During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

Despite a large number of applications in the field of health care, military, ecology or agriculture, the Wireless Underground Sensor Network (WUSN) faces the problem of wireless Underground Communication (WUC) which largely attenuate the signal on the ground. For the case of precision agriculture, the motes are buried and they have to check the good growth of plants by verifying data like the water content. However, due to soil composition, the wave signal is attenuated as it travels across the ground. Thus, before a real deployment of WUSN, the prediction of the path loss due to signal attenuation underground is an important asset for the good network functioning. In this paper, we proposed a WUSN path loss for precision agriculture called WUSN-PLM. To achieve it, the proposed model is based on an accurate prediction of the Complex Dielectric Constant (CDC). WUSN-PLM allows evaluating the path loss according to the different types of communication (Underground-to-Underground, Underground to Aboveground and Aboveground to Underground). On each communication type, WUSN-PLM takes into account reflective and refractive wave attenuation according to the sensor node burial depth. To evaluate WUSN-PLM, intensive measurements on real sensor nodes with two different pairs of transceivers have been conducted on the botanic garden of the University Cheikh Anta Diop in Senegal. The results show that the proposed model outperforms the existing path loss models in different communication types. The results show that our proposed approach can be used on real cheap sensor with 87.13% precision and 85% balanced accuracy.

The Internet of Things has developed greatly over the past decade to cater to many diverse applications across almost all fields of life. Many of these applications can either profit or even explicitly require deployment underground, such as precision agriculture, but also land, pipeline, or mine monitoring. Underground deployment offers many advantages, such as concealment of the devices for their protection. However, the underground environment is also very challenging, especially for wireless communications and energy harvesting. In this survey and tutorial, we offer a comprehensive view of the complete topic, from theoretical foundations of wireless communications underground, through system architectures and applications, to energy harvesting options. These topics cannot be viewed separately from each other, as they are deeply intertwined and all of them need to be considered before a possible deployment. We will show that wireless underground sensor networks have a great potential for a variety of applications and are an intriguing alternative to overground deployments. We will describe the state of the art in a tutorial style, so that beginners can also profit. Last but not least, we will identify remaining challenges to guide researchers in this area.

The Wireless Underground Sensor Networks (WUSNs) become an attractive field due to its applications. On these applications, links between nodes are made through wireless underground communication of electromagnetic waves. However, due to soil complexity, the wave signal is attenuated as it travels across the ground. The prediction of the path loss due to signal attenuation across the ground depends on several parameters like soil composition, soil particle sizes, moisture or temperature. Designing an accurate path loss model for WUSNs remains a key issue on Internet Of Underground Things (IOUT). In this paper, we proposed a new approach for path loss prediction in WUSNs. In order to achieve it, we firstly analyze the existing path losses in literature. Secondly, we evaluated and compared the Complex Dielectric Constant (CDC) derivation schemes. Thirdly, we proposed a new path loss model that uses a better CDC prediction than the existing path losses. To validate our approach, measurements in real experimental field were made and real sensor nodes are used. The conducted experimentations show that our proposed path loss is more accurate than the existing path loss models with the lowest errors. Furthermore, we show the efficiency of our approach by considering ±3% error of the soil moisture sensor.

The proliferation of constrained Internet of Things (IoT) devices requires scalable over‐the‐air (OTA) firmware update mechanisms. Conventional approaches, predominantly based on one‐to‐one communication, such as Bluetooth Low Energy (BLE) Device Firmware Update (DFU), suffer from a scalability bottleneck that makes fleet‐wide updates inefficient and energy‐intensive. To address this limitation, this paper proposes FragCast, a broadcast‐based OTA dissemination protocol that leverages the one‐to‐many communication paradigm of BLE advertising channels. The proposed solution implements an application‐layer protocol that transports firmware fragments within custom‐formatted BLE advertising packets. A detailed simulation framework was developed to analyse the protocol performance under different scanning profiles and fleet sizes of up to 100 devices. The proposed mechanism was also experimentally validated on Arduino UNO R4 Wi‐Fi boards equipped with ESP32‐S3 modules. Results show that the broadcast architecture enables efficient fleet‐wide dissemination, with discovery latency remaining nearly constant as the number of receivers increases. In the evaluated configuration, a fleet of 100 devices can be updated in approximately 40 s using the Aggressive scanning profile and about 90 s using an energy‐efficient iOS‐like profile. These findings demonstrate that BLE advertising provides a practical and scalable foundation for OTA firmware dissemination in large fleets of constrained IoT devices.