A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web—known as the Semantic Web—which has been defined as “the conceptual structuring of the Web in an explicit machine-readable way.”1 This definition does not differ too much from the one used for defining an ontology: “An ontology is an explicit, machinereadable specification of a shared conceptualization.”2 In fact, new ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires—solving the heterogeneous data exchange in this heterogeneous environment. Here, we don’t decide which language is best of the Semantic Web. Rather, our goal is to help developers find the most suitable language for their representation needs.

/pdf/ontology-languages-for-the-semantic-web-90f8gpqdsc.pdf

Ontology languages for the Semantic Web

A Mobile Ad Hoc Networks (MANETs) is a group of mobile nodes which cooperate in forwarding packets in a multi-hop fashion without any centralized administration. One of its key challenges is routing. Many routing protocols for MANET have been proposed and the protocols can be classified as proactive routing and on demand routing protocols. This paper uses ns-2 as the simulation tool, 802.11 as the wireless MAC protocol, and AODV & DSDV as the routing protocol. We present the traces of communication between different mobile nodes using Routing Protocols in MANETs. To compare the performance of Proactive and Reactive routing protocol, we have to analyze the simulation results by graphical manner and trace file based on QoS metrics such as Throughput, Drop, Delay, Jitter etc. Here, we have analyzed the simulation result by traces files only. The performance differentials have been analyzed based on network load, mobility, and network size. INDEX TERMS DSDV, AODV, MANET, QoS, Network Simulator-2 (NS2).

/pdf/routing-protocols-in-mobile-ad-hoc-networks-56bxr90pzy.pdf

Routing Protocols in Mobile Ad-hoc Networks \

Nonlinearity and high volatility of financial time series have made it difficult to predict stock price. However, thanks to recent developments in deep learning and methods such as long short-term memory (LSTM) and convolutional neural network (CNN) models, significant improvements have been obtained in the analysis of this type of data. Further, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD) algorithms decomposing time series to different frequency spectra are among the methods that could be effective in analyzing financial time series. Based on these theoretical frameworks, we propose novel hybrid algorithms, i.e., CEEMD-CNN-LSTM and EMD-CNN-LSTM, which could extract deep features and time sequences, which are finally applied to one-step-ahead prediction. The concept of the suggested algorithm is that when combining these models, some collaboration is established between them that could enhance the analytical power of the model. The practical findings confirm this claim and indicate that CNN alongside LSTM and CEEMD or EMD could enhance the prediction accuracy and outperform other counterparts. Further, the suggested algorithm with CEEMD provides better performance compared to EMD.

Stock price prediction using deep learning and frequency decomposition

Device-to-device (D2D) communication is continuously evolving and engulfing a wide diversity of proximate services and applications, which is considered as one of the key technologies in the 5G ecosystem. It is argued that direct communications paradigm advantages may not be entirely captured without embracing the concepts of the social-domain. Following the explosion of social networks and smart mobile devices, social-interactions among mobile users enable the promotion of D2D co-operations. In this paper, we first present a systematic overview of the socially unaware technical challenges and the prospective applications to consolidate a background on D2D communications. Next, all the social features utilized in tackling and improving the technical issues and the applications are briefed, respectively. Then, we provide a comprehensive survey on socially aware D2D communications, where various features of social-domain are leveraged, such as ties, community, trust, and selfishness to tackle the D2D technical issues and improve the performance of prospective D2D applications. We categorize state-of-the-art socially aware D2D communications into two main sections. The first section considers technical issues, while the second section pertains to their applications to provide insightful information and motivate more in-depth studies in this area. For each aforementioned section, we overview technical aspects and classify the core contributions based on approaches used and the social features utilized. In the discussion section, we summarize the socially aware D2D communications and present lessons learned. Finally, we highlight the open research challenges in this field and predict future research trends.

https://ieeexplore.ieee.org/ielaam/9739/8443345/8332477-aam.pdf

A Survey on Socially Aware Device-to-Device Communications

Wireless Sensor Network is Multihop Self-configuring Wireless Network consisting of sen- sor nodes. The patterns of movement of nodes can be classified into different mobility models and each is characterized by their own distinctive features. The significance of this study is that there has been very limited investigations of the effect of mobility mod- els on routing protocol performance such as Packet Delivery Ratio, Throughput and Latency in Wire- less Sensor Network. In this paper, we have con- sidered the influence of pursue group and random based entity mobility models on the performance of Ad Hoc On-Demand Distance Vector Routing Pro- tocol (AODV) routing protocol. The simulation re- sults show that Pursue Group Mobility model is bet- ter than Random Based Entity model.

/pdf/performance-of-aodv-routing-protocol-using-group-and-entity-1qn84hsu6t.pdf

Performance of AODV Routing Protocol using Group and Entity Mobility Models in Wireless Sensor Networks

With the introduction of the Web 3.0 standards on the World Wide Web, there is a need to include semantic techniques and ontologies in the Web based Recommendation Systems. In order to build query relevant domains and make information retrieval more efficient, it required recommending ontologies based on the query. Most ontology recommendation systems do not preserve the associations and axioms between them rather ontology matching and clustering algorithms tend to deduce logics dynamically. In this paper, a semantic algorithm for ontology recommendation has been proposed, where query-relevant ontologies are recommended by preserving the relationships between the ontological entities. The semantic similarity is computed using the query and the concepts initially and further between the query and description logics which makes it a context-based ontology recommendation system. A strategic approach called as SemantoSim is proposed to compute the semantic similarity.

OntoYield: A Semantic Approach for Context-Based Ontology Recommendation Based on Structure Preservation

Wireless sensor network can be applied to both abom- inable and military environments. A primary goal in the design of wireless sensor networks is lifetime maximization, constrained by the energy capacity of batteries. One well-known method to reduce energy consumption in such networks is data aggregation. Providing efcient data aggregation while preserving data privacy is a chal- lenging problem in wireless sensor networks research. In this paper, we present privacy-preserving data aggregation scheme for additive aggregation functions. The Cluster-based Private Data Aggregation (CPDA)leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. Keywords—Aggregation, Clustering, Query Processing.

Secure Data Aggregation Using Clusters in Sensor Networks

Wireless Sensor Networks (WSN) usually consists of a large number of tiny sensors with limited computation capability, memory space and power resource. WSN's are extremely vulnerable against any kind of internal or external attacks, due to several factors such as resource constrained nodes and lack of tamper- resistant packages. To achieve security in Wireless Sensor Networks, it is important to encrypt messages sent among sensor nodes. In this paper, we propose a scheme called Modified Bloom's Scheme (MBS) that uses asymmetric matrices of keys in place of symmetric matrices in order to establish secret keys between node pairs. The network resilience against node capture attack is improved using the proposed scheme.

Resilience Against Node Capture Attack using Asymmetric Matrices in Key Predistribution Scheme in Wireless Sensor Networks

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. We propose Tree on DAG (ToD), a semistructured approach that uses Dynamic Forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node Mica2- based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.

/pdf/tree-on-dag-for-data-aggregation-in-sensor-networks-2ovg4c8bmo.pdf

S H Manjula

Papers

Performance of AODV Routing Protocol using Group and Entity Mobility Models in Wireless Sensor Networks

OntoYield: A Semantic Approach for Context-Based Ontology Recommendation Based on Structure Preservation

Secure Data Aggregation Using Clusters in Sensor Networks

Resilience Against Node Capture Attack using Asymmetric Matrices in Key Predistribution Scheme in Wireless Sensor Networks

Tree-on-DAG for Data Aggregation in Sensor Networks