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 the mapping step and coding step are introduced to provide value-preserving and order-preserving and, later, to enable arbitrary statistics support in the same query. MFSDA is suited for dynamic networks because these active nodes can be counted directly from aggregation data. The proposed scheme is tolerant to many types of attacks. The network load of the proposed scheme is balanced, and no significant bottleneck exists. The MFSDA includes two versions: MFSDA-I and MFSDA-II. The first one can obtain accurate results, while the second one is a more generalized version that can significantly reduce network traffic at the expense of less accuracy loss.
Highlights
Wireless sensor networks and mobile sensor networks [1,2,3,4] have received unprecedented attention because of their exciting potential applications in military, industrial and civilian areas
The statistical results obtained by multi-functional secure data aggregation (MFSDA)-I are accurate; the accuracy evaluation is only performed on the MFSDA-II
Let us compared MFSDA-I with MFSDA-II based on the wind direction dataset of TAO, where the network size is N = 2000
Summary
Wireless sensor networks and mobile sensor networks [1,2,3,4] have received unprecedented attention because of their exciting potential applications in military, industrial and civilian areas (e.g., environmental and habitat monitoring). Data aggregation [5,6,7,8,9,10,11,12,13,14] is one of the most important solutions in minimizing the transmitted data size in large-scale wireless networks and is one of the most important tasks in other distributed applications [15,16,17,18,19,20,21]. In-server aggregation, where data aggregation is performed directly at the server based on the raw data received from each client, is an energy cost approach in large-scale distributed systems. In-network aggregation (i.e., aggregating partial results at intermediate nodes along the routing path) significantly reduces the total communication cost and obtains load balance, especially when we only need the aggregation result instead of much raw data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
