Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

Designing a safe and reliable way for communicating the messages among the devices and humans forming the Opportunistic Internet of Things network (OppIoT) has been a challenge since the broadcast mode of message sharing is used. To contribute toward addressing such challenge, this paper proposes a Random Forest Classifier (RFC)‐based safe and reliable routing protocol for OppIoT (called RFCSec) which ensures space efficiency, hash‐based message integrity, and high packet delivery, simultaneously protecting the network against safety threats viz. packet collusion, hypernova, supernova, and wormhole attacks. The proposed RFCSec scheme is composed of two phases. In the first one, the RFC is trained on real data trace, and based on the output of this training, the second phase consists in classifying the encountered nodes of a given node as belonging to one of the output classes of nodes based on their past behavior in the network. This helps in proactively isolating the malicious nodes from participating in the routing process and encourages the participation of the ones with good message forwarding behavior, low packet dropping rate, high buffer availability, and a higher probability of delivering the messages in the past. Simulation results using the ONE simulator show that the proposed RFCSec secure routing scheme is superior to the MLProph, RLProph, and CAML routing protocols, chosen as benchmarks, in terms of legitimate packet delivery, probability of message delivery, count of dropped messages, and latency in packet delivery. The out‐of‐bag error obtained is also minimal     

An Efficient and Secure Data Forwarding Mechanism for Opportunistic IoT

Internet of Things (IoT) is a heterogeneous network of interconnected things where users, smart devices and wireless technologies, collude for providing services. It is expected that a great deal of devices will get connected to the Internet in the near future. Opportunistic networks(OppNet) are a class of disruption tolerant networks characterized by uncertain topology and intermittent connectivity between the nodes. Opportunistic Internet of Things(OppIoT) is an amalgamation of the OppNet and IoT exploiting the communication between the IoT devices and the communities formed by humans. The data is exposed to a wide unfamiliar audience and the message delivery is dependent on the residual battery of the node, as most of the energy is spent on node discovery and message transmission. In such a scenario where a huge number of devices are accommodated, a scalable, adaptable, inter-operable, energy-efficient and secure network architecture is required. This paper proposes a novel defense mechanism against black hole and packet fabrication attacks for OppIoT, GFRSA, A Green Forwarding ratio and RSA (Rivest, Shamir and Adleman) based secure routing protocol. The selection of the next hop is based on node’s forwarding behavior, current energy level and its predicted message delivery probability. For further enhancing the security provided by the protocol, the messages are encrypted using asymmetric cryptography before transmission. Simulations performed using opportunistic network environment (ONE) simulator convey that GFRSA provides message security, saves energy and outperforms the existing protocols, LPRF-MC (Location Prediction-based Forwarding for Routing using Markov Chain) and RSASec (Asymmetric RSA-based security approach) in terms of correct packet delivery by 27.37%, message delivery probability is higher by 34.51%, number of messages dropped are reduced by 15.17% and the residual node energy is higher by 14.08%.