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Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, the benefits of Blockchain are far beyond that. It has been recently used to support and augment many other technologies, including the Internet-of-Things (IoT). IoT, with the help of Blockchain, paves the way for futuristic smart environments, like smart homes, smart transportation, smart energy trading, smart industries, smart supply chains, and more. To enable these smart environments, IoT devices, machines, appliances, and vehicles, will need to intercommunicate without the need for a centralized trusted party. Blockchain can replace third trusted parties by providing secure means of decentralization in such trustless environments. They also provide security enforcement, privacy assurance, authentication, and other important features to IoT ecosystems. Besides the benefits of Blockchain-IoT integration for smart environments, other technologies also have important features and benefits that attracted the research community. Software-Defined Networking (SDN), Fog, Edge, and Cloud Computing technologies, for example, play an important role in enabling realistic IoT applications. Moreover, the integration of Machine Learning and Artificial Intelligence (AI) algorithms provides smart, dynamic, and autonomous decisionmaking capabilities for IoT devices in smart environments. To push the research further in this domain, we provide in this paper a comprehensive survey that includes state-of-the-art technological integration, challenges, and solutions for smart environments, and the role of Blockchain and IoT technologies as the building blocks of such smart environments. We also demonstrate how the level of integration between these technologies has increased over the years, which brings us closer to the futuristic view of smart environments. We further discuss the current need to provide general-purpose Blockchain platforms that can adapt to different design requirements of different applications and solutions. Finally, we provide a simplified architecture of futuristic smart environments that integrates all these technologies, showing the advantage of such integration.

As the movement for a vast implementation of IoT networks is rapidly accelerating, so many researchers are working to analyze the performance of RPL, the widely-used routing protocol for wireless sensor networks. The analysis usually involves a small number of metrics studied under a limited number of scenarios. In this paper however, we provide a comprehensive study for the performance of the two objective functions used in RPL; MRHOF and OF0, using the Cooja simulator in Contiki operating system. Using static-grid and mobile-random topologies with 25, 49, and 81 sender nodes including one sink node. Each topology was tested with three transmission ranges of 11, 20, and 50 meters to simulate sparse, moderate and dense networks. The selected metrics are convergence time, changes in DoDAG tree structures, average churn in the network, Average Power Consumption, Average Listen Duty Cycle, Average Transmit Duty Cycle, Average received packets, average lost packets, average duplicate packets, and average hop count. In fixed networks, the results show that OF0 usually perform better than MRHOF in terms of Energy Consumption, Convergence Time in the Static-Grid Topology, Listen Duty Cycle, and Transmit Duty Cycle.

Fog Computing has emerged as a solution to support the growing demands of real-time Internet of Things (IoT) applications, which require high availability of these distributed services. Intelligent workload distribution algorithms are needed to maximize the utilization of such Fog resources while minimizing the time required to process these workloads. These load balancing algorithms are critical in dynamic environments with heterogeneous resources and workload requirements along with unpredictable traffic demands. In this paper, load balancing is provided using a Reinforcement Learning (RL) algorithm, which optimizes the system performance by minimizing the waiting delay of IoT workloads. Unlike previous studies, the proposed solution does not require load and resource information from Fog nodes, which makes the algorithm dynamically adaptable to possible environment changes over time. This also makes the algorithm aware of the privacy requirements of Fog service providers, who might like to hide such information to prevent competing providers from calculating better pricing strategies. The proposed algorithm is interactively evaluated on a Discreteevent Simulator (DES) to mimic a practical deployment of the solution in real environments. In addition, we evaluate the algorithm's generalization ability on simulations longer than what it was trained on, which, to the best of our knowledge, has never been explored before. The results provided in this paper show how our proposed approach outperforms baseline load balancing methods under different workload generation rates.