2016
DOI: 10.3390/s16040583
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Secure and Cost-Effective Distributed Aggregation for Mobile Sensor Networks

Abstract: Secure data aggregation (SDA) schemes are widely used in distributed applications, such as mobile sensor networks, to reduce communication cost, prolong the network life cycle and provide security. However, most SDA are only suited for a single type of statistics (i.e., summation-based or comparison-based statistics) and are not applicable to obtaining multiple statistic results. Most SDA are also inefficient for dynamic networks. This paper presents multi-functional secure data aggregation (MFSDA), in which t… Show more

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Cited by 2 publications
(1 citation statement)
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References 34 publications
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“…Aggregation functions 1) Expanded Scaleable Functions: In-network aggregation functions such as COUNT, SUM, MIN, and MAX [25] are scaleable in that the size of the data does not increase as the number of data sources increases. The functions MEAN and STANDARD-DEVIATION, frequently regarded as In-server aggregation functions [25], can be added to the range of scaleable functions, by including auxiliary variables required to perform incremental evaluation of the function [26]. This requires a slight increase in the fixed data length, over the size of the actual data values themselves, but this is a fixed overhead, and does not significantly decrease the efficiency of the aggregation (as shown in II-D below).…”
Section: A Variables and Equationsmentioning
confidence: 99%
“…Aggregation functions 1) Expanded Scaleable Functions: In-network aggregation functions such as COUNT, SUM, MIN, and MAX [25] are scaleable in that the size of the data does not increase as the number of data sources increases. The functions MEAN and STANDARD-DEVIATION, frequently regarded as In-server aggregation functions [25], can be added to the range of scaleable functions, by including auxiliary variables required to perform incremental evaluation of the function [26]. This requires a slight increase in the fixed data length, over the size of the actual data values themselves, but this is a fixed overhead, and does not significantly decrease the efficiency of the aggregation (as shown in II-D below).…”
Section: A Variables and Equationsmentioning
confidence: 99%