Security has become a vital factor for any Internet of things network but it is of paramount importance for Internet of Health Things (IoHT). IoHT also known as Internet of Medical Things (IoMT) is integration of IoT and healthcare environment, where fragile data related to the patients is transmitted from IoT devices to server. During this transmission, if, any eavesdropping or intrusion occurs then it will not only lead to the serious mutilation of entire network but this data will be handled maliciously for wrong doings as well. Therefore, a proper security is indispensable for IoHT based equipments due to exposure to different attacks. Security of IoHT has been the burning issue in last couple of years. In this regard different security models, surveys, frameworks have been presented. In this paper, a proposed Identified Security Attributes (ISA) framework is presented to evaluate the security features of IoHT based device in healthcare environment. The proposed framework uses hybrid MCDM methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework works in two phase: in first phase the weights of attributes are derived by using AHP method and in second phase security assessment of alternatives is performed based upon security criteria by using TOPSIS method. The outcomes of proposed security assessment framework demonstrate that the reliable and secure alternative among alternatives is selected in IoMT system. This approach can be used as a guideline for future use in IoMT systems or any other IoT based domain. To the best of our knowledge, it is novel approach to address the security assessment of IoT and these MCDM methods have never been used before for assessment and decision making in IoHT system for security.

PurposeIn recent years, a number of researchers have attempted to make an integration of sustainability with supply chain risk management. These studies have led to valued insights into this issue, though there is still a lack of knowledge about the mechanisms by which sustainability-related issues are materialized as risks in the supply chain management.Design/methodology/approachThe paper aims to provide a comprehensive framework to evaluate the sustainability risk in the supply chain management mechanism. To do so, a novel approach using the double normalization-based multiple aggregation (DNMA) approach under the intuitionistic fuzzy (IF) environment is extended to identify, rank and evaluate the sustainability risk factors in supply chain management.FindingsTo provide comprehensive sustainability risk factors, this study has conducted a survey using interview and literature review. In this regard, this study identified 36 sustainability risk factors in supply chain management of the manufacturing firms in five different groups of risk, including sustainable operational risk factors, economic risk factors, environmental risk factors, social risk factors, and sustainable distribution and recycling risk factors. The results of this paper found that the poor planning and scheduling was the important sustainability risk in supply chain management of the manufacturing firms, followed by the environmental accidents, production capacity risk, product design risk and exploitative hiring policies. In addition, the results of the study found that the extended approach was effective and efficient in evaluating the sustainability risk factors in supply chain management of the manufacturing firms.Originality/valueThree aggregation methods based on the normalization techniques are discussed. A DNMA method is proposed under intuitionistic fuzzy sets (IFSs). To propose a broad procedure for identifying and classifying sustainability risk factors (ESFs) in supply chain management. To rank the sustainability risk factor, the authors utilize a procedure for evaluating the significance degree of the sustainability risk factor in supply chain management.

In the digital world of today, any enterprise that deals with the amounts of data in Warehouse Management Systems (WMS) are an important component. Furthermore, the amount of data being raisedand its complexity have become more challenging to maintain the WMS efficiency. Therefore, a device is required, which can manage such complexities autonomously with no human intervention. In this paper, Hybrid Machine Learning with the Internet of Things (HML-IoT) improves isolated doors. Furthermore, operating machine performance in the factory of hazardous goods. Decision-Making Algorithm (DMA) Data from the customer’s holding space’s dangerous goods warehouses shall be checked using separated doors. Thispaper’s significant aspect is that inventory and inventory operation’s organizational performance can be increased, further logistics costs minimized utilizing the fair use of isolated doors. Finally, the HML-IoT model integrated hazardous goods warehouse with isolated doors has been contrasted with the current one, demonstrating that the previous one has greater efficacy.

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The automated deployment of the internet of things (IoT) and the human-machine interface provides the best advancement for dispersed warehouse scheduling management (WSM). In this paper, superior data systematic move toward warehouse scheduling management (WSM) has been suggested using the computational method to allow smart logistics. Furthermore, this paper introduces the human-machine interface framework (HMI) using IoT for collaborative warehouse order fulfillment. It consists of a layer of physical equipment, an ambient middleware network, a framework of multi-agents, and source planning. This approach is chosen to enhance the reaction capabilities of decentralized warehouse scheduling management in a dynamic environment. The simulation outcome has been performed, and the suggested method realizes a high product delivery ratio (96.5%), operational cost (94.9%), demand prediction ratio (96.5%), accuracy ratio (98.4%), and performance ratio (97.2%).