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Fifth generation (5G), the next generation telecommunications will be striking the markets in near future. Device‐to‐device (D2D) communication would be a part of 5G to serve communication needs for billions of connected devices to support high data rate ultrareliable low latency communications. Indoor 5G will be relying on distributed small cell solutions and D2D along with machine‐to‐machine connections. Machine learning is one of the most promising tools for providing the best set of solutions to learn the influential scenarios and certain parameters of the communication networks. This research proposes reinforcement‐learning‐based latency controlled D2D connectivity (RL‐LCDC) algorithm and its Q‐learning approach in an indoor D2D communication network for strong 5G connectivity with minimum latency. The proposed approach, RL‐LCDC efficiently discovers the neighbors, decides the D2D link, and adaptively controls the communication range for maximum network connectivity. The results show that RL‐LCDC optimizes the connectivity with lower end‐to‐end delay and better energy efficiency with efficient convergence time when compared with other conventional schemes.

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In this paper, we apply a Vector AutoRegression (VAR) based trust model over the Backpressure Collection Protocol (BCP), a collection mechanism based on dynamic backpressure routing in Wireless Sensor Networks (WSN) and show that the VAR trust model is suited for resource constraint networks. The backpressure scheduling is known for being throughput-optimal. However, it is usually assumed that nodes cooperate with each other to forward the network traffic. In the presence of malicious nodes, the throughput optimality no longer holds and this affects the network performance in collection tree applications of sensor networks. We apply an autoregression based scheme to embed trust into the link weights, making it more likely for trusted links to be scheduled. The novelty in our approach is that the notion of trust can be easily incorporated in a new state of the art distributed and dynamic routing Backpressure Collection Protocol in sensor networks. We have evaluated our work in a real sensor network testbed and shown that by carefully setting the trust parameters, substantial benefit in terms of throughput can be obtained with minimal overheads. Our performance analysis of VAR in comparison with other existing trust models demonstrate that even when 50% of network nodes are malicious, VAR trust offers approximately 73% throughput and ensures reliable routing, with a small trade-off in the end-to-end packet delay and energy consumptions.

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