Internet of Things (IoT) is a perfect candidate to realize efficient observation and management for Smart City concept. This requires deployment of large number of wireless devices. However, replenishing batteries of thousands, maybe millions of devices may be hard or even impossible. In order to solve this problem, Internet of Energy Harvesting Things (IoEHT) is proposed. Although the first studies on IoEHT focused on energy harvesting (EH) as an auxiliary power provisioning method, now completely battery-free and self-sufficient systems are envisioned. Taking advantage of diverse sources that the concept of Smart City offers helps us to fully appreciate the capacity of EH. In this way, we address the primary shortcomings of IoEHT; availability, unreliability and insufficiency by the Internet of Hybrid Energy Harvesting Things (IoHEHT). In this work, we survey the various EH opportunities, propose an hybrid EH system, and discuss energy and data management issues for battery-free operation. We mathematically prove advantages of hybrid EH compared to single source harvesting as well. We also point out to hardware requirements and present the open research directions for different network layers specific to IoHEHT things for Smart City concept.

Internet of Things (IoT) is envisioned to bring the Internet connection to every object/ service/process to seamlessly and efficiently observe, manage, and control pervasive systems. This necessitates the employment of wireless standalone devices in excessive numbers. However, periodic maintenance of thousands, maybe millions of batteries will add massive workload and replenishment costs to the operation. In order to alleviate this problem, we introduce a totally new energy harvesting paradigm based on utilizing ambient electric-field in the vicinity of lighting elements. A low voltage prototype is designed, constituted, and evaluated on a generic $4\times 18\text{W}$ -T8 ceiling-type fluorescent troffer. Empirical results disclose the availability of 1.5 J of energy that can be gathered in 30 min when a copper plate, i.e., the harvester, covered by a reflective dielectric is employed. The design issues to achieve the best performance attainable are addressed in both theoretical and experimental manners. The physical model of the proposed technique and an applicable circuit diagram for its execution are provided. We also point out possible application areas, and protocol stack requirements specific to our proposal to conveniently enable self-configuring IoT services, which are free from battery constraints.

The Internet of Bio-Nano Things (IoBNT) is a transformative communication framework characterized by heterogeneous networks comprising both biological entities and artificial micro/nano-scale devices, so-called Bio-Nano Things (BNTs), interfaced with conventional communication networks for enabling innovative biomedical and environmental applications. Realizing the potential of IoBNT requires the development of new and unconventional communication technologies, such as molecular communications, as well as the corresponding transceivers, bio-cyber interfacing technologies connecting the biochemical domain of IoBNT to the electromagnetic domain of conventional networks, and miniaturized energy harvesting and storage components for the continuous power supply to BNTs. Graphene and related materials (GRMs) exhibit exceptional electrical, optical, biochemical, and mechanical properties, rendering them ideal candidates for addressing the challenges posed by IoBNT. This perspective article highlights recent advancements in GRM-based device technologies that are promising for implementing the core components of IoBNT. By identifying the unique opportunities afforded by GRMs and aligning them with the practical challenges associated with IoBNT, particularly in the materials domain, our aim is to accelerate the transition of envisaged IoBNT applications from theoretical concepts to practical implementations while also uncovering new application areas for GRMs.

Humankind mimics the processes and strategies that nature has perfected and uses them as a model to address its problems. This has led to a new communication technology, molecular communication (MC), using molecules to encode, transmit, and receive information. Despite extensive research, an innate MC method found abundantly in nature—olfactory or odor communication—has not been thoroughly studied using information and communication technologies (ICT). Existing studies focus on digitizing this sense and developing actuators without examining odor-based information coding and MC principles, significantly limiting its application potential. Hence, cross-disciplinary research is needed to uncover the fundamentals of this unconventional communication modality from an ICT perspective. The ways of natural odor MC in nature need to be anatomized and engineered for end-to-end communication among humans and human-made things to enable several multi-sense augmented reality technologies reinforced with olfactory senses for novel applications and solutions in the Internet of Everything (IoE). This paper introduces odor-based molecular communication (OMC) and thoroughly examines olfactory systems, exploring odor communication in nature, including odor information, channels, reception, spatial perception, and cognitive functions. Additionally, a comprehensive comparison of various communication systems sets the foundation for further investigation. By highlighting OMC’s unique characteristics, advantages, and potential applications, this paper lays the groundwork for modeling end-to-end OMC channels, designing OMC transmitters and receivers, and developing innovative OMC techniques.

The integration of serverless edge computing and the Industrial Internet of Things (IIoT) has the potential to optimize industrial production. However, cold start latency is one of the main challenges in this area, resulting in resource waste. To address this issue, we propose a new machine learning-based resource management framework called MASTER which utilizes an extreme gradient boosting (XGBoost) model to predict the cold start latency for Industry 4.0 applications for performance optimization. Furthermore, we created a new cold start dataset using an IIoT scenario (i.e. predictive maintenance) to validate the proposed MASTER framework in serverless edge computing environments. We have evaluated the performance of the MASTER framework using a real-world serverless platform, Google Cloud Platform for single-step prediction (SSP) and multiple-step prediction (MSP) operations and compared it with existing frameworks that used deep deterministic policy gradient (DDPG) and long short-term memory (LSTM) models. The experimental results show that the XGBoost-based resource management framework is the most successful model in predicting cold start with mean absolute percentage error (MAPE) values of 0.23 in SSP and 0.12 in MSP. It has been also identified that the Linear Regression model (utilized in the MASTER framework) has the least computational time (0.03 seconds) as compared to other deep learning and machine learning models considered in this work. Finally, we compare the energy consumption and $\text{CO}_{2}$ emissions of all models to emphasize resource awareness.