Web Based Cross Layer Optimization Technique for Energy Efficient WSN

In recent days, wireless sensor networks (WSN) plays a major role in the real time applications like military battlefield surveillance, industrial process monitoring, machine health monitoring and so on. In WSN, selecting the cluster head (CH) is the challenging task. CH selection is done by considering parameters of single layer only. In cross layer protocol more than one layers are considered for inter related parameters such as integration of MAC/physical layer and integration routing/MAC/physical layers. The main drawback of layer-based approach is not considering the effect on improvement of particular layer parameter to other layer parameters. In this paper, new cross layer technique for energy efficient module is designed to address the energy efficiency issues, which is common to all layers and used to optimize the energy from one layer parameter by others. Nowadays everything is possible with the help of Internet, so sharing the information between WSN and TCP through the energy efficient cross layer can be done. It is done with transport layer to enhance the application filed to be reliably connected to the web. In this paper, dynamically adapted sleep scheduling mechanism is used with residual energy of each node. Virtual end-to-end packet rate selection and congestion control feedback mechanism are considered for end to end delay. This reduces the packet loss with the support of data-rate adaptation technique.

     

Blind Decoding of Massive MIMO Uplink Systems Based on the Higher Order Cumulants

In a developing country like India, there is an exponential rise in population nutrition requirement. To meet up with both the ends, the agricultural techniques should be perfected for optimal yield and quality. Irrigation and soil property monitoring system using sensors can be automated and operated wirelessly to achieve optimal water supply control and surveillance. The objective of this paper, is to automate the whole wireless sensor network (WSN) system with a control over water pumps and dripper valves. The humidity, temperature and pH sensor’s percepts provide a feedback, to control the water content of the soil. The system has an low-cost and energy reliable ZigBee for sensor data transformation, high-range GPRS system for data storing and analysis, and the whole system is powered by Solar panels which makes it self-sustainable. Customizable options for different crop with different requirements make it a versatile WSN system for automated irrigation based water management.     

Opportunistic scheduling and incentive mechanism for OFDMA networks with D2D relaying

The device-to-device (D2D) relaying is considered one of promising technologies to improve the spectral efficiency and extend the coverage of the cellular system with low additional costs. In the system with D2D relaying, some of user equipments (UEs) can act as relay stations (RSs) that forward other UEs’ data from/to the base station (BS). Compared with the RS, the D2D relaying has several advantages such as low deployment costs and high flexibility. We study an opportunistic subchannel scheduling problem in the OFDMA cellular network with D2D relaying in this paper. We formulate a stochastic optimization problem to maximize the sum-rate of the system with D2D relaying while satisfying the minimum average data rate requirement for each UE, and then develop an opportunistic scheduling algorithm by solving it. Due to a high computational complexity of the optimal scheduling algorithm, we also propose a heuristic algorithm with a lower computational complexity. In addition, since UEs that participate in D2D relaying sacrifice their resources to relay other UEs’ data, we also study incentive mechanisms to compensate their sacrifices. Through simulation results, we show the performance of our algorithms and the effects of our incentive mechanisms.     

Quantization of deep neural networks for accumulator-constrained processors

We introduce an Artificial Neural Network (ANN) quantization methodology for platforms without wide accumulation registers. This enables fixed-point model deployment on embedded compute platforms that are not specifically designed for large kernel computations (i.e. accumulator-constrained processors). We formulate the quantization problem as a function of accumulator size, and aim to maximize the model accuracy by maximizing bit width of input data and weights. To reduce the number of configurations to consider, only solutions that fully utilize the available accumulator bits are being tested. We demonstrate that 16 bit accumulators are able to obtain a classification accuracy within 1% of the floating-point baselines on the CIFAR-10 and ILSVRC2012 image classification benchmarks. Additionally, a near-optimal 2 × speedup is obtained on an ARM processor, by exploiting 16 bit accumulators for image classification on the All-CNN-C and AlexNet networks.     

A novel fault detection method for VSC-HVDC transmission system of offshore wind farm

VSC-HVDC transmission system is going to become the most economical way of power delivery for large and remote offshore wind farms. An accurate and fast fault detection method is necessary to protect sensitive devices of these systems and maintain uninterrupted power delivery. This paper investigates an innovative technique for recognizing DC zone faults including HVDC cable faults and unbalancing of DC capacitor bank. Sheath voltage is presented as a novel criterion for detecting abnormal situations in the system. Transient voltage of cable sheath and Wavelet transform are used to identify different types of DC faults. Extensive simulation examples are performed on EMTDC–PSCAD platform and post-processed using MATLAB. The results illustrate that the proposed technique gives a robust performance and can be applied to protection scheme of offshore wind farms.     

Failure analysis of power transformer for effective maintenance planning in electric utilities

In this paper, power transformer failures are analyzed and their root causes are systematically investigated in Tamil Nadu Transmission Corporation Limited (TANTRANSCO)/Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO) electric utilities, based on 196 failure cases from the year 2009–2013. Failure analysis is conducted in two phases. Initially, voltage level, geographical zone and power transformer components based failure analysis are performed through statistical analysis. Secondly, the most significant factors that cause power transformer failures are identified by using root cause analysis (RCA). Finally, current maintenance practice is reviewed and an effective maintenance planning has been proposed for implementation in order to prevent these failures and to maintain the power transformers in good operating condition during their life cycle. This study provides a practical guidance to help maintenance personnel for the best utilization of the power transformer in electric utilities.     

I-CARES: advancing health diagnosis and medication through IoT

Internet of Things (IoT) is changing the way many sectors operate and special attention is paid to promoting healthy living by employing IoT based technologies. In this paper, a novel approach is developed with IoT prototype of Wireless Sensor Network and Cloud based system to provide continuous monitoring of a patient’s health status, ensuring timely scheduled and unscheduled medicinal dosage based on real-time patient vitals measurement, life-saving emergency prediction and communication. The designed integrated prototype consists of a wearable expandable health monitoring system, Smart Medicine Dispensing System, Cloud-based Big Data analytical diagnostic and Artificial Intelligence (AI) based reporting tool. A working prototype was developed and tested on few persons to ensure that it is working according to expected standards. Based on the initial experiments, the system fulfilled intended objectives including continuous health monitoring, scheduled timely medication, unscheduled emergency medication, life-saving emergency reporting, life-saving emergency prediction and early stage diagnosis. In addition, based on the analysis reports, physicians can diagnose/decide, view medication side effects, medication errors and prescribe medication accordingly. The proposed system exhibited the ability to achieve objectives it was designed using IoT to alleviate the pressure on hospitals due to crowdedness in hospital care and to reduce the healthcare service delays.

     

Design and Compressive Analysis of Junctionless Multigate FinFET Towards Low Power and High Frequency Applications

Silicon - Advances in microelectronics have enabled smaller technical nodes, lower threshold voltages, and greater working frequencies. Even though VLSI circuit performance and power consumption...     

Eigenanalysis-based small signal stability of the system of coupled sustainable microgrids

Until now, microgrids were assumed to operate either in grid-connected or islanded modes. As a third alternative, neighboring microgrids may interconnect to support each other during faults in a section of one MG, or in the course of overloading. They may also interconnect to reduce the cost of electricity generation in their systems. Thereby, a microgrid will experience a significant transformation in its structure, when coupled with one or more neighboring microgrids. Before the formation of the system of coupled microgrids, the stability of the new system is vital to be cautiously examined to intercept the transformation, if instability is to occur. An eigenanalysis-based small signal stability evaluation technique is developed in this paper which can be used to allow/deny the interconnection of the microgrids. The analysis also defines the suitable range of control parameters for the energy resources of the CMG to guarantee the stability of the new system, if it was determined unstable. Numerical analyses, realized in MATLAB, are performed for the interconnection of a few sample microgrids. The accuracy of the developed technique is validated by comparing its results with the time-domain performance of the similar system, realized in PSCAD/EMTDC.     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.