Manoj Kumar

Capacity and interference aware link scheduling with channel assignment in wireless mesh networks

Wireless mesh networks (WMNs) are receiving lot of attention in recent years due to the unique features such as low cost and easy deployment. The optimal routing in these networks depends on the link capacities determined by link scheduling. To increase the throughput and network capacity, use of multiple channels and interfaces has been introduced in these networks. But the presence of interference limits the use of link and channel assignment in these types of networks. In this paper, we present a novel approach for link scheduling and channel assignment (CA) to improve the overall capacity and throughput of WMNs. The problem of link scheduling and CA is formulated as a Linear Programming (LP) problem with associated constraints. Links are sorted and placed in a particular group based upon link cost metric (LCM). The LCM value is used to group the links together and placed in appropriate group for CA. As the links are sorted and placed in appropriate groups the corresponding entries are made in Link Assignment Matrix (LAM). Once the links are grouped using LCM, then CA for these links is done and corresponding entries are made in Channel Assignment Matrix (CAM). Two algorithms, one for link scheduling and other for CA, are proposed. Simulation results show that proposed scheme outperforms the earlier approaches with respect to packet delivery fraction, end-to-end delay, aggregate throughput, fairness index and packet loss rate.

Neural Network Based Energy Efficient Clustering and Routing in Wireless Sensor Networks

Energy is a valuable resource in Wireless Sensor Networks (WSNs). The status of energy consumption should be continuously monitored after network deployment. The information about energy status can be used to early notify both sensor nodes and Network Deployers about resource depletion in some parts of the network. It can also be used to perform energy-efficient routing in WSNs. In this paper, we propose a neural network based clustering and energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with specified constraints. Cluster head selection is done using adaptive learning in neural networks followed by routing and data transmission. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs. Keywords: Sensor networks; Energy Efficient Routing, Linear Programming, Neural Networks

Remediation of Dyes from Aquatic Ecosystems by Biosorption Method Using Algae

Industrialisation and urbanisation are the root cause of increasing pollution. Water is of utmost necessity for the survival of all living beings and is subjected to pollution by industrial effluents, domestic sewage and agricultural runoffs. New environment friendly techniques to curb water pollution due to the increasing usage of toxic chemicals are needed. Extensive use of dyes today cause contamination of water bodies, loss of aquatic biota, poisoning of agricultural fields and spread of mutagens and carcinogens in organisms. Dyes of synthetic origin need to be particularly eliminated from aquatic ecosystems to reduce their toxic effects. Traditional methods of effluent treatment have failed to provide effective results because of high cost, nonselectiveness of adsorbents, toxic sludge produced by the adsorption process and other factors. In the case of nonconventional dye removal processes, researches are being done to introduce cost-effective and efficient technologies. Biosorption by macroalgae and microalgae is a promising method for treatment of wastewater. Phycoremediation of toxic compounds, heavy metals (e.g. chromium, lead and nickel) and colour from the aquatic ecosystem is a natural, eco-friendly and cost-effective technique. Easy availability and growth of algae, efficient uptake of dyes and other contaminants, some value-added merits like carbon sequestration and formation of less toxic sludge are the major advantages of using algae for biosorption.

Temporal variation of “solar dimming” induced by composite and carbonaceous aerosols: Observations from mineral‐rich eastern Indian region

Composite and carbonaceous aerosol radiative forcing (RF) over Ranchi (23.5°N, 85.3°E) in eastern India at monthly and seasonal scales during February 2011 to January 2012 is derived from mean optical properties obtained from Sun-sky radiometer and a radiative transfer model. Ranchi is located on the Chotanagpur plateau at 650 m above mean sea level; the region is unique with dense open active mines, a source of mineral aerosols with opposing optical properties such as coal and limestone. Diurnal mean composite aerosol RF at the surface, in the spectral band 0.3–3.0 µm increases from winter (December, January, February) to premonsoon (March, April, May) with maximum (−65 Wm−2) aerosol RF in March that is associated with highest black carbon (BC) aerosol optical depth (AOD), 0.05. Minimum surface aerosol RF occurs in July with minimum values of AOD and AODBC. Aerosol RF at top of the atmosphere is maximum (−17 Wm−2) in April, and is positive (+1.2 Wm−2) in March. On an average, carbonaceous aerosols (BC) contribute ~8.8% to total AOD and about 60% to total atmospheric absorption. A rapid increase of BC is seen in the postmonsoon with 1.0 µg m−3 in September to 3.0 µg m−3 in October. Aerosol-induced solar dimming is about 9.4% (premonsoon), 7.0% (monsoon), 10.6% (postmonsoon), and 10.2% (winter) of the surface radiation. Seasonal mean aerosol heating rate is observed to be maximum in premonsoon (~1.15°K/d−1), followed by postmonsoon, while winter and monsoon experience minimum (~0.45°K/d−1) heating, assuming typical aerosol profiles of the model.

Spectral analysis of wind and temperature components during lightning in pre-monsoon season over Ranchi

In this paper, the variations in the surface layer flux parameters as well as spectral parameters in the Atmospheric Surface Layer (ASL) during lightning period have been analysed. This analysis has been done in the pre-monsoon season over Ranchi region, which is a lightning prone zone in India. During this stochastic event not only the behaviour of surface layer parameters has been observed, but other derived parameters like Monin–Obukhov stability parameter (z/L), Turbulent Kinetic Energy, Momentum Flux and Sensible Heat Flux have also been considered. For the first time, spectral characteristics of wind and temperature component have been analysed and a comparison has been made between lightning and clear day for both the components. A distinct spectral characteristic of wind and temperature components is noticed during the lightning period. The outcome of the results will have important implications in future studies on ASL during lightning in India.

Phytoremediation: A Multidimensional and Ecologically Viable Practice for the Cleanup of Environmental Contaminants

The humungous load of pollutants added to the environment every day by the human activities is one of the major menaces facing by the world. Toxic substances released into the ecosystems are said to create imbalance to the equilibrium of the environment. Phytoremediation is a set of processes which have been considered as one of the most sustainable approaches to combat the problem of contaminants. Phytoremediation is considered to be more effective in comparison with traditional techniques because of the added benefits provided by the plants. The mechanisms adapted by the plants for extraction, accumulation, stabilization and degradation of contaminants from the polluted sites have been explored in this chapter. Various floral species which have been reported by several researchers that have the potential to remediate contaminated sites are listed in this report. The bioenergy crops, medicinal plants, trees and weeds have been found to be the best options for phytoremediation. Phytoremediation has proven to have a holistic approach which can help in restoration of contaminated sites with production timber, essential oils, energy, and employment to the rural peoples and with several other ecosystem services.

Lightning alarm system using stochastic modelling

It is true that such a long-range forecasting of lightning is not possible, the reason being the abrupt high value of the parameters at the time of lightning strike as compared to other weather conditions. But still a system that will predict the occurrence of lightning over few minutes or few hours will be beneficial for protection of lives and equipments. In this work, atmospheric electric field data are used for devising an alarm system for lightning. With the help of Markov chain stochastic modelling of the electric field data, probabilities of a lightning strike are calculated.

Mitigation of abiotic stresses in Lycopersicon esculentum by endophytic bacteria

In this paper, we emphasize the role of endophytic bacterial strains viz. Bacillus subtilis, two strains of Pseudomonas sp. in mitigating drought and salinity stress in tomato plants. Uninoculated or control plants showed arrested growth; while endophytic bacteria inoculated plants had appreciably higher plant biomass and chlorophyll contents under draught and salinity stress. The improving effects of endophytic bacterial inoculation were also substantiated by the augmented water potential and diminished electrolytic leak. Endophytic bacterial treated plants had copiously present K+ and P+ ions while less Na+ ion concentration in contrast to uninoculated under stress. The oxidative stress was reduced by endophytes through lessened activities of total polyphenol and enzymes catalase, peroxidase, polyphenol oxidase, as compared to the uninoculated plants. Uninoculated plants exhibited up-regulation of stress-responsive abscisic acid as compared to endophyte treated plants, while salicylic acid (SA) and gibberellin 4 were considerably higher in endophytic treated plants. In inference, application of endophytic bacterial strains may be used in relegated agricultural lands to upsurge crop sustainability and productivity.

Characterization of the atmospheric boundary layer from radiosonde observations along eastern end of monsoon trough of India

In this paper, a comparison of two methods for the calculation of the height of atmospheric boundary layer (ABL), using balloon-borne GPS radiosonde data is presented. ABL has been characterized using vertical profiles of meteorological parameter. The gradient of virtual potential temperature (𝜃v) profile for the determination of mixed layer heights (MLH) and the mean value of turbulent flow depth (TFD) obtained from the vertical profile of Bulk Richardson Number (RiB) have been used in this study. One-year data have been used for the study. There is large seasonal variability in MLH with a peak in the summer and winter whereas the TFD remained steady throughout the year. Results from the present study indicate that the magnitudes of TFD are often larger than the MLH.