E2IA-HWSN: Energy Efficient Dual Intelligent Agents based Data Gathering and Emergency Event Delivery in Heterogeneous WSN Enabled IoT

Heterogeneous sensors are equipped with a limited battery source that is concerned with network lifetime problems. However, this problem can be tackled with the effective design of WSN-IoT by clustering and sleep scheduling mechanisms. This paper addresses this issue by presenting novel ideas involved in the WSN operations such as grid construction, cluster head selection, sleep scheduling, and data gathering by intelligent Agents (iAgents). An energy-efficient dual iAgents based Heterogeneous WSN (E2IA-HWSN) is proposed. iAgents are used in this paper to automatically collect the sensed data from IoT sensors. In this E2IA-HWSN, a 3?×?3 grid is built and each cell is sub-divided into four in which cluster heads (CH) are selected in each sub-division, followed by ring partitioning for selecting a CH present at the center. Multi-Objective Harris Hawks optimization (MO-HHO) algorithm is used to select CH and supernode, here to minimize the energy consumption of CH, the supernode takes responsibility to assign sleep schedules to devices. The scheduling slots are assigned only after a sensor reaches below the energy threshold. For scheduling, the Bayes rule-based Markov model (BR-MM) is applied with the determination of residual energy and sensed packet counts. Generator de Bits Pseudo Aleatorios (GBPA) eliminates redundant data in CH and then inter-cluster routing is performed in case of emergency events. If not, then the CH waits for the arrival of iagents, the trajectory of iAgents is dynamically predicted with Deep Policy Gradient (DDPG). The implementation is carried out in NS3.26 and the results show betterment to the well-known methods.

     

Experimental investigation on pyramid solar still with single and double collector cover—Comparative study

Sun radiation is the heat energy source for solar still. That should be utilized maximum for increasing the evaporative rate at the top surface of the brine. The pyramid shape solar still (PSSS) can receive solar input radiation from all four directions. In this research, the top layer of conventional pyramid shape solar still (CPSSS) is covered with air‐packed cover and analysed the effect in the modified still after air packed in between the two glasses, finally, that result is compared with the CPSSS. The air inside the two glasses will be receiving maximum radiation and retain in it. It can be used as top side insulation and which can ensure the maximum insulation for heat energy inside the PSSS. The CPSSS and air‐packed pyramid shape solar still (APPSSS) were fabricated and experimented. The experiments were conducted at an ambient condition of the Chennai, Tamil Nadu (12.9416°N, 80.2362°E). The CPSSS gives good yield at evening when an increase in wind velocity. The APPSSS gives a lower performance as compared to the CPSSS due to the air‐packed cover.     

Exploitation of an insulated air chamber as a glazed cover of a conventional solar still

The countries of North Africa suffer from a serious problem that affects a large number of people. The trouble is the provisioning of drinking water. Several techniques are used but solar distillation; which seems to be an appropriate, economical, and easy solution. Conventional solar distiller is used in the Saharan areas but the yield of this device is low, that's why there are several studies that are focused on improving this distiller. The technique of double glazing gives an increase in the efficiency of a solar collector; hence, the idea of using the same technique in a conventional solar distiller. Two covers of glasses separated by 1 cm, isolated from the four sides was put on a distiller of dimensions 0.5 × 0.5 m. The results show that this technique minimizes the distiller's yield by 56.52% when compared with the conventional solar distiller.     

Tangible nanocomposites with diverse properties for heart valve application

Cardiovascular disease claims millions of lives every year throughout the world. Biomaterials are used widely for the treatment of this fatal disease. With the advent of nanotechnology, the use of nanocomposites has become almost inevitable in the field of biomaterials. The versatile properties of nanocomposites, such as improved durability and biocompatibility, make them an ideal choice for various biomedical applications. Among the various nanocomposites, polyhedral oligomeric silsesquioxane-poly(carbonate-urea)urethane, bacterial cellulose with polyvinyl alcohol, carbon nanotubes, graphene oxide and nano-hydroxyapatite nanocomposites have gained popularity as putative choices for biomaterials in cardiovascular applications owing to their superior properties. In this review, various studies performed utilizing these nanocomposites for improving the mechanical strength, anti-calcification potential and hemocompatibility of heart valves are reviewed and summarized. The primary motive of this work is to shed light on the emerging nanocomposites for heart valve applications. Furthermore, we aim to promote the prospects of these nanocomposites in the campaign against cardiovascular diseases.     

Identifying research facilitators in an emerging Asian Research Area

We introduce a novel set of metrics for triadic closure among individuals or groups to model how co-authorship networks become more integrated over time. We call this process of triadic, third-party mediated integration, research facilitation. We apply our research facilitation or RF-metrics to the development of the Pan-Asian SNP (PASNP) Consortium, the first inter-Asian genomics network. Our aim was to examine if the consortium catalyzed research facilitation or integration among the members and the wider region. The PASNP Consortium is an ideal case study of an emerging Asian Research Area because its members themselves asserted a regional Asian identity. To validate our model, we developed data mining software to extract and match full author and institutional information from the PDFs of scientific papers.     

Statistical analysis of mammographic features and its classification using support vector machine

This study aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.     

Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates

This study considers the scheduling problem observed in the burn-in operation of semiconductor final testing, where jobs are associated with release times, due dates, processing times, sizes, and non-agreeable release times and due dates. The burn-in oven is modeled as a batch-processing machine which can process a batch of several jobs as long as the total sizes of the jobs do not exceed the machine capacity and the processing time of a batch is equal to the longest time among all the jobs in the batch. Due to the importance of on-time delivery in semiconductor manufacturing, the objective measure of this problem is to minimize total weighted tardiness. We have formulated the scheduling problem into an integer linear programming model and empirically show its computational intractability. Due to the computational intractability, we propose a few simple greedy heuristic algorithms and meta-heuristic algorithm, simulated annealing (SA). A series of computational experiments are conducted to evaluate the performance of the proposed heuristic algorithms in comparison with exact solution on various small-size problem instances and in comparison with estimated optimal solution on various real-life large size problem instances. The computational results show that the SA algorithm, with initial solution obtained using our own proposed greedy heuristic algorithm, consistently finds a robust solution in a reasonable amount of computation time.