Capabilities for in-network data processing are also critical for effective and efficient data acquisition processes [3], especially when dealing with sensor-based data acquisition from rapidly changing environments.
Second, it provides programming abstractions and system software support for developing in-network data processing mechanisms.
The former supports complex querying of the sensor system, while the latter enables development of in-network data processing mechanisms such as aggregation, adaptive interpolation and assimilation.
This new technology is capable of achieving effective measurements on par with its predecessor, and introduces new possibilities of large-scale networks of hundreds of sensors made possible by low installation costs and scalable in-network data processing.
The reduced bandwidth consumption by in-network data processing methods enable enhanced query processing throughput.
The exploitation of in-network data processing techniques can reduce the aforementioned complexities.
There are also other distributed approaches for target tracking that are based on cluster [6, 33, 38] and tree [17, 31, 37] organizations for in-network data processing.
The future implementation of in-network data processing is envisioned as the provision of network-as-a-service (NaaS) in the cloud environment which is fully orchestrated for redundancy elimination and query optimization [57].
The authors of [57] proposed an in-network data processing technique for bandwidth reduction by customizing routing algorithms, eliminating network redundancy (by caching frequent packets), and reducing on-path data.
Node clustering, in-network data processing, data fusion, and network coding are some of the measures taken to reduce the amount of data that is processed, sensed, or transmitted.
