Efficient and Reliable Task Scheduling, Network Reprogramming, and Data Storage for Wireless Sensor Networks

Wireless sensor networks (WSNs) typically consist of a large number of resource-constrained nodes. The limited computational resources afforded by these nodes present unique development challenges. In this dissertation, we consider three such challenges. The first challenge focuses on minimizing energy usage in WSNs through intelligent duty cycling. Limited energy resources dictate the design of many embedded applications, causing such systems to be composed of small, modular tasks, scheduled periodically. In this model, each embedded device wakes, executes a task-set, and returns to sleep. These systems spend most of their time in a state of deep sleep to minimize power consumption. We refer to these systems as almost-always-sleeping (AAS) systems. We describe a series of task schedulers for AAS systems designed to maximize sleep time. We consider four scheduler designs, model their performance, and present detailed performance analysis results under varying load conditions. The second challenge focuses on a fast and reliable network reprogramming solution for WSNs based on incremental code updates. We first present VSPIN, a framework for developing incremental code update mechanisms to support efficient reprogramming of WSNs. VSPIN provides a modular testing platform on the host system to plug-in and evaluate various incremental code update algorithms. The framework supports Avrdude, among the most popular Linux-based programming tools for AVR microcontrollers. Using VSPIN, we next present an incremental code update strategy to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg\u27s Algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce the edit map size. Finally, we present experimental results exploring the reduction in data size that it enables. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions - leading to significantly lower energy costs for wireless sensor network reprogramming. The third challenge focuses on enabling fast and reliable data storage in wireless sensor systems. A file storage system that is fast, lightweight, and reliable across device failures is important to safeguard the data that these devices record. A fast and efficient file system enables sensed data to be sampled and stored quickly and batched for later transmission. A reliable file system allows seamless operation without disruptions due to hardware, software, or other unforeseen failures. While flash technology provides persistent storage by itself, it has limitations that prevent it from being used in mission-critical deployment scenarios. Hybrid memory models which utilize newer non-volatile memory technologies, such as ferroelectric RAM (FRAM), can mitigate the physical disadvantages of flash. In this vein, we present the design and implementation of LoggerFS, a fast, lightweight, and reliable file system for wireless sensor networks, which uses a hybrid memory design consisting of RAM, FRAM, and flash. LoggerFS is engineered to provide fast data storage, have a small memory footprint, and provide data reliability across system failures. LoggerFS adapts a log-structured file system approach, augmented with data persistence and reliability guarantees. A caching mechanism allows for flash wear-leveling and fast data buffering. We present a performance evaluation of LoggerFS using a prototypical in-situ sensing platform and demonstrate between 50% and 800% improvements for various workloads using the FRAM write-back cache over the implementation without the cache

Characterization of plant growth promoting rhizobia from root nodule of <i>Crotolaria pallida</i> grown in Assam

210-216From five different sites of Assam, 22 bacterial strains were isolated from the root nodules of Crotolaria pallida. The isolates were subjected to morphological and physiological characterization, and compared with reference strains Rhizobium leguminoserum MTCC-99, Bradyrhizobium japonicum MTCC-120 and Mesorhizobium thiogangeticum MTCC-7001. Although isolates showed close similarity in their morphological features, they had wide variation in their physiological features. All the 10 selected isolates were found to be potent phosphate solubilizer and the isolate DCP1 showed the highest phosphate solubilization efficiency (PSE; 187%). IAA production was detected in 8 isolates with the highest production (66 µg/mL) by MKCP1. The isolates also showed wide variation in their pH and salt tolerance ability. NifH gene analysis revealed the presence of nifH gene in 6 isolates. The result of PCR-RFLP grouped the isolates into three different 16S rDNA types; 2 isolates were related to B. japonicum MTCC-120, 6 isolates were related to R. leguminoserum MTCC-99 and 2 isolates were related to M. thiogangeticum MTCC-7001

Lost sales reduction and quality improvement with variable lead time and fuzzy costs in an imperfect production system

This article investigates the effects of lost sales reduction and quality improvement in an imperfect production process under imprecise environment with simultaneously optimizing reorder point, order quantity, and lead time. This study assumes that the demand during lead time follows a mixture of normal distributions and the cost components are imprecise and vague. Under these assumptions, the aim is to study the lost sales reduction and the quality improvement in an uncertainty environment. The objective function in fuzzy sense is defuzzified using Modified Graded Mean Integration Representation Method (MGMIRM). For the defuzzified objective function, theoretical results are developed to establish optimal policies. Finally, some numerical examples and sensitivity analysis are provided to examine the effects of non-stochastic uncertainty

Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network

The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF) by using Weglin's design criteria based on 1/3&amp;ndash;2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF) as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive

Development of a Fuzzy Economic Order Quantity Model of Deteriorating Items with Promotional Effort and Learning in Fuzziness with a Finite Time Horizon

This study investigates an economic order quantity model of deteriorating items, where demand is fuzzy in nature and depends on promotional effort with full backorder for a given time horizon. The learning effect in the fuzzy environment is added in this model. A constant deterioration rate is assumed. Under these circumstances, a mathematical model is developed to curtail the total cost over a finite time horizon by determining the replenishment order quantity, number of replenishments, and the fraction of the replenishment cycle when inventory is positive. A solution algorithm is developed to find the optimal solutions. The applicability of the proposed model is illustrated through numerical examples. To get further insights, sensitivity analysis is carried out for the main parameters in crisp, fuzzy, and fuzzy-learning environments

Barium, calcium and magnesium doped mesoporous ceria supported gold nanoparticle for benzyl alcohol oxidation using molecular O2

In the era of sustainable energy, catalysis using gold nanoparticles has drawn considerable attention from world researchers. Oxidation of benzyl alcohol by molecular O2 is an atom efficient path to synthesize benzaldehyde. Nanocrystalline ceria has been proven as a useful support to disperse gold nanoparticles since last few years, however there are a few reports on mesoporous ceria supported gold nanoparticles. In this work a systematic investigation was carried out to improve the activity of Au/CeO2 catalyst by incorporating Ba2+, Ca2+ and Mg2+ cations into the ceria lattice through a sol–gel procedure. Both the doped ceria and ceria supported gold nanoparticles are characterized by BET S.A, XRD, TEM, SAXS, XPS, TPR, CO2-TPD techniques. BET S.A measurements show the mesoporous oxides where H3 hysteresis loops are found. The decrease in the crystallite size of ceria after doping by metal cations is observed in the XRD measurement. The TEM and HRTEM characterization shows the nanocrystalline particle size around 30–50 nm and gold nanoparticles around 10–15 nm in size. Distribution in the particle size for doped ceria have been obtained using SAXS measurements where narrow distributions of ceria particles are found in the 10–20 nm range. The existence of oxide vacancies and the mixture of Ce3+/Ce4+ oxidation states are observed for doped ceria materials in the XPS investigation. The strong gold-support interaction was also evidenced by XPS characterization where oxidic gold was found on the doped ceria surface. Lowering of the reduction peak in ceria after gold nanoparticle deposition was observed from TPR investigation whereas the change in basic site distribution is observed from CO2 TPD experiment, instigating new insights into the surface properties of the catalysts. The catalytic activities of the catalysts were determined for benzyl alcohol oxidation reactions using molecular O2. The catalytic activity was in the order of Au/Ba–CeO2 4 Au/Ca–CeO2 4 Au/Mg–CeO2 4 Au/CeO2. The synergistic effect of gold nanoparticles and dopant cations to the ceria was explained in this work

A Review on Mechanistic Insight of Plant Derived Anticancer Bioactive Phytocompounds and Their Structure Activity Relationship

Cancer is a disorder that rigorously affects the human population worldwide. There is a steady demand for new remedies to both treat and prevent this life-threatening sickness due to toxicities, drug resistance and therapeutic failures in current conventional therapies. Researchers around the world are drawing their attention towards compounds of natural origin. For decades, human beings have been using the flora of the world as a source of cancer chemotherapeutic agents. Currently, clinically approved anticancer compounds are vincristine, vinblastine, taxanes, and podophyllotoxin, all of which come from natural sources. With the triumph of these compounds that have been developed into staple drug products for most cancer therapies, new technologies are now appearing to search for novel biomolecules with anticancer activities. Ellipticine, camptothecin, combretastatin, curcumin, homoharringtonine and others are plant derived bioactive phytocompounds with potential anticancer properties. Researchers have improved the field further through the use of advanced analytical chemistry and computational tools of analysis. The investigation of new strategies for administration such as nanotechnology may enable the development of the phytocompounds as drug products. These technologies have enhanced the anticancer potential of plant-derived drugs with the aim of site-directed drug delivery, enhanced bioavailability, and reduced toxicity. This review discusses mechanistic insights into anticancer compounds of natural origins and their structural activity relationships that make them targets for anticancer treatments