Abstract:
The fog-assisted Internet-of-Things (IoT) is gaining
interest due to its large number of devices, which can lead to
more duplicate data transmission over the internet. This paper
proposes task distribution and secure deduplication over
Cluster-based IoT, implementing four layers: IoT Devices
Layer, Fog Layer, Cloud Layer, and Service Layer. In the IoT
devices layer, devices sense air pollutants and are
authenticated to the cloud server using Edwards Curve-based
Elliptic Curve Cryptography (EC-ECC). Adaptive Rewards
Optimized Deep Reinforcement Learning (ARO-DRL) is used
for cluster-head selection at the first layer. In the fog layer,
SHA-3 is proposed for duplicate verification, and the Emperor
Penguin Optimization Algorithm is used to choose the best fog
node. Packet Scrutinization Algorithm is used in the fog node
to analyze packet features, including DDoS attack packets. A
proxy server is deployed between the cloud server and fog node
for queue modeling. In the cloud layer, a hybrid cloud
environment is used to protect organizations' data in a highly
secure manner. IoT devices are divided into sensitive and
nonsensitive devices, with sensitive data encrypted using RC6,
AES, and Fiestel encryption schemes. The overall environment
is assumed to be decentralized, with security invoked to IoT
devices to provision Quality of Service (QoS) by avoiding
attackers. Experiments were conducted and analyzed using
NS3 with Java programming, and simulation results showed
improvements in average latency, user satisfaction, network
lifetime, energy consumption, and security strength.
