# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 4 Energy Efficient GA-Based Multipath Routing
### Multipath Formation Using Genetic Algorithm
#### 4.2.4 Mutation
Moreover, the variation between node energy consumption and overall mean energy dissipation of the network was kept to a minimum which provides assurance that the proposed algorithm maintained load balance across the nodes in the network thereby prolonging the life span of the network. As depicted in Figure 8, EMRGA showed better, but only slightly improved, maximum network lifetime compared to MRP when there were 300 nodes. However, for more than 300 nodes EMRGA showed significant improvement in the network lifetime compared with the other four algorithms. This ensures that our proposed algorithm was never influenced by the density of nodes in the network. Minimum energy dissipation and better load balancing across the network were achieved when using EMRGA compared with the existing algorithms. This was due to two reasons. The first is because the dynamic clustering technique is employed only when a specific event happens in the network, and the second is due to dynamically selecting the best path using the load balancing function for data delivery. Figure 8: Network lifetime. Figure 7: Standard deviations. ## 6 Conclusions

In this paper, an energy-aware multipath routing technique based on GA wasexplained. We introduced a cost function which considers distance to the center of the event area and base station along with the residual energy of the node for choosing CH, and this improved the energy dissipation of the network. Furthermore, we applied GA for searching for multiple routes for data transmission between CH and the base station. A load balancing function was also used to evaluate and dynamically select the best path. In general, EMRGA maximized the working time of the network by evenly sharing the load across every sensor node. It also minimized the energy consumption of the network compared to existing protocols. This work can be further improved to a network of the same application of mobile sensor nodes with suitable speed. [PERSON] performed the experiment and the analyses of the data and wrote the draft paper. [PERSON] reviewed, restructured and rewrote most part of the paper and provided senior expertise advice in the preparation, presentation of the algorithms and discussion of the results. [PERSON] provided initial expertise advice. We would like to thank the anonymous reviewers for their constructive comments and [PERSON] for her help. The authors declare no conflict of interest. ## References

* [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON] An overview of wireless sensor networks technology and evolution. _Sensors_**2009**, \(9\), 6869-6896. [CrossRef] [PubMed]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON] Enhancement of Compressed Image Transmission in WMSNs Using Modified \(\upmu\)-Nonlinear Transformation. _IEEE Commun. Lett._**2018**, _22_, 934-937. [CrossRef]

Figure 11: Network lifetime. Figure 12: Average energy. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images. _Sensors_**2019**, _19_, 946. [CrossRef] [PubMed]
* [PERSON] et al. (2010) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Wireless multimedia sensor networks: Current trends and future directions. _Sensors_**2010**, _10_, 6662-6717. [CrossRef] [PubMed]
* [PERSON] (2016) [PERSON] Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. _Plant Dis._**2016**, _100_, 241-251. [CrossRef] [PubMed]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A wireless multimedia sensor network platform for environmental event detection dedicated to precision agriculture. _arXiv_**2018**, arXiv:1806.03237. * [PERSON] and [PERSON] (2016) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Wireless sensor network for wildlife tracking and behavior classification of animals in Dofana. _IEEE Commun. Lett._**2016**, _20_, 2534-2537.