Aggregating secure data in wireless sensor networks

K. Parameswari, M. Raseen
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引用次数: 2

Abstract

In a large sensor network, in-network data aggregation significantly reduces the amount of communication overhead and energy consumption. Due to less amount of data transmission, so that the average sensor lifetime and the overall bandwidth utilization are improved. As wireless sensor networks are usually deployed in remote and hostile environments to transmit sensitive information. Sensor nodes are prone to node compromise attacks and security issues such as data confidentiality and integrity are extremely important. Recently, A robust aggregation framework called synopsis diffusion which combines multipath routing schemes with duplicate-insensitive algorithms to accurately compute aggregates (e.g., predicate Count, Sum) in spite of message losses resulting from node and transmission failures. However, this aggregation framework does not address the problem of false sub aggregate values contributed by compromised nodes resulting in large errors in the aggregate computed at the base station, which is the root node in the aggregation hierarchy. This is an important problem since sensor networks are highly vulnerable to node compromises due to the unattended nature of sensor nodes and the lack of tamper-resistant hardware. The proposed algorithm is a light weight verification algorithm by which the base station can determine if the computed aggregate (predicate Count and Sum) includes any false contribution. Through theoretical analysis and extensive simulation study, it is shown that the proposed algorithm outperforms other existing approaches.
在无线传感器网络中聚合安全数据
在大型传感器网络中,网络内数据聚合可以显著降低通信开销和能耗。由于传输的数据量较少,使得传感器的平均寿命和整体带宽利用率都得到了提高。由于无线传感器网络通常部署在偏远和恶劣环境中传输敏感信息。传感器节点容易受到节点妥协攻击,数据保密性和完整性等安全问题尤为重要。最近,一种名为概要扩散的鲁棒聚合框架将多路径路由方案与重复不敏感算法相结合,可以在节点和传输失败导致消息丢失的情况下准确计算聚合(如谓词计数、Sum)。然而,这个聚合框架并没有解决由受损节点产生的假子聚合值的问题,这导致在作为聚合层次结构中的根节点的基站上计算的聚合出现较大错误。这是一个重要的问题,因为由于传感器节点的无人值守性质和缺乏防篡改硬件,传感器网络极易受到节点妥协的影响。该算法是一种轻量级的验证算法,通过该算法,基站可以确定计算的聚合(谓词Count和Sum)是否包含任何虚假贡献。通过理论分析和广泛的仿真研究,表明该算法优于现有的其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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