An adaptive reliable transport protocol based on automatic reSend reQuest(ASQ) technique for wireless sensor networks

There are many wireless sensor network(WSN) applications which require reliable data transfer between the nodes. Several techniques including link level retransmission, error correction methods and hybrid Automatic Repeat re- Quest(ARQ) were introduced into the wireless sensor networks for ensuring reliability. In this paper, we use Automatic reSend request(ASQ) technique with regular acknowledgement to design reliable end-to-end communication protocol, called Adaptive Reliable Transport(ARTP) protocol, for WSNs. Besides ensuring reliability, objective of ARTP protocol is to ensure message stream FIFO at the receiver side instead of the byte stream FIFO used in TCP/IP protocol suite. To realize this objective, a new protocol stack has been used in the ARTP protocol. The ARTP protocol saves energy without affecting the throughput by sending three different types of acknowledgements, viz. ACK, NACK and FNACK with semantics different from that existing in the literature currently and adapting to the network conditions. Additionally, the protocol controls flow based on the receiver's feedback and congestion by holding ACK messages. To the best of our knowledge, there has been little or no attempt to build a receiver controlled regularly acknowledged reliable communication protocol. We have carried out extensive simulation studies of our protocol using Castalia simulator, and the study shows that our protocol performs better than related protocols in wireless/wire line networks, in terms of throughput and energy efficiency.

A weighted average based external clock synchronization protocol for wireless sensor networks

Clock synchronization is an extremely important requirement of wireless sensor networks(WSNs). There are many application scenarios such as weather monitoring and forecasting etc. where external clock synchronization may be required because WSN itself may consists of components which are not connected to each other. A usual approach for external clock synchronization in WSNs is to synchronize the clock of a reference node with an external source such as UTC, and the remaining nodes synchronize with the reference node using an internal clock synchronization protocol. In order to provide highly accurate time, both the offset and the drift rate of each clock with respect to reference node are estimated from time to time, and these are used for getting correct time from local clock reading. A problem with this approach is that it is difficult to estimate the offset of a clock with respect to the reference node when drift rate of clocks varies over a period of time. In this paper, we first propose a novel internal clock synchronization protocol based on weighted averaging technique, which synchronizes all the clocks of a WSN to a reference node periodically. We call this protocol weighted average based internal clock synchronization(WICS) protocol. Based on this protocol, we then propose our weighted average based external clock synchronization(WECS) protocol. We have analyzed the proposed protocols for maximum synchronization error and shown that it is always upper bounded. Extensive simulation studies of the proposed protocols have been carried out using Castalia simulator. Simulation results validate our theoretical claim that the maximum synchronization error is always upper bounded and also show that the proposed protocols perform better in comparison to other protocols in terms of synchronization accuracy. A prototype implementation of the proposed internal clock synchronization protocol using a few TelosB motes also validates our claim.

Effect of Metal Ions (Ag, Co, Ni, and Pd) on the Visible Light Degradation of Rhodamine B by Carbon-Covered Alumina-Supported TiO2 in Aqueous Solutions

Metal-ion (Ag, Co, Ni, and Pd) doped TiO2 nanocatalysts were successfully embedded on carbon-covered alumina supports. The CCA-embedded catalysts were crystalline and had a high surface area compared to the free metal-ion doped titania nanocatalysts while they still retained the anatase phase of the core TiO2. These catalysts were photocatalytically active under solar light irradiation. Rhodamine B was used as a model pollutant and the reactivity followed a pseudo-first-order reaction kinetics. The reaction rate of the CCA-supported catalysts was Pd > Ag > Co > Ni. Among the ratios of the CCA:catalyst used, it was found that the 1:1 ratio had the fastest reaction rate, followed by the 1:2 ratio, while the 2:1 ratio exhibited the lowest reaction rate. The CCA/metal-ion doped titania were found to have photocatalytic activities comparable with those of CCA-supported titania.

Photocatalytic degradation with combustion synthesized WO3 and WO3-TiO2 mixed oxides under UV and visible light

The photocatalytic activity of commercial titanium dioxide under UV and visible radiation was improved by composites of tungsten trioxide (WO3) with TiO2. WO3 was prepared by solution combustion synthesis and the mixed oxides/composites of WO3-TiO2 were prepared in different weight ratios (0, 0.10, 0.15, 0.20, 0.25, 0.50, 0.75, and 1) by physical mixing. These catalysts were characterized by XRD, DRS, BET, SEM, TEM, pH drift method, TGA and photoluminescence. The photocatalytic activity varies with the WO3 loading in the composites. The optimum loading of WO3 in the composites was found to be 15 wt% for both UV and visible radiation. This loading showed faster dye degradation rate than commercial TiO2 (TiO2-C) and WO3 (WO3-C). The effect of initial concentrations of methylene blue (MB) and orange G (OG) and the effect of the functional group on dye degradation was studied with both anionic and cationic dyes with 15 wt% WO3-TiO2. (C) 2012 Elsevier B.V. All rights reserved.

Capacity of fading gaussian channel with an energy harvesting sensor node

Network life time maximization is becoming an important design goal in wireless sensor networks. Energy harvesting has recently become a preferred choice for achieving this goal as it provides near perpetual operation. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over a fading AWGN channel with perfect/no channel state information provided at the transmitter. We obtain an achievable rate when there are inefficiencies in energy storage and the capacity when energy is spent in activities other than transmission.

Queuing theoretic and information theoretic capacity of energy harvesting sensor nodes

Energy harvesting sensor networks provide near perpetual operation and reduce carbon emissions thereby supporting `green communication'. We study such a sensor node powered with an energy harvesting source. We obtain energy management policies that are throughput optimal. We also obtain delay-optimal policies. Next we obtain the Shannon capacity of such a system. Further we combine the information theoretic and queuing theoretic approaches to obtain the Shannon capacity of an energy harvesting sensor node with a data queue. Then we generalize these results to models with fading and energy consumption in activities other than transmission.

Information capacity of energy harvesting sensor nodes

Sensor nodes with energy harvesting sources are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting `green communication'. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over an AWGN channel and show that the capacity achieving energy management policies are related to the throughput optimal policies. We also obtain the capacity when energy conserving sleep-wake modes are supported and an achievable rate for the system with inefficiencies in energy storage.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Multiple transmitter localization and communication footprint identification using sparse reconstruction techniques

This paper considers the problem of identifying the footprints of communication of multiple transmitters in a given geographical area. To do this, a number of sensors are deployed at arbitrary but known locations in the area, and their individual decisions regarding the presence or absence of the transmitters' signal are combined at a fusion center to reconstruct the spatial spectral usage map. One straightforward scheme to construct this map is to query each of the sensors and cluster the sensors that detect the primary's signal. However, using the fact that a typical transmitter footprint map is a sparse image, two novel compressive sensing based schemes are proposed, which require significantly fewer number of transmissions compared to the querying scheme. A key feature of the proposed schemes is that the measurement matrix is constructed from a pseudo-random binary phase shift applied to the decision of each sensor prior to transmission. The measurement matrix is thus a binary ensemble which satisfies the restricted isometry property. The number of measurements needed for accurate footprint reconstruction is determined using compressive sampling theory. The three schemes are compared through simulations in terms of a performance measure that quantifies the accuracy of the reconstructed spatial spectral usage map. It is found that the proposed sparse reconstruction technique-based schemes significantly outperform the round-robin scheme.

Active-contour-based automated image quantitation techniques for Western Blot analysis

Western Blot analysis is an analytical technique used in Molecular Biology, Biochemistry, Immunogenetics and other Molecular Biology studies to separate proteins by electrophoresis. The procedure results in images containing nearly rectangular-shaped blots. In this paper, we address the problem of quantitation of the blots using automated image processing techniques. We formulate a special active contour (or snake) called Oblong, which locks on to rectangular shaped objects. Oblongs depend on five free parameters, which is also the minimum number of parameters required for a unique characterization. Unlike many snake formulations, Oblongs do not require explicit gradient computations and therefore the optimization is carried out fast. The performance of Oblongs is assessed on synthesized data and Western Blot Analysis images.

Non-invasive human breath sensor

In this paper, the design and development of a novel low-cost, non-invasive type sensor suitable for human breath sensing is reported. It can be used to detect respiratory disorders like bronchial asthma by analyzing the recorded breathing pattern. Though there are devices like spirometer to diagnose asthma, they are very inconvenient for patient's use because patients are made to exhale air through mouth forcefully. Presently developed sensor will overcome this limitation and is helpful in the diagnosis of respiratory related abnormalities. Polyvinylidene fluoride (PVDF) film in cantilever configuration is used as a sensing element to form the breath sensor. Two identical sensors are mounted on a spectacle frame, such that the tidal flow of inhaled and exhale air will impinge on sensor, for sensing the breathing patterns. These patterns are recorded, filtered, analyzed and displayed using CRO. Further the sensor is calibrated using a U-tube water manometer. The added advantage of piezoelectric type sensing element is that it is self powered without the need of any external power source.

Fracture processes of self compacting Concrete using image analysis

The mode I fracture toughness of concrete can be experimentally determined using three point bend beam in conjunction with digital image correlation (DIC). Three different geometrically similar sizes of beams are cast for this study. To study the influence of fly ash and silica fume on fracture toughness of SCC, three SCC mixes are prepared with and without mineral additions. The scanning electron microscope (SEM) images are taken on the fractured surface to add information on fracture process in SCC. From this study, it is concluded that the fracture toughness of SCC with mineral addition is higher when compared to those without mineral addition.

Limit laws for coverage in backbone-sensor networks

We study coverage in sensor networks having two types of nodes, namely, sensor nodes and backbone nodes. Each sensor is capable of transmitting information over relatively small distances. The backbone nodes collect information from the sensors. This information is processed and communicated over an ad hoc network formed by the backbone nodes, which are capable of transmitting over much larger distances. We consider two models of deployment for the sensor and backbone nodes. One is a PoissonPoisson cluster model and the other a dependently thinned Poisson point process. We deduce limit laws for functionals of vacancy in both models using properties of association for random measures.

Evaluation of kissing bond in composite adhesive lap joints using digital image correlation: Preliminary studies

The assembly of aerospace and automotive structures in recent years is increasingly carried out using adhesives. Adhesive joints have advantages of uniform stress distribution and less stress concentration in the bonded region. Nevertheless, they may suffer due to the presence of defects in bond line and at the interface or due to improper curing process. While defects like voids, cracks and delaminations present in the adhesive bond line may be detected using different NDE methods, interfacial defects in the form of kissing bond may go undetected. Attempts using advanced ultrasonic methods like nonlinear ultrasound and guided wave inspection to detect kissing bond have met with limited success stressing the need for alternate methods. This paper concerns the preliminary studies carried out on detectability of dry contact kissing bonds in adhesive joints using the Digital Image Correlation (DIC) technique. In this attempt, adhesive joint samples containing varied area of kissing bond were prepared using the glass fiber reinforced composite (GFRP) as substrates and epoxy resin as the adhesive layer joining them. The samples were also subjected to conventional and high power ultrasonic inspection. Further, these samples were loaded till failure to determine the bond strength during which digital images were recorded and analyzed using the DIC method. This noncontact method could indicate the existence of kissing bonds at less than 50% failure load. Finite element studies carried out showed a similar trend. Results obtained from these preliminary studies are encouraging and further tests need to be done on a larger set of samples to study experimental uncertainties and scatter associated with the method. (C) 2013 Elsevier Ltd. All rights reserved.

Bilateral smoothing of gradient vector field and application to image segmentation

Medical image segmentation finds application in computer-aided diagnosis, computer-guided surgery, measuring tissue volumes, locating tumors, and pathologies. One approach to segmentation is to use active contours or snakes. Active contours start from an initialization (often manually specified) and are guided by image-dependent forces to the object boundary. Snakes may also be guided by gradient vector fields associated with an image. The first main result in this direction is that of Xu and Prince, who proposed the notion of gradient vector flow (GVF), which is computed iteratively. We propose a new formalism to compute the vector flow based on the notion of bilateral filtering of the gradient field associated with the edge map - we refer to it as the bilateral vector flow (BVF). The range kernel definition that we employ is different from the one employed in the standard Gaussian bilateral filter. The advantage of the BVF formalism is that smooth gradient vector flow fields with enhanced edge information can be computed noniteratively. The quality of image segmentation turned out to be on par with that obtained using the GVF and in some cases better than the GVF.