Analyzing packet loss, whether resulting from communication challenges or malicious attacks, is vital for broadcast authentication protocols. It ensures legitimate and continuous authentication across networks. While previous studies have mainly focused on countering Denial of Service (DoS) attacks' impact on packet loss, our research introduces an innovative investigation into packet loss and develops data recovery within variant TESLA protocols. We highlight the efficacy of our proposed hybrid TLI-µTESLA protocol in maintaining continuous and robust connections among network members, while maximizing data recovery in adverse communication conditions. The study examines the unique packet structures associated with each TESLA protocol variant, emphasizing the implications of losing each type on the network performance. We also introduce modifications to variant TESLA protocols to improve data recovery and alleviate the effects of packet loss. Using Java programming language, we conducted simulation analyses that illustrate the adaptability of variant TESLA protocols in recovering lost packet keys and authenticating previously buffered packets, all while maintaining continuous and robust authentication between network members. Our findings also underscore the superiority of the hybrid TLI-µTESLA protocol in terms of packet loss performance and data recovery, alongside its robust cybersecurity features, including confidentiality, integrity, availability, and accessibility. Additionally, we demonstrated the efficiency of our proposed protocol in terms of low computational and communication requirements compared to earlier TESLA protocol variants, as outlined in previous publications.

The rapid integration of IoT devices into everyday decision-making processes underscores the need for continuous user authentication and data integrity checking during network communication, all while minimizing energy consumption to extend device lifespan. This paper introduces the Bio-Integrated Hybrid TESLA protocol, which is a fully symmetric and energy-efficient authentication protocol designed for resource-constrained IoT devices. Based on the Hybrid TLI-lTESLA protocol, this innovative solution prioritizes high cybersecurity levels and minimal computational requirements for continuous authentication. An innovative advancement involves eliminating the public cryptography process during the synchronization stage of TESLA protocols. Instead, biometric authentication through distorted fingerprint and EEG templates is employed, to establish a non-shared symmetric session key, utilized only once. Furthermore, neither the key nor the original biometric templates are transmitted over the network, ensuring user identity preservation and effectively resolving the key distribution challenge inherent in symmetric cryptography. By offloading intensive tasks to servers and avoiding the storage or transmission of biometric data, the proposed approach conserves IoT device energy and enhances cybersecurity. Simulation analyses and cybersecurity assessments demonstrate successful synchronization, privacy preservation, and low computational demands compared to existing protocols, making the Bio-Integrated Hybrid TESLA protocol a significant advancement in IoT authentication.

When deploying a wireless sensor network (WSN) identifying optimal locations for the nodes is challenging, especially so if the environment is either (i) unknown or (ii) obstacle-rich. We proposes BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi algorithm to address this challenge. Among the features of the approach are the restrictions placed upon the abilities of the nodes: (i) all information is sensed locally as well as (ii) subject to communication noise. Performance is measured as (i) the percentage of area covered, (ii) the total distance traveled by the nodes, (iii) the cumulative energy consumption and (iv) the uniformity of nodes’ distribution. Obstacle constellations and noise levels are studied systematically and a collision-free recovery strategy for failing nodes is proposed. Results obtained from extensive simulations show the algorithm outperforming previously reported approaches in both, convergence speed, as well as deployment cost.