Over the last decade, the Internet of Things (IoT) domain has grown dramatically, from ultra-low-power hardware design to cloud-based solutions, and now, with the rise of 5G technology, a new horizon for edge computing on IoT devices will be introduced. A wide range of communication technologies has steadily evolved in recent years, representing a diverse range of domain areas and communication specifications. Because of the heterogeneity of technology and interconnectivity, the true realisation of the IoT ecosystem is currently hampered by multiple dynamic integration challenges. In this context, several emerging IoT domains necessitate a complete re-modeling, design, and standardisation from the ground up in order to achieve seamless IoT ecosystem integration. The Internet of Nano-Things (IoNT), Internet of Space-Things (IoST), Internet of Underwater-Things (IoUT) and Social Internet of Things (SIoT) are investigated in this paper with a broad future scope based on their integration and ability to source other IoT domains by highlighting their application domains, state-of-the-art research, and open challenges. To the best of our knowledge, there is little or no information on the current state of these ecosystems, which is the motivating factor behind this article. Finally, the paper summarises the integration of these ecosystems with current IoT domains and suggests future directions for overcoming the challenges.
Wireless sensor networks (WSN) are commonly used in remote environments for monitoring and sensing. These devices are typically powered by batteries, the performance of which varies depending on environmental (such as temperature and humidity) as well as operational conditions (discharge rate and state-of-charge, SOC). As a result, assessing their technical viability for WSN applications requires performance evaluation based on the aforementioned stimuli. This paper proposes an efficient method for examining battery performance parameters such as capacity, open-circuit voltage (OCV) and SOC. Four battery types (lithium-ion, lithium-polymer, nickel-metal hydride and alkaline) were subjected to IEEE 802.15.4 protocol-based discharge rates to record the discharge characteristics. Furthermore, the combined effect of discharge rates on battery surface temperature and OCV variations was investigated. Shorter relaxation times (4-8 h) were observed in lithium-based batteries, resulting in faster energy recovery while maintaining rated capacity. It was observed that nearly 80% of the voltage region was flat, with minor voltage variations during the discharge cycle. Furthermore, lithium-based batteries experienced negligible changes in surface temperatures (approx. 0.03°C) with respect to discharge rates, making them the best battery choice for low-power applications such as WSNs.
Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.
In a Battery Management System (BMS), cell balancing plays an essential role in mitigating inconsistencies of state of charge (SoCs) in lithium-ion (Li-ion) cells in a battery stack. If the cells are not properly balanced, the weakest Li-ion cell will always be the one limiting the usable capacity of battery pack. Different cell balancing strategies have been proposed to balance the non-uniform SoC of cells in serially connected string. However, balancing efficiency and slow SoC convergence remain key issues in cell balancing methods. Aiming to alleviate these challenges, in this paper, a hybrid duty cycle balancing (H-DCB) technique is proposed, which combines the duty cycle balancing (DCB) and cell-to-pack (CTP) balancing methods. The integration of an H-bridge circuit is introduced to bypass the selected cells and enhance the controlling as well as monitoring of individual cell. Subsequently, a DC-DC converter is utilized to perform CTP balancing in the H-DCB topology, efficiently transferring energy from the selected cell to/from the battery pack, resulting in a reduction in balancing time. To verify the effectiveness of the proposed method, the battery pack of 96 series-connected cells evenly distributed in ten modules is designed in MATLAB/Simulink software for both charging and discharging operation, and the results show that the proposed H-DCB method has a faster equalization speed 6.0 h as compared to the conventional DCB method 9.2 h during charging phase. Additionally, a pack of four Li-ion cells connected in series is used in the experiment setup for the validation of the proposed H-DCB method during discharging operation. The results of the hardware experiment indicate that the SoC convergence is achieved at ~ 400 s.
The Internet of Things (IoT) is an extensive network of heterogeneous devices that provides an array of innovative applications and services. IoT networks enable the integration of data and services to seamlessly interconnect the cyber and physical systems. However, the heterogeneity of devices, underlying technologies and lack of standardization pose critical challenges in this domain. On account of these challenges, this research article aims to provide a comprehensive overview of the enabling technologies and standards that build up the IoT technology stack. First, a layered architecture approach is presented where the state-of-the-art research and open challenges are discussed at every layer. Next, this research article focuses on the role of middleware platforms in IoT application development and integration. Furthermore, this article addresses the open challenges and provides comprehensive steps towards IoT stack optimization. Finally, the interfacing of Fog/Edge Networks to IoT technology stack is thoroughly investigated by discussing the current research and open challenges in this domain. The main scope of this study is to provide a comprehensive review into IoT technology (the horizontal fabric), the associated middleware and networks required to build future proof applications (the vertical markets).