Parametric Optimization of Complementary Split-Ring Resonator Dimensions for Planar Antenna Size Miniaturization

Wireless Personal Communications -     

Cell‐Laden Hydrogels for Multikingdom 3D Printing

Living materials are created through the embedding of live, whole cells into a matrix that can house and sustain the viability of the encapsulated cells. Through the immobilization of these cells, their bioactivity can be harnessed for applications such as bioreactors for the production of high‐value chemicals. While the interest in living materials is growing, many existing materials lack robust structure and are difficult to pattern. Furthermore, many living materials employ only one type of microorganism, or microbial consortia with little control over the arrangement of the various cell types. In this work, a Pluronic F127‐based hydrogel system is characterized for the encapsulation of algae, yeast, and bacteria to create living materials. This hydrogel system is also demonstrated to be an excellent material for additive manufacturing in the form of direct write 3D‐printing to spatially arrange the cells within a single printed construct. These living materials allow for the development of incredibly complex, immobilized consortia, and the results detailed herein further enhance the understanding of how cells behave within living material matrices. The utilization of these materials allows for interesting applications of multikingdom microbial cultures in immobilized bioreactor or biosensing technologies.     

Secure and efficient anonymous authentication scheme for three-tier mobile healthcare systems with wearable sensors

The mobility and openness of wireless communication technologies make Mobile Healthcare Systems (mHealth) potentially exposed to a number of potential attacks, which significantly undermines their utility and impedes their widespread deployment. Attackers and criminals, even without knowing the context of the transmitted data, with simple eavesdropping on the wireless links, may benefit a lot from linking activities to the identities of patient’s sensors and medical staff members. These vulnerabilities apply to all tiers of the mHealth system. A new anonymous mutual authentication scheme for three-tier mobile healthcare systems with wearable sensors is proposed in this paper. Our scheme consists of three protocols: Protocol-1 allows the anonymous authentication nodes (mobile users and controller nodes) and the HSP medical server in the third tier, while Protocol-2 realizes the anonymous authentication between mobile users and controller nodes in the second tier, and Protocol-3 achieves the anonymous authentication between controller nodes and the wearable body sensors in the first tier. In the design of our protocols, the variation in the resource constraints of the different nodes in the mHealth system are taken into consideration so that our protocols make a better trade-off among security, efficiency and practicality. The security of our protocols are analyzed through rigorous formal proofs using BAN logic tool and informal discussions of security features, possible attacks and countermeasures. Besides, the efficiency of our protocols are concretely evaluated and compared with related schemes. The comparisons show that our scheme outperforms the previous schemes and provides more complete and integrated anonymous authentication services. Finally, the security of our protocols are evaluated by using the Automated Validation of Internet Security Protocols and Applications and the SPAN animator software. The simulation results show that our scheme is secure and satisfy all the specified privacy and authentication goals.     

ORuML: Optimized Routing in wireless networks using Machine Learning

Routing is a process of selecting a path in a network for delivering a packet from source node to destination node. Successful delivery of a message is a challenge, and therefore, this paper proposes an algorithm for a wireless network called Optimized Routing in wireless networks using Machine Learning (ORuML), which uses machine learning algorithm namely, K‐nearest neighbor (KNN), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR), to predict the network type of the source and destination nodes. The ML model is trained by using characteristic features of a node collected in real time such as battery power utilization, available internal storage, IP address, and range of a node. Intuitively, the MLR should outperform KNN and SVM in terms of accuracy and Area under ROC Curve (AUC). The proposed algorithm determines whether the source and destination nodes are co‐located and also, determines the best neighboring hop for efficient routing.     

Visual Speech Recognition Using Optical Flow and Hidden Markov Model

Wireless Personal Communications - The present work proposes audio-visual speech recognition with the use of Gammatone frequency cepstral coefficient (GFCC) and optical flow (OF) features with...     

A Novel Heterogeneous Clustering Protocol for Lifetime Maximization of Wireless Sensor Network

The network lifetime of Wireless Sensor Network (WSN) is one of the most challenging issues for any network protocol. The nodes in the network are densely deployed and are provided with limited power supply. The routing strategy is treated as an effective solution to improve the lifetime of the network. The cluster based routing techniques are used in the WSN to enhance the network lifespan and to minimize the energy consumption of the network. In this paper, an energy efficient heterogeneous clustering protocol for the enhancement of the network lifetime is proposed. The proposed protocol uses the sensor energy for the clustering process in a well-organized manner to maximize the lifetime of network. The MATLAB simulator is used for implementing the clustering model of proposed protocol and for measuring the effectiveness of the proposed technique the comparison is performed with the various existing approaches such as Stability Election Protocol, Distributed Energy Efficient Clustering and Adaptive Threshold Energy Efficient cross layer based Routing.

     

Symptotics: a framework for estimating the scalability of real-world wireless networks

We present a framework for non-asymptotic analysis of real-world multi-hop wireless networks that captures protocol overhead, congestion bottlenecks, traffic heterogeneity and other real-world concerns. The framework introduces the concept of symptotic scalability to determine the number of nodes to which a network scales, and a metric called change impact value for comparing the impact of underlying system parameters on network scalability. A key idea is to divide analysis into generic and specific parts connected via a signature—a set of governing parameters of a network scenario—such that analyzing a new network scenario reduces mainly to identifying its signature. Using this framework, we present the first closed-form symptotic scalability expressions for line, grid, clique, randomized grid and mobile topologies. We model both TDMA and 802.11, as well as unicast and broadcast traffic. We compare the analysis with discrete event simulations and show that the model provides sufficiently accurate estimates of scalability. We show how our impact analysis methodology can be used to progressively tune network features to meet a scaling requirement. We uncover several new insights, for instance, on the limited impact of reducing routing overhead, the differential nature of flooding traffic, and the effect real-world mobility on scalability. Our work is applicable to the design and deployment of real-world multi-hop wireless networks including community mesh networks, military networks, disaster relief networks and sensor networks.     

Two-Dimensional Supramolecular Polymerization of a Bis-Urea Macrocycle into a Brick-Like Hydrogen-Bonded Network

We report on a dendronized bis-urea macrocycle 1 self-assembling via a cooperative mechanism into two-dimensional (2D) nanosheets formed solely by alternated urea-urea hydrogen bonding interactions. The pure macrocycle self-assembles in bulk into one-dimensional liquid-crystalline columnar phases. In contrast, its self-assembly mode drastically changes in CHCl₃ or tetrachloroethane, leading to 2D hydrogen-bonded networks. Theoretical calculations, complemented by previously reported crystalline structures, indicate that the 2D assembly is formed by a brick-like hydrogen bonding pattern between bis-urea macrocycles. This assembly is promoted by the swelling of the trisdodecyloxyphenyl groups upon solvation, which frustrates, due to steric effects, the formation of the thermodynamically more stable columnar macrocycle stacks. This work proposes a new design strategy to access 2D supramolecular polymers by means of a single non-covalent interaction motif, which is of great interest for materials development.     

The complex role of the triphenylmethyl motif in anticancer compounds

Compounds incorporating the triphenylmethyl motif constitute an emerging family of potent anticancer agents. Although several small molecules containing this pharmacophore have now been identified, the mechanism of cell death induction for some of these compounds is unknown. In an effort to define their mechanism of action, and to distinguish subtypes within the group of compounds containing the triphenylmethyl moiety, we have created novel triphenylmethyl-containing small molecules and have evaluated them in a battery of biological assays. Here we show that several phosphonate and phosphonochloridates possessing the triphenylmethyl motif potently induce death of multiple cancer cell lines in culture. Further assays evaluating the ability to cause cell cycle arrest, inhibit tubulin polymerization, dissociate mitochondrial-bound hexokinase in cancer cells, and inhibit calcium-dependent potassium ion channels indicate that triphenylmethyl-containing compounds can be placed into at least four distinct categories, each with a different mechanism of action.