CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.     

Iodine-catalyzed N-alkylation of tosylhydrazones with benzylic alcohols

A new and highly efficient method for the C–N bond formation using molecular iodine-catalyzed N-alkylation reaction of tosylhydrazones with benzylic/benzhydryl alcohols at room temperature in methylene chloride is described. A variety of tosylhydrazones reacted readily with various substituted benzylic alcohols in presence of 20 mol % iodine under mild reaction conditions to produce the corresponding biologically active N-alkylated compounds in good to excellent yields.     

Physicochemical and electrochemical properties of Gd3+-doped ZnSe thin films fabricated by single-step electrochemical deposition process

Journal of Solid State Electrochemistry - Gd3+ (gadolinium)-doped ZnSe thin films (1 to 5 mol%) are grown onto indium-doped tin oxide (ITO) glass substrate by single-step electrochemical...     

High performance dye-sensitized solar cell based on 2-mercaptobenzimidazole doped poly(vinylidinefluoride-co-hexafluoropropylene) based polymer electrolyte

In this work, the influence of 2-mercaptobenzimidazole (2-MCBI) on poly(vinylidinefluoride-co-hexafluoropropylene)/KI/I₂ (PVDF-HFP/KI/I₂) polymer electrolytes were studied. The pure and different weight percentage ratios (20, 30, 40 and 50%) of 2-MCBI doped PVDF-HFP/KI/I₂ electrolytes were prepared by a solution casting technique. The as-prepared polymer electrolyte films were characterized using various techniques such as Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), X-ray diffractometer (XRD), alternating current (AC)-impedance analysis. The addition of 2-MCBI with pure PVDF-HFP/KI/I₂ was found to increase the ionic conductivity of electrolyte. Among the various additions, 30 wt% 2-MCBI doped PVDF-HFP/KI/I₂ showed the highest room temperature ionic conductivity values than the others. The dye-sensitized solar cell (DSSC) fabricated using this optimized polymer electrolyte achieved a high power conversion efficiency of 4.40% than the pure PVDF-HFP/KI/I₂ (1.74%) at similar experimental conditions. Thus, the 2-MCBI doped polymer electrolyte has proven to be an effective substitute to the liquid electrolyte in DSSCs.     

Electrodeposited carbon-supported nickel sulfide thin films with enhanced stability in acid medium as hydrogen evolution reaction electrocatalyst

A single-step electrochemical deposition of NiS thin films incorporating various carbon nanomaterials as support is described. Advantages of this method are as follows: It is simpler and can be easily scaled up, the precursors employed are cheaper, and the deposition method is energy effective requiring no further heat treatments. Benefits of carbon nanomaterials as catalyst support are manifold including high conductivity and stability. The carbon-supported thin films exhibit high hydrogen evolution activity, low Tafel slopes, and improved double layer capacitance. A remarkable enhancement in the stability of the thin films in the acid medium has been observed. Specifically, NiS/carbon nanofibers have shown the highest activity, lowest Tafel slope, and retained more than 90% of its initial activity after the stability tests.

Open image in new window

Graphical abstract NiS/carbon thin films were fabricated through a highly energy-efficient method as active and highly stable electrocatalysts in acid medium for hydrogen evolution reaction.

     

Electrodeposited Co<Subscript>1-x</Subscript>Mo<Subscript>x</Subscript>S thin films as highly efficient electrocatalysts for hydrogen evolution reaction in acid medium

Efficient and cost-effective electrocatalysts for hydrogen evolution reaction (HER) are significant for the advancement of effective water splitting reaction. Herein, we describe the growth of cobalt molybdenum sulfide (CoMoS) at different composites of tightly packed nanocrystals, prepared by one step process of simple electrodeposition method on fluorine-doped tin oxide (FTO) substrate as highly active and low-cost HER electrocatalyst. The prepared electrocatalysts were characterized via various analytical techniques. The HER activity was evaluated through electrochemical methods such as cyclic voltammetry technique and impedance analysis. Exhaustive electrochemical examinations show that the Co_0.95Mo_0.05S achieved higher cathodic current density value of 14.3 mA/cm² at η?=?250 mV with a lowest Tafel slope value towards HER. Furthermore, the active surface area of the as-deposited composite materials has been calculated by cyclic voltammetry analysis. Hence, the present work illustrates that the Co_0.95Mo_0.05S composite can serve as an encouraging cost-effective substitute to platinum-based electrocatalyst for HER.     

Synthesis of Ni3V2O8@graphene oxide nanocomposite as an efficient electrode material for supercapacitor applications

Journal of Solid State Electrochemistry - In the present investigation, we have synthesized Ni3V2O8@GO composite by co-precipitation method and designed as a new anode material for supercapacitor...     

Benzylation of β-dicarbonyl compounds and 4-hydroxycoumarin using TMSOTf catalyst: a simple, mild, and efficient method

The direct benzylation of 1,3-dicarbonyl compounds and 4-hydroxycoumarin with a wide variety of benzylic alcohols was achieved using trimethylsilyl trifluoromethanesulfonate as an efficient catalyst. The reaction proceeded under very mild conditions at room temperature providing the desired products in good to excellent yields.     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.     

Synthesis of various carbon incorporated flower-like MoS2 microspheres as counter electrode for dye-sensitized solar cells

Journal of Solid State Electrochemistry - A flower-like molybdenum disulfide microspheres and various carbon materials (acetylene black, vulcan carbon, multi-walled carbon nanotubes, carbon...