WSN下分簇算法和秘密比较协议的研究
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摘要
WSN(Wireless Sensor Network)是继Internet之后的又一次信息革命,将改变人们的生活,促进生产力的发展,进一步加强信息世界与物质世界之间的联系。WSN由传感器、无线传输模块和信息中心组成,其工作过程为传感器采集数据,通过无线传输模块将数据转发至数据中心。节点采用采用微电子设备,电池供电,在计算能力、通信能力以及存储能力都是受限的,很难更换电池或者部署节点替换。延长WSN的生命周期可以降低网络部署成本,延长网络的工作时间。分簇算法是一种网络管理方式,簇由簇头和簇成员组成。簇内成员将数据发送至簇头,簇头再将数据发送至目的地。簇头选举算法是分簇算法的核心,是分簇的基础。分簇网络的通信分为簇内通信与簇间通信,簇间通信过程中簇成员将数据发送到所在簇的簇头,簇间通信是簇头之间的通信。在WSN中,簇头承担较重的数据转发任务,会因消耗能量过多导致失效。簇头轮转机制是要求簇内成员轮流担任簇头,均衡网络负载,延长WSN的生命周期。高连通的算法为数据转发提供了更多的路由选择,对于负载均衡具有重要意义。
由于WSN节点处于开放环境,无线信号很容易被侦听,安全传输数据成为WSN推广的关键问题之一。传统的安全算法在计算要求、通信要求以及存储空间上的要求,WSN节点很难满足这样的要求,即使可以运行也会导致节点能量耗尽。设计轻量级的安全算法对构建WSN下的安全协议具有重要的意义,算法应该尽可能降低对计算能力、通信能力以及存储能力的要求。秘密比较协议要实现的是的具有隐私数据的双方,在不透露各自隐私数据的情况,对数据进行比较,该协议是安全多方计算的基础协议之一。研究轻量级的秘密比较协议不仅对构建实用的安全协议具有重要的意义,而且为WSN资源受限环境下的安全计算协议设计提供了基础。
本文的研究围绕着WSN下分簇算法和秘密比较协议展开,其主要工作有：
对WSN下的分簇算法进行了研究,针对“热点”问题,提出了高连通负载均衡的分簇算法。分簇算法中,节点可以属于不同的簇,可以请求不同的簇头进行数据转发。节点轮流充当簇头,从而实现均衡负载。高连通的网络使得簇内成员在数据转发具有多选择性,根据节点的情况进行负载均衡。算法增强了簇的稳定性,提高了网络的鲁棒性,均衡网络负载,解决了热点问题,并最终延长了网络的生命周期。
在安全多方计算基础协议方面,利用Range-Encoding编码技术,提出了高效和公平的秘密比较协议。该协议没有采用常用的加密算法,而是采用编码技术,大大降低算法对计算能力、存储能力的要求,适用于WSN节点。在不借助于可信第三方的条件下,实现参与方在协议执行中的地位对等公平。
WSN(Wireless Sensor Network) is another revolution on information since Internet, and it will change people's life and will enhance the communication between the real world and the Internet. WSN consists of sensor modules, wireless transmission modules and the center of information(Sink Node). Sensors which are deployed in the monitor region collect data, and then send data to Sink Node with the help of wireless transmission modules. WSN node with micro-electronic is limited in the computation power for micro-cpu, communication power for battery-powered and storage power.Once nodes are deployed in the monitor region, it's hard to replace the battery or to replace bad nodes with a new node. It can reduce the cost of WSN and prolong the time of nodeswhen the lifetime of WSN is prolonged. Clustering algorithm is a way of managing the network. A cluster consists of cluster heads and many cluster members which send data to cluster head. The data those sent to cluster head will send to sink node with the help of other nodes. The core of clustering algorithm is cluster head election and the basic of clustering algorithm. The communication in a cluster network consists of intra-cluster communicationandinter-cluster communication. Cluster head send more data than cluster members and consume more power, so it will die earlier than cluster members. Cluster head rotation makes cluster members to be cluster head, balances the load of network and prolongs the lifetime. High connectivity algorithm provides more router chances and balances the load.
Nodes are deployed in an open environment, the signal can received by anyone who maybe anenemy. It is a key to extend the application of WSN that make sure the signal is transmitted safely. Nodes in WSN are hardly to meet the demands for computation power, communication power and storage power of tradition algorithms. Even if tradition algorithm can be run on the node, the energy of battery can be used up. It is necessary to design the light-weight algorithm for constructing the secure protocol in WSN. The new algorithm should reduce the requirements of computation power, communication power and storage power. The light-weight private comparison is the foundation of secure protocols.
The cluster members collect the data which is interesting by the center of information, and then send the data to the center of information. Cluster head election algorithm is the core and the basic of clustering algorithm. Communication in a clustering network consists of two parts: communication of cluster members, communication of cluster heads. More data should be transmitted by cluster head, so cluster head will consume more energy than the other. The cluster head rotation mechanism requires that more nodes can service as cluster head, and the result is that each node consumes equal energy. No nodes consume much more energy, there will no path is cut. High connectivity algorithm can provide more choices for node choosing next hop when data transmitted, and the significance of prolonging the lifetime is important.
Private comparison can be described as following:two parties owning private data wants to get the relation of their data while the data should not be known by the other. Private comparison is mentioned by Yao in1982, and it is the basic of SMC(Secure Multi-Party Computation). WSN node is resource-constrained (computation power, communication power, storage power), and tradition algorithms are not suited for WSN nodes for the high demands of computation power, communication power, and storage power. It is important that design high weight basic secure algorithm which has a low demand on resources for constructing SMC protocols in WSN.
In this paper, we concentrate on high connectivity load balancing clustering algorithm and private comparison.
Having researched on some clustering algorithms, we mentioned a high connectivity and load balancing clustering algorithm to solve the 'hot-spot'. In this algorithm, node can belongs to different clusters, and selects different cluster heads to transmit data. The load of communication is balanced by different nodes as cluster head. High connectivity provides more choices for cluster members changing next hop, and makes load balancing possible. This algorithm enhances the stability of the cluster, improves the robustness of the network, provides load balancing and prolongs the lifetime of WSN.
To design low weight basic secure multi-part computation algorithm, we mentioned an efficient and fair private comparison protocol based on Range-Encoding. This protocol gives up tradition technologies, and selects coding technology to solve private comparison problem. This protocol is suited for WSN nodes for low demand of computation and storage. Two parties are equal in this protocol in that case no TTP(Third Trusted Party) is joined.
由于WSN节点处于开放环境,无线信号很容易被侦听,安全传输数据成为WSN推广的关键问题之一。传统的安全算法在计算要求、通信要求以及存储空间上的要求,WSN节点很难满足这样的要求,即使可以运行也会导致节点能量耗尽。设计轻量级的安全算法对构建WSN下的安全协议具有重要的意义,算法应该尽可能降低对计算能力、通信能力以及存储能力的要求。秘密比较协议要实现的是的具有隐私数据的双方,在不透露各自隐私数据的情况,对数据进行比较,该协议是安全多方计算的基础协议之一。研究轻量级的秘密比较协议不仅对构建实用的安全协议具有重要的意义,而且为WSN资源受限环境下的安全计算协议设计提供了基础。
本文的研究围绕着WSN下分簇算法和秘密比较协议展开,其主要工作有：
对WSN下的分簇算法进行了研究,针对“热点”问题,提出了高连通负载均衡的分簇算法。分簇算法中,节点可以属于不同的簇,可以请求不同的簇头进行数据转发。节点轮流充当簇头,从而实现均衡负载。高连通的网络使得簇内成员在数据转发具有多选择性,根据节点的情况进行负载均衡。算法增强了簇的稳定性,提高了网络的鲁棒性,均衡网络负载,解决了热点问题,并最终延长了网络的生命周期。
在安全多方计算基础协议方面,利用Range-Encoding编码技术,提出了高效和公平的秘密比较协议。该协议没有采用常用的加密算法,而是采用编码技术,大大降低算法对计算能力、存储能力的要求,适用于WSN节点。在不借助于可信第三方的条件下,实现参与方在协议执行中的地位对等公平。
WSN(Wireless Sensor Network) is another revolution on information since Internet, and it will change people's life and will enhance the communication between the real world and the Internet. WSN consists of sensor modules, wireless transmission modules and the center of information(Sink Node). Sensors which are deployed in the monitor region collect data, and then send data to Sink Node with the help of wireless transmission modules. WSN node with micro-electronic is limited in the computation power for micro-cpu, communication power for battery-powered and storage power.Once nodes are deployed in the monitor region, it's hard to replace the battery or to replace bad nodes with a new node. It can reduce the cost of WSN and prolong the time of nodeswhen the lifetime of WSN is prolonged. Clustering algorithm is a way of managing the network. A cluster consists of cluster heads and many cluster members which send data to cluster head. The data those sent to cluster head will send to sink node with the help of other nodes. The core of clustering algorithm is cluster head election and the basic of clustering algorithm. The communication in a cluster network consists of intra-cluster communicationandinter-cluster communication. Cluster head send more data than cluster members and consume more power, so it will die earlier than cluster members. Cluster head rotation makes cluster members to be cluster head, balances the load of network and prolongs the lifetime. High connectivity algorithm provides more router chances and balances the load.
Nodes are deployed in an open environment, the signal can received by anyone who maybe anenemy. It is a key to extend the application of WSN that make sure the signal is transmitted safely. Nodes in WSN are hardly to meet the demands for computation power, communication power and storage power of tradition algorithms. Even if tradition algorithm can be run on the node, the energy of battery can be used up. It is necessary to design the light-weight algorithm for constructing the secure protocol in WSN. The new algorithm should reduce the requirements of computation power, communication power and storage power. The light-weight private comparison is the foundation of secure protocols.
The cluster members collect the data which is interesting by the center of information, and then send the data to the center of information. Cluster head election algorithm is the core and the basic of clustering algorithm. Communication in a clustering network consists of two parts: communication of cluster members, communication of cluster heads. More data should be transmitted by cluster head, so cluster head will consume more energy than the other. The cluster head rotation mechanism requires that more nodes can service as cluster head, and the result is that each node consumes equal energy. No nodes consume much more energy, there will no path is cut. High connectivity algorithm can provide more choices for node choosing next hop when data transmitted, and the significance of prolonging the lifetime is important.
Private comparison can be described as following:two parties owning private data wants to get the relation of their data while the data should not be known by the other. Private comparison is mentioned by Yao in1982, and it is the basic of SMC(Secure Multi-Party Computation). WSN node is resource-constrained (computation power, communication power, storage power), and tradition algorithms are not suited for WSN nodes for the high demands of computation power, communication power, and storage power. It is important that design high weight basic secure algorithm which has a low demand on resources for constructing SMC protocols in WSN.
In this paper, we concentrate on high connectivity load balancing clustering algorithm and private comparison.
Having researched on some clustering algorithms, we mentioned a high connectivity and load balancing clustering algorithm to solve the 'hot-spot'. In this algorithm, node can belongs to different clusters, and selects different cluster heads to transmit data. The load of communication is balanced by different nodes as cluster head. High connectivity provides more choices for cluster members changing next hop, and makes load balancing possible. This algorithm enhances the stability of the cluster, improves the robustness of the network, provides load balancing and prolongs the lifetime of WSN.
To design low weight basic secure multi-part computation algorithm, we mentioned an efficient and fair private comparison protocol based on Range-Encoding. This protocol gives up tradition technologies, and selects coding technology to solve private comparison problem. This protocol is suited for WSN nodes for low demand of computation and storage. Two parties are equal in this protocol in that case no TTP(Third Trusted Party) is joined.
引文
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[2]Estrin, Deborah, et al. Next century challenges:Scalable coordination in sensor networks[C]. //Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking. ACM,1999.
[3]Agre, Jon. and Loren Clare. An integrated architecture for cooperative sensing networks[J]. Computer 33.5 (2000):106-108.
[4]Steere, David C., et al.Research challenges in environmental observation and forecasting systems[C]. Proceedings of the 6th annual international conference on Mobile computing and networking. ACM,2000.
[5]A Yao. Protocols for secure computations [C].//Proceeding of the 23th IEEE Symposium on Founda-tions of Computer Science. Los Alamitos, CA:IEEE Computer Society Press, 1982, pp.160-164.
[6]Cachin C. Efficient private bidding and auctions with an oblivious third party[C]//Proceedings of the 6th ACM conference on Computer and communications security. ACM,1999:120-127.
[7]Pinkas B, Schneider T, Smart N P, et al. Secure two-party computation is practical[M]//Advances in Cryptology-ASIACRYPT 2009. Springer Berlin Heidelberg,2009: 250-267.
[8]Li S D, Dai Y Q, You Q Y. Efficient solution to Yao's Millionaires' problem[J]. Dianzi Xuebao(Acta Electronica Sinica),2005,33(5):769-773.
[9]Lin H Y, Tzeng W G. An efficient solution to the millionaires' problem based on homomorphic encryption[C]//Applied Cryptography and Network Security. Springer Berlin Heidelberg,2005:456-466.
[10]秦波，秦慧,周克复,等.常数复杂性的百万富翁协议[J].西安理工大学学报,2005,21(2)：149-152.
[11]罗永龙,黄刘生,荆巍巍等.保护私有信息的叉积协议及其应用[J].计算机学报,2007.30(2)：248-254.
[12]Catrina O, Saxena A. Secure computation with fixed-point numbers[M]//Financial Cryptography and Data Security. Springer Berlin Heidelberg,2010:35-50.
[13]Wen L, Shou-shan L, Yong-bin W. Secure Multi-Party Comparing Protocol Based on Multi-Threshold Secret Sharing Scheme[C]//Wireless Communications Networking and Mobile Computing (WiCOM),2010 6th International Conference on. IEEE,2010:1-4.
[14]Zhao Y, Liu Y, Wang J, et al. An lmproved Secure Two-Party Bargaining Protocol[J]. Journal of University of Electronic Science and Technology of China.2007,36(3):538-540.
[15]Xiao Q, Luo S, Chen P, et al. Research on the problem of secure multi-party ranking under semi-honest model[J]. Acta Electronica Sinica,2008,36(4):709.
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