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Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

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With the proliferation of Internet of Things (IoT), a large number of devices are expected to connect to the Internet over different networks due to various demands and characteristics of IoT devices (IoTDs). One of the networks that will connect IoTDs to the Internet is cellular networks. The 3rd Generation Partnership Project (3GPP) proposes a connectivity model for cellular networks in which IoTDs connect to the network via relay devices. In this model, discovering the relay devices is a challenging task due to energy budget constraints, mobility of devices, and collision of discovery messages. Since IoTDs are constrained devices, it is important that they perform discovery with the minimum energy consumption by intermittently sleeping and waking up. In this paper, we propose signature-based and energy-efficient relay discovery protocol (SERDP) using Zadoff-Chu sequences. Since Zadoff-Chu is a Constant Amplitude Zero Autocorrelation (CAZAC) sequence, we use the sequence for pointing the active slots of the devices within the proposed three-stage original period structure. The probabilistic analysis and simulation results show that SERDP discovers more devices with less energy consumption as well as preventing collisions, as compared with the existing protocols.