# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
The Node Placement Algorithm retains the basic principles of MobileCC and reacts upon the occurrence of congestion. It resolves the problem by efficiently and effectively relocating mobile nodes. The main idea is that the Alternative Path Creation mechanism starts when existing congestion control algorithms fail. The algorithm consists of two variations: a dynamic node placement algorithm that solves the problem locally and a direct node placement algorithm that creates a new direct path to the sink, which consists only of mobile nodes. The extended version of the Node Placement Algorithm, called the Energy Node Placement Algorithm, reuses the mobile relay nodes already in use in the network for resolving congestion, network disconnection, energy holes, or security attack problems occurring in the network. The basic idea of placing mobile nodes in the network to mitigate the problem to be solved is retained, and the focus is set on the energy consumption of the active mobile nodes in the network. Considering the energy levels of a mobile node can be useful in re-using it in a different area of the network or in replacing it on time before causing a new problem in the network. A preliminary version of the Node Placement Algorithm is presented in [8], while a preliminary version of the Energy Node Placement Algorithm appeared in [9]. The current work extends the discussion and findings of these previous research works and evaluates them against multiple energy models inspired by different types of mobile robots. It presents a theoretical analysis of the algorithms, as well as a comparison of the energy models. WSNs are descendants of the new emerging technology of the Internet of Things (IoT) [10]. An IoT-enabled WSN [1] is defined as the WSN in an IoT-based system. Each sensor node is defined as an IoT-enabled sensor node that can monitor the environment and collect real-time data. An IoT-enabled WSN consists of an end-user connected to the Internet, which is also connected to an access point. The access point is represented by the base station of the WSN where all sensor nodes are connected to. In this respect, the algorithmic mechanisms proposed in our paper can be directly applied to IoT-enabled WSNs. The contributions of the current work are the following: (a) we are the first to propose a solution that utilizes mobile nodes as alternative paths in order to unload the flows of the affected area in the network, (b) introduce a time-efficient solution for decreasing the correspondence time upon detecting a network problem, (c) consider the energy consumption of the mobile node prolonging its lifetime, and (d) evaluate different energy models of the mobile node to determine the most striving energy factor. The paper is organized as follows. In Section 2, we present related work. In Section 3, we provide an overview of the MobileCC Framework, of which our proposed algorithms are part of. Then, in Section 4, we present the Node Placement algorithm, with its two variations, Dynamic (Section 4.1) and Direct Path (Section 4.2), and in Section 5, we present an energy-efficient extension of the Dynamic variation of the Node Placement algorithm, called the Energy Node Placement algorithm. In Section 6, the evaluation of the proposed algorithms is presented. The evaluation is divided into three parts. In the first part, the Node Placement algorithm, including both variations, is compared with a congestion control algorithm (Section 6.3.1). In the second and third parts, the Energy Node Placement algorithm is evaluated in different reuse scenarios (Section 6.3.2) and under different energy consumption models (Section 6.3.3), respectively. We conclude with a discussion in Section 7.