Search

The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

The advanced technology Internet of Things (IoT), a recent development in wireless sensor networks, provides a way to connect surrounding physical objects to the Internet in a dynamic way. There is an advantage of using IoT technology protocols as it requires less network bandwidth, minimum code space and hence reduces access time. Major IoT provider companies have several IoT products in the gadget market to make consumers’ life effortlessly easy. Several research gaps regarding the scalability of IoT with other platforms have been identified by reviewing the existing literature and also explained essential information about the integration of IoT with cloud computing. Major technology big giants Apple, Google, and Amazon's products are differentiated on the basis of measurement parameters such as their prices, streaming options, customisable appearance methods, major partnership companies related information appearing. A prototype of Apple's Home Kit and Samsung SmartThings is developed to show the characteristics of smart home products for the user, which helps in better decision making of IoT platform providers.

The vision of the Internet of Vehicles (IoV) has acquired widespread acceptance as a result of the development of wireless technology. Its ability to provide drivers with various mobile services has made it an attractive application. Blockchain based security solution is considered good in various applications of IoV till now. But all the frameworks and architectures developed so far in IoV adopt the load of blockchain. So, it is generally deployed on or above edge computing devices, keeping lower structure of the frameworks traditional as managing blockchain with low computing devices on the physical layer is really a challenge. Securing communication in an IoV environment requires ensuring strong authentication and encryption. Although limited efforts are accessible for strong authentication and secure communication but, the lightweight characteristic is absent from this list. This article is proposing a lightweight secured framework for blockchain based IoV that offers strong authentication and secure communication. After securely authorization of the vehicle, it introduces the concept of branched blockchain taking into the consideration of most recently used block, so that the low powered devices can also adopt the security benefits of the blockchain. The existing mining systems are not designed to handle the needs of the IoV at the physical layer. Some of the key features of the proposed framework are lightweight blockchain for physical layer and above, load balancing, scalability, availability and decentralization. According to the relative research, which takes into account factors like computation and transmission costs, our suggested framework outperforms already implemented ones. This framework can act as base of new research verticals where anyone is looking forward for a lightweight blockchain variant applicable on physical layer of IoV. The existing mining systems are not designed to handle the needs of the Internet of Vehicles at the physical layer. This framework can act as base of new research verticals where anyone is looking forward for a lightweight blockchain variant applicable on physical layer of Internet of Vehicles.

Health monitoring of dairy cattle plays a vital role for increasing the dairy products supply worldwide. Nowadays farmers are showing less interest in dairy sector as animals are suffering from various ailing health issues, unpredictable killing diseases, and advanced breeding costs. Therefore, it is necessary for farmers to adopt efficient technical methods for cattle health monitoring to increase the milk production supply. This study documented various wireless sensor network (WSN)-based automatic health monitoring systems for monitoring various diseases of dairy cattle. The main objective of WSN-based intelligent monitoring systems in farm automation is to monitor the health of dairy cattle on regular basis. This monitoring system needs to be installed in local and remote locations of farms that will assist the concerned farmers in monitoring their cattle activities from diverse locations for the whole day. All collected factors from the automated system will be stored in a database. Subsequently, with the help of farm automation, farmers can retrieve information for the execution of correct farm control strategies. Moreover, WSN is low-cost technology which is specific for the identification of diseases in dairy animals. This revolution in advanced technological farm automation will aid in improving the productivity rate with the reduction of human intervention. This review study concludes all cattle monitoring systems along with various issues and challenges.

Spinodoid structures, also called spinodoid metamaterials, are non-periodic cellular structures that mimic spinodal topologies that are observed during diffusion-driven phase separation processes. Computationally efficient to model, spinodoid structures can be fabricated using additive techniques and offer an attractive route to the design of multifunctional structures. In this paper, we investigate the normal incidence sound absorption behavior of four distinct spinodoid topologies: isotropic, cubic, columnar, and lamellar. We fabricate the test samples using the fused filament fabrication process and systematically study the effect of the Gaussian Random Field (GRF) parameters on their underlying open pore network and sound absorption behavior. We employ a watershed segmentation-based image processing approach to correlate their pore and throat radii distributions to the GRF parameters. The normal incidence sound absorption properties are experimentally measured using the two-microphone impedance tube test method. Finally, we use a particle-swarm-based inverse characterization approach to extract the bulk properties necessary to model their acoustical behavior by using the Johnson-Champoux-Allard formulation. Our results show that the open pore network and acoustical properties of spinodoid structures are primarily a function of their relative density and wavenumber. Further, while the absorption behavior of the isotropic, cubic, and columnar spinodoids is similar, the lamellar spinodoids display unique low-frequency sound absorption behavior. Overall, all four spinodoid topologies provide favorable sound absorption characteristics, which may be tuned by varying the GRF parameters. The presented work advances the state-of-the art by establishing the feasibility of using additive manufacturing to enable non-periodic porous structures for that can be tuned to simultaneously provide mechanical stiffness and noise dampening capabilities.

We investigate the possibility of additively manufacturing fibrous sound absorbers using fused deposition modeling. Two methods for 3D printing fibers are proposed. The fiber bridging method involves extruding the filament between two points with no underlying supports. The extrude-and-pull method involves extruding a filament droplet before pulling away the extruder rapidly to generate thin fibers. Both methods can produce fibers with aspect ratios greater than 100. Optical microscopy is used to investigate the effect of various printing parameters on the fiber characteristics. The sound absorption coefficient of samples printed using the two techniques are measured using a two-microphone normal incidence impedance tube setup. Effects of printing parameters and fiber density variables are experimentally studied. The experimental studies are supported by the Johnson-Champoux-Allard semi-empirical analytical model informed using an inverse characterization approach. The analytical model is then utilized to understand the effect of fiber parameters on the acoustical transport parameters. It is observed that the two methods result in individual fibers with distinct characteristics. On average, the fiber bridging method results in thicker fibers, which results in comparatively higher sound absorption. However, the extrude-and-pull method results in fibers with hair-like characteristics (thick base with progressively decreasing thickness) and one may easily incorporate it within existing additive manufacturing routines to add fibers to a base surface, thus opening up a new route towards fiber-enhanced multifunctional structures.