Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with any GW in a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligent MAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches.     

Design of an optimized traffic-aware routing algorithm using integer linear programming for software-defined networking

The number of internet users and connected devices has dramatically expanded due to the recent technological boom and the benefits that the internet of things offers to ease our lives. Network scheduling, quality of service, resource allocation, and security issues are now being addressed via software-defined networking (SDN). SDN has several benefits over traditional networks, including global centralized control, managing network traffic, and separating the forwarding and control plane. The work done in this paper aims to design and implement a traffic-aware routing framework based on routing optimization presented as an integer linear programming (ILP) to improve heterogeneous traffic flows' quality of service (QoS) in a simulated SDN environment. With the knowledge that the routing problem is a nondeterministic polynomial-time-hard problem, the proposed scheme aims to decrease the computational routing time to make the ILP-based routing system more suitable for real-time processing. The simulation results illustrate that the proposed framework reduces the computational time by 23% and 49% for Abilene and Goodnet topology, respectively. Additionally, with 1000 flows in the network, the suggested scheme reduces the number of network flows that violate the QoS by 9% and 22% (with Abilene topology) and 16% and 51% (with Goodnet topology) as compared to the existing shortest path delay and sway methods, respectively.     

A study on tool wear of tungsten carbide cutters in edge trimming of CFRP

Journal of Mechanical Science and Technology - Carbon fiber reinforced polymer (CFRP) composites are widely used in the aerospace field because of their outstanding lightweight material...     

A dual-band semi-circular patch antenna for WiMAX and WiFi-5/6 applications

A semi-circular patch antenna (SCPA) is designed for WiMAX and WiFi-5/6 communications. The footprint of the proposed SCPA is only 30 × 40 × 1.575 mm³, which is composed of a semi-circular patch and a partial ground plane. The main strength of this work is that the estimated wide dual-frequency span of 2.39–3.75 GHz and 5.39–7.18 GHz are contributed with two sharp resonances at 2.77 and 6.46 GHz, respectively. The proposed SCPA has been practically tested after fabrication. The measured results confirm that the designed antenna achieved the bandwidth necessity for WiMAX 1.5 (2.5–2.69 GHz), WiMAX 2 (3.4–3.6 GHz), WiFi-5 (5.15–5.85 GHz) and WiFi-6 (5.925–7.125 GHz). In the two resonance frequencies of 2.77 and 6.46 GHz, the designed antenna achieved a gain of 2.558 and 4.109 dB, respectively. In addition, the SCPA also manifests good radiation properties and achieves an average efficiency of more than 90%. A professional version of the 3D electromagnetic simulator is utilized to examine the effect of diffident parameters and model the tested antenna.     

Low-Cost Customized Color-Blind Contact Lenses using 3D Printed Molds

Prominence of color perception in our day-to-day routine is unequivocally pronounced, yet visual ramifications due to color vision deficiency (CVD) or color blindness impede carriers of this disorder from functioning normally. To circumvent this deficiency, patients opt for tinted glasses/contact lenses to complement their color distinction capabilities. Red-green color blindness, the most prevalent form of CVD, can be alleviated using such glasses/lenses that filter out problematic wavelengths (540–580 nm). Nonetheless, nearly all contact lenses established by companies and developed by researchers are tinted throughout their entire surface, causing patients discomfort and needless attention as people can easily note their deficiency. Ideally, the tint within the lens should only cover the eye's pupil as it is responsible for perceiving light. Hence herein, CVD contact lenses are fabricated by solely tinting the midportion of commercial lenses utilizing two additively manufactured molds with 4 and 8 mm-diameter holes to emulate the humans’ average pupil size. The tinted lenses filter light effectively at 530–590 nm with their transmission dip being at 558 nm. The contact lenses show excellent wettability and water retention capabilities along with demonstrating superior wavelength-filtering properties to most of the commercial and research-based CVD wearables.     

Erucic acid ameliorates the lipopolysaccharide-induced memory deficit in rats through inhibited inflammation cytokines expression/caspase 3/NF-κB pathways

Erucic acid is a single unsaturated fatty acid that falls under the omega-9 fatty acid family. It was suggested to treat Wistar rats with lipopolysaccharide (LPS)-induced memory impairment and minimize cognitive impairment. A total of 30 animals were randomized: group I was normally treated group, group II was administered with LPS, group III was treated with LPS along with erucic acid at the dose of 10 mg kg^–1 p.o.^–1, group IV was treated with LPS along with erucic acid at 20 mg kg^–1 p.o.^–1 and group V was the erucic acid per se group provided at the dose of 20 mg kg^–1 p.o.^–1 per se. Behavioral tests were evaluated by using the Morris water maze and Y-maze. Biochemical analysis including acetylcholine esterase (AChE), choline acetyltransferase (ChAT), glutathione (GSH), catalase activity (CAT), superoxide dismutase (SOD), and nitric oxide (NO) along with proinflammatory mediators tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), caspase 3, and neuroinflammatory biomarker (nuclear factor kappa B-NF-κB) were measured. Erucic acid produced substantial behavioral improvement in the Y-maze test, including spontaneous alterations and reduced latency time during acquisition, and a longer duration of time in the consolidation phase undergoing the MWM test. Furthermore, erucic acid improved the AChE, proinflammatory markers, and oxidative stress as well as restoring endogenous antioxidant levels, ChAT, caspase 3, and NF-κB levels. Erucic acid may be a therapeutic component for conditions related to memory disorders such as memory impairment, enhances memory functioning, and protects against neuronal damage.     

Hybrid Smart Contracts for Securing IoMT Data

Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient’s health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store this highly valuable health related data in a secure, safety, easy access and less cost-effective manner. In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work (PBFT-PoW). The implementation is done using cloud technology setup with smart contracts (PBFT-PoW). The accuracy rate of PBFT is 90.15%, for PoW is 92.75% and our proposed work PBFT-PoW is 99.88%.