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Wireless sensor nodes (WSNs) are widely used in implementation of the new concepts such as the Internet of Things (IoT) and smart homes and societies. These WSNs use batteries as the sole power source which has numerous problems such as limited shelf life, require charging points, deep discharge, and sensitivity to temperature, and require periodic replacement and recharging. Harvesting ambient energies such as hydrokinetic, vibration, and thermal from the surrounding of WSNs provide a promising solution to the power‐related problems. This research illustrates the design, development, and testing of a microplanar coil (MPC)‐based hydrokinetic‐based electromagnetic‐type energy harvester for WSNs applications. The developed MPC‐based hydrokinetic energy harvester (H‐EH) utilizes the flow of water in domestic pipelines such as in the kitchen and converts it into useful electrical energy. An indigenous setup was developed to test the prototype in‐lab. The H‐EH produces 548 μW at a flow rate of 7 L/min at an optimum load resistance of 9 Ω. This work opens up new possibilities for developing MPC‐based H‐EH that can enhance the efficiency of WSNs. The research will also help to achieve self‐sustained long‐term operation of WSNs in advanced technologies such as IoTs, smart homes, and smart cities. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) is a complex system that consists of resource types such as vehicles, humans, and sensors. Although the Internet of Vehicles is complex, it improvises communication among vehicles on the roads. Quality of service (QoS) enabled the cooperative driving system (CDS) based on 5G technology, enabling vehicles to communicate and cooperate to improve road traffic efficiency. Due to the high vehicle density and limited resources (bandwidth) of current network infrastructure, sometimes a better channel that meets the requirements of cooperative driving is not available that causes network congestion, which directly influences the overall QoS of the CDS. To overcome this, we proposed a 5G network-based architecture for CDS that incorporates a D2D technology-based resource allocation scheme. The proposed network architecture and cooperative behavior-based scheme helps in improving QoS for CDS. We implemented our proposed scheme by incorporating the density-based scattered clustering algorithm with noise for vehicular clustering. The proposed scheme's performance shows significant improvement in terms of throughput compared with existing D2D approaches., Author(s): Nafeesa kalsoom [1]; Iftikhar Ahmad (corresponding author) [1,2]; Roobaea Alroobaea [3]; Muhammad Ahsan Raza [4]; Samina Khalid [1]; Zaheed Ahmed [5]; Ihsan Ali (corresponding author) [2] 1. Introduction The [...]

The date palm (Phoenix dactylifera L.) industry generates around 1 million tons of date seeds annually. These seeds contain dietary fibers, fatty acids, proteins, and other phytochemical compounds that, if extracted, could be utilized in food, medicine, cosmetics, and energy production. Extracting bioactive compounds from date seeds requires cell degradation to release the intracellular content. Enzymatic hydrolysis (EH) is a biochemical process used to release bioactive molecules stored in date seeds and subsequently produce valuable products. The aim of this review was to highlight the role of EH in the extraction of useful bioactive compounds from the date seeds. Specifically, extraction of the dietary fibers (sugars), proteins, and fatty acids (oils) from the date seeds and their subsequent conversion to bio‐fuel (i.e., ethanol and diesel) are discussed in detail. This review may provide a useful reference for the users of date products. Novelty impact statement: The seeds of the date palm (Phoenix dactylifera L.) contain dietary fiber, fatty acids, proteins, and other phytochemicals that, if extracted, could be used in food, medicine, cosmetics, and energy. The use of enzymatic hydrolysis to extract these bioactive compounds from the date seeds and the associated parameters and their optimization are discussed in this study. [ABSTRACT FROM AUTHOR]

In the last decade, the Internet of Things (IoT) has grown to connect a large number of smart entities, devices, and components. These connected entities provide a wide range of services to improve the current society of end customers. The Industrial Internet of Things (IIoTs) are revolutionary systems that have linked manufacturing processes with Internet access in order to preciously increase quality of services. These systems have minimized the costs of production through collaboration with electronic objects, accumulating computing, advanced analytics, and smart perception techniques. A demanding analysis of the strengths and limitations of computational models of IIoT is an essential part of the industry and before deciding which approach to use and implement for enhancing usability. Therefore, the goal of this study is to provide feedback and information to the research community and identify patterns in recommendations for future research in the context of process, development, and monitoring of additional technologies of computational models for IIoT. This paper has presented a comprehensive summary of the existing literature on IIoT for providing details about modern industrial revolutions in the context of IIoT. Associated materials were searched and filtered for identification of relevant materials to the proposed study. These materials have been collectively studied with in-depth analysis and then summarized to condense the information of computation models for the readers as well as entrepreneurs. The study will facilitate research community and practitioners to develop novel techniques, algorithms, and tools to automate and facilitate IIoT. This will develop the field of IIoT and will enhance its usability. [ABSTRACT FROM AUTHOR]

Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare. [ABSTRACT FROM AUTHOR]

Low‐Level Laser Therapy (LLLT) has been investigated for the treatment of various dermatological disorders. Here, we investigate the efficacy of LLLT for the treatment of cutaneous leishmaniasis (CL). This study comprised of 53 patients (total 123 lesions) with a confirmed diagnosis of CL via positive smear of LD‐bodies. The CL lesions were classified in Grade I (ie, papule of size ≤1 cm) to Grade V (ie, vesicle formation, ulceration, and superadded infection of size >4 cm). All the patients were divided into group 1 with low grade (ie, Grade I and II) CL lesions and group 2 with high‐grade disease (ie, Grade III‐V). Red laser light (wavelength = 635 nm) was used for the lesion irradiation, with a light dose of 75 J/cm2 and at a low power of 300 mW. The treatment was divided into four sessions, one session per week. Disease assessment at 10 months follow‐up revealed complete response in 91% and partial response in 9% patients of group 1, while no response was observed in patients of group 2. LLLT offers a promising treatment modality for patients presenting with early‐stage (ie, Grade I and II) CL lesions. [ABSTRACT FROM AUTHOR]

For health monitoring of bridges, wireless acceleration sensor nodes (WASNs) are normally used. In bridge environment, several forms of energy are available for operating WASNs that include wind, solar, acoustic, and vibration energy. However, only bridge vibration has the tendency to be utilized for embedded WASNs application in bridge structures. This paper reports on the recent advancements in the area of vibration energy harvesters (VEHs) utilizing bridge oscillations. The bridge vibration is narrowband (1 to 40 Hz) with low acceleration levels (0.01 to 3.8 g). For utilization of bridge vibration, electromagnetic based vibration energy harvesters (EM-VEHs) and piezoelectric based vibration energy harvesters (PE-VEHs) have been developed. The power generation of the reported EM-VEHs is in the range from 0.7 to 1450000 μW. However, the power production by the developed PE-VEHs ranges from 0.6 to 7700 μW. The overall size of most of the bridge VEHs is quite comparable and is in mesoscale. The resonant frequencies of EM-VEHs are on the lower side (0.13 to 27 Hz) in comparison to PE-VEHs (1 to 120 Hz). The power densities reported for these bridge VEHs range from 0.01 to 9539.5 μW/cm3 and are quite enough to operate most of the commercial WASNs. [ABSTRACT FROM AUTHOR]

The prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes of false alarms is due to the usage of a raw dataset that contains redundancy. To resolve this issue, feature selection is necessary which can improve intrusion detection performance. Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. To avoid this problem, an optimization method is required. Evolutionary optimization approach like genetic algorithm (GA) has been used to search the most discriminative subset of transformed features. The particle swarm optimization (PSO) is another optimization approach based on the behavioral study of animals/birds. Therefore, in this paper a feature subset selection based on PSO is proposed which provides better performance as compared to GA. [ABSTRACT FROM AUTHOR]

Vehicular cloud computing is envisioned to deliver services that provide traffic safety and efficiency to vehicles. Vehicular cloud computing has great potential to change the contemporary vehicular communication paradigm. Explicitly, the underutilized resources of vehicles can be shared with other vehicles to manage traffic during congestion. These resources include but are not limited to storage, computing power, and Internet connectivity. This study reviews current traffic management systems to analyze the role and significance of vehicular cloud computing in road traffic management. First, an abstraction of the vehicular cloud infrastructure in an urban scenario is presented to explore the vehicular cloud computing process. A taxonomy of vehicular clouds that defines the cloud formation, integration types, and services is presented. A taxonomy of vehicular cloud services is also provided to explore the object types involved and their positions within the vehicular cloud. A comparison of the current state-of-the-art traffic management systems is performed in terms of parameters, such as vehicular ad hoc network infrastructure, Internet dependency, cloud management, scalability, traffic flow control, and emerging services. Potential future challenges and emerging technologies, such as the Internet of vehicles and its incorporation in traffic congestion control, are also discussed. Vehicular cloud computing is envisioned to have a substantial role in the development of smart traffic management solutions and in emerging Internet of vehicles. [ABSTRACT FROM AUTHOR]