传感器网络中一种非均匀的节点部署算法

传感器节点将收集到的数据传输到簇头,簇头将数据包聚合后再发送到基站。远离基站的簇头,因发送数据的能耗太高而提早死亡。为此,在研究了传感器网络中节点的能耗后,提出一种非均匀的节点部署算法,得出了一个部署传感器节点的密度函数,在远离基站的区域部署较多的节点。仿真实验表明,非均匀的节点部署算法能有效延长网络的生命周期。     

Simple,extensible and flexible random key predistribution schemes for wireless sensor networks using reusable key pools

Sensor nodes are tiny, low-power, computationally limited and battery constrained electromechanical devices. A sensor node contains a sensing unit and a wireless communication unit. Sensor nodes are deployed over a field for sensing an event data in the environment and transfer it towards a base station over its wireless channel. In a typical application, vast amount of sensor nodes are deployed over a field which constitute a sensor network. Sensor nodes must be customized for a specific sensor network application before the deployment. This customization is needed not only for underlying networking application, but also for security related configurations. Random key predistribution mechanisms have been proposed to provide security for wireless sensor networks. In the literature, there are well known random key predistribution schemes. Some of these schemes are secure, but quite complex to apply in real-world applications due to their node-based customization requirements, while some other are easily applicable but they do not offer reasonable security. In this paper, we propose random key predistribution schemes for wireless sensor networks that provide varying ranges of security. The proposed schemes are easily applicable in real world scenarios due to their simplicity and relaxed node customization requirements. In this respect, our schemes provide a tradeoff. Moreover, our proposed schemes show a good extensibility property. We assume prior deployment knowledge. We examine performance of our schemes and compare them with well known random key predistribution schemes.     

传感器网络中一种支持多分辨率区域查询的数据存储方法

在传感器网络中,考虑到节点的通信开销在节点总能量开销中的比重大,以及用户由粗到细分辨率的不同查询需求,有必要在传感器网络中建立支持多分辨率的数据存储机制.首先提出了一种支持多分辨率的数据压缩存储策略 MDCS,节点基于 MDCS在网内产生多分辨率的近似结果;其次,给出了一种基于 MDCS的区域查询处理方法,根据用户给定的分辨率阈值去网内作区域查询处理,并将结果返回给用户.模拟实验表明,基于 MDCS的区域查询处理方法能够高效、低能耗地支持多分辨率的区域查询操作.     

Variational Multi-Valued Velocity Field Estimation for Transparent Sequences

Motion estimation in sequences with transparencies is an important problem in robotics and medical imaging applications. In this work we propose a variational approach for estimating multi-valued velocity fields in transparent sequences. Starting from existing local motion estimators, we derive a variational model for integrating in space and time such a local information in order to obtain a robust estimation of the multi-valued velocity field. With this approach, we can indeed estimate multi-valued velocity fields which are not necessarily piecewise constant on a layer—each layer can evolve according to a non-parametric optical flow. We show how our approach outperforms existing methods; and we illustrate its capabilities on challenging experiments on both synthetic and real sequences.     

基于双簇头的无线传感器网络反馈信任模型

针对无线传感器网络存在恶意或妥协节点导致通信数据被攻击的问题,提出一种基于双簇头的反馈信任模型,以保障数据在传输、感知以及融合等环节的可靠性与完整性。利用节点间直接交互结果评估节点的直接信任,同时考虑通信、数据感知和融合信任,通过贝叶斯公式评估邻居节点的通信信任,使用时间滑动窗将节点历史信任作为直接信任的补充,动态调整直接与间接信任权重使综合信任评价更客观和准确。在此基础上,引入双簇头交互监测和基站信任反馈机制,主簇头和监督簇头根据本地数据空间相关性独立评估成员数据感知信任,基站利用双簇头数据融合结果的时间相关性评估数据融合信任,并将最终信任结果反馈到所有节点。仿真结果表明,该模型能有效检测异常数据和恶意节点,成功抵御选择性转发攻击、伪造本地数据攻击以及伪造融合数据攻击,在网络安全与能耗间实现良好的平衡。     

Bandwidth-constrained queries in sensor networks

Sensor networks consist of battery-powered wireless devices that are required to operate unattended for long periods of time. Thus, reducing energy drain is of utmost importance when designing algorithms and applications for such networks. Aggregate queries are often used by monitoring applications to assess the status of the network and detect abnormal behavior. Since radio transmission often constitutes the biggest factor of energy drain in a node, in this paper we propose novel algorithms for the evaluation of bandwidth- constrained queries over sensor networks. The goal of our techniques is, given a target bandwidth utilization factor, to program the sensor nodes in a way that seeks to maximize the accuracy of the produced query results at the monitoring node, while always providing strong error guarantees to the monitoring application. This is a distinct difference of our framework from previous techniques that only provide probabilistic guarantees on the accuracy of the query result. Our algorithms are equally applicable when the nodes have ample power resources, but bandwidth consumption needs to be minimized, for instance in densely distributed networks, to ensure proper operation of the nodes. Our experiments with real sensor data show that bandwidth-constrained queries can substantially reduce the number of messages in the network while providing very tight error bounds on the query result.     

无线传感器网络中基于小波的分布式存储

无线传感器网络主要用于从目标对象收集信息,由于其能源极其有限,分布式数据存储和查询得到越来越多人的注意.本文提出了一种基于小波构架的新型分布式存储方式,它使所有信息经小波压缩后平均分布于各个节点之中,构成小波系数空间存储结构树.通过仿真实验,表明这种算法在无线传感器网络的数据管理中获得了良好的效果:(1)通过简化小波变换消除了额外的计算和通信量,大大节省了数据管理所需的能耗;(2)利用传感器节点内和节点间的信息关联,有效提高了存储效率;(3)利用小波多分辨率的编码技术和小波系数空间结构树的自相似性,支持时空两个方向的快速查询.     

无线传感器网络智能信息处理研究

由大量微小传感器节点组成的无线传感器网络主要用于从目标对象收集信息,但由于节点资源受限,给无线传感器网络的信息处理带来了严峻挑战,为此,必须采取简单、高效的处理策略.本文综述了无线传感器网路环境下智能信息处理的最新研究进展,包括网内聚合、数据压缩和分布式存储和查询等方法,对各种算法的优缺点进行评述,并指出了其关键问题.孙优贤(1940-),男,教授,博士生导师,中国工程院院土,研究方向为复杂系统理论、分布控制系统以及企业综合自动化等.     

Scheduling to Minimize Staleness and Stretch in Real-Time Data Warehouses

We study scheduling algorithms for loading data feeds into real time data warehouses, which are used in applications such as IP network monitoring, online financial trading, and credit card fraud detection. In these applications, the warehouse collects a large number of streaming data feeds that are generated by external sources and arrive asynchronously. Data for each table in the warehouse are generated at a constant rate, different tables possibly at different rates. For each data feed, the arrival of new data triggers an update that seeks to append the new data to the corresponding table; if multiple updates are pending for the same table, they are batched together before being loaded. At time τ, if a table has been updated with information up to time r≤τ, its staleness is defined as τ−r.     

A key pre-distribution scheme for wireless sensor networks: merging blocks in combinatorial design

In this paper, combinatorial design followed by randomized merging strategy is applied to key pre-distribution in sensor nodes. A transversal design is used to construct a (v, b, r, k) configuration and then randomly selected blocks are merged to form the sensor nodes. We present detailed mathematical analysis of the number of nodes, number of keys per node and the probability that a link gets affected if certain number of nodes are compromised. The technique is tunable to user requirements and it also compares favourably with state of the art design strategies. An important feature of our design is the presence of more number of common keys between any two nodes. Further, we study the situation when properly chosen blocks are merged to form sensor nodes such that the number of intra-node common key is minimized. We present a basic heuristic for this approach and show that it provides slight improvement in terms of certain parameters than our basic random merging strategy. This paper is an extended and revised version of the paper presented in 8th Information Security Conference, ISC'05, pp. 89–103, Lecture Notes in Computer Science, vol. 3650, Springer Verlag. Dibyendu Chakrabarti received his Master of Technology in Computer Science in the year 1998 from the Indian Statistical Institute, Kolkata. Currently he is pursuing his Ph.D. from the Indian Statistical Institute, Kolkata. He is working in the area of Sensor Networks. Subhamoy Maitra received his Bachelor of Electronics and Telecommunication Engineering degree in the year 1992 from Jadavpur University, Kolkata and Master of Technology in Computer Science in the year 1996 from the Indian Statistical Institute, Kolkata. He has completed Ph.D. from the Indian Statistical Institute in 2001. Currently he is an Associate Professor at the Indian Statistical Institute. His research interest is in Cryptology, Digital Watermarking, and Sensor Networks. Prof. Bimal Roy obtained his Master's degree from the Indian Statistical Institute, Calcutta, India in 1979 and Ph.D. from University of Waterloo, Canada in 1982. He is currently a professor at the Indian Statistical Institute, Kolkata. His research area includes Cryptography, Security, Combinatorics etc. His special topics of interest are: Sensor Networks, Visual Cryptography, Hash Functions and Stream Ciphers.