scholarly journals MCEEP-BDA: Multilevel Clustering Based -Energy Efficient Privacy-Preserving Big Data Aggregation in Large-Scale Wsn

2019 ◽  
Vol 9 (1) ◽  
pp. 6779-6785
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Large Scale ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Communication Overhead

In current scenario, the Big Data processing that includes data storage, aggregation, transmission and evaluation has attained more attraction from researchers, since there is an enormous data produced by the sensing nodes of large-scale Wireless Sensor Networks (WSNs). Concerning the energy efficiency and the privacy conservation needs of WSNs in big data aggregation and processing, this paper develops a novel model called Multilevel Clustering based- Energy Efficient Privacy-preserving Big Data Aggregation (MCEEP-BDA). Initially, based on the pre-defined structure of gradient topology, the sensor nodes are framed into clusters. Further, the sensed information collected from each sensor node is altered with respect to the privacy preserving model obtained from their corresponding sinks. The Energy model has been defined for determining the efficient energy consumption in the overall process of big data aggregation in WSN. Moreover, Cluster_head Rotation process has been incorporated for effectively reducing the communication overhead and computational cost. Additionally, algorithm has been framed for Least BDA Tree for aggregating the big sensor data through the selected cluster heads effectively. The simulation results show that the developed MCEEP-BDA model is more scalable and energy efficient. And, it shows that the Big Data Aggregation (BDA) has been performed here with reduced resource utilization and secure manner by the privacy preserving model, further satisfying the security concerns of the developing application-oriented needs.

scholarly journals A Secure Privacy-Preserving Data Aggregation Model in Wearable Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Changlun Zhang ◽  
Chao Li ◽  
Jian Zhang
Keyword(s):  
Data Aggregation ◽  
Privacy Preservation ◽  
Rapid Development ◽  
Cluster Head ◽  
Privacy Preserving ◽  
Base Station ◽  
Sensor Nodes ◽  
Mixed Data ◽  
Communication Overhead ◽  
Aggregation Model

With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid the leak of data at the cluster head in inner-cluster. Furthermore, a mechanism is given to locate the compromised nodes. The analysis shows that the model is robust to many attacks and has a lower communication overhead.

scholarly journals Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xu Xia ◽  
Zhigang Chen ◽  
Wei Wei
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Coal Mine ◽  
Data Aggregation ◽  
Delay Time ◽  
Assessment Model ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Working Face ◽  
State Of The Environment

More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

scholarly journals Query Privacy Preserving for Data Aggregation in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
X. Liu ◽  
X. Zhang ◽  
J. Yu ◽  
C. Fu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Experimental Simulation ◽  
Wireless Sensor ◽  
Communication Overhead

Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and energy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs makes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the abovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce energy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by employing a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and the experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the private data from being compromised.

Study of Mine Environment Monitoring System Based on Wireless Sensor Network

2013 ◽  
Vol 278-280 ◽  
pp. 809-812
Author(s):  
Jun Wang ◽  
Feng Wang ◽  
Shu Ren Han
Keyword(s):  
Monitoring System ◽  
Data Storage ◽  
Large Scale ◽  
Nonlinear Problems ◽  
Original Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Mine Environment ◽  
Multi Sensor Data Fusion

There are a large number of sensors to detect information in mine environment system, which provides the original data for the prevention and treatment of mine accidents. According to data flow of data acquisition, data transmission, data storage and data processing, this paper used CC2530 to design the wireless sensor nodes, analyzed the Zigbee network topology underground and designed an optimized PEGASIS protocol. The multi-sensor data fusion method was applied to the multi-parameter and large-scale mine data, solved the nonlinear problems in the multi-feature selection and extraction and also improved the performance of mine monitoring system.

scholarly journals A large data processing algorithm for energy efficiency in a heterogeneous cluster

2018 ◽  
Vol 17 ◽  
pp. 03023
Author(s):  
Lei Wang ◽  
Weichun Ge ◽  
Zhao Li ◽  
Zhenjiang Lei ◽  
Shuo Chen
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Energy Efficient ◽  
Large Scale ◽  
Large Data ◽  
Heterogeneous Cluster ◽  
Electricity Cost ◽  
Long Period ◽  
Large Sets ◽  
Efficient Processing

It is reportedi that the electricity cost to operate a cluster may well exceed its acquisition cost, and the processing of big data requires large scale cluster and long period. Therefore, energy efficient processing of big data is essential for the data owners and users. In this paper, we propose a novel algorithm MinBalance to processing I/O intensive big data tasks energy efficiently in heterogeneous cluster. In the former step, four greedy policies are used to select the proper nodes considering heterogeneity of the cluster. While in the latter step, the workloads of the selected nodes will be well balanced to avoid the energy wastes caused by waiting. MinBalance is a universal algorithm and cannot be affected by the data storage strategies. Experimental results indicate that MinBalance can achieve over 60% energy reduction for large sets over the traditional methods of powering down partial nodes.

Energy-Efficient and Secure Data Forwarding Mechanism for Balancing Cluster Lifetime for Huge Size Wireless Sensor Networks

2019 ◽  
Vol 16 (9) ◽  
pp. 3961-3964
Author(s):  
Charu Sharma ◽  
Rohit Vaid
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Base Station ◽  
Packet Transmission ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor

In designing Wireless Sensor Networks, energy efficiency and security should be considered very critically. Energy efficiency is achieved through data aggregation which eliminates the transmission of redundant data while security is achieved by preserving confidentiality among sensor node and the base station. In this paper, an energy efficient and secure cluster based aggregation mechanism is presented. In this model, for energy efficiency the network is divided into tracks and sectors so the cluster head’s are uniformly selected from the whole network. To achieve security the cluster head’s perform data aggregation with the help of some pattern codes and only distinctive data is transmitted from sensor nodes in encrypted form. To perform aggregation, the sensor nodes do not need to know about the actual sensor data therefore there is no need to use any encryption or decryption schemes between nodes and cluster head. Performance evaluation shows proposed model works better to enhance the network lifetime, security, average residual energy, and average packet transmission ratio than conventional data aggregation models.

scholarly journals A Distributed Collision-Free Data Aggregation Scheme for wireless sensor network

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879584 ◽  
Author(s):  
Danyang Qin ◽  
Yan Zhang ◽  
Jingya Ma ◽  
Ping Ji ◽  
Pan Feng
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Aggregation ◽  
Large Scale ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Delay ◽  
Free Data ◽  
Aggregation Scheme ◽  
Aggregation Technology

Due to the advantages of large-scale, data-centric and wide application, wireless sensor networks have been widely used in nowadays society. From the physical layer to the application layer, the multiply increasing information makes the data aggregation technology particularly important for wireless sensor network. Data aggregation technology can extract useful information from the network and reduce the network load, but will increase the network delay. The non-exchangeable feature of the battery of sensor nodes makes the researches on the battery power saving and lifetime extension be carried out extensively. Aiming at the delay problem caused by sleeping mechanism used for energy saving, a Distributed Collision-Free Data Aggregation Scheme is proposed in this article to make the network aggregate data without conflicts during the working states periodically changing so as to save the limited energy and reduce the network delay at the same time. Simulation results verify the better aggregating performance of Distributed Collision-Free Data Aggregation Scheme than other traditional data aggregation mechanisms.

Large-Scale Sensor Network Analysis

2013 ◽  
pp. 314-347 ◽  
Author(s):  
Joaquin Vanschoren ◽  
Ugo Vespier ◽  
Shengfa Miao ◽  
Marvin Meeng ◽  
Ricardo Cachucho ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Vital Signs ◽  
Sensor Data ◽  
Atmospheric Conditions ◽  
Big Data Applications ◽  
The World ◽  
Sheer Size ◽  
Effective Use

Sensors are increasingly being used to monitor the world around us. They measure movements of structures such as bridges, windmills, and plane wings, human’s vital signs, atmospheric conditions, and fluctuations in power and water networks. In many cases, this results in large networks with different types of sensors, generating impressive amounts of data. As the volume and complexity of data increases, their effective use becomes more challenging, and novel solutions are needed both on a technical as well as a scientific level. Founded on several real-world applications, this chapter discusses the challenges involved in large-scale sensor data analysis and describes practical solutions to address them. Due to the sheer size of the data and the large amount of computation involved, these are clearly “Big Data” applications.

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