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A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, are often aggregated (or compressed). This compression is done by a subset of the sensors called 'aggregators.' Inasmuch as sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, the authors first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS) protocol. Then, they generalize it to an aggregation hierarchy and extend EPAS to Hierarchical EPAS. The optimal number of aggregators with generalized compression and power--consumption models was derived, and fully distributed algorithms for aggregator selection were presented. Simulation results show that the algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols and are thus applicable to a broad spectrum of application environments. Index Terms--Energy efficient, hierarchy, wireless sensor networks.

A new model makes it possible to exchange information periodically between individuals in the communication networks by scheduling communications. It also provides communication which is efficient and avoids message collision. This model also calculates the time required to gossip, which is the most general information exchange problem. Synchronous communication schemes are very effective besides being simple.

Abstract: An ad hoc network is a multihop wireless communication network supporting mobile users. Network performance degradation is a major problem as the network becomes larger. Clustering is an approach to simplify the network structure and thus alleviate the scalability problem. One method that has been proposed to form clusters is to use weakly-connected dominating sets [Y.P. Chen, A.L. Liestman, Approximating minimum size weakly-connected dominating sets for clustering mobile ad hoc networks, in: The Third ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’02), 2002, pp. 165–172; Y.P. Chen, A.L. Liestman, A zonal algorithm for clustering ad hoc networks, International Journal of Foundations of Computer Science 14(2) (2003) 305–322]. Here, we present a zonal distributed algorithm to maintain weakly-connected dominating sets as the network structure changes. When the zones are small, the algorithm is essentially localized; when the zones are large, it behaves more globally. The size of the weakly-connected dominating set obtained also varies depending on the choice of zone size, with larger zones generally resulting in smaller weakly-connected dominating sets. Experiments provide evidence that this maintenance algorithm keeps the size of the weakly-connected dominating set approximately the same as its initial size and does not compromise the network connectivity. [Copyright &y& Elsevier]

A real-time system must be reliable if a failure to meet its timing specifications might endanger human life, damage equipment, or waste expensive resources. Applications that require remote operation, timing accuracy, and long periods of activity need mechanisms to support reliability. Fault tolerance improves reliability by incorporating redundancy into the system design. A deadline mechanism has been proposed to provide fault tolerance in real-time software systems. The mechanism trades the accuracy of the results of a service for timing precision. Two independent algorithms are provided for each service subject to a deadline. The primary algorithm produces a good quality service, although its real-time reliability may not be assured. The alternate algorithm is reliable and produces an acceptable response. This paper introduces an algorithm to generate an optimal schedule for the deadline mechanism and discusses a simple and efficient implementation. The schedule ensures the timely completion of the alternate algorithm despite a failure to complete the primary algorithm within real time. [ABSTRACT FROM AUTHOR]