Establishing reliable and efficient multicast services in heterogeneous networks under an erasure channel is one of the current priorities in coding theory, particularly in Network Coding (NC) over data networks. Additionally, the increasing number of clients with mobile devices with high processing capabilities and the prevalence of non-delay tolerant traffic have led to the demand for non-feedback multicast schemes regarding distributed resource management. Current communication platforms lack gradual and dynamic coding control based on the type of data being transmitted. This paper proposes a reliable transmission scheme based on a concatenation of systematic coding and Random Linear Network Coding (RLNC) called Fulcrum coding. This hybrid rateless coding scheme with distributed characteristics allows the implementation of an adaptive resource management system to increase the decoding probability during data reception. The method ultimately results in a higher network throughput and shorter transmission times (RTT) per packet by implementing efficient Forward Error Correction (FEC).

We formalize and evaluate an adaptation of the Middleware Fulcrum coding scheme, developed under a Fog computing architecture, for 5G-IoT mobile devices. This adaptation includes a D2D extension with distributed coding, specifically designed to address the challenges posed by a dynamic topology that involves heterogeneous devices in terms of processing capabilities. The proposed method is established at the application layer level to allow for the mapping of packet segments to a finite field scheme (Galois field), which allows for exploiting the algebraic properties of the system to exercise data retrieval with encoded information. In addition, it will enable dynamic adjustment of the coding parameters according to the processing capacity of the communication devices, wireless channel conditions, network topology, and type of stream in the system. In this sense, high-performance coding is distributed to nodes that can offer higher coding complexity in the design, effectively improving the wireless network’s performance in many aspects, such as delay, energy consumption, and goodput. By characterizing the system as an algebraic system, it is possible to exploit the advantages of FEC through decoding to reduce the signaling in the system.

In the present study, rainfall rate statistics for radio propagation applications collected during 1‐year measurement with a 1‐min integration time in three locations of Venezuela are presented and analyzed for the first time in the country. Specifically, the statistics of the worst‐month (WM) are assessed and compared with the Recommendation ITU‐R P.841‐7 prediction model. The results indicate that the model does not accurately predict the statistics of the WM in the country. Also, the performance of Recommendation ITU‐R P.837‐7 (Annex 1 and Annex 2) in estimating the rain rate for a 1‐min integration time at the three locations is examined, with the ITU‐R P.837‐7 Annex 1 exhibiting the best performance and providing results that encourage its use in the prediction of complementary cumulative distributions of rain rate in the country.

The proposed architecture defines an adaptation of the Fulcrum coding system towards a Fog computing architecture. The distributed coding scheme allows the adjustment of the coding density according to the complexity of each communication device in the network, enabling the system to concentrate the system's performance to those that would allow for a high complexity in the coding if it significantly affects the delay network. These changes enable eliminating redundant information in the network, thus shortening the system delay. Additionally, thanks to Fulcrum coding defining a code concatenation based on RLNC coding, it allows the system to control the coding parameters from the application layer directly. The proposed architecture is an extension of the IoT-Cloud scheme, which allows establishing distributed performance, whose efficiency is controlled by a Fog-RLNC server and a series of Microcasting-type services.

The rapid expansion of the IoT has led to increasing concerns about security, particularly in the early stages of communication where many IoT application-layer protocols, such as CoAP and MQTT, lack native support for secure key exchange. This absence exposes IoT systems to critical vulnerabilities, including dictionary attacks, session hijacking, and MitM threats, especially in resource-constrained environments. To address this challenge, this paper proposes the integration of OWL, a password-authenticated key exchange (PAKE) protocol, into existing IoT communication frameworks. OWL introduces a lightweight and secure mechanism for establishing high-entropy session keys from low-entropy credentials, without reliance on complex certificate infrastructures. Its one-round exchange model and resistance to both passive and active attacks make it particularly well-suited for constrained devices and dynamic network topologies. The originality of the proposal lies in embedding OWL directly into protocols like CoAP, enabling secure session establishment as a native feature rather than as an auxiliary security layer. Experimental results and formal analysis indicate that OWL achieves reduced authentication latency and lower computational overhead, while enhancing scalability, resilience, and protocol performance. The proposed solution provides an innovative, practical, and efficient framework for securing IoT communications from the foundational protocol level.

The exponential growth of the Internet of Things (IoT) has amplified the demand for energy-conscious, low-latency, and efficient data transmission techniques. This study proposes an adaptive distributed coding scheme combining Fulcrum Code and Model-Agnostic Meta-Learning (MAML) to optimize IoT communications in ultralow-latency environments. Fulcrum Code improves packet resilience by adjusting redundancy to channel conditions, while MAML optimizes coding parameters in real-time to improve transmission efficiency. Forward Error Correction (FEC) and Hybrid Automatic Repeat Request (HARQ) further balance error correction and retransmission overhead. Simulations show significant reductions in transmission time and energy consumption, particularly in high-packet-loss scenarios. This approach ensures robust performance in resource-constrained IoT devices, preserving battery life while supporting critical applications such as smart cities, Industry 4.0, and the tactile Internet. By optimizing trade-offs between resilience and efficiency, this scheme advances scalable and energy-efficient communication for next-generation IoT systems.