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In a wireless Industrial Internet of Things (IIoT) network, enforcing security is a challenge due to the large number of devices forming the network and their limited computation capabilities. Furthermore, different security attacks require specifically tailored security protocols to prevent their occurrence. As an alternative to these conventional centralized security protocols, the application of Blockchain (BC) and Deep learning (DL) for securing IIoT networks hold great potential. BC facilitates security by being an immutable record of the changes happening in a network. Coalition Formation theory aids decentralization and promotes energy efficiency. And to enforce a state‐of‐the‐art attack detection technique, Deep learning provides an adaptive and reliable platform. Thus, in this paper, a security framework that facilitates generalized security for the IIoT network using BC and Coalition Formation theory is proposed. Additionally, we promote a sophisticated deep learning‐based classification algorithm to efficiently classify malicious and benign devices in IIoT scenarios. In the proposed model, connection links can only be established if the details of the connection are mined on the BC by the “sender” device. Therefore, we propose a Proof of Reliance algorithm that dynamically increases the computational difficulty to prevent malicious devices from attacking the network. Through simulations, it is experimentally proven that malicious devices can never attack the network when the proposed framework is employed for IIoT security.

Social opportunistic networks are a subclass of opportunistic networks which rely upon the predictability and established patterns of human social behaviour to facilitate the sharing of information by performing message routing. While the forwarding step purely depends upon the movement paradigm of nodes in the network, the said movement makes the network unreliable due to frequent disconnections and delay, and spasmodically connected environment. Various protocols have been developed so far for routing in such networks, whose primary aim is to ensure efficient and reliable message delivery. This study proposes a heuristic-based scheme for routing messages from the source to the destination using the Ant Algorithm. Problems related to frequent disconnections and inefficient routing are overcome in the proposed protocol through features such as pheromones. This protocol is thoroughly examined via simulation and analysis to assess the performance with other routing protocols in social opportunistic networks under various parameters. The performance criterion for the comparisons includes overhead ratio, average residual energy, average latency, number of dead nodes and average buffer time. The examinations have shown that the authors algorithm is superior to protocols such as Prophet, Epidemic and ProWait on the basis of average latency and buffer time.

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