Showing papers on "Data transmission published in 2018"

Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces

Abstract: In this paper, we consider the potential of data transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS), which is a newly proposed concept and conceptually goes beyond contemporary massive MIMO technology. First, we consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array in a perfect line-of-sight propagation environment. Under the condition that the surface area is sufficiently large, the received signal after a matched-filtering operation can be closely approximated by a sinc-function-like intersymbol interference channel. Second, we analyze a normalized capacity measured per unit surface, for a fixed transmit power per volume unit with different terminal deployments. As terminal density increases, the limit of the normalized capacity [nats/s/Hz/volume-unit] achieved when wavelength $\lambda$ approaches zero is equal to half of the transmit power per volume unit divided by the noise spatial power spectral density. Third, we show that the number of independent signal dimensions that can be harvested per meter deployed surface is $2/\lambda$ for one-dimensional terminal deployment, and $\pi /\lambda ^2$ per square meter for two- and three-dimensional terminal deployments. Finally, we consider implementations of the LIS in the form of a grid of conventional antenna elements, and show that the sampling lattice that minimizes the surface area and simultaneously obtains one independent signal dimension for every spent antenna is the hexagonal lattice.

Deep Joint Source-Channel Coding for Wireless Image Transmission

Abstract: We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the ``cliff effect'', and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Abstract: A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared with application of the original AMP approach.

Cooperative Content Caching in 5G Networks with Mobile Edge Computing

Abstract: Along with modern wireless networks being content-centric, the demand for rich multimedia services has been growing at a tremendous pace, which brings significant challenges to mobile networks in terms of the need for massive content delivery. Edge caching has emerged as a promising approach to alleviate the heavy burden on data transmission through caching and forwarding contents at the edge of networks. However, existing studies always treat storage and computing resources separately, and neglect the mobility characteristic of both the content caching nodes and end users. Driven by these issues, in this work, we propose a new cooperative edge caching architecture for 5G networks, where mobile edge computing resources are utilized for enhancing edge caching capability. In the architecture, we focus on mobility-aware hierarchical caching, where smart vehicles are taken as collaborative caching agents for sharing content cache tasks with base stations. To further utilize the caching resource of smart vehicles, we introduce a new vehicular caching cloud concept, and propose a vehicle-aided edge caching scheme, where the caching and computing resources at the wireless network edge are jointly scheduled. Numerical results indicate that the proposed scheme minimizes content access latency and improves caching resource utilization.

Event-Triggered Asynchronous Guaranteed Cost Control for Markov Jump Discrete-Time Neural Networks With Distributed Delay and Channel Fading

Abstract: This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.

Hybrid PLC/Wireless Communication for Smart Grids and Internet of Things Applications

Abstract: This paper outlines important characteristics of hybrid power line/wireless data communication system for smart grid (SG) and Internet of Things (IoT) applications. Moreover, we discuss the hybrid systems advantages in comparison to nonhybrid ones. These advantages are demonstrated not only in the technical point of view, but also in the infrastructural perspective. Also, we highlight a connection between the capillarity of IoT and the communication infrastructure provided by SG. Furthermore, we address the environmental influence on wireless and power line communications. Additionally, we present ergodic achievable data rate expressions for the hybrid power line/wireless channel (HPWC) and hybrid power line/wireless single-relay channel (HSRC) and provide performance analyses by considering four different cases, which are associated with the relative relay position in the single-relay channel model. Based on numerical results, we show that HPWC and HSRC offer a more reliable data transmission path when compared to power line or wireless system working alone.

On-chip silicon photonic signaling and processing: a review

Abstract: The arrival of the big data era has driven the rapid development of high-speed optical signaling and processing, ranging from long-haul optical communication links to short-reach data centers and high-performance computing, and even micro-/nano-scale inter-chip and intra-chip optical interconnects. On-chip photonic signaling is essential for optical data transmission, especially for chip-scale optical interconnects, while on-chip photonic processing is a critical technology for optical data manipulation or processing, especially at the network nodes to facilitate ultracompact data management with low power consumption. In this paper, we review recent research progress in on-chip photonic signaling and processing on silicon photonics platforms. Firstly, basic key devices (lasers, modulators, detectors) are introduced. Secondly, for on-chip photonic signaling, we present recent works on on-chip data transmission of advanced multi-level modulation signals using various silicon photonic integrated devices (microring, slot waveguide, hybrid plasmonic waveguide, subwavelength grating slot waveguide). Thirdly, for on-chip photonic processing, we summarize recent works on on-chip data processing of advanced multi-level modulation signals exploiting linear and nonlinear effects in different kinds of silicon photonic integrated devices (strip waveguide, directional coupler, 2D grating coupler, microring, silicon-organic hybrid slot waveguide). Various photonic processing functions are demonstrated, such as photonic switch, filtering, polarization/wavelength/mode (de)multiplexing, wavelength conversion, signal regeneration, optical logic and computing. Additionally, we also introduce extended silicon+ photonics and show recent works on on-chip graphene-silicon photonic signal processing. The advances in on-chip silicon photonic signaling and processing with favorable performance pave the way to integrate complete optical communication systems on a monolithic chip and integrate silicon photonics and silicon nanoelectronics on a chip. It is believed that silicon photonics will enable more and more emerging advanced applications even beyond silicon photonic signaling and processing.

18 km low-crosstalk OAM + WDM transmission with 224 individual channels enabled by a ring-core fiber with large high-order mode group separation

Abstract: The space domain is regarded as the only known physical dimension of lightwaves left to be exploited for optical communications. Very recently, much research effort has been devoted to using orbital angular momentum (OAM) spatial modes to increase the transmission capacity in fiber-optic communications. However, long-distance low-crosstalk high-order OAM multiplexing transmission in fiber is quite challenging. Here we design and fabricate a graded-index ring-core fiber to effectively suppress radially high-order modes and greatly separate high-order OAM mode groups. By exploiting high-order OAM mode group multiplexing, together with wavelength-division multiplexing (WDM), i.e., 12.5 Gbaud 8-array quadrature amplitude modulation (8-QAM) signals over OAM+4 and OAM+5 modes on 112 WDM channels (224 individual channels), we experimentally demonstrate 8.4 Tbit/s data transmission in an 18 km OAM fiber with low crosstalk. Multiple-input multiple-output digital signal processing is not required in the experiment because of the large high-order mode group separation of the OAM fiber. The demonstrations may open a door to find more fiber-optic communication and interconnect applications exploiting high-order OAM modes.

Label-less Learning for Traffic Control in an Edge Network

Abstract: With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

General architectures for on-chip optical space and mode switching

Abstract: The optical switches for single-mode operation cannot be directly utilized in optical communication and interconnect systems adopting mode-division multiplexing In this paper, three general architectures for on-chip optical space and mode switching are proposed, which are optimized for optical space switching, optical space switching plus local optical mode switching, and global optical mode switching, respectively A silicon thermo-optic 2×2 four-mode optical switch is demonstrated The minimum and maximum optical link insertion losses are 160 and 209 dB (including ∼6 dB coupling loss), respectively, in the wavelength range of 1525–1565 nm, while the optical signal-to-noise ratios of the optical links are larger than 153 dB The optical power penalty at a bit error rate of 10−9 varies from 10 to 56 dB for 40 Gbps data transmission through different optical links This work provides a systematic solution to on-chip information switching for different physical and mode channels

Classification and Detection of PMU Data Manipulation Attacks Using Transmission Line Parameters

Abstract: Modern power grids are increasingly relying on real-time data, such as those from phasor measurement units (PMUs), for their control and management operations. Due to its dependence on the Internet for data transfer, the grid is susceptible to a wide range of cyber-attacks. Among these, data manipulation attacks are of particular interest in the context of PMU data, due to their potential for causing widespread damage. In such attacks, the adversary changes the measurements in order to bias the estimate of system states. In this paper, we propose an effective and simple-to-implement mechanism for detecting such attacks. The proposed methodology is based on evaluating the equivalent impedances of transmission lines. Being independent of the conventional bad data detection scheme, it is also able to detect the so called “false data injection attacks.” Extensive simulation results using real PMU data have been provided in order to verify the accuracy of the proposed detector.

An Inductive and Capacitive Combined Parallel Transmission of Power and Data for Wireless Power Transfer Systems

Abstract: A new parallel transmission method of power and data is proposed for peer-to-peer wireless power transfer (WPT) systems. Essentially, data are modulated and transferred via high-frequency electric field generated by the parasitic capacitances of the coupling coils and the metal shield plates, while power is transferred through relatively low-frequency magnetic field generated by the coupling coils. Coupling structure and operation principle are illustrated and analyzed. Besides, the signal to noisy ratio performance is studied and optimized. With the proposed method, a 40 W prototype is built and the data transmission rate reaches 230 kbps. Experimental results have verified that the proposed method is valid and has the advantages of good flexibility and large spatial position offset redundancy. Because the method does not do any modification on the main circuit of the WPT system, it also has advantages of low cost and easy to implement.

Wireless Power Transfer and Data Collection in Wireless Sensor Networks

Abstract: In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using wireless power transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, whereas the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes’ energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semidecentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.

Multipath QUIC: A Deployable Multipath Transport Protocol

Abstract: QUIC is the emerging transport layer protocol, providing encrypted, stream-multiplexed, low-latency data transfer. In this paper, we propose multipath-enabled QUIC (MPQUIC) to leverage multiple network interfaces, such as WiFi and LTE on today's mobile devices. We show how our MPQUIC design conceptually evolves beyond existing multipathing protocols, such as MPTCP, as it provides fine-grained stream-to-path scheduling, reduced head-of-line blocking, and faster subflow establishment. We present an userland implementation of MPQUIC that is deployable without operating system changes. Our evaluation results show that MPQUIC increases throughput in comparison to traditional QUIC, TCP and even the currently de facto multipath transport protocol MPTCP. First real world measurements confirm that MPQUIC is deployable in the Internet to reduce download times. Moreover, we show that MPQUIC's conceptual advantages over MPTCP efficiently reduce head-of-line blocking in heterogeneous environments. With multipathing support, QUIC is ready to become the universal stream transport protocol in today's Internet.

Inverse-Design and Demonstration of Ultracompact Silicon Meta-Structure Mode Exchange Device

Abstract: Data exchange among different mode channels is indispensable for optical communication system adopting mode-division multiplexing. Traditional mode exchange device is complex in procedure and large in footprint, which makes it not suitable for dense and large-scale photonic integration. Utilizing the degree of freedom of silicon meta-structure, we design an ultracompact and optically broadband mode exchange device between TE0 and TE1 modes by the step-by-step inverse-design method considering the axisymmetric constraint. Simulation result shows that it is robust to a temperature variation of 100 K and a fabrication error of ±20 nm. The fabricated device is 4 × 1.6 μm2 in footprint. The simulated conversion efficiencies are over 73% and 71% for TE0 to TE1 and TE1 to TE0 within the whole C-band, and the experimental results are about 10% lower than the simulation. 40 Gbps OOK and 25 GBaud PAM-4 data transmission through the device are carried out, which shows good signal quality. We envision that the device...

All optical switching and associated technologies: a review

Abstract: Optical computation is the most desirable technology that enhances the speed, data transmission rate and processing power by replacing the electronics with the optical switches. Optical switching is efficiently performed in high speed signal processing by all optical gates. This paper reviews the progressive development of the optical switching technology, highlights the different technologies of all optical gates and other switching circuits in all optical processing. Basic gates and other logic circuits in optical computing based on nonlinear regimes using semiconductor optical amplifier (SOA), fiber and photonic crystals are discussed, compared and the challenges along with future direction is outlined.

Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks

Abstract: Load balance is a vital goal for battery-powered wireless sensor networks (WSNs). In this letter, we propose a novel load balancing strategy for data transmission of WSNs, namely, super links-based data drainage, which makes full use of the advantages of super nodes with more powerful hardware and greater communication capacity to realize data traffic redistribution. Being different from conventional passive late-remedy approaches, this is a positive and early-intervention strategy. Specifically, an evaluation function is designed to select appropriate start points and end points of super links, and the core idea is to transfer data from locations relatively far from the sink with a jump of data traffic to those near the sink with little data traffic. Extensive simulations are conducted to validate the effectiveness and advantages of the new strategy.

Parallelized Kalman Filters for Mitigation of the Excess Phase Noise of Fast Tunable Lasers in Coherent Optical Communication Systems

Abstract: Numerical and experimental investigations are carried out on the performance of parallelized Kalman filters applied for mitigation of the excess phase noise of fast tunable lasers. Based on the characterization of the phase noise of a sampled grating distributed Bragg reflector (SG-DBR) laser, the proposed carrier phase recovery (CPR) scheme using Kalman filters is introduced. By performing simulations of data transmission with various advanced modulation formats in the presence of the excess phase noise, the Kalman filter based CPR scheme shows its ability to overcome the excess phase noise and this method is suitable for parallel processing. Then the results are further demonstrated by 12.5 Gbaud QPSK and 16-QAM transmission experiments employing the SG-DBR laser. We find that the Kalman filters have better performance than the 2nd-order phase-locked loop in parallel systems due to a better phase noise tolerance. The bit error rate performance is also examined in the whole tuning range (∼30 nm) of the tunable laser, which further proves the feasibility of the proposed scheme.

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Abstract: A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared to application of the original AMP approach.

Packet Structure and Receiver Design for Low Latency Wireless Communications With Ultra-Short Packets

Abstract: Fifth generation wireless standards require much lower latency than what current wireless systems can guarantee. The main challenge in fulfilling these requirements is the development of short packet transmission, in contrast to most of the current standards, which use a long data packet structure. Since the available training resources are limited by the packet size, reliable channel and interference covariance estimation with reduced training overhead are crucial to any system using short data packets. In this paper, we propose an efficient receiver that exploits useful information available in the data transmission period to enhance the reliability of the short packet transmission. In the proposed method, the receive filter (i.e., the sample covariance matrix) is estimated using the received samples from the data transmission without using an interference training period. A channel estimation algorithm to use the most reliable data symbols as virtual pilots is employed to improve quality of the channel estimate. Simulation results verify that the proposed receiver algorithms enhance the reception quality of the short packet transmission.

Integrating Communications and Control for UAV Systems: Opportunities and Challenges

Abstract: Unmanned aerial vehicle (UAV) communications play an important role in building the space-air-ground network and realizing the seamless wide-area coverage thanks to its long-range connectivity, high maneuverability, flexible deployment, and low latency. Different from the traditional ground-only communications, the control techniques tightly impact UAV communications, which could be jointly designed to enhance the performance of data transmission. In this paper, we explore the opportunities and challenges of combining the communications and control in UAV systems. For single-UAV scenario, we introduce a new frequency-dependent 3D channel model. We then show how to perform channel tracking with a flight control system as well as how to mechanically and electronically formulate the transmission beams. For multi-UAV scenario, we present new techniques as cooperative communications, self-positioning, trajectory design, resource allocation, and seamless coverage. In the end, we provide some interesting discussions over communication protocol, secrecy, 3D dynamic topology heterogeneous networks, and low-cost design for practical UAV applications.

An adaptive method for data reduction in the Internet of Things

Abstract: Enormous amounts of dynamic observation and measurement data are collected from sensors in Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) applications such as environmental monitoring However, continuous transmission of the sensed data requires high energy consumption Data transmission between sensor nodes and cluster heads (sink nodes) consumes much higher energy than data sensing in WSNs One way of reducing such energy consumption is to minimise the number of data transmissions In this paper, we propose an Adaptive Method for Data Reduction (AM-DR) Our method is based on a convex combination of two decoupled Least-Mean-Square (LMS) windowed filters with differing sizes for estimating the next measured values both at the source and the sink node such that sensor nodes have to transmit only their immediate sensed values that deviate significantly (with a pre-defined threshold) from the predicted values The conducted experiments on a real-world data show that our approach has been able to achieve up to 95% communication reduction while retaining a high accuracy (ie predicted values have a deviation of ±05 from real data values)

The Trade Off Between Different Modulation Schemes for Maximum Long Reach High Data Transmission Capacity Optical Orthogonal Frequency Division Multiplexing (OOFDM)

Abstract: This paper shows the trade off between different modulation techniques such as multi level quadrature amplitude modulation, multi level phase shift keying, and multi level differential phase shift keying for upgrading direct detection optical orthogonal frequency division multiplexing systems with possible transmission distance up to 15,000 km and total bit rate of 2.56 Tb/s. The 2.56 Tb/s signal is generated by multiplexing 64 OFDM signals with 40 Gb/s for each OFDM. Variations of optical signal to noise ratio (OSNR), signal to noise ratio (SNR), and bit error rate (BER) are studied with the variations of transmission distance. Maximum radio frequency power spectrum, and output electrical power after decoder are measured for different multi level modulation techniques with carrier frequency. It is observed that multi level QAM has presented better performance than multi level PSK and finally multi level DPSK in optical OFDM systems. Maximum output power after decoder is enhanced with both 32-PSK, and 64-QAM. Quadrature signal amplitude level at encoder is upgraded with 64-QAM. It is noticed that OSNR, SNR, and BER are improved using 4-QAM OFDM system than either QPSK or 4-DPSK.

Buffer-Aided Physical-Layer Network Coding With Optimal Linear Code Designs for Cooperative Networks

Abstract: In this paper, we propose buffer-aided physical-layer network coding (PLNC) techniques for improving data transmission over cooperative networks In particular, we develop buffer-aided PLNC schemes and relay pair selection algorithms for direct-sequence code-division multiple access (DS-CDMA) systems We devise PLNC techniques based on optimal linear network coding matrices according to the maximum likelihood and minimum mean-square error design criteria in order to generate the network coded symbols that are sent to the destination In the proposed buffer-aided PLNC schemes, relay pair selection algorithms are developed to obtain the relay pair and the packets in the buffer entries with the best performance and the associated link combinations are used for the data transmission An analysis of the computational complexity of the proposed techniques along with their sum-rate analysis is carried out Simulation results show that the proposed techniques significantly outperform previously reported approaches

Method and apparatus for beam indication in next generation wireless systems

Abstract: A method of a user equipment (UE) for a beam indication in a wireless communication system is provided. The method includes receiving, from a base station (BS), downlink control information (DCI) including scheduling information for a data transmission on a downlink data channel, wherein the DCI includes an index of a spatial quasi-co-location (QCL) configuration, comparing a time offset between the data transmission and the DCI with a threshold that is pre-configured at the UE, and calculating a receive (Rx) beam based on the index of the spatial QCL configuration or a pre-configured spatial QCL assumption, receiving the data transmission based on the time offset.

Long-Range Big Quantum-Data Transmission.

Abstract: We introduce an alternative type of quantum repeater for long-range quantum communication with improved scaling with the distance. We show that by employing hashing, a deterministic entanglement distillation protocol with one-way communication, one obtains a scalable scheme that allows one to reach arbitrary distances, with constant overhead in resources per repeater station, and ultrahigh rates. In practical terms, we show that, also with moderate resources of a few hundred qubits at each repeater station, one can reach intercontinental distances. At the same time, a measurement-based implementation allows one to tolerate high loss but also operational and memory errors of the order of several percent per qubit. This opens the way for long-distance communication of big quantum data.

Low-Latency and Energy-Balanced Data Transmission Over Cognitive Small World WSN

Abstract: Energy balancing and faster data transfer over a wireless sensor network (WSN) is an important problem in applications like cyber-physical systems, Internet of things, and context-aware pervasive systems. Addressing this problem leads to increased network lifetime and improved network feasibility for real time applications. In WSNs, sensor nodes transfer the data using multihop data transmission model. The large number of hops required for data transmission leads to poor energy balancing and large data latency across the network. In this paper, we utilize a recent development in social networks called small world characteristics for proposing a novel method of low-latency and energy-balanced data transmission over WSN. Small world WSN (SW-WSN) exhibits low average path length and high average clustering coefficient. A cognitive SW-WSN is developed by adding new links between a selected fraction of nodes and the sink. A new data routing method is also proposed by optimizing energy cost of the links. This method yields uniform energy consumption and faster data transfer. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of network lifetime, residual energy, and data latency over the WSN. Experimental results obtained indicate that the proposed cognitive small world model achieves energy balancing, increases network lifetime, improves energy efficiency, and reduces data latency when compared to results obtained using various state-of-the-art approaches. The results are motivating enough for the proposed method to be used in large and medium scale network applications.