Search

Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras which are used to detect and categorise passing vehicles. Simple sensors, such as pneumatic tubes, are successfully deployed for counting passing vehicles but are not useful for vehicle tracking or re-identification. Smart cameras, on the other hand, collect comprehensive information but suffer from occlusion, patchy coverage, and compromised vision in adverse weather and visibility. This work explores a novel ITS data source based on an optical fibre which acts as an uninterrupted length of virtual sensors using a distributed acoustic sensor (DAS) system. Based on real DAS data collected in the field, we first present a study of latent DAS features that uniquely identify a given vehicle, otherwise referred to as the vehicle signature. We formulate a classification problem that examines incoming DAS data to extract vehicle signatures and identify the different types of vehicle. To this end, we implement different classification methods and present a comparative performance analysis that reveals novel insights into the potential role of DAS for ITS applications. This work is a pilot study of DAS for vehicle classification that is driven by real DAS data and validated by promising results where a vehicle’s type is correctly identified with 94% accuracy and the size of a vehicle with 95% accuracy.

Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes.

A novel Radio Frequency Identification (RFID) based sensor supporting touch detection and localization features is proposed in this work. The formulated sensor leverages chipless variant of RFID technology for the design of a passive fully-printable frequency domain-based sensor-incorporated tag. The sensor is composed of square resonators arranged in a peculiar fashion laid down across a $3\times2$ grid. The proposed sensor incorporated-tag readily keeps track of human-digit position, allowing for tracking of finger-swipes which, in turn, can potentially be used for recognition of unlock patterns and security codes. Performance of the sensor is analyzed using its Radar Cross Section (RCS) response observable in the spectral domain. Each constituent resonant-element making up the sensor resonates at a single frequency represented by a distinct dip in the RCS response. The spectral dip drifts well outside of its allocated band upon occurrence of a touch event. A functional prototype of the sensor tag is fabricated on a 0.508 mm thick Rogers RT/Duroid® 5880 laminate is scrutinized of its electromagnetic performance. The sensor possesses a compact physical footprint equal to 45 mm $\times55$ mm. The obtained results solidify the suitability of the proposed sensor for deployment in secure access control settings prevalent in smart cities and connected home applications.

In this paper, we analyze the impact of buffer-aided full-duplex successive relay selection schemes with energy harvesting capability of relay nodes in amplifying and forward (AF) and decode and forward (DF) relaying environments for the Internet of Things networks. We propose to select a relay pair based on the energy harvested and signal strength at relay and destination to receive and transmit in the same time slot, respectively. Contrary to the previous relay pair selection schemes which are based on the signal strength only and cause the relay overuse problem, the proposed scheme ensures the balanced use of energy of relay nodes. The proposed relay selection scheme is implemented with the time switching (TS) and power splitting (PS)-based energy harvesting models in AF and DF relaying environments separately. Furthermore, we derive the closed-form expression of the outage probability and average throughput for both the TS and PS approaches in the DF and AF relaying modes. We compare the proposed relay selection scheme with the S-MMRS scheme and prove that the proposed scheme significantly reduces the outage probability and improves the average throughput. Furthermore, the analytical findings are reinforced with the extensive Monte Carlo simulations.

In this paper, a compact and fully passive bit encoding circuit, capable of operating as a chipless radio frequency identification (RFID) tag is presented. The structure consists of novel concentric trefoil-shaped slot resonators realized using Rogers RT/duroid® 5880 laminate, occupying a physical footprint of 13.55 $\times $ 13.55 mm2. Each resonating element is associated with a particular data bit, having a 1:1 resonator-to-bit correspondence. Bit sequences are configured through introducing modifications in the geometric structure either by addition or exclusion of each nested slot resonator. Such changes manifest directly in the electromagnetic signature of the tag as presence or absence of corresponding resonant peaks. The proposed 10-bit tag offers minimized inter-resonator mutual coupling and insensitivity to changes in polarization and incident angles thereby demonstrating orientation independent functionality. Moreover, error-free encoding is achieved through stabilizing the shift in resonant frequencies for a variety of different geometric configurations and orientation of the structure. The tag operates within the license-free ultra-wideband ranging from 5.4 to 10.4 GHz, providing spectral bit capacity and bit density of 2 bits/GHz and 5.44 bits/cm2 respectively.

This paper ideates a novel texture descriptor that retains its classification accuracy under varying conditions of image orientation, scale, and illumination. The proposed Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP) texture descriptor also remains insensitive to additive white Gaussian noise. The wavelet decomposition stage of the OMTLBP provides robustness to photometric variations, while the two subsequent stages - overlapped multi-oriented fusion and multi-scale fusion - provide resilience against geometric transformations within an image. Isolated encoding of constituent pixels along each scale in the joint histogram enables the proposed descriptor to capture both micro and macro structures within the texture. Performance of the OMTLBP is evaluated by classifying a variety of textured images belonging to Outex, KTH-TIPS, Brodatz, CUReT, and UIUC datasets. The experimental results validate the superiority of the proposed method in terms of classification accuracy when compared with the state-of-the-art texture descriptors for noisy images.

With the rapid growth of network traffic and the enhancement of the quality of experiences of users, Information-Centric Networking (ICN), which is a content-centric network architecture with named data caching and routing, is proposed to improve the multimedia content distribution efficiency. In arbitrary topology, cache nodes and users are randomly distributed and connected, hence it is challenging to achieve an optimal caching placement under this situation. In this paper, we propose a caching placement algorithm for arbitrary topology in ICN. We formulate an optimization problem of proactive caching placement for arbitrary topology combined with multi-hop forwarding, with an objective to optimize the user delay and the load balancing level of the nodes simultaneously. Since the original problem is NP-hard, we solve the formulated caching placement problem in two sub-problems, content replica allocation sub-problem and content replica placement sub-problem. First, in the content replica allocation sub-problem, the replica number of each content is obtained by utilizing the auction theory. Second, the replica number of each content is used as a constraint for the content replica placement sub-problem, which is solved by matching theory. The caching placement algorithm combined with multi-hop NRR forwarding maximizes the utilization of cache resources in order to achieve better caching performance. The numerical results show that significant hop count savings and load balancing level improvement are attainable via the proposed algorithm.

In this work, an integrated antenna system with Defected Ground Structure (DGS) is presented for Fourth Generation (4G) and millimeter (mm)-wave Fifth Generation (5G) wireless applications and handheld devices. The proposed design with overall dimensions of 110 mm × 75 mm is modeled on 0.508 mm thick Rogers RT/Duroid 5880 substrate. Radiating structure consists of antenna arrays excited by the T-shape 1 × 2 power divider/combiner. Dual bands for 4G centered at 3.8 GHz and 5.5 GHz are attained, whereas the 10-dB impedance bandwidth of 24.4 - 29.3 GHz is achieved for the 5G antenna array. In addition, a peak gain of 5.41 dBi is demonstrated across the operating bandwidth of the 4G antenna array. Similarly, for the 5G mm-wave configuration the attained peak gain is 10.29 dBi. Moreover, significant isolation is obtained between the two antenna modules ensuring efficient dual-frequency band operation using a single integrated solution. To endorse the concept, antenna prototype is fabricated and far-field measurements are procured. Simulated and measured results exhibit coherence. Also the proposed design is investigated for the beam steering capability of the mm-wave 5G antenna array using CST®MWS®. The demonstrated structure offers various advantages including compactness, wide bandwidth, high gain, and planar configuration. Hence, the attained radiation characteristics prove the suitability of the proposed design for the current and future wireless handheld devices.

A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented.

Increased energy consumption becomes a major issue in 5G cellular networks, which inspires the network operators to deploy renewable energy sources. However, due to the fluctuating nature of renewable energy sources, the energy harvested by base stations (BSs) may not fit for their load conditions. The transmit power of the BS needs to be redesigned again. Hence, this paper considers power control in energy cooperation enabled millimeter wave networks, to alleviate the harvested energy imbalance problem and reduce the energy waste. Each BS is solely powered by renewable energy sources and the harvested energy is allowed to be transferred between BSs. Each BS needs to determine whether the energy should be stored in the battery or transferred to others at each time slot. In this paper, power control is formulated as a stochastic optimization problem, aiming at maximizing the time average network utility while keeping the network stable. An online algorithm called dynamic energy-aware power allocation is proposed based on Lyapunov optimization, which does not need to acquire any statistical knowledge of channels and traffic arrivals. Simulation results show that compared with the power control scheme without energy cooperation, the proposed algorithm with energy cooperation can achieve higher network sum rate while reducing the delay and the required battery capacity.