Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

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The performance of the envisioned 6G network is fundamentally constrained by the uncontrollable and random wireless communication channel. Intelligent reflecting surfaces (IRSs) have emerged as one of the potential solutions to overcome this challenge by smartly controlling the incident signal to enhance the energy efficiency and spectrum efficiency of the 6G network. In addition, the future 6G network will incorporate several enabling technologies, including artificial intelligence and machine learning (AI/ML), integrated terrestrial and non-terrestrial (TNT) networks, multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), and terahertz/millimeter wave (THz/mmWave) communication techniques. Therefore, this paper provides a contemporary and comprehensive overview of the envisioned IRS-empowered 6G networks from the perspective of its architecture, deployment strategy, integration of IRS technology with other 6G-enabling technologies, and physical layer security (PLS). Finally, we highlight design challenges and future research directions aimed at improving 6G network performance.INDEX TERMS Intelligent reflecting surfaces, Terahertz communication, Terrestrial and non-terrestrial (TNT) networks, Unmanned Aerial Vehicles, Reinforcement learning, Ultra-reliable and Low-latency communications.

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A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and intelligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infrastructure. With the deployment of the Internet of Things (IoT), smart cities' infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renewable energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges.

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