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Wireless sensor network (WSN) technology is becoming increasingly crucial in the context of the Fourth Industrial Revolution. This has led to the development of various protocols aimed at enhancing energy efficiency, with LEACH, PEGASIS, and EEACP being key among them. Despite these advancements, the EEACP protocol exhibits significant limitations in its current form, particularly regarding energy expenditure during data transmission phases. A primary inefficiency arises from suboptimal path configurations during data reception, which can severely impact the network’s longevity and effectiveness. To address these challenges, the newly proposed Advanced EEACP (O3EACP) protocol introduces strategic optimizations in data routing toward the sink node. This refinement aims to minimize energy drain during transmissions, thereby extending the operational lifespan of the network and bolstering its overall resilience. This enhancement not only promises a reduction in energy consumption but also a notable improvement in network sustainability, essential for supporting the robust, interconnected frameworks envisioned in the latest industrial revolution.

Asking and answering questions are common activities in both the workplace and everyday life. Knowledge-sharing websites have become a popular resource for obtaining instant and searchable answers. However, users of these sites may encounter challenges in acquiring timely and appropriate content from user-provided answers owing to factors such as limited expertise, spam, and time constraints. Identifying trustworthy experts who can provide relevant and reliable answers in knowledge-sharing communities is crucial to overcome this issue. In this study, we propose a solution to the problem of identifying credible experts on knowledge-sharing sites by introducing the CredibleExpertRank algorithm. Our algorithm calculates a CredibleExpert score based on two main factors: activity and credibility. The credibility score is determined by analyzing users’ interactions related to questioning, answering, recommending, and mining users’ opinions, while the activity score reflects the user’s level of participation on the platform. We conducted experiments to evaluate the performance of the CredibleExpertRank algorithm, using user satisfaction measures for answers to given questions. Our findings confirmed that the credible experts identified by our algorithm provided more relevant and timely answers compared to other ordinary users. The timely nature of the credible experts’ answers was due to the reflection of their activity factor, while the superior performance in relevance was attributed to the high recommendation rate of their answers and positive evaluations received from opinion mining results. Our study undertakes an extensive investigation focused on the identification and prioritization of credible experts, revealing their profound advantages in significantly enhancing the overall quality of knowledge-sharing platforms. We proposed the CredibleExpertRank algorithm as a powerful method for effectively identifying trustworthy experts and giving priority to their answers. Through a meticulous process of experimental evaluation, we provide compelling evidence that this approach leads to substantial improvements in both search efficiency and reliability on knowledge-sharing sites. By highlighting the potential benefits derived from the identification of credible experts, our study underscores their pivotal role in elevating the overall performance of knowledge-sharing platforms.