Abstract
With the rapid increase in number of sensing devices for healthcare, researchers are getting growing interest due to wide application support and new challenging scenarios. Internet of Things (IoT) comprises of huge number of connected devices with a variety of sensing support especially in medical health parameters sensing. In these scenarios, secure data collection and transmission to centralized servers is quite challenging to protect against several attacks for illegal data access. Existing solutions suffer from storage, communication and energy overheads. To resolve these issues, this paper presents a FoG assisted scheme for healthcare data aggregation in a secure and efficient manner.. We have involved the peer-to-peer communication between healthcare sensing devices and wearables to share secret data with an aggregating node that can share data with FoG server. In this scenario, an aggregator may be away from FoG server and cannot transmit data directly. However, it can share the encrypted data with the neighboring aggregator to transmit data to FoG server by appending in its current aggregated data. FoG server can extract the required values from the data and can save in the local repository that can be further updated later in cloud repositories. For these functionalities, we have presented two algorithms for message receiving at aggregator and message extraction at FoG server. Moreover, compression mechanism is also presented to further reduce the communication costs. We have performed simulations using TCL and C files in NS2.35 to generate trace files and then executed AWK scripts to extract results. Results prove the supremacy of proposed scheme over existing schemes in terms of storage, communication, transmission ratio, energy consumption and resilience.
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This work was funded by the National Natural Science Foundation of China for Grant 61471035 and 61774014
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Ullah, A., Said, G., Sher, M. et al. Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN. Peer-to-Peer Netw. Appl. 13, 163–174 (2020). https://doi.org/10.1007/s12083-019-00745-z
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DOI: https://doi.org/10.1007/s12083-019-00745-z