GIANLUCA PANNOZZO MASTER’S THESIS

 

Title: Comparison between  Generic Aggregation Protocol (GAP) and Gossip-Based Aggregation Protocol.

 

Aggregation refers to a set of functions that provide global information about a distributed system. These functions operate on numeric values distributed over the system and can be used to count network size, determine extreme values, compute averages, products or sums.

In the last years, especially with the development of wireless sensor networks, data aggregation has received many attentions  by the researchers. A fundamental challenge in the design of WSNs is to maximize their lifetimes since sensor nodes are equipped with limited lifetime battery. Data aggregation and in-network processing techniques are investigated  as efficient approaches to achieve significant energy savings by combining data arriving from different sensor nodes at some aggregation points en route, eliminating redundancy, and minimizing the number of transmission before forwarding data to management station.

 

GAP is an aggregation protocol developed by Mads Dam and Rolf Stadler at KTH. This protocol computes Aggregation functions building a spanning tree over the network, and has shown to provide good performances in terms of time of convergence, robustness and scalability. The main drawback of GAP is the large number of messages exchanged.

This master’s project is divided into two parts. The main goals of the first part are:

·        To develop  a new version of GAP in order to reduce the load over the network.

·        To implement  a Gossip Based Aggregation Protocol.

·        To compare performances of protocols above. Simulations are carried out on ISP topology, using a simulator called SIMPSON.

The topic of the second part is TCA-GAP, where TCA stands for Threshold Crossing Alert. TCA-GAP is  a new protocol developed  for computing TCAs  in a scalable and robust manner. An alert alarm is raised whenever a management variable exceeds an upper threshold, and is cleared when the variable decreases below a lower threshold.

 

e-mail: g.pannozzo@tin.it