Highly Sensitive Carbon Nanotubes Coated Etched Fiber Bragg Grating Sensor for Humidity Sensing

The sensing of relative humidity (RH) at room temperature has potential applications in several areas ranging from biomedical to horticulture, paper, and textile industries. In this paper, a highly sensitive humidity sensor based on carbon nanotubes (CNTs) coated on the surface of an etched fiber Bragg grating (EFBG) sensor has been demonstrated, for detecting RH over a wide range of 20%-90% at room temperature. When water molecules interact with the CNT coated EFBG, the effective refractive index of the fiber core changes, resulting in a shift in the Bragg wavelength. It has been possible to achieve a high sensitivity of similar to 31 pm/% RH, which is the highest compared with many of the existing FBG-based humidity sensors. The limit of detection in the CNT coated EFBG has been found to be similar to 0.03 RH. The experimental data shows a linear response of Bragg wavelength shift with increase in humidity. This novel method of incorporating CNTs on to the FBG sensor for humidity sensing has not been reported before.

Emergency Handling in MICS based Body Area Network

Body Area Network, a new wireless networking paradigm, promises to revolutionize the healthcare applications. A number of tiny sensor nodes are strategically placed in and around the human body to obtain physiological information. The sensor nodes are connected to a coordinator or a data collector to form a Body Area Network. The tiny devices may sense physiological parameters of emergency in nature (e.g. abnormality in heart bit rate, increase of glucose level above the threshold etc.) that needs immediate attention of a physician. Due to ultra low power requirement of wireless body area network, most of the time, the coordinator and devices are expected to be in the dormant mode, categorically when network is not operational. This leads to an open question, how to handle and meet the QoS requirement of emergency data when network is not operational? Emergency handling becomes more challenging at the MAC layer, if the channel access related information is unknown to the device with emergency message. The aforementioned scenarios are very likely scenarios in a MICS (Medical Implant Communication Service, 402-405 MHz) based healthcare systems. This paper proposes a mechanism for timely and reliable transfer of emergency data in a MICS based Body Area Network. We validate our protocol design with simulation in a C++ framework. Our simulation results show that more than 99 p ercentage of the time emergency messages are reached at the coordinator with a delay of 400ms.

Adaptive Sleep-Wake Control using Reinforcement Learning in Sensor Networks

The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.

Quick, Decentralized, Energy-Efficient One-Shot Max Function Computation Using Timer-Based Selection

In several wireless sensor networks, it is of interest to determine the maximum of the sensor readings and identify the sensor responsible for it. We propose a novel, decentralized, scalable, energy-efficient, timer-based, one-shot max function computation (TMC) algorithm. In it, the sensor nodes do not transmit their readings in a centrally pre-defined sequence. Instead, the nodes are grouped into clusters, and computation occurs over two contention stages. First, the nodes in each cluster contend with each other using the timer scheme to transmit their reading to their cluster-heads. Thereafter, the cluster-heads use the timer scheme to transmit the highest sensor reading in their cluster to the fusion node. One new challenge is that the use of the timer scheme leads to collisions, which can make the algorithm fail. We optimize the algorithm to minimize the average time required to determine the maximum subject to a constraint on the probability that it fails to find the maximum. TMC significantly lowers average function computation time, average number of transmissions, and average energy consumption compared to approaches proposed in the literature.

Electron-Rich Triphenylamine-Based Sensors for Picric Acid Detection

This paper demonstrates the role of solvent in selectivity and sensitivity of a series of electron-rich compounds for the detection of trace amounts of picric acid. Two new electron-rich fluorescent esters (6, 7) containing a triphenylamine backbone as well as their analogous carboxylic acids (8, 9) have been synthesized and characterized. Fluorescent triphenylamine coupled with an ethynyl moiety constitutes p-electron-rich selective and sensitive probes for electron-deficient picric acid (PA). In solution, the high sensitivity of all the sensors toward PA can be attributed to a combined effect of the ground-state charge-transfer complex formation and resonance energy transfer between the sensor and analyte. The acids 8 and 9 also showed enhanced sensitivity for nitroaromatics in the solid state, and their enhanced sensitivity could be attributed to exciton migration due to close proximity of the neighboring acid molecules, as evident from the X-ray diffraction study. The compounds were found to be quite sensitive for the detection of trace amount of nitroaromatics in solution, solid, and contact mode.

An Optoelectronic Profiling and Ranging Sensor for Monitoring of Perimeters

Electronic monitoring of perimeters plays vital roles in homeland security, management of traffic and of humanwildlife conflict. This paper reports the design and development of an optical beam-interruption-based ranging and profiling sensor for monitoring perimeters. The developed sensor system can determine the distance of the object from the sensing units and its temporal height profile as the object crosses the system. Together, these quantities can also be used to classify the object and to determine its speed. The sensor is designed, fabricated, and evaluated. The design enables compact construction, high sensitivity, and low measurement crosstalk. The evaluation demonstrates accuracy better than 98.5% in the determination of height and over 94% in determination of the distance of an object from the sensing units. Finally, a strategy is proposed to classify the objects based on the obtained height profiles. The strategy is demonstrated to correctly classify the objects despite differences in their speed and the location at which they cross the system.

Energy Sharing for Multiple Sensor Nodes With Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Relay Selection with Channel Probing in Sleep-Wake Cycling Wireless Sensor Networks

In geographical forwarding of packets in a large wireless sensor network (WSN) with sleep-wake cycling nodes, we are interested in the local decision problem faced by a node that has ``custody'' of a packet and has to choose one among a set of next-hop relay nodes to forward the packet toward the sink. Each relay is associated with a ``reward'' that summarizes the benefit of forwarding the packet through that relay. We seek a solution to this local problem, the idea being that such a solution, if adopted by every node, could provide a reasonable heuristic for the end-to-end forwarding problem. Toward this end, we propose a local relay selection problem consisting of a forwarding node and a collection of relay nodes, with the relays waking up sequentially at random times. At each relay wake-up instant, the forwarder can choose to probe a relay to learn its reward value, based on which the forwarder can then decide whether to stop (and forward its packet to the chosen relay) or to continue to wait for further relays to wake up. The forwarder's objective is to select a relay so as to minimize a combination of waiting delay, reward, and probing cost. The local decision problem can be considered as a variant of the asset selling problem studied in the operations research literature. We formulate the local problem as a Markov decision process (MDP) and characterize the solution in terms of stopping sets and probing sets. We provide results illustrating the structure of the stopping sets, namely, the (lower bound) threshold and the stage independence properties. Regarding the probing sets, we make an interesting conjecture that these sets are characterized by upper bounds. Through simulation experiments, we provide valuable insights into the performance of the optimal local forwarding and its use as an end-to-end forwarding heuristic.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Developing acetylcholinesterase-based inhibition assay by modulated synthesis of silver nanoparticles: applications for sensing of organophosphorus pesticides

A novel and highly sensitive sensing strategy for the detection of organophosphorus compounds (OPs) based on the catalytic reaction of acetylcholinesterase (AChE) and acetylcholine (ATCh) during the modulated synthesis of silver nanoparticles (AgNPs) has been developed. The enzymatic hydrolysis of ATCh by AChE yields thiocholine (TCh), which induces the aggregation of AgNPs during synthesis, and the absorption peak at 382 nm corresponding to AgNPs decreases. The enzymatic reaction can be regulated by OPs, which can covalently bind to the active site of AChE and decrease the TCh formation, thereby decreasing the aggregation and significantly enhancing the absorption peak at 382 nm. The proposed system achieved good linearity and limits of detection of 0.078 nM and 2.402 nM for trichlorfon and malathion, respectively, by UV-visible spectroscopy. Further, the sensitivity of the proposed system was demonstrated through the determination of OPs in different spiked real samples. The described work shows the potential application for further development of a colorimetric sensor for other OP pesticide detection during the synthesis of AgNPs using enzyme-based assays.

A TDMA-based Energy Aware MAC (TEA-MAC) Protocol for Reliable Multicast in WSNs

Multicast in wireless sensor networks (WSNs) is an efficient way to spread the same data to multiple sensor nodes. It becomes more effective due to the broadcast nature of wireless link, where a message transmitted from one source is inherently received by all one-hop receivers, and therefore, there is no need to transmit the message one by one. Reliable multicast in WSNs is desirable for critical tasks like code updation and query based data collection. The erroneous nature of wireless medium coupled with limited resource of sensor nodes, makes the design of reliable multicast protocol a challenging task. In this work, we propose a time division multiple access (TDMA) based energy aware media access and control (TEA-MAC) protocol for reliable multicast in WSNs. The TDMA eliminates collisions, overhearing and idle listening, which are the main sources of reliability degradation and energy consumption. Furthermore, the proposed protocol is parametric in the sense that it can be used to trade-off reliability with energy and delay as per the requirement of the underlying applications. The performance of TEA-MAC has been evaluated by simulating it using Castalia network simulator. Simulation results show that TEA-MAC is able to considerably improve the performance of multicast communication in WSNs.

High-Performance Sensors Based on Resistance Fluctuations of Single-Layer-Graphene Transistors

One of the most interesting predicted applications of graphenemonolayer-based devices is as high-quality sensors. In this article, we show, through systematic experiments, a chemical vapor sensor based on the measurement of lowfrequency resistance fluctuations of single-layer-graphene field-effect-transistor devices. The sensor has extremely high sensitivity, very high specificity, high fidelity, and fast response times. The performance of the device using this scheme of measurement (which uses resistance fluctuations as the detection parameter) is more than 2 orders of magnitude better than a detection scheme in which changes in the average value of the resistance is monitored. We propose a number-densityfluctuation-based model to explain the superior characteristics of a noisemeasurement-based detection scheme presented in this article.

Stability analysis of thick piezoelectric metal based FGM plate using first order and higher order shear deformation theory

This paper presents the stability analysis of functionally graded plate integrated with piezoelectric actuator and sensor at the top and bottom face, subjected to electrical and mechanical loading. The finite element formulation is based on first order and higher order shear deformation theory, degenerated shell element, von-Karman hypothesis and piezoelectric effect. The equation for static analysis is derived by using the minimum energy principle and solutions for critical buckling load is obtained by solving eigenvalue problem. The material properties of the functionally graded plate are assumed to be graded along the thickness direction according to simple power law function. Two types of boundary conditions are used, such as SSSS (simply supported) and CSCS (simply supported along two opposite side perpendicular to the direction of compression and clamped along the other two sides). Sensor voltage is calculated using present analysis for various power law indices and FG (functionally graded) material gradations. The stability analysis of piezoelectric FG plate is carried out to present the effects of power law index, material variations, applied mechanical pressure and piezo effect on buckling and stability characteristics of FG plate.

QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that the number of hops in the path from each sensor to its BS is bounded by h(max), and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios.

As-You-Go Deployment of a 2-Connected Wireless Relay Network for Sensor-Sink Interconnection

A person walks along a line (which could be an idealisation of a forest trail, for example), placing relays as he walks, in order to create a multihop network for connecting a sensor at a point along the line to a sink at the start of the line. The potential placement points are equally spaced along the line, and at each such location the decision to place or not to place a relay is based on link quality measurements to the previously placed relays. The location of the sensor is unknown apriori, and is discovered as the deployment agent walks. In this paper, we extend our earlier work on this class of problems to include the objective of achieving a 2-connected multihop network. We propose a network cost objective that is additive over the deployed relays, and accounts for possible alternate routing over the multiple available paths. As in our earlier work, the problem is formulated as a Markov decision process. Placement algorithms are obtained for two source location models, which yield a discounted cost MDP and an average cost MDP. In each case we obtain structural results for an optimal policy, and perform a numerical study that provides insights into the advantages and disadvantages of multi-connectivity. We validate the results obtained from numerical study experimentally in a forest-like environment.