One of the central communication infrastructures of the Internet of Things (IoT) is the IEEE 802.15.4 standard, which defines Low Rate Wireless Personal Area Networks (LR- WPAN). In order to share the medium fairly in a non-beacon-enabled mode, the standard uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The nature of connected objects with respect to various resource constraints makes them vulnerable to cyber attacks. One of the most aggressive DoS attacks is the greedy behaviour attack which aims to deprive legitimate nodes to access to the communication medium. The greedy or selfish node may violate the proper use of the CSMA/CA protocol, by tampering its parameters, in order to take as much bandwidth as possible on the network, and then monopolize access to the medium by depriving legitimate nodes of communication. Based on the analysis of the difference between parameters of greedy and legitimate nodes, we propose a method based on the threshold mechanism to identify greedy nodes. The simulation results show that the proposed mechanism provides a detection efficiency of 99.5%.

In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people’s abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly’s global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances.

With the rapid advancement of mobile devices and wireless networks, spatial crowdsourcing has emerged as a transformative e-market platform. It enables requesters to outsource tasks requiring a physical presence at specific locations, while workers autonomously select and complete these tasks for a reward. In this study, we address the task scheduling challenge under the Worker-Selected Tasks mode, where a single worker is assigned to a set of spatially distributed tasks. Each task is characterized by a geographic location and a strict deadline. The primary objective is to maximize the number of tasks completed by the worker while guaranteeing all temporal constraints are met. This involves strategically sequencing tasks to optimize both spatial route efficiency and deadline adherence. We propose a bio-inspired approach based on the Firefly Algorithm (FA). We further introduce an Enhanced Firefly Algorithm that incorporates adaptive parameter control, local search heuristics, and genetic crossover operations to improve convergence speed and solution quality. A comprehensive experimental evaluation was conducted using both real-world and synthetic datasets to assess the performance and computational complexity of the proposed method. Computational experiments demonstrate that the proposed algorithm performs competitively compared to existing solution methods.