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 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 An Energy Efficient Secure Data Aggregation in Wireless Sensor Networks

2021 ◽  
Author(s):  
Jenice Prabu A ◽  
Hevin Rajesh D
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Data Communication ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Malicious Nodes ◽  
Aggregated Data

Abstract In Wireless sensor network, the major issues are security and energy consumption. There may be several numbers of malicious nodes present in sensor networks. Several techniques have been proposed by the researchers to identify these malicious nodes. WSNs contain many sensor nodes that sense their environment and also transmit their data via multi-hop communication schemes to the base station. These sensor nodes provides power supply using battery and the energy consumption of these batteries must be low. Securing the data is to avoid attacks on these nodes and data communication. The aggregation of data helps to minimize the amount of messages transmitted within the network and thus reduces overall network energy consumption. Moreover, the base station may distinguish the encrypted and aggregated data based on the encryption keys during the decryption of the aggregated data. In this paper, two aspects of the problem is concerned, we investigate the efficiency of data aggregation: first, how to develop cluster-based routing algorithms to achieve the lowest energy consumption for aggregating data, and second, security issues in wsn. By using Network simulator2 (NS2) this scheme is simulated. In the proposed scheme, energy consumption, packet delivery ratio and throughput is analyzed. The proposed clustering, routing, and protection protocol based on the MCSDA algorithm shows significant improvement over the state-of - the-art protocol.

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

scholarly journals Efficient Data Collection using Mobile Sink Scheme in IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1360-1371
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Collection ◽  
Large Scale ◽  
Performance Metrics ◽  
Route Planning ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Energy Hole

Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

Energy Consumption Analysis of Secure and Clustered Wireless Sensor Network

2014 ◽  
Vol 5 (1) ◽  
pp. 15-36 ◽  
Author(s):  
Ashim Pokharel ◽  
Ethiopia Nigussie
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Aggregation ◽  
Secure Communication ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Design Strategies

Due to limited energy resources, different design strategies have been proposed in order to achieve better energy efficiency in wireless sensor networks, and organizing sensor nodes into clusters and data aggregation are among such solutions. In this work, secure communication protocol is added to clustered wireless sensor network. Security is a very important requirement that keeps the overall system usable and reliable by protecting the information in the network from attackers. The proposed and implemented AES block cipher provides confidentiality to the communication between nodes and base station. The energy efficiency of LEACH clustered network and with added security is analyzed in detail. In LEACH clustering along with the implemented data aggregation technique 48% energy has been saved compared to not clustered and no aggregation network. The energy consumption overhead of the AES-based security is 9.14%. The implementation is done in Contiki and the simulation is carried out in Cooja emulator using sky motes.

scholarly journals EGCIR: Energy-Aware Graph Clustering and Intelligent Routing Using Supervised System in Wireless Sensor Networks

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4072
Author(s):  
Tanzila Saba ◽  
Khalid Haseeb ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Ayman Altameem ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Graph Clustering ◽  
Base Station ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Intelligent Routing

In recent times, the field of wireless sensor networks (WSNs) has attained a growing popularity in observing the environment due to its dynamic factors. Sensor data are gathered and forwarded to the base station (BS) through a wireless transmission medium. The data from the BS is further distributed to end-users using the Internet for their post analysis and operations. However, all sensors except the BS have limited constraints in terms of memory, energy and computational resources that degrade the network performance concerning the network lifetime and trustworthy routing. Therefore, improving energy efficiency with reliable and secure transmissions is a valuable debate among researchers for critical applications based on low-powered sensor nodes. In addition, security plays a significant cause to achieve responsible communications among sensors due to their unfixed and variable infrastructures. Keeping in view the above-mentioned issues, this paper presents an energy-aware graph clustering and intelligent routing (EGCIR) using a supervised system for WSNs to balance the energy consumption and load distribution. Moreover, a secure and efficient key distribution in a hierarchy-based mechanism is adopted by the proposed solution to improve the network efficacy in terms of routes and links integrity. The experimental results demonstrated that the EGCIR protocol enhances the network throughput by an average of 14%, packet drop ratio by an average of 50%, energy consumption by an average of 13%, data latency by an average of 30.2% and data breaches by an average of 37.5% than other state-of-the-art protocols.

A QoS aware Framework to support Minimum Energy Data Aggregation and Routing in Wireless Sensor Networks

2009 ◽  
Vol 1 (4) ◽  
pp. 91-106 ◽  
Author(s):  
Neeraj Kumar ◽  
R.B. Patel
Keyword(s):  
Energy Consumption ◽  
Data Aggregation ◽  
Control Mechanism ◽  
Minimum Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Combine Data ◽  
Energy Data ◽  
Energy Constrained

In a wireless sensor network (WSN), the sensor nodes obtain data and communicate its data to a centralized node called base station (BS) using intermediate gateway nodes (GN). Because sensors are battery powered, they are highly energy constrained. Data aggregation can be used to combine data of several sensors into a single message, thus reducing sensor communication costs and energy consumption. In this article, the authors propose a QoS aware framework to support minimum energy data aggregation and routing in WSNs. To minimize the energy consumption, a new metric is defined for the evaluation of the path constructed from source to destination. The proposed QoS framework supports the dual goal of load balancing and serving as an admission control mechanism for incoming traffic at a particular sensor node. The results show that the proposed framework supports data aggregation with less energy consumption than earlier strategies.

scholarly journals Greenhouse industry 4.0 – digital twin technology for commercial greenhouses

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Daniel Anthony Howard ◽  
Zheng Ma ◽  
Christian Veje ◽  
Anders Clausen ◽  
Jesper Mazanti Aaslyng ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Industry 4.0 ◽  
Influential Factors ◽  
Sensor Data ◽  
Greenhouse Production ◽  
Production Chain ◽  
Improve Energy Efficiency ◽  
Digital Twin ◽  
Digital Twins

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

Trusted Data Aggregation with Low Energy in Wireless Sensor Networks

2014 ◽  
Vol 596 ◽  
pp. 519-527 ◽  
Author(s):  
Tristan Daladier Engouang ◽  
Yun Liu ◽  
Zhen Jiang Zhang
Keyword(s):  
Energy Consumption ◽  
Elliptic Curve ◽  
Data Aggregation ◽  
Data Privacy ◽  
Processing Technique ◽  
Low Energy ◽  
Sensor Nodes ◽  
Security Requirements ◽  
Constrained Environments ◽  
Trusted Data

— Scientific researches advancements achieving miniaturization of Micro-electromechanical Systems, has enable smarts autonomous embedded devices know as sensor nodes, that are developed on numerous platforms using the proprietary of hardware and software with capability to communicate wirelessly. Today more than ever these sensors are continuously spreading to civilian usage side of things and in many others applications , including military, security, medical, environments, and animal among others, to sense specific occurrence of desired event and may carried very important data on physical device such as the mica-mote. Hence a plethora of security protocols arises in order to mitigate the risks of malicious attacks such as eavesdrop communications, or data alteration, using cryptographic techniques such as Elliptic curve for data privacy, accuracy, integrity, efficiency, and reducing the energy consumption by mote and processors. We focus on securing data by all means. Hence the in-network processing technique is used to reduce considerably the energy consumption, considering that sensors deployments in inaccessible and resource-constrained environments. After the drawback, we investigate the secured data aggregation and give security requirements that are mandatory to a Trusted Data Aggregation with Low Energy model to satisfy. The security and energy performances are analysed comparing with others methods. Index Terms—TDALE, Aggregation, Elliptic curve, RSA, Security, Energy

Key Technology for Wireless Sensor Data Fusion

2014 ◽  
Vol 701-702 ◽  
pp. 505-509
Author(s):  
Zhi Qiang Kang ◽  
Rui Feng Cheng ◽  
Wei Chu
Keyword(s):  
Data Fusion ◽  
Low Power ◽  
Coal Mine ◽  
Collection Efficiency ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Support Pressure ◽  
Single Chip ◽  
Large Power ◽  
Redundant Data

Using wireless sensors network (WSN) to collect coal mine hydraulic support pressure data , the method to solve the drawbacks of wired sensors . But the lack of WSN transmission performance in the coal mine : the transmission distance is short , large power consumption , short life cycle . Improve working methods for WSN : DIGI low-power sensor nodes ; using low-power single-chip Wake and Sleep MPS430149 at checking time ; core is a data fusion process , in the process of collecting data from all nodes , the use of local computing and storage nodes the ability to process the data , removing redundant data and minimize the amount of data transmitted within the network , improve data collection efficiency , achieve energy saving role in enhancing the network of life . The results show that the method is effective in improving the life and efficiency of WSN .

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