## 6 Conclusions  
In this article, we have proposed the CH-active algorithm that relocates solar-powered CHs in the center of the mass, in order the total energy consumption in WSN to be reduced, and the network lifespan to be extended. Actually, by relocating the CH to the center of the masses, the CH minimizes the distance to the other cluster members and saves energy while receiving data.  
In our framework, an additional solar-powered battery is used only to relocate the CHs, and not for processing and communication tasks. The CH-active algorithm uses the idea of CH relocation that is based on the selfish herd hypothesis. Simulation results show that when proper parameters are used, the CH-active algorithm is more stable and superior to LEACH with regard to the extension of WSN lifespan.  
In this article, we also examined the situation where CH sensors do not have enough energy to position themselves in the center of the mass, but they can reach a certain percentage of the distance from their initial position to the center. This modification of CH-active is called the CH-partially-active algorithm. Our simulations have shown that moving CH to only 25% of the distance to the center of mass, contributes significantly to the lifetime of the WSN. The difference in active nodes in relation  
Figure 8: Average energy per node (1000 m \(\times\) 1000 m terrain).  
Figure 7: Average energy per node (500 m \(\times\) 500 m terrain).  
to the CH-active algorithm in most cases is less than 5. This result significantly extends the possible scope of the application of the proposed algorithm.  
CH-active and CH-partially-active algorithms constitute a new paradigm set for further research in order to invent even better relocation algorithms. In the near future, we will study how relocating other non-CH nodes can impact network performance. Also, the impact of a problematic solar charging on network performance will be considered.  
Conclusively, CH-active is unsuitable to be applied at a WSN application, where changes in the sensing field (data) are very frequent. For example, CH-active is unsuitable for a WSN application in an industrial automation context, where changes on the sensing field are very frequent and periodical and intensive reporting is required. CH-active is ideally applied only to \"slow\" WSN applications, such as daily reporting of the radioactivity and other types of pollution, reporting for weather data, etc. For the CH-active algorithm, the optimum WSN application covers wider sensing fields and in a harsh environment with periodical and not intensive reporting.  
[PERSON] conceived and developed the ideas behind the research. [PERSON] and [PERSON] developed the proposed algorithms, designed and performed the experiments, and wrote the paper; [PERSON] wrote the paper, analyzed the simulation results, validated the methodology, and reviewed and edited the paper.  
This research received no external funding.  
The authors would like to thank the editor and anonymous referees for their valuable comments that improved the quality of this paper.  
The authors declare no conflicts of interest.  
The following abbreviations are used:  
\begin{tabular}{l l} BS & Base station \\ CH & Cluster head \\ LEACH & Low-energy adaptive clustering hierarchy \\ PCLA & Partial coverage with learning automata \\ PSO & Particle swarm optimization \\ TDMA & Time-division multiple access \\ WSN & Wireless sensor network \\ \end{tabular}