Abstract
Data aggregation is a fundamental building block of modern distributed systems. Averaging based approaches, commonly designated gossip-based, are an important class of aggregation algorithms as they allow all nodes to produce a result, converge to any required accuracy, and work independently from the network topology. However, existing approaches exhibit many dependability issues when used in faulty and dynamic environments. This paper describes and evaluates a fault tolerant distributed aggregation technique, Flow Updating, which overcomes the problems in previous averaging approaches and is able to operate on faulty dynamic networks. Experimental results show that this novel approach outperforms previous averaging algorithms; it self-adapts to churn and input value changes without requiring any periodic restart, supporting node crashes and high levels of message loss, and works in asynchronous networks. Realistic concerns have been taken into account in evaluating Flow Updating, like the use of unreliable failure detectors and asynchrony, targeting its application to realistic environments.

Abstract
Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like COUNT, SUM, and AVERAGE. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Abstract
This paper presents a new load flow formulation to solve active and passive electric distribution networks. The fundamental idea discussed here is how to obtain the power flow solution by using the elements of a unique quasi-symmetric matrix called TRX in the iterative process. The method is formulated for single-phase balanced and three-phase unbalanced radially operated networks. It works with real variables as opposed to complex variables used in previous backward/forward sweep algorithms discussed in literature. The proposed TRX matrix constitutes a complete database by including information of network topology structure as well as branch impedances of the distribution feeder. Data arrangement is suitable to be exchanged under standard Common Information Model (CIM) under Distribution Management Systems (DMS) environment allowing an efficient computation of the state of the system for on-line and off-line study applications. The proposed methodology was applied on a group of IEEE test systems and a real distribution system of 49,000 nodes.

Abstract
This paper discusses an integrated framework for cost allocation of energy losses of electric distribution systems under liberalised energy markets. As key contribution is developed an allocation strategy based upon Locational and Uniform Incremental Prices. The locational incremental prices send efficient economical signals to distributed generators with incentives for loss minimisation and uniform incremental pricing are applied to consumers avoiding non-discriminatory access to the network due to the consumer's geographical localisation. Proposed methodology has been tested and compared with different loss allocation procedures reported in literature under the scope of the social welfare theory in order to assess its impact in the market equilibrium. Copyright © 2007 Inderscience Enterprises Ltd.

Abstract
Data aggregation is a fundamental building block of modern distributed systems. Averaging based approaches, commonly designated gossip-based, are an important class of aggregation algorithms as they allow all nodes to produce a result, converge to any required accuracy, and work independently from the network topology. However, existing approaches exhibit many dependability issues when used in faulty and dynamic environments. This paper extends our own technique, Flow Updating, which is immune to message loss, to operate in dynamic networks, improving its fault tolerance characteristics. Experimental results show that the novel version of Flow Updating vastly outperforms previous averaging algorithms; it self adapts to churn without requiring any periodic restart, supporting node crashes and high levels of message loss.

Abstract
Aggregation of data values plays an important role on distributed computations, in particular, over peer-to-peer and sensor networks, as it can provide a summary of some global system property and direct the actions of self-adaptive distributed algorithms. Examples include using estimates of the network size to dimension distributed hash tables or estimates of the average system load to direct load balancing. Distributed aggregation using nonidempotent functions, like sums, is not trivial as it is not easy to prevent a given value from being accounted for multiple times; this is especially the case if no centralized algorithms or global identifiers can be used. This paper introduces Extrema Propagation, a probabilistic technique for distributed estimation of the sum of positive real numbers. The technique relies on the exchange of duplicate insensitive messages and can be applied in flood and/or epidemic settings, where multipath routing occurs; it is tolerant of message loss; it is fast, as the number of message exchange steps can be made just slightly above the theoretical minimum; and it is fully distributed, with no single point of failure and the result produced at every node.