Bio-medical waste management is critical for ensuring public health and environmental sustainability. However, due to the inherent ambiguities and complexities involved with waste characteristics and disposal techniques, measuring the efficiency of bio-medical waste management systems presents major hurdles. This study provides a Fuzzy TOPSIS-based (Technique for Order Preference by Similarity to Ideal Solution) strategy for thorough bio-medical waste management assessment. The suggested method combines the benefits of fuzzy logic and TOPSIS, allowing for the incorporation of subjective judgments and ambiguity in the evaluation procedure. Initially, a thorough set of criteria is constructed based on a review of current literature and recommendations from experts, comprising Environmental Impact, Compliance with Regulations, Health and Safety, Technological Feasibility, and Cost-effectiveness. To accurately represent the inherent ambiguity and imprecision in decision-making, each criterion is evaluated using linguistic variables. Furthermore, the Fuzzy TOPSIS approach is used to rate various bio-medical waste management systems depending on how well they perform in comparison to the identified criteria. The language judgments are represented as fuzzy numbers, and the idea of closeness coefficients is used for calculating the relative distance between each alternative and the ideal answer. An investigation in a healthcare facility is performed to demonstrate the feasibility and effectiveness of the suggested strategy. To assess numerous waste management approaches, the study uses real-world data on waste management practices, expert opinions, and linguistic analyses. The study’s findings emphasize the benefits of using a Fuzzy TOPSIS-based technique to evaluate bio-medical waste management. According to the findings of this research study, recycling is the best choice because it has the potential to reduce waste, recover resources, and preserve the environment. It assists decision-makers to account for uncertainties and subjectivity, increases transparency and consistency in decision-making, and aids in choosing of the best waste management system. The proposed approach advances sustainable waste management practices in the bio-medical area and provides a helpful tool for policymakers and practitioners looking to enhance waste management systems.

The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.