In Network Processing and Data Aggregation in

Benefits1. It is a completely distributed and localized solution for the periodic aggregationproblem and is not error prone to node failures (unlike the solutions that make use ofaggregation-trees) due to the broadcast nature of the communications that are performedbetween the nodes.2. Simulations showed that it achieves significant energy savings for some thresholdparameters (Th).DrawbacksIt needs the exchange of big volumes of information between the nodes when thecomputing aggregate is more complex than the cases of MAX/MIN (e.g. MEDIAN,HISTOGRAM etc.).Gossip-Based Computation of aggregates [9]In this paper [9], a new theoretical method is proposed for the computation ofaggregates (like sums, averages, random samples) with gossip-style protocols. But whyshould someone use a gossip based protocol? Here are some characteristics of them thatare enough to convince someone about the benefits from using them:a) It is known that gossip-based algorithms perform well in terms of scalability.b) Each node communicates with one or a few nodes of its neighborhood, thus, onlylocal information is neededc) Simplicity of design and operation. Usually, gossip-style protocols do not requireerror recovery mechanisms, while often incurring only moderate overhead, compared tooptimal deterministic protocols such as the construction of data dissemination treesd) They achieve high stability under stress and disruptions. In comparison,traditional techniques have absolute guarantees, but are unstable or fail to make progressduring periods of even modest disruption.It is pointed out that the point-to-point Uniform Gossip protocol is not suitable forwireless sensor networks. Instead, an alternative distributed broadcast-based algorithm isproposed, namely flooding. The convergence of the flooding algorithm is analyzed by