A new data aggregation approach for WSNs based on open pits mining

Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding the vast use of smart objects and applications, one of the big challenges is to collect and analyse the data. Sensor energy limitations and data redundancy are the primary challenges in these networks and reduce network lifetime as well. Therefore, the nodes try to eliminate redundant data, before transferring it to the central station. Data aggregation in IoT such as wireless sensor network plays an important role because in IoT there are heterogeneous data collected from different sources which need more energy to send data. One of the solutions to reduce energy, in this case, is to process and aggregate data prior to sending it. Data aggregation is an effective technique in reducing the data redundancy as well as improving energy efficiency; It also increases the lifespan of Wireless Sensor Networks. Integrating and combining relevant and identical data prevents sending additional packets, and minimizes the redundancy, saves energy, and increases network lifetime. The main purpose of this paper is to provide a new data aggregation method based on the open-pit mining idea efficiently. In this approach, the wireless sensor network is divided into several clusters, and in each cluster, a central node is specified, around which some hypothetical pits are considered to aggregate and send data.

[1]  Majid Bagheri,et al.  Forest Fire Modeling and Early Detection using Wireless Sensor Networks , 2009, Ad Hoc Sens. Wirel. Networks.

[2]  Ngoc-Tu Nguyen,et al.  Challenges, Designs, and Performances of a Distributed Algorithm for Minimum-Latency of Data-Aggregation in Multi-Channel WSNs , 2018, IEEE Transactions on Network and Service Management.

[3]  Ali Kadhum M. Al-Qurabat,et al.  Two level data aggregation protocol for prolonging lifetime of periodic sensor networks , 2019, Wirel. Networks.

[4]  Athanasios V. Vasilakos,et al.  Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs , 2015, ACM Trans. Sens. Networks.

[5]  Pravin Chandra,et al.  Analysis of Data Aggregation Techniques in WSN , 2019 .

[6]  Kumbesan Sandrasegaran,et al.  A survey on data aggregation techniques in IoT sensor networks , 2020, Wirel. Networks.

[7]  Shama Siddiqui,et al.  A survey on data aggregation mechanisms in wireless sensor networks , 2015, 2015 International Conference on Information and Communication Technologies (ICICT).

[8]  Jaideep Srivastava,et al.  PWave: A Multi-source Multi-sink Anycast Routing Framework for Wireless Sensor Networks , 2007, Networking.

[9]  I. Arockiarani,et al.  COSINE SIMILARITY MEASURE FOR ROUGH INTUITIONISTIC FUZZY SETS AND ITS APPLICATION IN MEDICAL DIAGNOSIS , 2018 .

[10]  Jun Tong,et al.  V-Matrix-Based Scalable Data Aggregation Scheme in WSN , 2019, IEEE Access.

[11]  Ravindra C. Thool,et al.  UML Based Modeling for Data Aggregation in Secured Wireless Sensor Network , 2016 .

[12]  Junsheng Zhang,et al.  Aggregated multi-attribute query processing in edge computing for industrial IoT applications , 2019, Comput. Networks.

[13]  Pradeep Kumar Singh,et al.  Comparison and Analysis on Artificial Intelligence Based Data Aggregation Techniques in Wireless Sensor Networks , 2018 .

[14]  Ahmad Makui,et al.  An Extended Fuzzy PROMETHEE based on Fuzzy Rule based System for Supplier Selection Problem , 2015 .

[15]  S. Swamynathan,et al.  A Comparative Study and Analysis of Data Aggregation Techniques in WSN , 2015 .

[16]  Zongfeng Zou,et al.  Wireless sensor network routing method based on improved ant colony algorithm , 2019, J. Ambient Intell. Humaniz. Comput..

[17]  Jenn-Wei Lin,et al.  Efficient Fault-Tolerant Routing in IoT Wireless Sensor Networks Based on Bipartite-Flow Graph Modeling , 2019, IEEE Access.

[18]  Wen-Hwa Liao,et al.  Data aggregation in wireless sensor networks using ant colony algorithm , 2008, J. Netw. Comput. Appl..

[19]  John Anderson,et al.  An analysis of a large scale habitat monitoring application , 2004, SenSys '04.

[20]  Pavani Movva,et al.  Novel Two-Fold Data Aggregation and MAC Scheduling to Support Energy Efficient Routing in Wireless Sensor Network , 2019, IEEE Access.

[21]  S. Taruna,et al.  Optimal Clustering in Zone Based Protocol of Wireless Sensor Network , 2012 .

[22]  Chuang Lin,et al.  Attribute-Aware Data Aggregation Using Potential-Based Dynamic Routing in Wireless Sensor Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.

[23]  Fagui Liu,et al.  An Energy Aware Adaptive Kernel Density Estimation Approach to Unequal Clustering in Wireless Sensor Networks , 2019, IEEE Access.

[24]  Leonidas J. Guibas,et al.  Lightweight sensing and communication protocols for target enumeration and aggregation , 2003, MobiHoc '03.

[25]  Won-Gyu Lee,et al.  A Potential Based Routing Protocol for Mobile Ad Hoc Networks , 2009, 2009 11th IEEE International Conference on High Performance Computing and Communications.

[26]  Sushma Jain,et al.  Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions , 2017, Wirel. Pers. Commun..

[27]  B. Mahalakshmi,et al.  ADA: Data Aggregation Scheme for Dynamic Application Using PBDR in Wireless Sensor Networks , 2014 .

[28]  Anindya Basu,et al.  Routing using potentials: a dynamic traffic-aware routing algorithm , 2003, SIGCOMM '03.