Distributed data filtering in logistics wireless sensor networks based on transmission relevance

Abstract

Energy-efficient operation is mandatory in wireless sensor networks due to the limited energy budget of sensor nodes. Considering the potential application of wireless sensor networks in logistics, cost efficiency is another major requirement due to high cost pressure. To save on data transmissions within such sensor deployments, which account for the major part of energy consumption and monetary costs, we develop a method for data filtering based on an in-network determination of transmission relevance of sensor data. Our approach explicitly incorporates interdependencies between wireless sensor nodes and their measurements and data transmissions. It contributes to efficiency in wireless sensor networks by filtering out irrelevant data and enables a subsequent reduction of unnecessary transmissions from a network-wide view, while being able to still offer real-time data provision with sufficient data fidelity to stakeholders. The benefits of our approach are indicated by preliminary evaluation results.

Autonomous Proactive Data Management in Support of Pervasive Edge Applications

“…When DS2 feeds our model, we observe similar outcomes with 𝜖 ∈ {0. 43 that the proposed model behaves well when a high number of tasks are processed to deliver the final filters. This is because the variability of the requests manages to lead to filters that clearly depict and affect the underlying data stored in every node.…”

“…Data selectivity or data filtering is adopted to select the incoming data that will be hoisted locally in a node. Any selectivity/filtering model should be designed to detect the dependencies between nodes, their datasets and incoming tasks [42,43]. Actually, the processing of the constraints that the incoming tasks impose is a useful knowledge to identify and conclude the appropriate local data filter.…”

Going all the way - Detecting and transmitting events with wireless sensor networks in logistics

“…As means for the local detection of events on motes, we employ our approach from [29], [30], which is based on the application of so-called "scoresheets". These scoresheets allow users to individually define in a very fine-grained way diverse context parameters according to their information needs, which are afterwards used to locally assess sensor measurements on motes to detect events.…”