online optimization throughout the network at runtime. By decoupling the processing stages from the data flow, we allow for self-organizing meta-level optimizations of data placement in the network. Moreover, this approach even makes inter-algorithmic optimizations possible, if different algorithms process similar data within their step-wise processing logic. With the introduction of the access-centric storage paradigm, we prove to reduce network load and query latency at the same time at runtime.