MaxLife is capable of calculating optimal data aggregation structures and thereby extending network lifetime.
Application dependent approaches [6 8] support the creation of optimal network-wide data aggregation structures, but restrict the topology of the distributed application.
Prior works have suggested several approaches to decrease energy cost during data transmission process via data aggregation tree structure.
Specific middleware for data acquisition, data aggregation, data structuring, and transformation, so as to feed all functionalities and services in a generic way.
The MDM system comprises of the following: Specific middleware for data acquisition, data aggregation, data structuring, and transformation, so as to feed all functionalities and services in a generic way.
We also evaluate and compare our protocol with a prototype tree-based technique for data aggregation (i.e., structure based) and show that our protocol is better suited for MANETs and remains scalable under high mobility.
In this section, we compare the performance of our protocol with a structured approach for one-shot duplicate insensitive data aggregation that involves maintaining network structures such as spanning trees.
The inherent mobility of the application and of the cell phone users warrants distributed structure-free data aggregation and on-the-fly routing.
In terms of overhead, EZ-AG outperforms existing structure-free data aggregation by a factor of at least log(N) and achieves the lower bound for aggregation message overhead.
According to the underlying route structure, the proposed data aggregation schemes can be categorized into four classes: tree-based data aggregation scheme, cluster-based data aggregation scheme, multi-path data aggregation scheme, and hybrid data aggregation scheme [8].
