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。     

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。     

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....     

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.     

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...     

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.     

PABM-EDCF: parameter adaptive bi-directional mapping mechanism for video transmission over WSNs

To support and keep high quality of video transmission over wireless sensor networks, this paper proposes a parameter adaptive bi-directional cross-layer mapping algorithm on the basis of the operation mechanism of IEEE 802.11e Enhanced Distributed Coordination Function (EDCF) supporting video service differentiation, named PABM-EDCF. Instead of classifying video data to a specific access category in 802.11e network, our proposed adaptive cross-layer scheme makes use of the hierarchy characteristic of video stream, dynamically maps video data to the appropriate access categories according to both the significance of the different video frames and the network traffic load. The significance passes from the application layer to the media access layer through a cross-layer architecture. In order to prevent the network congestion and keep the high transmission quality, the proposed algorithm adopts bi-directional floating mapping algorithm and congestion awareness mechanism based on the queue length and frame types. The mapping parameters are updated according to the network condition in time. Our simulation results indicate: the proposed method (a) improves the video transmission quality; (b) optimizes the management and utilization of queue resources; and (c) yields superior performance (under different loads) over 802.11e, static mapping and adaptive mapping schemes.     

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.     

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.     

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.     

Efficient storage and retrieval of probabilistic latent semantic information for information retrieval

Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationships from a document set. The probabilistic latent semantic index (PLSI) has been used to store PLSA information, but unfortunately the PLSI uses excessive storage space relative to a simple term frequency index, which causes lengthy query times. To overcome the storage and speed problems of PLSI, we introduce the probabilistic latent semantic thesaurus (PLST); an efficient and effective method of storing the PLSA information. We show that through methods such as document thresholding and term pruning, we are able to maintain the high precision results found using PLSA while using a very small percent (0.15%) of the storage space of PLSI.     

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.     

Load-balanced and energy-aware opportunistic routing with adaptive duty cycling for multi-channel WSNs

The Journal of Supercomputing - By increasing the internet of things (IoT) applications, multi-channel wireless sensor networks (WSNs) are widely used as a promising technology. Due to power...     

Optimal tree lists for information storage and retrieval

Combining the concepts of doubly-chained tree and list one may produce files with expected search time smaller than that of the pure tree. A best such structure is found to lie within a very large set of candidates, but a sort of convexity property is found to prevail and it is possible to devise extremely efficient solution procedures for even quite large problems of this variety.