To reach the goal of sustainable agriculture, smart farming is taking advantage of the Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) paradigm. These smart farms are designed to be run by interconnected devices and vehicles. Some enormous potentials can be achieved by the integration of different IoT technologies to achieve automated operations with minimum supervision. This paper outlines some major applications of IoT and UAV in smart farming, explores the communication technologies, network functionalities and connectivity requirements for Smart farming. The connectivity limitations of smart agriculture and it’s solutions are analysed with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming. While in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas of Australia. Finally, we conclude the paper by identifying future research challenges on this topic and outlining directions to address those challenges.

IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders.

The growing popularity of electric vehicles (EV) is creating an increasing burden on the power grid in Bangladesh due to massive energy consumption. Due to this uptake of variable energy consumption, environmental concerns, and scarcity of energy lead to investigate alternative energy resources that are readily available and environment friendly. Bangladesh has enormous potential in the field of renewable resources, such as biogas and biomass. Therefore, this paper proposes a design of a 20 kW electric vehicle charging station (EVCS) using biogas resources. A comprehensive viability analysis is also presented for the proposed EVCS from technological, economic, and environmental viewpoints using the HOMER (Hybrid Optimization of Multiple Energy Resources) model. The viability result shows that with the capacity of 15–20 EVs per day, the proposed EVCS will save monthly $16.31 and $29.46, respectively, for easy bike and auto-rickshaw type electric vehicles in Bangladesh compare to grid electricity charging. Furthermore, the proposed charging station can reduce 65.61% of CO₂ emissions than a grid-based charging station.

Chipless radio frequency identification (RFID) sensors have enormous potential for underground mining applications since this technology has the potential to meet all the requirements, such as wireless communication, maintenance-free, long lifespan, and battery-free, required to deploy in harsh underground environments. This paper has analysed existing frequency domain chipless RFID sensor technologies to understand suitability for potential applications, such as environmental condition monitoring, support system stability detection, and health monitoring of mining structures in underground mining sites. In light of the analysis, the paper has presented a new classification based on sensing materials and intrinsic sensing parameters such as effective length, coupling, conductivity and permittivity. Furthermore, the analysis has considered accuracy, performance and readability using 3D Electromagnetic (EM) analysis software, CST Studio Suite. Finally, based on our critical analysis and experimental simulations, the paper has identified challenges that need to be addressed by future research for chipless RFID sensors in the underground mining application domain.

Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs).

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols.