A Lossless Convergence Method for Reducing Data Fragments on WSN

This article focuses on the most common application scenarios for data collection and uploading in WSN (Wireless Sensor Networks). First, we measure the energy consumption of widely used hardware. According to the characteristics of transmission energy consumption, a MIP (mixed integer programming) model called FAT-WSN (fragmentation aggregation transmission WSN) is proposed to minimize the number of data fragments. Moreover, we propose an iterative solution for this MIP problem with elasticity and low complexity. The main optimization method for this model is to adjust topology and traffic distribution. It focuses on optimizing the number of data transfers without modifying any data and without introducing a compression calculation burden. Finally, simulation and small-scale real node verifications are performed for the FAT-WSN scheme. The experimental results show that FAT-WSN can effectively reduce the number of data transmission and reception, thereby reducing energy consumption and improving network life. Compared with the MinST model, JGDC (Jointly Gaussian Distributed Compress) model and AMREST (Approximately Maximum min-Residual Energy Steiner Tree) model, the network life can be increased by 10%-30% without extending the calculation time.

[1]  Soumya K. Ghosh,et al.  Adaptive data aggregation and energy efficiency using network coding in a clustered wireless sensor network: An analytical approach , 2014, Comput. Commun..

[2]  Abdelkamel Tari,et al.  A distributed multi-path routing algorithm to balance energy consumption in wireless sensor networks , 2017, Ad Hoc Networks.

[3]  Jia Li,et al.  A sparsity feedback-based data gathering algorithm for Wireless Sensor Networks , 2018, Comput. Networks.

[4]  Hwa-Chun Lin,et al.  An Approximation Algorithm for the Maximum-Lifetime Data Aggregation Tree Problem in Wireless Sensor Networks , 2017, IEEE Transactions on Wireless Communications.

[5]  Ivan Stojmenovic,et al.  Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

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

[7]  Li Ma,et al.  An Adjustable Model in Data Collection Scenario for WSN , 2017, 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC).

[8]  Khaled Shuaib,et al.  Dependable wireless sensor networks for reliable and secure humanitarian relief applications , 2014, Ad Hoc Networks.

[9]  Sang Hyuk Son,et al.  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks , 2016, TOSN.

[10]  Xin Jin,et al.  Deployment guidelines for achieving maximum lifetime and avoiding energy holes in sensor network , 2013, Inf. Sci..

[11]  Simone Silvestri,et al.  A Framework for the Inference of Sensing Measurements Based on Correlation , 2018, ACM Trans. Sens. Networks.

[12]  Guihai Chen,et al.  Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks , 2014, TOSN.

[13]  Syed Hassan Ahmed,et al.  FAT-WSN: A Non Destructive and Secure Aggregation Strategy for Energy Saving in WSN , 2019, 2019 IEEE Globecom Workshops (GC Wkshps).

[14]  Kai-Hsiang Ke,et al.  A LoRa wireless mesh networking module for campus-scale monitoring: demo abstract , 2017, IPSN.

[15]  Zhi Zhang,et al.  Cluster-based energy-efficient transmission using a new hybrid compressed sensing in WSN , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[16]  Shan Chang,et al.  Energy-efficient data sensing and routing in unreliable energy-harvesting wireless sensor network , 2018, Wirel. Networks.

[17]  W. Dorigo,et al.  A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset , 2016 .

[18]  José López Vicario,et al.  Data Aggregation and Principal Component Analysis in WSNs , 2016, IEEE Transactions on Wireless Communications.

[19]  Lei Shu,et al.  An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs , 2016, IEEE Transactions on Industrial Informatics.

[20]  Sajal K. Das,et al.  Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree-Based Wireless Sensor Networks , 2015, IEEE/ACM Transactions on Networking.

[21]  Kai-Hsiang Ke,et al.  Monitoring of Large-Area IoT Sensors Using a LoRa Wireless Mesh Network System: Design and Evaluation , 2018, IEEE Transactions on Instrumentation and Measurement.

[22]  Tao Gu,et al.  A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks , 2017, IEEE Transactions on Wireless Communications.

[23]  Lokman Sboui,et al.  gTBS: A green Task-Based Sensing for energy efficient Wireless Sensor Networks , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[24]  Jean Schwoerer,et al.  Capacity limits of LoRaWAN technology for smart metering applications , 2017, 2017 IEEE International Conference on Communications (ICC).

[25]  Nadeem Javaid,et al.  Energy consumption model for density controlled divide-and-rule scheme for energy efficient routing in wireless sensor networks , 2016, Int. J. Ad Hoc Ubiquitous Comput..

[26]  Hui Wu,et al.  Constructing a Shortest Path Overhearing Tree with Maximum Lifetime in WSNs , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[27]  Seungmin Rho,et al.  Heuristic Approach for Stagnation Free Energy Aware Routing in Wireless Sensor Networks , 2016, Ad Hoc Sens. Wirel. Networks.

[28]  Qing Zhao,et al.  On the lifetime of wireless sensor networks , 2005, IEEE Communications Letters.

[29]  Seung Jun Baek,et al.  Energy-Efficient Collection of Sparse Data in Wireless Sensor Networks Using Sparse Random Matrices , 2017, ACM Trans. Sens. Networks.

[30]  Martin W. P. Savelsbergh,et al.  Adaptive Kernel Search: A heuristic for solving Mixed Integer linear Programs , 2017, Eur. J. Oper. Res..

[31]  Lorenzo Bruzzone,et al.  LaPS: LiDAR-assisted Placement of Wireless Sensor Networks in Forests , 2019, ACM Trans. Sens. Networks.

[32]  Song Guo,et al.  Maximizing Lifetime of Data-Gathering Trees with Different Aggregation Modes in WSNs , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).