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Approximately one-third of human food is derived from flowering plants, and 80% of those plants require honeybees for pollination. However, climate change is affecting bees as well as the beekeeping industry. Having continuous information on the condition of honey bee colonies would be helpful in studying new diseases such as colony collapse disorder, as well as in developing novel beekeeping tools to improve the health management of bee colonies. In this study, an audio monitoring system is established based on Internet of Thing (IoT) technology to record the sounds produced by bees. A colony status classification model coupled with a machine learning algorithm is used to analyze the sound data of bee colonies under various treatments to classify the colony conditions, such as queenless, virus-infected, and pesticide-contaminated. With an edge computing framework, the classification is done by the monitoring system, which greatly reduces data transmission delay and cloud computing burden. The field test results show that the accuracy and F 1 -score for the classification model for the beehive under various treatments reach 98.8% and 0.98, respectively. The proposed IoT-based audio monitoring system for beehives can assist beekeepers and users in managing bee colonies more effectively by providing automatic, accurate, and real-time monitoring information regarding the current colony conditions. [ABSTRACT FROM AUTHOR]

Recently, the progression in Internet of Thing (IoT) has extensively influenced the model of wireless networking. As numerous wireless devices were emerged, the new Medium Access Control (MAC) and routing protocols has been introduced for assuring the end-to-end network efficiency. This paper aims to focus on the IEEE 802.15.4 MAC standards, where the security field is included in MAC header. The sensitive data security authentication on this MAC header is completely based on the method with the introduction of optimum authentication key using partially homomorphic encryption named ElGamal public key cryptosystem. More importantly, in this work, the ElGamal public key cryptosystem for securing IoT data is progressed by generating the optimal private key as well. For this optimal selection of key, this paper aims to establish a new hybrid optimization algorithm that hybrids the concept of Lion Algorithm (LA) and Cuckoo Search Algorithm (CS) named Cuckoo Mated-Lion Algorithm (CM-LA). Finally, the performance of the proposed working strategy is compared over other state-of-the-arts models by concerning various attacks, convergence and key sensitivity analysis, respectively. [ABSTRACT FROM AUTHOR]

Development of Internet of Thing requires the high efficiency indoor energy harvesting solution using photovoltaic cells. This study presents the experimental investigation of the power performance of the solar harvester using crystalline silicon (c-Si) and Cu(In, Ga)Se 2 (CIGS) photovoltaic cells. Experimental studies include the optical environment setting, indoor source selection, the light source calibration and power measurements. The measured efficiencies of c-Si and CIGS cells are in the range of 1.5%–7.4% with strong source-dependency. Efficiencies of power conversion module varies in the range of 65%–75%. Furthermore, the efficiencies of solar cells and modules vary with the change of level of illuminance. Correction factors of cell efficiency and module efficiency are derived from experimental data. In general, c-Si and CIGS cells showed acceptable performance under wideband light source. However, their power outputs are limited under the various narrowband indoor light sources. Future development of the indoor-used photovoltaic cells may focus on the narrowband indoor light applications. • Both c-Si and CIGS cells showed limited performance for narrowband sources. • Indoor cell efficiencies are between 1.5 and 7.4% that are lower than outdoor values. • Power conversion efficiency of harvester is within 65%–75% under indoor sources. • Power of 15.6 cm by 15.6 cm cell c-Si harvester is ranged from 1.0 mW to 75.0 mW. • New cell technologies are required for high power output of narrowband sources. [ABSTRACT FROM AUTHOR]

The 5th industrial revolution is characterized by an extensive interconnection of embedded devices, which offer a range of services, including the monitoring of their environments. Images captured from remote cameras require enhancements for effective analysis. Despite recent progress in single-image super-resolution techniques by yielding impressive results through deep convolutional neural networks, the complexity of these advanced models renders them impractical for use on miniaturized Internet of Things (IoT) devices, primarily due to their limited computational capabilities and memory constraints. Furthermore, the rapid evolution of IoT devices necessitates efficient image super-resolution techniques, while existing advanced methods, based on deep convolutional neural networks, are too resource-intensive for these devices, and this gap highlights the need for a more suitable solution. In this study, we introduce a lightweight, efficient super-resolution model specifically designed for IoT devices. This model incorporates a novel deep residual feature distillation block (DRFDB), which leverages a depthwise-separable convolution block (DCB) for effective feature extraction. The focus is on reducing computational and memory demands without compromising on image quality. The proposed DCB extracts coarse features from given input features as calculation units, using two operations, depthwise and pointwise convolutions. These two operations are able to significantly reduce the number of parameters and floating-point operations while maintaining a PSNR value higher than the 90% threshold. We modify the proposed DCB and introduce a multi-kernel depthwise-separable convolution block (MKDCB) to fine-tune the model. The experiments, conduct on various standard datasets such as DIV2K, Set5, Set14, Urban100, and Manga109 by demonstrating that our model significantly outperforms existing methods in terms of both image quality and computational efficiency. The model shows improved performance metrics like PSNR, while requiring fewer parameters and less memory usage, making it highly suitable for IoT applications. This study presents a breakthrough in super-resolution for IoT devices, balancing high-quality image reconstruction with the limited resources of these devices. [ABSTRACT FROM AUTHOR]

Crowd counting in specific places has recently been considered as a significant contribution in many applications in terms of security and economic values. Recently, the Kingdom of Saudi Arabia has considered new ways and methods to diversify sources of income, where many non-traditional establishments in several fields have been initiated and put in place. However, controlling the number of visitors and participants to events and exhibitions has always been a challenge, as it has always been considered as an important success factor to any event. Smart public places approach is one of the inevitable directions of development in Saudi Arabia, where security, comfort, and safety of crowds is to be controlled and managed using machine learning techniques, more specifically, IoT-based crowd counting techniques. Such a technology will not only help in resolving security and safety problems, but also will play a significant role in reducing waiting time for visitors, by giving indicators, projections and advices on crowded places. In this paper, a mobile-based model is proposed for counting people in high and low crowded public places in Saudi Arabia under various scene conditions with no prior knowledge. The proposed model is built based on pre-trained convolutional neural network (CNN) called VGG-16 with some modifications on the last layer of the CNN to increase the efficiency of the training model. In addition to the improvement of efficiency, the proposed method accepts images of arbitrary sizes/scales as inputs. The applicability of the proposed method has been evaluated by incorporating IoT architecture, where surveillance cameras to be connected to the Internet to capture live pictures of different public places. To achieve this goal, New and special Saudi people dataset as well as some other existing dataset, have been produced and used to train the network. The result shows a significant improvement to the efficiency of the DCNN over the existing counting networks. [ABSTRACT FROM AUTHOR]

A Microgrid (MG) is an independent grid with both load and its own generation that has the option to be connected to the grid or can be islanded. Intelligent monitoring and control of MG make it as a building block for smart grid technology. With automation and distributed control, load and generation management can be done remotely. We are currently running a campus MG with high efficiency combined heat and power (CHP) generation and renewable energy sources. The space heating is achieved by the recaptured unused heat from CHP generators. The rotatory loads connected to the MG is monitored by the smart wireless enabled sensors that can be accessed by using the internet. A distributed control system (DCS) from our industry partner ABB Inc. (800- XA system) is providing remote control and protection of MGs critical components using multiple industry standard communication protocol (Modbus, Profibus etc.). The study shows that the controlling of water flow to the CHP generator by using remote automation technology provide higher thermal recovery for space heating. Also the smart monitoring and distributed control provide energy savings and warning for preventive maintenance [ABSTRACT FROM AUTHOR]

The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission. [ABSTRACT FROM AUTHOR]

A monitoring platform is designed, set up and used to store the measured indoor climate data such a temperature and humidity from an air-conditioned classroom to the purpose of thermal comfort evaluation. In addition, the measured current using smart current sensors at the power cables, which supply the air conditioning and ventilation system, was transmitted to the monitoring platform and calculated by soft code for the energy consumption when the air conditioner is switched on. The detail of energy and indoor climate monitoring system can be found in previous publication as well as the monitoring platform. In this paper, the data stored in the monitoring platform are used for the analysis of temperature and humidity distribution in the air conditioned room as well as the thermal comfort based on the predicted mean vote, PMV, with various occupant clothes simulated criteria. Temperature and humidity sensors are installed at various positions around the room provided 24/7 climate monitoring. In order to illustrate the thermal comfort, the comfort index is indicated by the calculation of the predicted mean vote that ranges from -3 to +3 where the zero meant neutral. The results show that the occupants wore long sleeve shirt with jacket and trouser for male, and short sleeve shirt with jacket and full-length skirt for female would reach the thermal comfort at lower temperature than others. In other words, the waste energy could be reduced simply by setting up higher cold temperature cooperated with switching off the air conditioner sometime before the end of the last class of the day, according to the indoor temperature curve analysis, which could save energy for roughly 18%. [ABSTRACT FROM AUTHOR]

In this paper, we have developed a wearable cuffless device for daily blood pressure (BP) measurement. We incorporated the light based sensor and other hard wares in a small volume for BP detection. With optimized algorithm, the real-time BP reading could be achieved, the data could be presented in the screen and be transmitted by internet of things (IoT) for history data comparison and multi-terminal viewing. Thus, further analysis provides the probability for diet or sports suggestion and alarm. We have measured BP from more than 60 subjects, compare to traditional mercury blood pressure meter, no obvious error in both systolic blood pressure (SBP) and diastolic blood pressure (DBP) are detected. Such device can be used for continues non-invasion BP detection, and further data docking and health analysis could be achieved. [ABSTRACT FROM AUTHOR]

Flash Flood is a natural disaster that floods away large area in a sudden manner. Bangladesh is affected by this natural disaster and loses valuable assets regularly. We currently have developed a system that has cloud infrastructure for intelligent long term data processing and low cost sensing devices that are able to make local decisions in its surrounding area. The system is adopted to work in a developing country such as low cost sensing that is locally available along with ability to adapt to sparse network infrastructure. Our system is called Shonabondhu: Golden Friend in Bengali, and we share our design, development and evaluation cycle of this system. [ABSTRACT FROM AUTHOR]

Despite the rapid development of the Internet of Things technologies where more and more medical sensors and gadgets are connected to the Internet, limited energy resources due to transmission, and security are still main challenges. Most cases, patients wear multiple medical devices that transmit sensed medical data wirelessly to servers, which might cause big traffic on the communication networks, which in turn cause high energy consumption. Therefore, using the data aggregation, we can considerably reduce the energy consumption by eliminating redundant data; yet collected data must be fully protected. Secure data collection and transfer to centralized servers in healthcare applications employing IoT is quite challenging to protect against several attacks for illegal data access. For this reason, massive security measures should be taken to ensure that patients' data can only be accessed by legitimate users. This paper proposes EPPADA: Efficient Privacy‑Preserving Authentication and Data Aggregation scheme in conjunction with Homomorphic Encryption concepts to meet requirements of healthcare using IoT with green computing technologies. The main objective of this proposed scheme is to decrease the communication overhead and energy consumption while maintaining safe and secure aggregation of the healthcare data between medical sensors and cloud servers. The proposed system is experimentally developed using E-health sensor shield V2.0 platform. Based on security analysis that has the most extensive set of security features, our multi-objective approach enhances the End-to-End Delay, Computational Cost, Communication Overhead, besides maintaining security features. [ABSTRACT FROM AUTHOR]

• Both wearable and implantable PENG and TENG as energy sources are thoroughly discussed • Self-powered tactile sensors based on PENG and TENG are highlighted. • Wireless data and energy transmission for PENGs and TENGs are investigated for IoT sensory system. • PENG and TENG hybridized devices and other approaches for better sustainability of IoT networks are introduced. Nowadays, the human body is gradually digitized by various wearable and implantable electronics. The establishment of a sustainable wireless internet of thing (IoT) sensory system is urged by developing the energy harvesters and self-powered sensors based on specific scenarios. As one of the dominant energy sources from the human body, mechanical energy offers a great opportunity for piezoelectric nanogenerator (PENG) and triboelectric nanogenerator (TENG) to accomplish the tasks. Both two techniques are featured with simple conversion mechanisms and easy integration for wearable and implantable devices. In this review, starting from the fundamental principle, both wearable and implantable PENG and TENG are thoroughly discussed, with different mechanical stimuli, including force, strain, vibration, etc., and various materials, such as ceramic, polymer, textile/nanowires, etc. Furthermore, based on the outputs from PENG and TENG, the self-powered physical sensors are investigated to exploit the potential of motion recognition or physiological signals monitoring. Besides, the introduction of hybrid systems also provides an overview of the joint function of PENG and TENG for enhancing both power generation and sensing performances. For achieving the IoT sensory network, the representative researches on wireless transmission of both energy and sensing signals are investigated according to the different transmission ranges. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding the sustainable wireless sensory system specifically for the human body. [ABSTRACT FROM AUTHOR]

• Recent advancements in ICT have sped up the development of new healthcare services. • We focus on the Tele-Rehabilitation as a Service (TRaaS) concept. • Remote rehabilitation big data need to be processed in hospital Clouds. • We used the Lokomat data format as sample. • We compared the performances of four of the major NoSQL DBMS solutions. Nowadays, recent advancements in ICT have sped up the development of new services for smart cities in different application domains. One of these is definitely healthcare. In this context, remote patient monitoring and rehabilitation activities can take place either in satellite hospital centres or directly in citizens' homes. Specifically, using a combination of Cloud computing, Internet of Things (IoT) and big data analytics technologies, patients with motor disabilities can be remotely assisted avoiding stressful waiting times and overcoming geographical barriers. This paper focuses on the Tele-Rehabilitation as a Service (TRaaS) concept. Such a service generates healthcare big data coming from remote rehabilitation devices used by patients that need to be processed in the hospital Cloud. Specifically, after a feasibility analysis, by using a Lokomat dataset as sample, we measured and compared the performances of four of the major NoSQL DBMS(s) demonstrating that the document approach well suits our case study. [ABSTRACT FROM AUTHOR]

The table of contents entry A sensing scheme based on internet of thing to detect O 3 at room temperature is achieved by tailoring the sensing performances of MWCNTs with non-covalent functionalization. The sensor array based on MWCNTs makes the discriminative and sensitive detection of O 3 possible within 30 s at room temperature. • Multi-walled carbon nanotubes were noncovalently functionalized to develop the chemiresistive sensor array with the tailoring responses. • The sensor array displayed the excellent endurance to humidity and the good long-term stability. • The sensor array can identify O 3 , NO 2 and other analytes within 30 s in the real-world detection. • This method may lead to a valuable application based on internet of thing for the low-cost monitoring of gaseous pollutants. Carbon nanotubes are considered as one of the leading candidate materials for the high-performance, internet of thing (IoT)-based gas sensor operating at room temperature. Multi-walled carbon nanotubes (MWCNTs) as a sensing platform were noncovalently functionalized by six kinds of functional modifiers to discriminatively detect ozone (O 3). Compared with the MWCNTs, the responses of functionalized MWCNTs to 5 ppm of O 3 were improved by about 68.8–258.3% and the highest response reached 34.4%. The maximum response of functionalized MWCNTs to 100% relative humidity was just 5.6%, displaying the excellent endurance to humidity. After two months, the decrease in the response value of the most sensitive sensor to O 3 was no more than 15%, showing the good long-term stability. Furthermore, the sensor array was optimized according to principal component analysis and achieved the discriminative detection of six analytes in 30 s at room temperature. For the most sensitive sensor of the sensor array, the theoretical limit of detection for O 3 is determined to be 24.2 ppb. Importantly, it could be coupled with the image recognition and cloud computing for a rapid diagnostic detection of gaseous pollutants under the background of the rapid development of IoT. [ABSTRACT FROM AUTHOR]