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The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, we, for the first time, propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed timesaving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

The bioprinting technique with specialized tissue production allows the study of biological, physiological, and behavioral changes of cancerous and non-cancerous tissues in response to pharmacological compounds in personalized medicine. To this end, to evaluate the efficacy of anticancer drugs before entering the clinical setting, tissue engineered 3D scaffolds containing breast cancer and derived from the especially patient, similar to the original tissue architecture, can potentially be used. Despite recent advances in the manufacturing of 3D bioprinted breast cancer tissue (BCT), many studies still suffer from reproducibility primarily because of the uncertainty of the materials used in the scaffolds and lack of printing methods. In this review, we present an overview of the breast cancer environment to optimize personalized treatment by examining and identifying the physiological and biological factors that mimic BCT. We also surveyed the materials and techniques related to 3D bioprinting, i.e, 3D bioprinting systems, current strategies for fabrication of 3D bioprinting tissues, cell adhesion and migration in 3D bioprinted BCT, and 3D bioprinted breast cancer metastasis models. Finally, we emphasized on the prospective future applications of 3D bioprinted cancer models for rapid and accurate drug screening in breast cancer.

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 h of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

The demand for energy has increased tremendously around the whole world due to rapid urbanization and booming industrialization. Energy is the major key to achieving an improved social life, but energy production and utilization processes are the main contributors to environmental pollution and greenhouse gas emissions. Mitigation of the energy crisis and reduction in pollution (water and air) difficulties are the leading research topics nowadays. Carbonaceous materials offer some of the best solutions to minimize these problems in an easy and effective way. It is also advantageous that the sources of carbon-based materials are economical, the synthesis processes are comfortable, and the applications are environmentally friendly. Among carbonaceous materials, activated carbons, graphene, and carbon nanotubes have shown outstanding performance in mitigating the energy crisis and environmental pollution. These three carbonaceous materials exhibit unique adsorption properties for energy storage, water purification, and gas cleansing due to their outstanding electrical conductivity, large specific surface areas, and strong mechanical strength. This paper reviews the synthesis methods for activated carbons, carbon nanotubes, and graphene and their significant applications in energy storage, water treatment, and carbon dioxide gas capture to improve environmental sustainability.

Novel conjugated carbon dots (CDs) were synthesized as two-photon active photosensitisers to unleash lethal reactive oxygen species (ROS) for nucleus-targeting photodynamic therapy (PDT). To enhance the therapeutic efficiency and preclude non-specific CD uptake, we employed a combination of folic acid and curcumin for two-photon NIR-triggered ROS generation and enhanced internalization in the nucleus. Consequently, enhanced destruction of cancer cells occurred by directly attacking the DNA. The intrinsic ROS generation and nucleus-targeting ability of CDs introduced multifunctional two-photon active nanoprobes within a single platform for enhanced PDT in oral cancer theranostics.

IoT enables a new set of novel applications that promise to transform the physical world as we know it today. Current IoT applications have demonstrated the feasibility and promise of this technology, but there is still a large space open for innovation. We present our vision for the next generation of IoT devices: IoT devices that are autonomous, in terms of energy and communication, as well as cost-efficient to deploy. We present enabling-technologies to realize our vision and open research challenges.

With the rapid development of sensor networks and embedded computing technologies, miniaturized wearable healthcare monitoring devices have become practically feasible. For many of these devices, accelerometer-based user authentication systems by gait analysis are becoming a hot research topic. However, a major bottleneck of such system is it requires continuous sampling of accelerometer, which reduces battery life of wearable sensors. In this paper, we present KEH-Gait , which advocates use of output voltage signal from kinetic energy harvester (KEH) as the source for gait recognition. KEH-Gait is motivated by the prospect of significant power saving by not having to sample the accelerometer at all. Indeed, our measurements show that, compared to conventional accelerometer-based gait detection, KEH-Gait can reduce energy consumption by 82.15 percent. The feasibility of KEH-Gait is based on the fact that human gait has distinctive movement patterns for different individuals, which is expected to leave distinctive patterns for KEH as well. We evaluate the performance of KEH-Gait using two different types of KEH hardware on a data set of 20 subjects. Our experiments demonstrate that, although KEH-Gait yields slightly lower accuracy than accelerometer-based gait detection when single step is used, the accuracy problem can be overcome by the proposed Probability-based Multi-Step Sparse Representation Classification (PMSSRC). Moreover, the security analysis shows that the EER of KEH-Gait against an active spoofing attacker is 11.2 and 14.1 percent using two different types of KEH hardware, respectively.

On-body health monitoring involves continuous sensing and wireless data transmissions, which severely impacts the battery life of wearable devices. However, rapid advances in kinetic energy harvesting (KEH) soon might obviate the need for battery recharging or replacement by harnessing human motion energy. The authors discuss the challenges and opportunities of KEH technology to help realize the ultimate vision of fully autonomous IoT-based healthcare.

Biocompatible and photostable fluorescent probes are crucial yet challenging to develop for visualising and tracking biological functions and interactions that occur in living organisms. We synthesised biocompatible CQDs with 15% QY and tested them for cellular and scaffold imaging at variable depths. The CQDs, synthesised from collagen under controlled and benign conditions in a hydrothermal reactor, were characterised for their key physicochemical properties. The fluorescence characteristics were determined using two-photon microscopy, and based on our results we propose a mechanism for CQD luminescence by combining the carbogenic core and edge-effect contributions to the PL behaviour. The bioimaging of cells embedded in a luminescent 3D printed scaffold showed that CQDs enable imaging at depths of about 1500 μm under biomimetic conditions. Real-time videography and imaging tests showed differential visualisation of individual cells and scaffold. The excellent photostability and non-photobleaching characteristics of CQDs make them suitable for long-term whole cell and tissue imaging via multi-photon microscopy.

How to effectively delivering therapeutic agents, including γ-secretase inhibitors (GSIs), into live cells, remains a significant challenge. This study assessed the effect of Notch signaling inhibition by examining levels of the Notch1 intracellular domain (N1ICD) in cultured oral cancer cells analyzed with random stitched images (2D) and 3D visualizations using confocal microscopy and quantitative gene analysis. Substantially, we have developed a novel method to assist the delivery of γ-secretase inhibitor, DAPT, into live cells in the presence of an effective minimum concentration of Triton-X100 (0.001%) without damaging cell activity and membrane integrity assessed with cell proliferation assays. The images obtained in this study showed that DAPT alone could not block the γ-secretase inhibitor despite inhibiting cell growth. Further analysis of quantitative gene expressions of Notch signaling canonical pathway to verify the effectiveness of the novel method for delivering inhibitor into live cells, displayed deregulation of Notch1, Delta-like ligand 1 (DLL1) and hairy and enhancer of split 1 (Hes1). Our data suggest that Notch1/Hes1 signaling pathway is deactivated using DAPT with a low dose of Triton-X100 in this cancer cells. And the finding also suggests that Notch1 could be engaged by DLL1 to promote differentiation in oral cancer cells. Using this approach, we demonstrate that Triton-X100 is a promising and effective permeabilization agent to deliver γ-secretase inhibitor DAPT into live oral epithelial cells. This strategy has the potential to implicate in the treatment of cancer diseases.

A new model is here proposed to investigate the effects of nano-ferroliquid under the influence of low oscillating over stretchable rotating disk. The basic governing equations are formulated under the effects of magnetic field. The resulting system of partial differential equations is first reduced in non-dimensional form by using proper transformations and then reduced coupled system of differential equations is solved analytically by means of homotopy analysis method (HAM). The physical interpretation of velocity and temperature towards different emerging parameters such as particle concentration and effective magnetization parameter are discussed graphically. The physical parameters such as shear stress at wall, heat transfer rate through wall, boundary layer thickness and volume flow rate in axial direction are also presented in tabular form. Finally a comparison with the existing literature is made as a limiting case of the reported problem and found in good agreement.

Advances in nanotechnology are paving the way for a new generation of sensor networks at the nanoscale, promising radically new applications in medical, biological, and chemical fields. At the nanoscale, it is difficult to use large batteries for prolonged operations, necessitating energy harvesting as the most viable solution for these nanosensor networks. Unfortunately, the rate at which energy can be harvested at the nanoscale may not be sufficient to simultaneously power all of the conventional device components, including the microprocessor, memory, sensor, and the communication radio. We propose a simplified architecture for energy-harvesting nanosensor networks that uses the energy signatures of monitored events for efficient event recognition at the sink. Using the instantaneous harvested energy to transmit a short pulse of proportional amplitude, the proposed architecture obviates the need for several conventional sensor node elements, including the microprocessor, memory, and sensor. In this article, we validate the feasibility of the architecture using a novel application of nanosensor networks for microscopic monitoring of chemical reactors, and we demonstrate that different types of reactions can be detected accurately at a remote sink.

Porous nitrogen-doped graphene samples were synthesized and tuned via pyrolysis of solid nitrogen precursor dimethyl-aminoterephthalate with graphene oxide as template. Our investigations show that the extent of thermal treatment, total concentration of nitrogen and the nature of nitrogen moieties play important roles in enhancing oxygen reduction reaction (ORR) and CO 2 uptake. N-doped graphene synthesized at 650 °C (NG-650) with specific BET surface area of 278 m 2 /g, exhibits enhanced CO 2 sorption capacity of 4.43 mmol/g (at 298 K, 1 bar) with exceptional selectivity (CO 2 :N 2 = 42) and cyclic regeneration stability. In contrast, nitrogen-doped graphene synthesized at 750 °C (NG-750) demonstrated excellent catalytic activity for ORR via favourable 4e − transfer, performance stability with tests conducted up to 5000 cycles, and is unaffected by methanol cross-over effect. Thus, NG-750 shows potential to replace metal-based electrodes for fuel cell application. The comparative results for ORR with non-doped and nitrogen-doped graphene electrodes showed that graphitic nitrogen sites play vital role in enhancing catalytic activity.

We characterize terahertz wireless channels for extracting data from nanoscale sensors deployed within human lungs. We discover that the inhalation and exhalation of oxygen and carbon dioxide causes periodic variation of the absorption coefficient of the terahertz channel. Channel absorption drops to its minimum near the end of inhalation, providing a window of opportunity to extract data with minimum transmission power. We propose an algorithm for nanosensors to estimate the periodic channel by observing signal-to-noise ratio of the beacons transmitted from the data sink. Using real respiration data from multiple subjects, we demonstrate that the proposed algorithm can estimate the minimum absorption interval of the periodic channel with 98.5% accuracy. Our analysis shows that by confining all data collections during the estimated low-absorption window of the periodic channel, nanosensors can reduce power consumption by six orders of magnitude. Finally, we demonstrate that for wireless communications within human lungs, 0.1–0.12 THz is the least absorbing spectrum within the terahertz band.

Internet browsing over cellular networks is a major source of power consumption in mobile devices. A device could potentially reduce its browsing power consumption by downloading a page from the browser cache of a proximate device over the short-range low-power non-cellular device-to-device (D2D) wireless link, such as Bluetooth. Using an Android-based cooperative browser prototype, this paper experimentally characterizes the power saving opportunity of such D2D cache cooperation. In particular, we model the impact of cellular network power management protocol and the dynamic components of web pages on the power saving of D2D cooperation. We find that the power saving is limited (upper bounded) by the power management parameter values of cellular networks, which could vary significantly from operator to operator. Based on previously reported cellular power management data and the data obtained from our own experiments, the upper bound can vary from as low as 15-51%. Our model also shows that the existence of dynamic components in the web page decreases the power saving further proportional to the amount of dynamic components. We validate the model with power measurement data from smartphones using real cellular networks for Internet access and Bluetooth connectivity for D2D cache access. By downloading most popular web pages and studying the distribution of their dynamic components, we find that the expected amount of dynamic component in a popular page is rather small, allowing D2D cooperation to achieve device power saving close to (95% of) its upper bound.

It is reported that mobile users spend most of their time on texting SMS, Social Networking, Emailing, or sending instant messaging (IM), all of which involve text input. There are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each one of them engages a different set of hardware and consequently consumes different amounts of battery energy. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three input modalities. We find that the length of interaction, or the message length, determines the most energy efficient modality. For short interactions, less than 14-30 characters, SK is the most energy efficient. For longer interactions, however, STT significantly outperforms both SK and Swype. When message length distributions of popular text activities are considered, STT provides near optimal energy consumption without requiring the user to predict the message length and decide between SK and STT. In terms of battery life, the choice of input modality makes significant differences. If users always choose SK for all their text activities, they will consume nearly 50% of the phone battery each day. Choosing STT over SK can save 30%-40% of the battery depending on the choice of STT software.

Hypertext transfer protocol (HTTP) is the fundamental mechanics supporting web browsing on the Internet. An HTTP server stores large volumes of contents and delivers specific pieces to the clients when requested. There is a recent move to use HTTP for video streaming as well, which promises seamless integration of video delivery to existing HTTP-based server platforms. This is achieved by segmenting the video into many small chunks and storing these chunks as separate files on the server. For adaptive streaming, the server stores different quality versions of the same chunk in different files to allow real-time quality adaptation of the video due to network bandwidth variation experienced by a client. For each chunk of the video, which quality version to download, therefore, becomes a major decision-making challenge for the streaming client, especially in vehicular environment with significant uncertainty in mobile bandwidth. In this paper, we demonstrate that for such decision making, the Markov decision process (MDP) is superior to previously proposed non-MDP solutions. Using publicly available video and bandwidth datasets, we show that the MDP achieves up to a 15x reduction in playback deadline miss compared to a well-known non-MDP solution when the MDP has the prior knowledge of the bandwidth model. We also consider a model-free MDP implementation that uses Q-learning to gradually learn the optimal decisions by continuously observing the outcome of its decision making. We find that the MDP with Q-learning significantly outperforms the MDP that uses bandwidth models.

Doping of graphene has emerged as a key strategy to improve the electrocatalytic performance of the oxygen reduction reaction (ORR). Activated graphene co-doped with iodine and nitrogen atoms (NIG) was developed in this work using a facile scalable approach. The onset potential, current density, and four-electron reduction pathway of the newly developed catalyst were significantly improved. The charge-transfer resistance of co-doped NIG was found to be much lower than nitrogen-doped graphene (NG); furthermore, the stability of NIG and its resistance to methanol crossover were also improved. The synergistically enhanced ORR performance of NIG was found to be a result of a high strain and size advantage of the larger iodine atom clusters (compared to nitrogen), which facilitate the simultaneous enrichment of anode electrons and O2 and H2 O molecule transport at catalytic sites, inducing four-electron transfer in a single step. These results are promising for application in alkaline fuel cells.

Recent advancements in energy-harvesting hardware have created an opportunity for realizing batteryless wearables for continuous and pervasive human activity recognition (HAR). Unfortunately, power consumption of accelerometers used in conventional HAR is relatively high compared to the amount of power that can be harvested practically, which limits the usefulness of energy harvesting. Here, the authors present and evaluate a novel energy-harvesting wearable sensor architecture, HAR from Kinetic Energy (HARKE), that doesn't require using an accelerometer. Using off-the-shelf products, the authors demonstrate that the voltage of a kinetic harvester exhibits distinguishable patterns to distinctly infer human activities. Their results demonstrate that HARKE is as accurate as an accelerometer-based HAR system, yet consumes only a small fraction of the limited harvested energy.

The design and development of efficient TiO 2 -based, hybrid, nanostructured photocatalysts has recently been receiving substantial attention for environmental remediation due to their excellent physiochemical properties. This article provides an overview of the synthesis strategies and characteristics of the next-generation TiO 2 -based hybrid photocatalysts, produced in combination with polymers (e.g., polyaniline, polypyrrole, polythiophene) and carbon nanomaterials (e.g., graphene, GO, CNT, carbon quantum dots, carbon nitride). The structural aspects, nanostructure formation process, parameters affecting catalytic activity, photocatalytic mechanisms and photocatalytic applications of TiO 2 -based catalysts for efficient photocatalytic degradation of gaseous/volatile organic pollutants in water/air are reviewed. Further, current research trends, means to increase catalytic performance and future prospects of high-performance TiO 2 -based hybrid photocatalytic materials, are briefly summarized.

A novel method for nitrogen doping of graphene via solid-state impregnation was developed using graphene oxide (GO) as the raw substrate and aminoterephthalic acid as the doping agent via a facile thermal treatment at 750 °C. The structure, morphology and chemical composition of the synthesised N-doped graphene were characterised using XRD, SEM, EDS and XPS. The N-graphene product exhibits homogeneous doping with high nitrogen content (∼6 at%) in four configurations: pyridinic-N, pyrrolic-N, pyridinic-N-oxide and graphitic-N. The electric double layer capacitor (EDLC) fabricated using an N-doped graphene electrode attained a specific capacitance of 210 F g−1 (at a current density of 1 A g−1), which was greater than the values attained by pristine graphene and a GO electrode by factors of about two and six, respectively. Our synthesised N-graphene shows supercapacitance at a low electrolyte concentration compared to supercapacitors reported in the literature for high electrolyte concentrations with similar electrodes. The EDLC device we constructed based on N-graphene showed excellent charge–discharge stability for tests of up to 5000 cycles with high capacity retention (>90%). A comparison of the electrochemical performance of GO, graphene and N-graphene demonstrated that doping with nitrogen can dramatically enhance capacitance.

Free-standing composite films based on graphene/polyaniline (PANI) nanostructure were synthesized for supercapacitor application. First, polyaniline nanospheres were prepared by microemulsion polymerization, followed by the incorporation of graphene oxide nanosheets by hierarchical organization. The cationic PANI nanospheres were attached to the anionic GO sheets by electrostatic interaction and hierarchical deposition of GO/PANI nanostructures on a membrane filter via targeted self-assembly. Subsequent in situ chemical reduction of GO using hydroiodic acid produced well-defined graphene/PANI nanostructures having interpenetrating network with 3D open structure. The in situ GO reduction enabled keeping the hierarchical organization intact. The flexible graphene/PANI film produced was tested as a supercapacitor electrode using voltammograms and Nyquist analysis. The electro-capacitance of the film (448 F/g) was enhanced by 60% through the synergistic combination of graphene and PANI nanostructures. About 81% capacity retention was achieved for the composite compared to 38% for PANI alone after subjecting the samples to 5000 cyclic operations.

The arsenic-iron sludge generated in most of the treatment systems around the world is discharged into the nearest watercourse, which leads to accumulative rise of arsenic and iron concentrations in water. In this study, attempts were made to use the arsenic-iron sludge in making bricks and to analyze the corresponding effects on brick properties. The water treatment plant sludge is extremely close to brick clay in chemical composition. So, the sludge could be a potential substitute for brick clay. This study involved the addition of sludge with ratios 3%, 6%, 9% and 12% of the total weight of sludge-clay mixture. The physical and chemical properties of the produced bricks were then determined and evaluated and compared to control brick made entirely from clay. Results of different tests indicated that the sludge proportion and firing temperature were the two key factors in determining the quality of bricks. The compressive strength of 3%, 6%, 9% and 12% sludge containing brick samples were found to be 14.1 MPa, 15.1 MPa, 9.4 MPa and 7.1 MPa, respectively. These results indicate that the compressive strength of prepared bricks initially increased and then decreased with the increase of sludge proportion. Leaching characteristics of burnt bricks were determined with the variation of pH at a constant temperature. The optimum amount of sludge that could be mixed with clay to produce good bonding of clay-sludge bricks was found to be 6% (safely maximum) by weight.

Network mobility (NEMO) allows various types of in-vehicle networks (e.g., WLANs inside public transport vehicle) to be seamlessly connected to the Internet. An on-board mobile router (MR) connects the moving network to the Internet by means of high-speed cellular mobile data services. Unlike terminal mobility, where the mobile hosts (MHs) connect to the cellular base station directly, MHs in NEMO encounter an additional wireless link (MR–MH) before they get connected to the Internet. In this paper, we first note the impact of this additional wireless link on the performance of the wireless enhancements of TCP and observe that the existing TCP enhancement schemes designed for conventional terminal mobility are not equally effective in NEMO. So, we propose an extension of TCP, called on-board TCP (obTCP), to effectively address the double wireless link related issues in NEMO. We compare obTCP against a classical scheme, called snoop, known for its effectiveness in terminal mobility, and analytically demonstrate that the performance gain of obTCP over snoop increases linearly with the delays, and non-linearly with the loss probabilities in the wireless links. Finally, we extend these analyses to obtain throughput models for snoop and obTCP in NEMO. The throughput models are validated through ns-2 simulations.

Aqueous phase exfoliation was developed for producing high-yield graphene nanosheets from expandedgraphite (EG). The process included ultrasonication with sodium dodecyl sulfate (SDS) emulsion in aque-ous phase. The high throughput exfoliation process was characterized by UV–vis spectroscopy, transmis-sion electron microscopy (TEM) and electrical impedance spectroscopy (EIS). Controlled sonicationexperiments revealed that optimum exfoliation corresponds to maxima in UV–vis spectra. TEM resultsshowed that the exfoliated graphene comprised nanoflakes having 65 layers ( 60%) and 610 layersfor 90% of the product. The potential use of this highly dispersed graphene was demonstrated by one-pot synthesis of graphene/polymer composite via in situ emulsion polymerization with styrene. The inte-grated role of SDS included adsorption and exfoliation of graphite, dispersion of graphene produced andassisting with micelle formation in emulsion. The high surface area graphene nanosheets as dispersedphase in polymeric nanocomposites showed significant improvement in thermal stability and electricalconductivity. 2013 Elsevier Inc. All rights reserved.

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbors for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbor positions. We contend and demonstrate that periodic beaconing regardless of the node mobility and traffic patterns in the network is not attractive from both update cost and routing performance points of view. We propose the Adaptive Position Update (APU) strategy for geographic routing, which dynamically adjusts the frequency of position updates based on the mobility dynamics of the nodes and the forwarding patterns in the network. APU is based on two simple principles: 1) nodes whose movements are harder to predict update their positions more frequently (and vice versa), and (ii) nodes closer to forwarding paths update their positions more frequently (and vice versa). Our theoretical analysis, which is validated by NS2 simulations of a well-known geographic routing protocol, Greedy Perimeter Stateless Routing Protocol (GPSR), shows that APU can significantly reduce the update cost and improve the routing performance in terms of packet delivery ratio and average end-to-end delay in comparison with periodic beaconing and other recently proposed updating schemes. The benefits of APU are further confirmed by undertaking evaluations in realistic network scenarios, which account for localization error, realistic radio propagation, and sparse network.

In the Southeast Asia region, arsenic contamination of groundwater has been reported in several countries and a large number of arsenic treatment units were installed in many regions. Microbial activity can greatly affect the mobilization of arsenic under anaerobic conditions when coupled with the oxidation of organic matter. In this study, a mathematical model was developed to predict the bioleaching of arsenic in response to organic matter contamination in the treatment unit. Calibration of the model was performed using the data of batch experiments, and finally the results obtained from the laboratory experiments were used to verify the developed model. The microbial decomposition of organic matter led to an anaerobic condition within the accumulated sludge in the sand filter and hence caused the bioleaching of arsenic. Under the inoperative condition of the treatment unit for 5, 10, and 15 days along with organic matter contamination of 15 mg/L as biochemical oxygen demand, the concentrations of bioleached arsenic were found to be 59, 184, and 275 μg/L, respectively. This study revealed the bioleaching potential of arsenic due to organic matter inclusion in the treatment process, which might contribute in designing safe options for drinking water.

We model the user's quality of experience (QoE) in a wireless voice over IP (VoIP) service as a function of the amount of effort the user has to put to continue her conversation. We assume that users would quit or terminate an ongoing call if they have to put more efforts than they could tolerate. Not knowing the tolerance threshold of each individual user, the service provider faces a decision dilemma of whether to fix the network problem immediately whenever he detects a user effort in the VoIP system, or ignore it with the hope that the user may still continue the call anyway. In this paper, we formulate the provider's dilemma as a non-cooperative game between the provider and the VoIP user experiencing a deteriorating QoE. We demonstrate that providers implementing the equilibrium solutions can expect to not only increase their revenues, but also reduce the number of cases when users quit out of frustration thus minimizing potential churning. We also discuss conditions under which a sophisticated user may or may not benefit from faking unwarranted efforts with a goal of receiving a better service from the provider. Finally, we conduct a subjective experiment of VoIP over WiFi, which verifies the key model assumption that perceptual quality is negatively correlated to the amount of effort the user has to put to continue the call.

It is widely evidenced that location has a significant influence on the actual bandwidth that can be expected from Wireless Wide Area Networks (WWANs), e.g., 3G. Because a fast-moving vehicle continuously changes its location, vehicular mobile computing is confronted with the possibility of significant variations in available network bandwidth. While it is difficult for providers to eliminate bandwidth disparity over a large service area, it may be possible to map network bandwidth to the road network through repeated measurements. In this paper, we report results of an extensive measurement campaign to demonstrate the viability of such bandwidth maps. We show how bandwidth maps can be interfaced with adaptive multimedia servers and the emerging vehicular communication systems that use on-board mobile routers to deliver Internet services to the passengers. Using simulation experiments driven by our measurement data, we quantify the improvement in Quality of Service (QoS) that can be achieved by taking advantage of the geographical knowledge of bandwidth provided by the bandwidth maps. We find that our approach reduces the frequency of disruptions in perceived QoS for both audio and video applications in high-speed vehicular mobility by several orders of magnitude.

Cooperative positioning (CP) can potentially improve the accuracy of vehicle location information, which is vital for several road safety applications. Although concepts of CP have been introduced, the efficiency of CP under real-world vehicular communication constraints is largely unknown. Our simulations reveal that the frequent exchange of large amounts of range information required by existing CP schemes not only increases the packet collision rate of the vehicular network but reduces the effectiveness of the CP as well. To address this issue, we propose simple easily deployable protocol improvements in terms of utilizing as much range information as possible, reducing range broadcasts by piggybacking, compressing the range information, tuning the broadcast frequency, and combining multiple packets using network coding. Our results demonstrate that, even under dense traffic conditions, these protocol improvements achieve a twofold reduction in packet loss rates and increase the positioning accuracy of CP by 40%.

Oxidation of ferrous ions by iron-oxidizing bacteria facilitates adsorption of arsenic on iron and subsequent co-precipitation. Based on this technique, a novel treatment process has been developed and tested under field conditions for simultaneous iron and arsenic removal from groundwater. In this study, a biological fixed bed reactor containing coconut husk and subsequent layers of iron matrix, charcoal and sand filter combined the biological and physicochemical treatment processes in a single system. Experimental results showed that arsenic could be efficiently treated from initial concentration of 500 μg L− 1 to residual (effluent) concentrations lower than 15 μg L− 1. Simultaneous significant iron removal efficiency (over 95%) was also achieved. Microbial water quality examination confirmed absence of any contamination in the treated water. Simple and inexpensive construction using locally available materials, considerable treatment performance as well as ease of operation and maintenance present the developed system as a promising one.

Arsenic is found to exist within the shallow zones of groundwater in many countries and a large number of drinking water treatment units have been installed to combat this arsenic poisoning. Microbial activity can greatly affect the mobilization of arsenic under anaerobic conditions when coupled with the oxidation of organic matter. In this study, the bioleaching of arsenic in response to organic matter inclusion in a drinking water treatment process has been elucidated. The decomposition of organic matter due to the microbial action has led to an anaerobic condition within the accumulated sludge in the treatment unit and hence caused the bioleaching of arsenic, above 130 μg/L, with the effluent water. Nevertheless, the biofilm structure, related to the oxygen penetration, had significant influence on the bioleaching of arsenic even in the circumferential aerobic condition of the biofilm. For a biofilm thickness around 400 μm, an anaerobic bottom part was noticed and hence a clear step-up concentration of arsenic was observed in the associated bulk liquid. This study suggests that organic matter would have a sufficiently important influence on the microbiological transformation of arsenic to warrant its consideration in designing safe remediation strategies in the context of arsenic removal processes for drinking water.

The energy expended by sensor nodes in data communication makes up a significant quantum of their total energy consumption. Consequently, a mathematical model that can accurately predict the communication traffic load of a sensor node is critical for designing efficient sensor network protocols. In this paper, we present an analytical model for estimating the per-node traffic load in a multi-hop wireless sensor network. We consider a typical scenario wherein, the sensor nodes periodically sense the environment and forward the collected samples to a sink using greedy geographic routing. The analysis incorporates the idealistic circular coverage radio model as well as a realistic model, log-normal shadowing. Our results confirm that irrespective of the radio model, the traffic load generally increases as a function of the node's proximity to the sink. However, in the immediate vicinity of the sink, the two radio models yield quite contrasting results. The ideal radio model reveals the existence of a volcano region near the sink, where the traffic load drops significantly. On the contrary, with the log-normal shadowing model, the opposite effect is observed, wherein the traffic load actually increases at a much higher rate as one approaches the sink, resulting in the formation of a mountain peak. The results from our analysis are validated by extensive simulations.

We investigate the effects of using dual transmission power to achieve quality-of-service (QoS) differentiation in IEEE 802.11 wireless LANs. Specifically, we assume all stations employ the standard IEEE 802.11 distributed coordination function (DCF), with the exception that some stations transmit at a higher power than others. Consequently, in the event of a collision involving frames from both power levels, a high-power frame is more likely to be received correctly as a result of the so-called capture effect (i.e. received at a power sufficiently higher than that of the interference, allowing it to be decoded correctly). This effectively leads to QoS differentiation between two classes, corresponding to the high-power and low-power stations. We develop a Markov model for the IEEE 802.11 DCF in a network with dual transmission power over a Rayleigh-fading channel, and use it to evaluate the resulting performance, in terms of the key metrics of throughput and delay. We explore how the performance of the service classes depends on the proportion of stations in each class and the transmission power ratio, focusing in particular on the bounds achieved in the limit case of 'ideal differentiation', characterised by a perfect capture probability of high-power frames. We find that significant performance differentiation between high-power and low-power stations is achieved even with transmission power ratios that are not very high, leading us to conclude that employing multiple transmission power levels is a viable and efficient approach for service quality differentiation in wireless LANs.