Fractional-Grasshopper Optimization Algorithm for the Sensor Activation Control in Wireless Sensor Networks

Wireless Personal Communications - The progression in wireless sensor network (WSN) has been increased and gained immense attention in computer vision. In WSN, a large number of sensors are...     

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.     

A genetic algorithm‐based flow update scheduler for software‐defined networks

Software‐defined networking (SDN) facilitates network programmability through a central controller. It dynamically modifies the network configuration to adapt to the changes in the network. In SDN, the controller updates the network configuration through flow updates, ie, installing the flow rules in network devices. However, during the network update, improper scheduling of flow updates can lead to a number of problems including overflowing of the switch flow table memory and the link bandwidth. Another challenge is minimizing the network update completion time during large‐network updates triggered by events such as traffic engineering path updates. The existing centralized approaches do not search the solution space for flow update schedules with optimal completion time. We proposed a hybrid genetic algorithm‐based flow update scheduling method (the GA‐Flow Scheduler). By searching the solution space, the GA‐Flow Scheduler attempts to minimize the completion time of the network update without overflowing the flow table memory of the switches and the link bandwidth. It can be used in combination with other existing flow scheduling methods to improve the network performance and reduce the flow update completion time. In this paper, the GA‐Flow Scheduler is combined with a stand‐alone method called the three‐step method. Through large‐scale experiments, we show that the proposed hybrid approach could reduce the network update time and packet loss. It is concluded that the proposed GA‐Flow Scheduler provides improved performance over the stand‐alone three‐step method. Also, it handles the above‐mentioned network update problems in SDN.     

Classification of complex environments using pixel level fusion of satellite data

The present study reports classification and analysis of composite land features using fusion images obtained by fusing two original hyperspectral and multispectral datasets. The high spatial-spectral resolution, multi-instrument and multi-period satellite images were used for fusion. Three pixel level fusion based techniques, Color Normalized Spectral Sharpening (CNSS), Principal Component Spectral Sharpening Transform (PCSST) and Gram-Schmidt Transform (GST), were implemented on the datasets. Performance evaluations of three fusion algorithms were done using classification results. The Support Vector Machine (SVM) and Gaussian Maximum Likelihood Classification (MLC) were used for classification using five types of images, viz. hyperspectral, multispectral and three fused images. Number of classes considered was eight. Sufficient number of ground field data for each class has also been acquired which was needed for supervise based classification. The accuracy was improved from 74.44 to 97.65% when the fused images were considered with SVM classifier. Similarly, the results were improved from 69.25 to 94.61% with original and fused data using MLC classifier. The fusion image technique was found to be superior to the single original image and the SVM is better than the MLC method.

     

A classification and systematic review of product line feature model defects

Product line (PL)-based development is a thriving research area to develop software-intensive systems. Feature models (FMs) facilitate derivation of valid products from a PL by managing commonalities and variabilities among software products. However, the researchers in academia as well as in the industries experience difficulties in quality assessment of FMs. The increasing complexity and size of FMs may lead to defects, which outweigh the benefits of PL. This paper provides a systematic literature review and key research issues related to the FM defects in PL. We derive a typology of FM defects according to their level of importance. The information on defects’ identification and explanations are provided with formalization. Further, corrective explanations are presented which incorporates various techniques used to fix defects with their implementation. This information would help software engineering community by enabling developers or modelers to find the types of defects and their causes and to choose an appropriate technique to fix defects in order to produce defect-free products from FMs, thereby enhancing the overall quality of PL-based development.

     

Graphene based tunable and wideband terahertz antenna for wireless network communication

A wide band terahertz dipole-antenna using graphene with tunable resonant frequency is proposed. Presence of graphene in the antenna is shown to electrically tune resonant frequency and to push the antenna to resonate with multibands in terahertz regime. The proposed terahertz antenna shows maximum of five tuning frequencies and better performance parameters such as return loss of ?? 39.7 dB, maximum directivity of 9.3 dB, five resonant tuning frequencies (multi resonances) at chemical voltage of 0.5 eV, maximum fractional bandwidth of 15.6%, maximum radiation efficiency of 21.5% and large bandwidth of 2.32 THz. Large bandwidth of the antenna can be very useful for highest possible data transfer among wireless devices. The proposed graphene based terahertz antenna has the dimensions of few micrometers so miniaturization i.e. size is reduced to 0.007 mm² which is suitable for size limited future applications such as Wireless Networks on Chip, software defined meta material and Wireless Nano Sensor Networks (WNSNs). Size of the proposed terahertz antenna is less than that reported in the literature. One reconfigurable and miniaturized antenna may replace a number of single function radiators, thereby also cost and size of a WNSNs can be abridged while performance is improved.

     

Traffic aware field-based routing for wireless sensor networks

Telecommunication Systems - Route estimation process often involves significant message exchanges among wireless sensor nodes while selecting the least cost path. Nodes along this path handle more...     

A fuzzy decision scheme for relay selection in cooperative wireless sensor network

In energy‐constrained military wireless sensor networks, minimizing the bit error rate (BER) with little compromise on network lifetime is one of the most challenging issues. This paper presents a new relay selection based on fuzzy logic (RSFL) scheme which provides balance between these parameters. The proposed scheme considers node's residual energy and path loss of the relay‐destination link as the input parameters for the selection of the relay node. The relay node selection by fuzzy logic is based on prioritizing higher residual energy and minimum path loss. To evaluate the performance on wireless sensor network, we compare the proposed scheme with the three existing relay selection strategies, ie, random, maximum residual energy based relay selection (MaxRes), and minimum energy consumption based relay selection (MinEnCon). The simulation results of the proposed scheme in terms of network lifetime, BER, Network Survivability Index (NSI), and average energy of network nodes have been presented and compared with different relay selection schemes. The simulation results show that the proposed RSFL scheme has the lowest BER, moderate network lifetime, average energy, and NSI.     

Unsupervised hyperspectral band selection using ranking based on a denoising error matching approach

In this paper, we focus on utilizing the image denoising method for ranking of significant bands in hyperspectral imagery. We make use of the fact that the denoising error of bands varies with the significant information content of the bands in hyperspectral imagery. The denoising error is computed for each band individually and compared using a matching parameter with the denoising error of a reference image. The reference image is selected to be the first principal component corresponding to the maximum information. Three matching parameters including mutual information (MI), correlation coefficient (r) and the structural similarity index (SSIM) were used for ranking the bands based on the match with the denoising error of the reference image. The proposed algorithm is tested using three datasets, namely, Indian Pines, Salinas and Dhundi. The Indian Pines and Salinas datasets were acquired from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and comprised rural and agricultural area. The Dhundi dataset of Hyperion comprises mostly of features corresponding to snow-covered mountainous regions. To assess the accuracy of the proposed method, a supervised classification was carried out using a random forest classifier with 20% training pixels selected randomly from the ground reference. The proposed method yielded significantly better results determined by the kappa coefficient (κ) of 0.756, 0.910 and 0.996 for the Indian Pines, Salinas and Dhundi datasets, respectively, over several other state of the art methods. The classification results of the proposed method also yielded better results than those obtained by the state-of-the-art methods for hyperspectral band selection.     

Characterization of Canadian biomass for alternative renewable biofuel

Biomass represents the renewable energy source and their use reduces the consumption of fossil fuels and limits the emission of CO₂, SO_x, NO_x and heavy metals. They are used in pyrolysis, gasification, combustion and co-combustion. Present study aims to highlight the common biomass available in Canada such as wheat straw, barley straw, flax straw, timothy grass and pinewood. The biomass samples were collected form Saskatoon, Canada and examined for their physical and chemical characteristics using static bomb calorimeter, XRD, TGA, ICP-MS, CHNSO, FT-IR and FT-NIR. The biomass samples were subjected to three-step extraction process, i.e. hexane, alcohol and water extraction separately, after extraction the raffinate biomass was acid hydrolyzed. The acid soluble fractions, which mainly contained degraded sugars, were analysed by HPLC and the lignin content was determined using acid insoluble fraction. The hexane extract (i.e. waxes), alcohol extract and lignin were characterized by FT-IR spectroscopy. Among all the biomass samples pinewood shows lower ash and lignin content, while shows higher calorific value, cellulose and hemicellulose content. The appreciable amount of hexane soluble in pinewood was due to the presence of terpene hydrocarbons. However among the agricultural biomass samples barley straw shows higher ash, wax and lignin content compared to wheat and flax straw. All these properties combined together have shown that pinewood, wheat and flax can act as the potential candidates for bio-energy production.