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This paper deals with the modalities of the Smart Automated Health Machine (AHM) using Internet of Things (IoT), a user-friendly health machine with an interactive graphical user interface for medical necessities. It is a virtual health check-up/self-screening/test system, aimed at being the first point of contact for patient screening, to monitor heart rate, blood pressure, ECG, oxygen saturation, and visual acuity of patients. In case of emergency, a doctor will be available online through a video call, based on the severity of patients' conditions, a call can be placed by a doctor to book an ambulance van or ambulance bike (based on the conditions). If a patient is unconscious, a direct call can be placed by any bystander or patient's carer to book an ambulance. In non-emergency cases, medical appointments can be booked -the system will also dispense medicines based on the health conditions and/or doctor's electronic prescription which can be selected among the list of available medicines. Each individual will be provided with a "smart health card", which will keep a record of patients' personal details, health conditions, prescriptions, and amount of tablets consumed. The AHM will provide all the necessary information, keep a record of different medical problems in different regions and different necessities required to be adopted. The system can also be called as "Self Healthcare Service (SHS)" or "Self Healthcare Units (SHU)"the initiatives are part of sustainable development goals (SDG-3) proposed by United Nations.We conducted a pilot evaluation with the patients, nurses and doctors. As an overall outcome, the practitioners feel the system can be adopted in an area where medical facility is not available immediately. Adopting this system in such regions not only help in medical emergencies/epidemic/pandemic such as COVID, it also increases the percentage of survival. The system can also be adopted in hospitals, pharmacies, schools, public places like bus/train stations, airports, markets or any commercial areas, zones prone to accidents in rural and urban areas and other places that can be quite useful and helpful to the public.

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system.