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Internet of Things (IoT) is rapidly growing and contributing drastically to improve the quality of life. Immense technological innovations and growth is a key factor in IoT advancements. Readily available low cost IoT hardware is essential for continuous adaptation of IoT. Advancements in IoT Operating System (OS) to support these newly developed IoT hardware along with the recent standards and techniques for all the communication layers are the way forward. The variety of IoT OS availability demands to support interoperability that requires to follow standard set of rules for development and protocol functionalities to support heterogeneous deployment scenarios. IoT requires to be intelligent to self-adapt according to the network conditions. In this paper, we present brief overview of different IoT OSs, supported hardware, and future research directions. Therein, we provide overview of the accepted papers in our Special Issue on IoT OS management: opportunities, challenges, and solution. Finally, we conclude the manuscript.

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Due to the limited availability of battery power of the acoustic node, an efficient utilization is desired. Additionally, the aquatic environment is harsh; therefore, the battery cannot be replaced, which leaves the network prone to sudden failures. Thus, an efficient node battery dissipation is required to prolong the network lifespan and optimize the available resources. In this paper, we propose four schemes: Adaptive transmission range in WDFAD-Depth-Based Routing (DBR) (A-DBR), Cluster-based WDFAD-DBR (C-DBR), Backward transmission-based WDFAD-DBR (B-DBR) and Collision Avoidance-based WDFAD-DBR (CA-DBR) for Internet of Things-enabled Underwater Wireless Sensor Networks (IoT, UWSNs). A-DBR adaptively adjusts its transmission range to avoid the void node for forwarding data packets at the sink, while C-DBR minimizes end-to-end delay along with energy consumption by making small clusters of nodes gather data. In continuous transmission range adjustment, energy consumption increases exponentially; thus, in B-DBR, a fall back recovery mechanism is used to find an alternative route to deliver the data packet at the destination node with minimal energy dissipation; whereas, CA-DBR uses a fall back mechanism along with the selection of the potential node that has the minimum number of neighbors to minimize collision on the acoustic channel. Simulation results show that our schemes outperform the baseline solution in terms of average packet delivery ratio, energy tax, end-to-end delay and accumulated propagation distance.