Flow‐based electromagnetic‐type energy harvester using microplanar coil for IoT sensors application

Battery is the sole power source for Internet of thing (IoT) sensors. Due to limited shelf life, the batteries are required to be replaced intermittently. This periodic replacement of batteries is inflated in terms of both logistics and time. This article illustrates conceptual design, development, and characterization of a flow‐based electromagnetic‐type energy harvester (F‐EH) using microplanar coil for IoT sensors application. The F‐EH converts hydro energy into useful electrical energy utilizing electromagnetic transduction mechanism. The microfabrication and macrofabrication techniques adopted to manufacture harvester's components are presented. The F‐EH has been successfully characterized by laboratory scale experimental flow test loop commissioned for this work. Experimentation with associated uncertainty analysis prevails that at a matching impedance, the F‐EH can generate a 686 μW of maximum power at an operating flow rate of 12 L/min with an uncertainty of 8.1%.     

LTL logistics networks with differentiated services

This research addresses the problem of designing a LTL logistics network over individual lanes by offering different delivery services (1-day, 2-day, 3-day). A network design is proposed which links lane-specific delivery service choices and service profitability to the hub location-allocation decisions. A hybrid-heuristic is developed which uses a search interval reduction technique with a meta-heuristic. The performance of the hybrid-heuristic is evaluated in a computational study. A factorial experiment design with real world data is used to analyze the network structure, service choices and shipment flows, which results in managerial insights about the design and operation of a LTL logistics network with differentiated services.     

A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids

With the explosive growth in computers and the growing scarcity in electric supply, reduction of energy consumption in large-scale computing systems has become a research issue of paramount importance. In this paper, we study the problem of allocation of tasks onto a computational grid, with the aim to simultaneously minimize the energy consumption and the makespan subject to the constraints of deadlines and tasks' architectural requirements. We propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution. In this cooperative game, machines collectively arrive at a decision that describes the task allocation that is collectively best for the system, ensuring that the allocations are both energy and makespan optimized. Through rigorous mathematical proofs we show that the proposed cooperative game in mere O(n mlog(m)) time (where n is the number of tasks and m is the number of machines in the system) produces a Nash Bargaining Solution that guarantees Pareto-optimally. The simulation results show that the proposed technique achieves superior performance compared to the Greedy and Linear Relaxation (LR) heuristics, and with competitive performance relative to the optimal solution implemented in LINDO for small-scale problems.     

Embedding Pyramids into 3D Meshes

The pyramid architecture is a powerful topology in the area of computer vision. On the other hand, the 3D mesh architecture possesses rich topological features which make it suitable for building scalable parallel processor systems. The usefulness of these two architectures has led us to consider the problem of embedding pyramids into 3D meshes, for which we present two solutions. The first solution, termednatural embedding,maps a pyramid into a 3D mesh such that each level of the pyramid is mapped to a single level of the 3D mesh. The second solution, termedmultiple embedding,allows simultaneous embedding of multiple pyramids into a single 3D mesh. The quality of both solutions is evaluated using dilation and expansion measures. Using the multiple embedding, we are able to obtain an average dilation of 1.26 and a near-optimal expansion of 1.12.     

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.     

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.     

WEDM of layered composite: analyzing material removal and cut quality issues

The use of cladded bimaterial composites has grown in the recent past as they offer a combination of properties at low cost. But the heterogeneity which is the inherent attribute of these composites makes it challenging to accurately cut via conventional means. Therefore, thermal cutting is commonly employed for their cutting which not only produce poor cut quality and deeper heat affected zones but also demand subsequent finishing operations. Wire electric discharge cutting (WEDM) is a proficient alternate but low material removal (MRR) and widen kerf slot (KW) due to sideways sparking limit its application. Moreover, both layers of material have different thermoelectric properties and are subjected to simultaneous cutting by a single moving wire electrode which lead to produce different spark strength against both layers. In this regard, the present study aims to investigate the cutting potential of WEDM for cladded bimaterial with a prior focus on both the aforesaid issues, i.e. MRR and KW. Considering the thermoelectric nature of the WEDM, workpiece-related parameters like orientation of work surface and layer thickness of each layer are taken as control variables in addition to the WEDM process parameters. Experimental results are thoroughly analyzed using statistical and SEM analysis.     

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.     

On exploiting task duplication in parallel program scheduling

One of the main obstacles in obtaining high performance from message-passing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplication-based scheduling can mitigate this overhead by allocating some of the tasks redundantly on more than one processor. In this paper, we focus on the problem of using duplication in static scheduling of task graphs on parallel and distributed systems. We discuss five previously proposed algorithms and examine their merits and demerits. We describe some of the essential principles for exploiting duplication in a more useful manner and, based on these principles, propose an algorithm which outperforms the previous algorithms. The proposed algorithm generates optimal solutions for a number of task graphs. The algorithm assumes an unbounded number of processors. For scheduling on a bounded number of processors, we propose a second algorithm which controls the degree of duplication according to the number of available processors. The proposed algorithms are analytically and experimentally evaluated and are also compared with the previous algorithms     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.