# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 7 Conclusions  
In this paper, we examined the concept of using mobile nodes in the network to alleviate congestion in WSNs. Initially, we presented a mechanism with two variations, which gets initiated when existing congestion control algorithms fail. The mechanism employs mobile nodes to either create disjoint paths of mobile nodes and route the excess traffic directly to the sink (Direct MobileCC), or to place a mobile node in such a position to create an alternative path by bridging two disjointed areas in the network and repeating the process if necessary (Dynamic MobileCC). Simulation results demonstrate that both variations can alleviate congestion. In doing so, Direct MobileCC demonstrates a better average source to sink delay and reduced packet drop, at the expense of mobile nodes used (almost double) and energy consumed, when compared to Dynamic MobileCC. In this part of the work, we have considered one instance of using alternative paths for alleviating congestion. Next, we extended the previous concept by considering the energy consumption of the mobile nodes used in the network to alleviate congestion in WSNs. We introduced the term of reuse by extending the Dynamic Node Placement algorithm of [6] into an energy-efficient solution. The extended version considers the energy consumption of the nodes in the network, where the mobile nodes added to the network use their energy level for every decision that needs to be made. This action is helpful in replacing an energy exhausted mobile node in time before new congestion occurs or in reusing it to a new position for resolving new congestion. The simulation results demonstrate that the proposed algorithm can effectively alleviate congestion in the network. The two methods used for allocating an alternative node show slight differences in the results, which is expected due to their algorithmic structure, where the allocation method uses more information messages and computation than the optimal method. In the case of the scenarios, it is noticeable that the least effective one is the one where no reuse occurs, with its result being the lowest in terms of packets andthe highest in terms of energy consumption. The other scenarios have similar reactions in terms of packets, whereas based on the energy consumption, they all have different reactions based on the number of mobile nodes that are used and needed. Finally, we evaluated our energy-efficient algorithm under different energy models. These models consider different mobile robots for their experimentation that were extracted on our simulation for the evaluation. We introduced the three energy models used, and then we ran the simulation for different scenarios. Based on the results, it is demonstrated, as expected, that the energy consumption of the network increases based on the number of nodes. When more nodes are active in the network, more energy is consumed. Additionally, when the energy model uses the time variable, it is noticeable that the results are increasing, as this variable changes constantly, whereas the distance variable changes only when a node moves, which is rarer. In general, it is demonstrated that our solution is able to be used effectively with different mobile robots. Future work aims to enhance the MobileCC framework with chargers [33,34] and to handle faults by coping with crash-prone or malicious nodes [35,36], as well as faulty communication links. Conceptualization, N.T., C.S., C.I., C.G. and V.V. ; methodology, N.T., C.S., C.I, C.G. and V.V. ; software, N.T. ; validation, N.T., C.S., C.I., C.G. and V.V. ; formal analysis, N.T., C.S., C.I., C.G. and V.V. ; writing--original draft preparation, N.T. ; writing--review and editing, N.T., C.S., C.I., C.G. and V.V. ; visualization, N.T., C.S., C.I., C.G. and V.V. ; supervision, C.G and V.V. All authors have read and agreed to the published version of the manuscript.