A method of determining maximum transmission rate in wireless sensor network

The lifetime of wireless sensor networks (WSNs) decreases with the increasing payload, such that extending the network lifetime has become a hot topic. This paper proposes a distance-based transmission rate sexlection algorithm, maximum emission rate (MER) determination, to select the pairwise maximum effective transmission rate in a given WSN environment. The proposed work is founded on two observations, one is the logarithmic relationship of internode communication distance to data transmission rate, the other is the linear relationship of receiving current to data transmission rate. The proposed work helps to reduce data transmission duration, and finally to increase the network lifetime. Simulation results show that the application of MER in existing network protocols extends 300% plus network lifetime while ensuring communication success rate.

[1]  Mo Li,et al.  A Survey on Topology Control in Wireless Sensor Networks: Taxonomy, Comparative Study, and Open Issues , 2013, Proc. IEEE.

[2]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[3]  Athanasios V. Vasilakos,et al.  Backpressure-based routing protocol for DTNs , 2010, SIGCOMM '10.

[4]  Witold Pedrycz,et al.  An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Zhang Yu-guo Analysis of Distance Measurement Based on RSSI , 2007 .

[6]  Feng Liang,et al.  Minimum distance clustering algorithm based on an improved differential evolution , 2014, Int. J. Sens. Networks.

[7]  Jiafu Wan,et al.  Towards Real-Time Indoor Localization in Wireless Sensor Networks , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

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

[9]  Driss Aboutajdine,et al.  Stochastic and Equitable Distributed Energy-Efficient Clustering (SEDEEC) for heterogeneous wireless sensor networks , 2011, Int. J. Ad Hoc Ubiquitous Comput..

[10]  Athanasios V. Vasilakos,et al.  EDAL: An Energy-Efficient, Delay-Aware, and Lifetime-Balancing Data Collection Protocol for Heterogeneous Wireless Sensor Networks , 2015, IEEE/ACM Transactions on Networking.

[11]  Azeddine Bilami,et al.  HEEP (Hybrid Energy Efficiency Protocol) based on chain clustering , 2011, Int. J. Sens. Networks.

[12]  Zhou Jie,et al.  An Improved Algorithm Based on LEACH in WSN , 2013 .

[13]  Jiafu Wan,et al.  Issues and Challenges of Wireless Sensor Networks Localization in Emerging Applications , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[14]  Li Qing,et al.  Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks , 2006, Comput. Commun..

[15]  Athanasios V. Vasilakos,et al.  Algorithm design for data communications in duty-cycled wireless sensor networks: A survey , 2013, IEEE Communications Magazine.

[16]  Athanasios V. Vasilakos,et al.  Directional routing and scheduling for green vehicular delay tolerant networks , 2012, Wireless Networks.