In-Network Aggregation Research Paper

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II. IN-NETWORK AGGREGATION TECHNIQUES We define the In-network aggregation process as follows: In-network aggregation is the global process of gathering and routing information through a multi-hop network, processing data at intermediate nodes with the objective of reducing resource consumption (in particular energy), thereby increasing network lifetime. We can distinguish the In-network aggregation process into two approaches as described below: 1. In-network aggregation with size reduction: refers to the process of combining and compressing data coming from different sources in order to reduce the information to be sent over the network. As an example, assume that a node receives two packets from two different sources containing the locally…show more content…
There are several types of aggregation functions [2, 11–18], and most of them are closely related to the specific sensor application. Nevertheless, we can identify some common paradigms for their classification: • Lossy and lossless: Aggregation functions can compress and merge data according to either a lossy or a lossless approach. In the first case the original values cannot be recovered after having merged them by means of the aggregation function. In addition, we may lose precision with respect to transmitting all readings uncompressed. In contrast, the second approach (lossless) allows us to compress the data by preserving the original information. This means that all readings can be perfectly reconstructed from their aggregate at the receiver…show more content…
Each node, on receiving the interest, rebroadcasts it to its neighbors. In addition, the node sets up interest gradients, that is, vectors containing the next hop that has to be used to propagate the result of the query back to the sink node (gradient setup). As an illustrative example (Fig. 1), if the sink sends an interest that reaches nodes a and b, and both forward the interest to node c, node c sets up two vectors indicating that the data matching that interest should be sent back to a and/or b. The strength of such a gradient can be adapted, which may result in a different amount of information being redirected to each neighbor. To this end, various metrics such as the node’s energy level, communication capability, and position within the network can be used. Each gradient is related to the attribute for which it has been set up. As the gradient setup phase for a certain interest is complete, only a single path for each source is reinforced and used to route packets toward the sink (path reinforcement and forwarding). A valuable feature of Directed Diffusion consists of the local interaction among nodes in setting up gradients and reinforcing paths. This allows for increased efficiency as there is no need to

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