A Convex Programming for Range Assignment to Optimize Lifetime in Network-Coding-Based-Wireless-Sensor Networks

Wireless sensor networks (WSNs) are known to be highly energy-constrained and  consequently lifetime is a critical metric in their design and implementation. Range assignment by adjusting the transmission powers of nodes create a energy-efficient topology for such networks while preserving other network issues, however, it may effect on the performance of other techniques such as network coding. This paper addresses the problem of lifetime optimization for WSNs where the network employs both range assignment and network-coding-based multicast. We formulate the problem and then reformulated it as convex optimization that offer a numerous theoretical or conceptual advantages. The proposed programming leads to efficient or distributed algorithms for solving the problem. Simulation results show that the proposed optimized mechanism decreases end-to-end delay and improve lifetime as compared by other conventional ones.     

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Wireless Personal Communications - The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station....     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Security and Privacy Analysis of Song–Mitchell RFID Authentication Protocol

Many applications, such as e-passport, e-health, credit cards, and personal devices that utilize Radio frequency Identification (RFID) devices for authentication require strict security and privacy. However, RFID tags suffer from some inherent weaknesses due to restricted hardware capabilities and are vulnerable to eavesdropping, interception, or modification. The synchronization and untraceability characteristics are the major determinants of RFID authentication protocols. They are strongly related to privacy of tags and availability, respectively. In this paper, we analyze a new lightweight RFID authentication protocol, Song and Mitchell, in terms of privacy and security. We prove that not only is the scheme vulnerable to desynchronization attack, but it suffers from traceability and backward traceability as well. Finally, our improved scheme is proposed which can prevent aforementioned attacks.     

Advanced Bioresponsive Multitasking Hydrogels in the New Era of Biomedicine

Advanced forms of hydrogels have many inherently desirable properties and can be designed with different structures and functions. In particular, bioresponsive multifunctional hydrogels can carry out sophisticated biological functions. These include in situ single-cell approaches, capturing, analysis, and release of living cells, biomimetics of cell, tissue, and tumor-specific niches. They can allow in vivo cell manipulation and act as novel drug delivery systems, allowing diagnostic, therapeutic, vaccination, and immunotherapy methods. In the present review of multitasking hydrogels, new approaches and devices classified into point-of-care testing (POCT), microarrays, single-cell/rare cell approaches, artificial membranes, biomimetic modeling systems, nanodoctors, and microneedle patches are summarized. The potentials and application of each format are critically discussed, and some limitations are highlighted. Finally, how hydrogels can enable an “all-in-one platform” to play a key role in cancer therapy, regenerative medicine, and the treatment of inflammatory, degenerative, genetic, and metabolic diseases is being looked forward to.     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.     

Origin of High Ionic Conductivity of Sc-Doped Sodium-Rich NASICON Solid-State Electrolytes

Substitution of liquid electrolyte with solid-state electrolytes (SSEs) has emerged as a very urgent and challenging research area of rechargeable batteries. NASICON (Na₃Zr₂Si₂PO₁₂) is one of the most potential SSEs for Na-ion batteries due to its high ionic conductivity and low thermal expansion. It is proven that the ionic conductivity of NASICON can be improved to 10⁻³ S cm⁻¹ by Sc-doping, of which the mechanism, however, has not been fully understood. Herein, a series of Na_3+xSc_xZr_2−xSi₂PO₁₂ (0 ≤ x  ≤  0.5) SSEs are prepared. To gain a deep insight into the ion transportation mechanism, synchrotron-based X-ray absorption spectroscopy (XAS) is employed to characterize the electronic structure, and solid-state nuclear magnetic resonance (SS-NMR) is used to analyze the dynamics. In this study, Sc is successfully doped into Na₃Zr₂Si₂PO₁₂ to substitute Zr atoms. The redistribution of sodium ions at certain specific sites is proven to be critical for sodium ion movement. For x ≤ 0.3, the promotion of sodium ion movement is attributed to sodium ion concentration increase at the Na2 sites and decrease at the Na1 and Na3 sites. For x > 0.3, the inhibition of sodium ion movement is due to the phase change from monoclinic to rhombohedral and an increasing impurity content.     

Voltage difference engineering in SOI MOSFETs: A novel side gate device with improved electrical performance

We studied the impact of voltage difference engineering in a silicon-on-insulator metal oxide semiconductor field-effect transistor (SOI-MOSFET) and compared the performance to that of a conventional SOI-MOSFET (C-SOI). Our structure, called a SIG-SOI MOSFET, includes main and side gates with an optimum voltage difference between them. The voltage difference leads to an inverted channel as an electrical drain extension under the side gate. This channel creates a stepped potential distribution along the channel that it cannot be seen in the C-SOI MOSFETs. The voltage difference controls the channel properly and two-dimensional two-carrier device simulations revealed lower threshold voltage variations, larger breakdown voltage, higher voltage gain, lower hot carrier effects, improved drain-induced barrier lowering, lower drain conductance, higher unilateral power gain, and lower leakage current compared to a C-SOI device. Thus, our proposed structure has higher performance than a typical C-SOI structure.