Table 2.

The fog-IoHT proposals in period [2019–2021]

Ref	Year	Approach	Purpose	Method	Accuracy	Delay	Energy	Security	Results
[39]	2019	Performance	Fog-IoHT to monitoring and diagnose the hypertension	Use ANN predict the hypertension attack and making-decision based on fog computing	Yes	Yes	No	No	Improve performance, and response time
[40]	2019	Security	Privacy ensured e-healthcare framework to secure patient information records	Use novel consensus-based access control method to ensure the reliability of the requester to EMR	No	Yes	No	Yes	Improve privacy of patient information record, and response time
[41]	2020	Security	Fog-IoHT to enhance efficiency and security	Use user authentication method through identity and elliptic curve cryptography technique to prevent security breaches	No	Yes	No	Yes	Improve performance and enhance security
[42]	2020	Performance	Fog-IoHT to reduced patient data retrieval time	Use the in-network caching of NDN and request aggregation of the content-centric networking	No	Yes	Yes	No	Reduce 28.5% patient data retrieval time
[43]	2020	Performance	Fog-IoHT architecture to saving and optimise energy consumption	Use the MILP method to optimize the number and location of fog devices at the network edge	No	Yes	Yes	No	Saving energy up to [36–52]% compared to Cloud-IoHT systems
[44]	2020	Performance	Fog-IoHT analyzes and identifies heart diseases automatically	Integrates deep learning with fog computing	No	Yes	No	No	Improve the execution time up to [10–43]% under different scenarios
[45]	2021	Offloading	Fog-IoHT offloading schema to optimal offload plan	Use the MSSP method to minimizing the total latency of offloading as well as consider how offloading perform	No	Yes	Yes	No	This solution can rapidly give to the approximate optimal results
[46]	2021	Security	Blockchain-fog-IoHT to improve privacy, security, and diagnostic accuracy diabetic and cardio disease	Use a blockchain to store patient health information and a neuro-fuzzy inference system to making-decision	Yes	Yes	No	Yes	Improve privacy and security; enhance diagnostic accuracy up to 81%
[47]	2021	Offloading	Fog-IoT to enhance performance the diagnosis and treatment of infected patients Coronavirus	Use non-linear and non-convex approaches to solve the suboptimal low-complexity problems	No	No	Yes	No	Reduce computing cost and response time
[48]	2021	Performance	Fog-IoHT to monitoring physical status of athletes	Use the 3D-acceleration method to record the collected health data state of athletes for workout activity	Yes	No	No	No	Improve health of athletes; provide timely warnings; gym activity recognition
[49]	2021	Offloading	Fog-IoHT to adaptive for large-scale healthcare applications	Use load balancing method to offloading among fog servers nodes	No	Yes	No	No	Improve performance compared to the Cloud systems
[50]	2021	Security	Fog-IoHT based on symmetric homomorphic cryptosystem to secure patient information	Use an symmetric homomorphic cryptosystem to privacy assured medical data aggregation	No	Yes	No	Yes	Improve privacy and security
[51]	2021	Performance	Fog-IoHT to reduce the delay, bandwidth and energy, enhance the reliability of system	Use a MOCSA algorithm to solve the multi‑objective optimization problem of both energy and latency	No	Yes	Yes	No	Improve the latency and saving energy up to [5–42]% and [28–43]%, respectively compared to several existing solutions