Search results

Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing protocols with multiple QoS constraints are necessary. In QoS routing, the basic problem is to find a path that satisfies multiple QoS constraints. Moreover, mobility, congestion and packet loss in dynamic topology of network also leads to QoS performance degradation of protocol.

In this paper, the authors proposed a multi-path selection scheme for QoS aware routing in mobile ad hoc network based on fractional cuckoo search algorithm (FCS-MQARP). Here, multiple QoS constraints energy, link life time, distance and delay are considered for path selection.

The experimentation of proposed FCS-MQARP is performed over existing QoS aware routing protocols AOMDV, MMQARP, CS-MQARP using measures such as normalized delay, energy and throughput. The extensive simulation study of the proposed FCS-based multipath selection shows that the proposed QoS aware routing protocol performs better than the existing routing protocol with maximal energy of 99.1501 and minimal delay of 0.0554.

This paper presents a hybrid optimization algorithm called the FCS algorithm for the multi-path selection. Also, a new fitness function is developed by considering the QoS constraints such as energy, link life time, distance and delay.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

In the present networking scenarios, MANETs mainly focus on reducing the consumed power of battery-operated devices. The transmission of huge data in MANETs is responsible for greater energy usage, thereby affecting the parameter metrics network performance, throughput, packet overhead, energy consumption in addition to end-to-end delay. The effective parameter metric measures are implemented and made to enhance the network lifetime and energy efficiency. The transmission of data for at any node should be more efficient and also the battery of sensor node battery usage should be proficiently applied to increase the network lifetime. The paper aims to discuss these issues.

In this research work for the MANETs, the improvement of energy-efficient algorithms in MANETs is necessary. The main aim of this research is to develop an efficient and accurate routing protocol for MANET that consumes less energy, with an increased network lifetime.

In this paper, the author has made an attempt to improve the genetic algorithm with simulated annealing (GASA) for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.

In this paper, the author has made an attempt to improve the GASA for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.

The aim of the research is to use an integrated approach – simulation and Six Sigma to improve the ambient air quality.

Integration of simulation and Six Sigma DMAIC methodology in a foundry had been used to improve the ambient air quality. Various elements of the Six Sigma toolkit such as Cause and Effect diagrams and Failure Mode and Effect Analysis have been used to discover the root causes underlying the problem and prioritize action and incorporate cost‐effective solutions. Simulation has been used to improve and control the environmental efficiency by monitoring the performance of the Venturi Scrubber – the pollution control equipment, by running the model under varying conditions.

The integrated application of Six Sigma and simulation has been successful in reducing particulate emissions from 200 milligrams per cubic meter to less than 20 milligrams per cubic meter and sulphur dioxide emissions from 45 milligrams per cubic meter to less than 4.5 milligrams per cubic meter, thus reducing air pollution.

Air pollution is a burning problem in the present scenario and foundry industries are one of the contributors to air quality degradation. The approach described in this paper is a step towards reducing air pollution due to foundry operations.

Integration of Six Sigma DMAIC methodology and simulation provides a novel cost‐effective strategy for monitoring and reducing air pollution resulting from foundry operations. This paper is useful for environmental division of foundry and other manufacturing industries.

This study aims to understand how climatic drivers of change will affect rural communities living in the hot semiarid region of Bhokardan Taluka of Jalna district in the Indian state of Maharashtra. In the context of the economic and social change they are experiencing, the concern is to evolve ways that enable them to cope with, adapt to and benefit from these challenges.

The focus of most of the climate change studies is on the short- to long-term trends of weather parameters such as rainfall, temperature and extreme weather events. The impact of climate variability and changing patterns on the local communities, the local economy, livelihoods and social life in specific geographies is less explored.

As the impacts of climatic and nonclimatic drivers of change are cross-sectoral, diverse, multidimensional, interlinked and dynamic, this study has adopted a transdisciplinary “research-in-use” approach involving multidisciplinary teams covering the aspects such as changes in land use and land cover, surface and groundwater status, edaphic conditions, crops and livestock, climate analysis including projected changes, socioeconomic analysis, people’s experience of climate variability and their current coping strategies and resilience (vulnerability) analysis of communities and various livelihood groups.

The study was based on the peoples’ perspective and recommendation based on the local communities ability to cope up with climate change. However, a statistical analysis perspective is missing in the present study.

Based on these findings, a set of implementation-focused recommendations are made that are aimed at conserving and enhancing the resilience of the foundations that uphold and sustain the social and economic well-being of the rural communities in Bhokardan taluka, namely, land, water, agriculture, livestock, food and nutrition security, livelihoods, market access and social capital.

Condition monitoring is an important aspect of the maintenance program for rotating machines. The vibration signature is usually analyzed to perform diagnostics of the health of the machine. The data acquisition and analysis were cumbersome in the analog era and dedicated instruments were required and the diagnostics was usually a laboratory‐based exercise and time consuming. With the advent of high‐speed microprocessors, the practice has completely changed. A modern continuous condition monitoring and diagnostics system is first described. With the recent advances in Internet‐based technologies, the condition monitoring procedures are poised for a quantum jump in the way the machines can be maintaned continuously on‐line at remote locations or checked through the Web for any faults. A specific example of the design of an Applet towards this goal is described in this paper.

In structural, earthquake and aeronautical engineering and mechanical vibration, the solution of dynamic equations for a structure subjected to dynamic loading leads to a high order system of differential equations. The numerical methods are usually used for integration when either there is dealing with discrete data or there is no analytical solution for the equations. Since the numerical methods with more accuracy and stability give more accurate results in structural responses, there is a need to improve the existing methods or develop new ones. The paper aims to discuss these issues.

In this paper, a new time integration method is proposed mathematically and numerically, which is accordingly applied to single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems. Finally, the results are compared to the existing methods such as Newmark's method and closed form solution.

It is concluded that, in the proposed method, the data variance of each set of structural responses such as displacement, velocity, or acceleration in different time steps is less than those in Newmark's method, and the proposed method is more accurate and stable than Newmark's method and is capable of analyzing the structure at fewer numbers of iteration or computation cycles, hence less time-consuming.

A new mathematical and numerical time integration method is proposed for the computation of structural responses with higher accuracy and stability, lower data variance, and fewer numbers of iterations for computational cycles.

The purpose of this paper is to propose an optimization method by combining artificial immune algorithm and finite element analysis to find the optimal exciting electrode of a piezoceramic plate type ultrasonic motor vibrator.

The artificial immune algorithm is selected as optimizer for its merit of fast convergence to global optimal solution. The finite element analysis is used to calculate the motion trajectory of contact point. The objective function is the work that the vibrator does to rotor. The design variables are the boundaries of exciting electrode on piezoceramic plate vibrator surface.

The calculated results and the experimental results show that using this method, both the position and the size of optimal exciting electrode of this ultrasonic motor can be quickly and accurately determined.

In order to successfully design an ultrasonic motor, both the position and the size of the exciting electrode must be investigated, so as to change more electric energy into mechanical energy. In this paper, an optimization method by combining artificial immune algorithm and finite element analysis is proposed for the exciting location optimization of a piezoceramic plate type ultrasonic motor to obtain large power output.

Changing climate has increasingly become a challenge for smallholder farmers. Identification of technical, institutional and policy interventions as coping and adaptation strategies and exploring risks of their adoption for smallholder farms are the important areas to consider. The aim of the present study was to carry out an in-depth analysis of adaptation strategies followed and the associated risk premium in technology adoption.

The study was carried out in the dryland systems of three Indian states – Andhra Pradesh, Karnataka and Rajasthan – and was based on a survey of 1,019 households in 2013. The flexible moment-based approach was used for estimating the stochastic production function, which allowed estimation of the relative risk premium that farmers are willing to pay while adopting the technologies to avoid crop production risks.

In all the three states, the risk premium (INR ha⁻¹) was higher for farm mechanization compared to supplemental irrigation, except in the case of Andhra Pradesh. The higher the level of technology adoption, the higher the risk premium that households have to pay. This can be estimated by the higher investment needed to build infrastructure for farm mechanization and supplemental irrigation in the regions. The key determinants of technology adoption in the context of smallholder farmers were climatic shocks, investment in farm infrastructure, location of the farm, farm size, household health status, level of education, married years, expected profit and livestock ownership.

Quantification of the risk premium in technology adoption and conducting associated awareness programs for farmers and decision-makers are important to strengthen evidence-based adoption decisions in the dryland systems of India.