Double Mobile sinks Architecture for WSN Data Gathering and Critical Events Detection

Data gathering and critical events detection are two essential functionalities for Wireless Sensor Network (WSN). In this paper, we propose double mobile sinks network architecture, where two mobile sink nodes visit the Cluster Heads (CHs) to collect the captured data, which is very energy effective in terms of energy transmission efficiency and reliable compared with the case of having one static sink node. Moreover, the proposed architecture provides a capable scheme for supporting critical and non-critical data, which assures a timely delivery for any critical event to the remote monitoring and decision-making center with minimal interference to the non-critical data. Our proposed architecture shows a superior performance in terms of packets transmission delay, and requires low buffer occupancy for the CHs nodes when compared to related work in the literature. Finally, the paper provides a preliminary hardware design and implementation for the proposed architecture.

[1]  Novella Bartolini,et al.  Hybrid Wireless Sensor Networks: A Prototype , 2019, INTERACT.

[2]  Ala’ Khalifeh,et al.  An Energy Efficient WSN Implementation for Monitoring and Critical Event Detection , 2019, 2019 2nd IEEE Middle East and North Africa COMMunications Conference (MENACOMM).

[3]  Olivier Berder,et al.  A Hybrid Model for Accurate Energy Analysis of WSN Nodes , 2011, EURASIP J. Embed. Syst..

[4]  Zhi Huang,et al.  An Energy Efficient Strategy for Single Mobile Sink in Event-driven Sensor Networks , 2012, Ad Hoc Sens. Wirel. Networks.

[5]  Khalid A. Darabkh,et al.  EA-CRP: A Novel Energy-aware Clustering and Routing Protocol in Wireless Sensor Networks , 2017, Comput. Electr. Eng..

[6]  Faruk Bagci,et al.  Cluster communication protocol for wireless sensor networks , 2016, Int. J. Sens. Networks.

[7]  Raed Mesleh,et al.  A low-interference decision-gathering scheme for critical event detection in clustered wireless sensor network , 2018, Phys. Commun..

[8]  Khalid A. Darabkh,et al.  Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks , 2019, Appl. Soft Comput..

[9]  Azer Bestavros,et al.  SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks , 2004 .

[10]  Sahel Alouneh,et al.  Performance Evaluation of DigiMesh and ZigBee Wireless Mesh Networks , 2018, 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[11]  Ye Xia,et al.  Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications , 2010, IEEE Transactions on Mobile Computing.

[12]  Jun Luo,et al.  MobiRoute: Routing Towards a Mobile Sink for Improving Lifetime in Sensor Networks , 2006, DCOSS.

[13]  Khalid A. Darabkh,et al.  A Survey of 5G Emerging Wireless Technologies Featuring LoRaWAN, Sigfox, NB-IoT and LTE-M , 2019, 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET).

[14]  Muhammad Usman Asad,et al.  Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs , 2018 .

[15]  Subir Halder,et al.  Lifetime Optimizing Clustering Structure Using Archimedes’ Spiral-Based Deployment in WSNs , 2017, IEEE Systems Journal.

[16]  Jang-Ping Sheu,et al.  An Obstacle-Free and Power-Efficient Deployment Algorithm for Wireless Sensor Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[17]  Khalid A. Darabkh,et al.  LiM-AHP-G-C: Life Time Maximizing based on Analytical Hierarchal Process and Genetic Clustering protocol for the Internet of Things environment , 2020, Comput. Networks.