WSNs中高效且适应性强的安全数据融合*

针对无线传感器网络中传感节点能量的有限性和无线链路的时变性,设计了一种高能效的、适应性强的安全数据融合算法EASDA。该算法在融合过程中基于非物理意义的模式码将传感器节点分成冗余集,在数据传输过程中利用数字喷泉码进行编码。仿真结果显示,该算法有效地减少了冗余数据的传输,使网络中能耗更加均衡,并且适应于任何删除信道。     

MELEACH 一个高效节能的WSN路由协议

无线传感器网络(简称WSN)一种资源严重受限的网络,特别是在供能方面.因此,如何有效地使用传感器节点的能量以延长WSN的生存时间,一直是WSN路由协议研究所关注的焦点. LEACH[1-2]作为一种WSN路由协议,以其优秀的节能效果和简单的规程而得到广泛的认可.本文基于LEACH提出了一个新的路由协议MELEACH(More Energy-efficient LEACH). 通过进一步缩短无线通信的平均距离并进一步改善节点间的负载平衡, MELEACH在保持LEACH原有优点的基础上实现了更好的节能效果.分析和实验表明,一个WSN在MELEACH下的生存时间要比在LEACH中长50%以上.     

基于RSSI的无线传感器网络迭代安全定位算法

分析了无线传感器网络节点定位过程中的安全性问题,针对接收信号强度指示(RSSI)测距技术,提出了一种结合梯度下降法和离群检测技术的安全定位算法.仿真和实验结果表明:使用该算法的无线传感器节点在存在攻击的环境下依然能够正常定位,当平均测距误差为0.7m时,定位误差为1m.     

一种安全有效的无线传感器网络数据收集方案

数据收集技术是无线传感器网络（WSNs）中的重要技术之一。针对无线传感器网络中存在恶意节点攻击可能造成数据包丢失,给出一种安全有效的无线传感器网络数据收集方案。该方案首先利用多路径路由和跟踪反馈机制构造出安全路径,然后再根据安全路径进行数据收集。性能分析结果表明：与基于随机分散路由的数据收集方案相比,所给方案可以大幅降低数据包被拦截率,具有更高的安全性。     

支持网内处理的无线传感器网络加密方案

随着无线传感器网络的广泛应用,网络安全成了很多传感器网络应用的关键.在研究现有的安全方案的基础上,提出了一种支持安全网内处理的无线传感器网络加密方案.它能提供网络数据内容的语义加密、数据源认证、数据完整性和数据新鲜.同时,通过安全的网内处理延长了无线传感器网络的生命期.     

高斯白噪声信道传感网络能量有效与可靠性研究

无线传感器网络节点的优化可以提高无线传感器网络的性能。基于传感网络的能量消耗特征及数据传输的可靠性与能量消耗间的关系,提出了一种跨层优化方法。它不仅能够均衡能量消耗,延长网络寿命,而且也可保证无线传感器网络(Wireless Sensor Networks,WSNs)在加性高斯白噪声信道(Additive White Gaussian Noise Channels,AWGN)下节点间数据传输的可靠性。首先,从数学上严格给出节点个数N*、节点的部署位置d*和节点的传输结构P*、优化问题有解的条件。其次,针对传感器网络距离Sink近的节点耗能较高而离Sink远的节点耗能较低的这一能量消耗特征以及节点数据传输的可靠性与能量消耗成正相关的情况,采用了跨层优化策略,即对离Sink节点近的节点适当降低其可靠性要求以减少能耗从而延长网络寿命,而对于离Sink节点远有能量剩余的节点提高其可靠性以充分利用其剩余能量,从而使得数据传输的可靠性在满足要求的情况下让网络能量消耗均衡,并延长网络寿命。最后,理论分析和实验结果表明,提出的跨层优化方法可以使网络寿命延长10％～90％,使网络的可靠性提高20％,具有较好的意义。     

MWC: an efficient and secure multi-cloud storage approach to leverage augmentation of multi-cloud storage services on mobile devices using fog computing

The Journal of Supercomputing - To store the sensitive and large amount of information on mobile devices is not recommended as it can be lost or stolen and the storage capacity is limited. Even the...     

适用于WSNs的拥塞自适应多径路由算法

针对无线传感器网络(WSNs)数据汇聚特性易导致网络拥塞的问题,结合改进AOMDV协议的多径建立、选择机制的缺陷,提出一种拥塞自适应的多径路由算法。新协议首先引入相关因子模型建立相互干扰度最小的路径集;其次建立路径拥塞信息采集、更新机制,并利用HELLO消息传递。最终源节点通过实时感知路径拥塞信息,自适应选择低拥塞路径来避免拥塞。仿真结果表明:改进的协议显著提高了分组投递速率,降低了端对端时延。     

一种基于迭代投票的无线传感器网络安全定位算法

绝大多数的定位算法都是基于信标节点的坐标信息计算未知节点的坐标信息,因此,信标节点的可信性至关重要.无线传感器网络在敌对环境中易受攻击,由此提出了一种安全定位算法.在算法中,节点间无需坐标交换,可直接迭代投票选出可信的信标节点进行定位,并通过仿真验证算法有效性.     

A novel energy-efficient encryption algorithm for secure data in WSNs

The Journal of Supercomputing - Wireless sensor networks (WSNs) are typically deployed environments, often very hostile and without assistance. A certain level of security must be provided....     

云储存中利用属性基加密技术的安全数据检索方案

本文提出了一种利用属性基加密(ABE)技术的安全数据检索方案。首先,利用ABE提供丰富的索引词表达能力,从而确保数据安全性;然后,通过平衡云服务提供商运行开销和其它用户参与基于云存储的信息检索服务;最后,使用加密运算替代穷尽搜索,使得搜索过程与现存数据库管理系统机制更加兼容。分析结果表明,相比其他几种较新的方案,本文方案在访问控制和快速搜索中具有更好的性能,且能在数据检索过程中确保数据安全性和用户隐私,适合应用于具有大量数据的云存储系统。     

An adaptive in-network aggregation operator for query processing in wireless sensor networks

A wireless sensor network (WSN) is composed of tens or hundreds of spatially distributed autonomous nodes, called sensors. Sensors are devices used to collect data from the environment related to the detection or measurement of physical phenomena. In fact, a WSN consists of groups of sensors where each group is responsible for providing information about one or more physical phenomena (e.g., group for collecting temperature data). Sensors are limited in power, computational capacity, and memory. Therefore, a query engine and query operators for processing queries in WSNs should be able to handle resource limitations such as memory and battery life. Adaptability has been explored as an alternative approach when dealing with these conditions. Adaptive query operators (algorithms) can adjust their behavior in response to specific events that take place during data processing. In this paper, we propose an adaptive in-network aggregation operator for query processing in sensor nodes of a WSN, called ADAGA (ADaptive AGgregation Algorithm for sensor networks). The ADAGA adapts its behavior according to memory and energy usage by dynamically adjusting data-collection and data-sending time intervals. ADAGA can correctly aggregate data in WSNs with packet replication. Moreover, ADAGA is able to predict non-performed detection values by analyzing collected values. Thus, ADAGA is able to produce results as close as possible to real results (obtained when no resource constraint is faced). The results obtained through experiments prove the efficiency of ADAGA.     

Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks

Due to the inherent characteristics of resource-constrained sensors, communication overhead is always a major concern in wireless sensor networks (WSNs). Data aggregation is an essential technique to reduce the communication overhead and prolong network lifetime. Since data aggregation results are usually used to make critical decisions, the accuracy of final aggregation results is very important. Furthermore, as wireless sensor networks are increasing being deployed in security-critical applications, we should take security into consideration as well. Therefore, for such applications, data aggregation protocols must be highly energy efficient and highly accurate while being able to prevent an adversary from stealing private data held by each sensor node. In this paper, we propose an energy-efficient and high-accuracy (EEHA) scheme for secure data aggregation. The main idea of our scheme is that accurate data aggregation is achieved without releasing private sensor readings and without introducing significant overhead on the battery-limited sensors. We conduct extensive simulations to evaluate the performance of EEHA. Our analysis and simulations show that EEHA is more efficient and accurate than the existing scheme.     

Towards an efficient storage and retrieval mechanism for large unstructured grids

The size of spatial scientific datasets is steadily increasing due to improvements in instruments and availability of computational resources. However, much of the research on efficient storage and access to spatial datasets has focused on large multidimensional arrays. In contrast, unstructured grids consisting of collections of simplices (e.g. triangles or tetrahedra) present special challenges that have received less attention. Data values found at the vertices of the simplices may be dispersed throughout a datafile, producing especially poor disk locality.Our previous work has focused on addressing this locality problem. In this paper, we reorganize the unstructured grid to improve locality of disk access by maintaining the spatial neighborhood relationships inherent in the unstructured grid. This reorganization produces significant gains in performance by reducing the number of accesses made to the data file. We also examine the effects of different chunking configurations on data retrieval performance. A major motivation for reorganizing the unstructured grid is to allow the application of iteration aware prefetching. Applying this prefetching method to unstructured grids produces further performance gains over and above the gains seen from reorganization alone.The work presented in this journal contains at least 40% new material not included in our conference paper (Akande and Rhodes 2013).     

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi-square test-based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi-square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real-time attack detection.     

Minimizing time risk in on-line bidding: An adaptive information retrieval based approach

Knowledge is of prime importance, particularly for the individuals who are involved in e-business. A lot of energy and time is wasted by the individuals in seeking required knowledge and information. In order to facilitate the individuals with required information, an efficient technique for the proper retrieval of knowledge is must. Almost all online business activities, particularly e-auction based firms are surrounded by various risk factors pertaining to time, security, brand etc. The main focus of the present paper is to analyze all such risk factors and further to categorize the same as per their degree of influence. A nominal group technique (NGT) based approach has been utilized to do the same that ranks the risk factors using agreed criteria based approach. Further, the paper proposed an adaptive information retrieval to resolve the problems related to time risk in online bidding process, while other risk factors has been tried to resolved by using corporate memory based data warehousing. Efficient knowledge retrieval along with the knowledge development and knowledge management became a backbreaking task for any organization. A corporate memory based approach has been utilized to represent the required knowledge stored in memory warehouse for its current and future usage. In underlying retrieval model, adaptiveness is achieved using genetic algorithm based matching function adaptation, where, a total of five matching functions viz. Jaccard’s coefficient, Overlap’s coefficient, Dice coefficient, Inclusion measure, and Cosine measures have been considered to determine the retrieval effectiveness. Later, effectiveness of information retrieval system is calculated in terms of well known parameters namely precision, recall, fallout and miss. Results of adaptive information retrieval using a weighted combination of matching functions are compared with individual matching functions.     

Shield: A stackable secure storage system for file sharing in public storage

With the increasing amount of personal data stored in public storage, users are losing control of their physical data, putting their data information at risk of theft or being compromised. Traditional secure storage systems either require users to completely trust the storage provider or impose the considerable burden of managing files on file owners; such systems are inapplicable in the practical cloud environment. This paper addresses these challenging problems by proposing a new secure system architecture and implementing a stackable secure storage system named Shield, in which a proxy server is introduced to be in charge of authentication and access control. We propose a new variant of the Merkle Hash Tree to support efficient integrity checking and file content update; further, we have designed a hierarchical key organization to achieve convenient keys management and efficient permission revocation. Shield supports concurrent write access by employing a virtual linked list; it also provides secure file sharing without any modification to the underlying file systems. A series of evaluations over various real benchmarks show that Shield causes about 7%∼13%$7\%\sim 13\%$ performance degradation when compared with eCryptfs but provides enhanced security for user’s data.     

Measuring in-network node similarity based on neighborhoods: a unified parametric approach

In many applications, we need to measure similarity between nodes in a large network based on features of their neighborhoods. Although in-network node similarity based on proximity has been well investigated, surprisingly, measuring in-network node similarity based on neighborhoods remains a largely untouched problem in literature. One challenge is that in different applications we may need different measurements that manifest different meanings of similarity. Furthermore, we often want to make trade-offs between specificity of neighborhood matching and efficiency. In this paper, we investigate the problem in a principled and systematic manner. We develop a unified parametric model and a series of four instance measures. Those instance similarity measures not only address a spectrum of various meanings of similarity, but also present a series of trade-offs between computational cost and strictness of matching between neighborhoods of nodes being compared. By extensive experiments and case studies, we demonstrate the effectiveness of the proposed model and its instances.     

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.