Open Access
Subscription Access
Open Access
Subscription Access
Analysis on Performance Comparison of Virtual Grid-Base Dynamic Route Adjustment in Wireless Detector Networks
Subscribe/Renew Journal
A virtual Grid-based dynamic routes adjustment scheme (virtual grid routing) for wireless sink-based wireless sensor networks is a recent introduction. Each mobile node in the network is capable of sensing, processing and communicating. In the present scenario, sensor networks are used in a variety of applications such as military, commercial, industrial, etc. which require constant monitoring and detection of specific event. The approach of efficient data delivery using communication of distance priority is used, avoiding the technique of previous schemes. Our method aims to reduce the routes reconstruction cost of sensor nodes while maintaining the most favorable routes to the mobile sink's recent location. It will improve the lifetime and reduce the cost consumption. This method highlights many routed schemes. A few such novel routing schemes are Virtual Grid based Dynamic Route Adjustment (VGDRA), Multiple Enhanced Specified-deployed Sub-sinks (MESS), Virtual Circle Combined Straight Routing (VCCSR), Hexagonal cell-based Data Dissemination (HexDD), Hierarchical Cluster-based Data Dissemination (HCDD), Backbone-based Virtual Infrastructure (BVI), Line-Based Data Dissemination (LBDD), Rail Road, Quadtree-based Data Dissemination (QDD), and Two-Tier Data Dissemination (TTDD). But, each scheme has its own advantages and disadvantages.
Keywords
VGDRA, MESS, VCCSR, HCDD, BVI, LBDD, QDD, TTDD, WDN, Grid Routing, Data Mining.
Subscription
Login to verify subscription
User
Font Size
Information
- J. Wang, J.Cao, S. Ji and J.H. Park, “Energy-Efficient Cluster-based Dynamic Routes Adjustment Approach for Wireless Sensor Networks with Mobile Sinks”, Journal of Supercomputing, Vol. 73, No. 7, pp. 3277-3290, 2017.
- J.S. Pan, L. Kong, T.W. Sung, P.W. Tsai and V. Snasel, “A Clustering Scheme for Wireless Sensor Networks based on Genetic Algorithm and Dominating Set”, Journal of Internet Technology, Vol. 19, No. 4, pp. 1111-1118, 2018.
- W. Qi, W. Liu, X. Liu, A. Liu, T. Wang, N.N. Xiong and Z. Cai, “Minimizing Delay and Transmission Times with Long Lifetime in Code Dissemination Scheme for High Loss Ratio and Low Duty Cycle Wireless Sensor Networks”, Sensors, Vol. 18, No. 10, pp. 3516-3523, 2018.
- S. Chen, C. Zhao and M. Wu, “Compressive Network Coding for Wireless Sensor Networks: Spatio-Temporal Coding and Optimization Design”, Computer Networks, Vol. 108, No. 1, pp. 345-356, 2016.
- X. Deng, Z. Tang, L.T. Yang, M. Lin and B. Wang, “Confident Information Coverage Hole Healing in Hybrid Industrial Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 5, pp. 2220-2229, 2017.
- Y. Liu, K. Ota, K. Zhang, M. Ma and N. Xiong, “QTSAC: An Energy-Efficient MAC Protocol for Delay Minimization in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 8273-8291, 2018.
- T.L. Duc, D.T. Le, V.V. Zalyubovski and D.S. Kim, “Towards Broadcast Redundancy Minimization in Duty‐Cycled Wireless Sensor Networks”, International Journal of Communication Systems, Vol. 30, No. 6, pp. 3108-3117, 2017.
- J. Tan, W. Liu, T. Wang, S. Zhang, A. Liu, M. Xie and M. Zhao, “An Efficient Information Maximization based Adaptive Congestion Control Scheme in Wireless Sensor Network”, IEEE Access, Vol. 7, pp. 64878-64896, 2019.
- L. Kong, J.S. Pan and V. Snasel, “An Energy-Aware Routing Protocol for Wireless Sensor Network based on Genetic Algorithm”, Telecommunication Systems, Vol. 67, No. 3, pp. 451-463, 2018.
- E.P.K. Gilbert, B. Kaliaperumal, E.B. Rajsingh and M. Lydia, “Trust based Data Prediction, Aggregation and Reconstruction using Compressed Sensing for Clustered Wireless Sensor Networks”, Computers and Electrical Engineering, Vol. 72, pp. 894-909, 2018.
- F. Ma, X. Liu, A. Liu, M. Zhao, C. Huang and T. Wang, “A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks”, Wireless Communications and Mobile Computing, Vol. 32, No. 2, pp. 1-16, 2018.
- K. Muthukumaran, K. Chitra and C. Selvakumar, “An Energy Efficient Clustering Scheme using Multilevel Routing for Wireless Sensor Network”, Computers and Electrical Engineering, Vol. 69, pp. 642-652, 2018.
- A. Agrawal, V. Singh, S. Jain and R.K. Gupta, “GCRP: Grid-Cycle Routing Protocol for Wireless Sensor Network with Mobile Sink”, AEU-International Journal of Electronics and Communications, Vol. 94, No. 2, pp. 1-11, 2018.
- W. Chen and I.J. Wassell, “Cost-Aware Activity Scheduling for Compressive Sleeping Wireless Sensor Networks”, IEEE Transactions on Signal Processing, Vol. 64, No. 9, pp. 2314-2323, 2016.
- J.S. Pan, L. Kong and P.W. Tsail, “α-Fraction First Strategy for Hierarchical Model in Wireless Sensor Networks”, Journal of Internet Technology, Vol. 19, No. 6, pp. 1717-1726, 2018.
- S.M. Amini, A. Karimi and M. Esnaashari, “Energy-Efficient Data Dissemination Algorithm based on Virtual Hexagonal Cell-Based Infrastructure and Multi-Mobile Sink for Wireless Sensor Networks”, Journal of Supercomputing, Vol. 76, No. 1, pp. 150-173, 2020.
Abstract Views: 215
PDF Views: 0