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Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

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Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

 Due to limited computational power and energy resources of sensor nodes, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However, such aggregation has been known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trust-worthiness of data and reputation of sensor nodes has become crucially important for WSN. As the performance of very low power processors dramatically improves and their cost is drastically reduced, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, which will make WSN less vulnerable to severe impact of compromised nodes. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that a number of existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging. We believe that so modified iterative filtering algorithms have a great potential for deployment in the future WSN.


 


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  • Model: PROJ5348
  • 999 Units in Stock
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This product was added to our catalog on Friday 16 September, 2016.

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