Energy-efficient Communication Research Articles

Distributed Beam Training for Intelligent Reflecting Surface Enabled Multi-Hop Routing

Intelligent reflecting surface (IRS) is an emerging technology to enhance the spectral and energy efficiency of wireless communications cost-effectively. This letter considers a new multi-IRS aided wireless network where a cascaded line-of-sight (LoS) link is established between the base station (BS) and a remote user by leveraging the multi-hop signal reflection of selected IRSs. As compared to the conventional single-/double-hop IRS system, multi-hop IRS system provides more pronounced path diversity and cooperative passive beamforming gains, especially in the environment with dense obstacles. However, a more challenging joint active/passive beamforming and multi-hop beam routing problem also arises for maximizing the end-to-end channel gain. Furthermore, the number of IRS-associated channel coefficients increases drastically with the number of IRS hops. To tackle the above issues, in this letter we propose a new and efficient beam training based solution by considering the use of practical codebook-based BS/IRS active/passive beamforming without the need of explicit channel estimation. Instead of exhaustively or sequentially searching over all combinations of active and passive beam patterns for each beam route, a distributed beam training scheme is proposed to reduce the complexity, by exploiting the (nearly) time-invariant BS-IRS and inter-IRS channels and the cooperative training among the BS and IRSs’ controllers. Simulation results show that our proposed design achieves the end-to-end channel gain close to that of the sequential beam search, but at a much lower training overhead and complexity.

An Energy Efficient Scheme for WPCN-NOMA Based Device-to-Device Communication

The capability of Non-Orthogonal Multiple Access (NOMA) for minimizing interference at the receiver using successive interference cancellation (SIC) makes it as a promising solution for 5G mobile communication. Device-to-device (D2D) communication is a part of the 5G network which provides frequency reuse in cellular networks and provides a better quality of service (QoS) to co-channel users. In this paper, we investigate the resource allocation and time scheduling algorithms for a NOMA based D2D communication system. In addition, to overcome the problem of energy at D2D transmitter and receivers, wireless powered communication network (WPCN) is used. Considering these issues, the objective of the proposed scheme is to maintain energy efficient communication among D2D transmitters and receivers without compromising energy efficiency (EE) of cellular users. To solve the fractional form of an optimization problem, we employ the Dinkelbach method. A many-to-many matching game approach is used for channel allocation and successive convex approximation for low complexity (SCALE) is used to control the power of the D2D transmitter. The simulation results demonstrate that by maximizing the EE and optimizing the time and transmission power of D2D groups, the energy efficiency of the network can be significantly improved in comparison to the other competitive schemes in the existing literature.

M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities

In recent years, the Green Internet of Things (G-IoT) has gained a lot of attention to developing energy-efficient communication systems. It consists of electronic devices and is integrated with numerous tight constraint sensors for observing the real world and provide communication services to end-users. However, optimal data collection and its management among the heterogeneous G-IoT objects are one of the main challenges. Many researchers are still proposing different solutions to cope with such problems and offering IoT-cloud paradigms for processing, storage, and scalability services. However, the data of smart cities is forwarded using the open-source IoT platform, and sensitive information may be compromised. Therefore, this research aims to propose a model of security measures using the Green Internet of Things with Cloud Integrated Data Management (M-SMDM) for Smart Cities. Firstly, it forms a long-run and energy-efficient connectivity using self-balancing trees and distributing load factors uniformly in green communication systems. Secondly, it addresses the problem of secret key distribution between peer nodes and attained trust for both partial and direct communication. In the end, it securing the transmission system from mobile gateways to application users against threats with improved overheads and data latency. The security analysis of the proposed M-SMDM model is done along with simulation-based experiments. The attained results disclose the importance of the proposed model in terms of network parameters compared to existing work.

A Stateless Spatial IPv6 Address Configuration Scheme for Internet of Things

The Internet of Things (IoT) is going to create a heterogeneous network of billions or trillions of connected things with self-aware, self-configuration capabilities that will exchange data with each other. This heterogeneous IoT network requires standard protocols, secure and energy-efficient wireless communication, and the allocation of unique identities such as IPv6 addresses. One of the key challenges in such a heterogeneous network is the maintenance of device location and unique identity to ensure the smooth functioning of location-aware applications. Hence, this paper presents a stateless spatiotemporal IPv6 addressing scheme called four-dimensional IP addressing (FDIPA). It is founded on the idea that two objects can not be in the same place at the same time. It uses the time and location information of the device to generate the target IPv6 address and assigns it to the device after verifying its uniqueness with the duplicate address detection (DAD) protocol. It not only maintains spatial and temporal information but also guarantees a unique identity for a large-sized network. Analytically, it is proved that FDIPA will always generate a unique identity if there is no technological limitation. However, the performance of FDIPA is evaluated based on metrics such as Assignment Success Rate (ASR), communication overhead, and total energy consumption in the assignment of a unique IPv6 address. The experimental results show that FDIPA achieves good ASR while keeping low energy consumption and less overhead in the network.

Short Word-Length Entering Compressive Sensing Domain: Improved Energy Efficiency in Wireless Sensor Networks

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R ≥ 1, where R is the sample reduction ratio of compressive sensing, and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise and transmission noise, owing to the noise shaping property of sigma-delta modulation.

Fundamental limits of high-efficiency silicon and compound semiconductor power amplifiers in 100-300 GHz bands

This paper reviews the requirements for future digital arrays in terms of power amplifier requirements for output power and efficiency and the device technologies that will realize future energy-efficient communication and sensing electronics for the upper millimeter-wave bands (100-300 GHz). Fundamental device technologies are reviewed to compare the needs for compound semiconductors and silicon processes. Power amplifier circuit design above 100 GHz is reviewed based on load line and matching element losses. We present recently presented class-A and class-B PAs based on a InP HBT process that have demonstrated record efficiency and power around 140 GHz while discussing circuit techniques that can be applied in a variety of integrated circuits.

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

PurposeInternet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.Design/methodology/approachFor the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.FindingsSimulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).Practical implicationsThe paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.Originality/valueThe proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.

Multi-RAT Orchestration Method for Heterogeneous Wireless Networks

Currently, energy efficiency (EE) of wireless communication is essential where many wireless networks with different Radio Access Technologies (RATs) coexist together. The RATs can be effectively selected and managed on a higher level to achieve maximum EE and save energy, e.g., save batteries. The approach to wireless traffic steering in mobile networks with a proof-of-concept solution is presented in this paper, owing to the developed high-level multi-RAT (multi-Radio Access Technology) heterogonous network orchestration approach. Based on the high-level network orchestrator, which traces network indicators, it is possible to decrease the user mobile terminal energy consumption, keeping traffic speed at an adequate level. The solution discussed was implemented in an experimental testbed with Software Defined Radio transmission systems. Downlink and uplink data links were toggled among different RATs according to the decisions that were taken by the end-to-end multi-RAT orchestrator based on the received proper network traffic-related indicators. The authors focused on finding an adequate algorithm that allowed for reduced power usage in the user terminal and made the attempt to verify how to reach the power reduction without introducing RAT-specific rules. The results showed that the proposed orchestration EE reduction was observed (from 11% to 42% for two different scenarios) in relation to the single LTE network deployment. The orchestration compared to the Wi-Fi network does not provide EE gain (−7% and 0%, respectively), but allows the user to achieve a higher data rate (23% and 39% gain, respectively), thus keeping the energy efficiency at almost the same level.

Robust Design for UAV-Enabled Multiuser Relaying System With SWIPT

Combination of wireless power transfer and unmanned aerial vehicle (UAV) is considered as an effective solution to achieving sustainable, flexible and energy-efficient communications. In this paper, we study a UAV-enabled multiuser relaying system with simultaneous wireless information and power transfer, where the users are capable of power splitting. Our aim is to jointly optimize the beamforming matrixes of the base station and UAV relay and the power splitting factors of users to maximize the sum harvested energy of users while satisfying the quality of service and the transmit power constraints. We propose an alternating optimization scheme to tackle the intractable no-convex problem. To reduce the computational cost, the low-complexity scheme based on singular value decomposition-zero forcing (SVD-ZF) technique is designed. Moreover, we discuss the convergence and computational complexity of the two proposed schemes. Simulation results verify the effectiveness of the proposed schemes.

Performance Analysis and User Association Optimization for Wireless Network Aided by Multiple Intelligent Reflecting Surfaces

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes.

Energy and Spectrum Efficient Blind Equalization With Unknown Constellation for Air-to-Ground Multipath UAV Communications

In unmanned aerial vehicle (UAV) communications, frequency-selective fading can severely deteriorate the quality of transmitted signal by generating undesired and disordered constellation diagrams due to scatters in the air-to-ground (ATG) mutipath channels. In this paper, we propose a low-overhead blind equalization method to combat frequency-selective fading in air-ground multipath UAV channels. Specifically, a pre-equalization method is proposed based on a constant modulus algorithm to restore the contour of the constellation diagram. Moreover, the similarity measure function and the difference measure function are derived using template matching to identify the constellation of M-ary quadrature amplitude modulation. Furthermore, we propose a weighted constant cross algorithm (WXA) to reduce the residual mean square error and construct a cross-shaped modulus value, by utilizing the statistical information of the identified normalized standard constellation diagrams and the equalizer output decision symbols' weighting value. The proposed method requires less information and no training sequences and pilots, therefore, if achieves energy and spectrum efficient ATG multipath UAV communications. Simulation results show that the proposed WXA algorithm can reduce the residual mean square error convergence value between -22dB and -25dB, making it very useful for the equalization of the frequency-selective fading channel in typical UAV communication scenarios.

MEES-WuR: Minimum Energy Coding With Early Shutdown for Wake-Up Receivers

One of the main challenges of wireless sensor networks is to maintain sensor nodes alive as long as possible, and a lot of efforts are dedicated to enable energy efficient communications. Wake-Up Receivers (WuRs) represent a promising solution for reducing the power consumption of nodes by enabling asynchronous communications. However, to achieve an ultra-low power consumption, WuRs circuits are kept as simple as possible, inducing a low sensitivity and thus a short range communication. As channel coding improves sensitivity, we propose to take advantage of the computing capability of the WuRs to apply a specific channel coding. The novelty resides in applying Minimum Energy coding with an Early Shutdown (MEES) of WuRs based on On-Off Keying (OOK) detectors. Both theoretical derivations and Monte-Carlo simulations show that the proposed coding scheme improves the reliability. Moreover, MEES has been implemented on a non-coherent WuR prototype, and it is shown through experimentation that WuR reliability can be raised up to 22% compared to uncoded communications. Moreover, both the energy consumption and the latency can be significantly decreased thanks to the shutdown mechanism of MEES.

Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control.

In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems are typically implemented over wireless sensor networks, where the sensing devices are battery-powered, and thus they have to manage energy constraints by implementing efficient communication schemas. Self-triggered control is an aperiodic sampling strategy capable of reducing the number of networked messages compared to traditional periodical sampling. In this paper, we propose an energy-efficient communication strategy for closed-loop control irrigation, implemented over a wireless sensor network, where event-driven soil moisture measurements are conducted by the sensing devices only when needed. Thereby, the self-triggered algorithm estimates the occurrence of the next sampling period based on the process dynamics. The proposed strategy was evaluated in a pecan crop field and compared with periodical sampling implementations. The experimental results show that the proposed adaptive sampling rate technique decreased the number of communication messages more than 85% and reduced power consumption up to 20%, while still accomplishing the system control objectives in terms of the irrigation efficiency and water consumption.

Performance Analysis of FISSER Model in Rural-Urban Cognitive Radio Networks

In the past few decades, Cognitive Radio network (CRN) has been regarded in the literature as the most promising technology for performing dynamic spectrum management. One of the major aspects of spectrum management is referred to as the spectrum-reconfiguration decision-making ability of CR users. Dynamic spectrum reconfiguration has previously been reported to improve the spectrum utilisation in CRN. In exploiting spectrum reconfiguration to improve spectrum utilisation, various approaches have been adopted to develop models. However, none of these research works has been evaluated in the urban and rural settlement context. In a CRN environment, the frequency availability is not static like that of traditional networks. There are resource-rich regions such as in rural areas, where many frequencies are available for the Secondary Users (SUs) and resource-poor regions such as urban areas, where there are only a few frequencies available for SUs. Hence, this paper proposed a novel Foraging Inspired Spectrum Selection and Reconfiguration (FISSER) model. The performance of the FISSER model has been analysed through computer simulations both in the rural and urban CRN, using high and low perceptual radii. A high perceptual radius has been shown in the literature to improve energy efficiency and data communication performance in ad hoc networks. However, the simulation results reported in this paper show that even though the high perceptual radius improves the nodes’ energy efficiency it does not achieve optimal data communication performance compared to when the nodes use a low perceptual radius. Also, the efficacy of FISSER model was tested by comparing it against the reinforcement learning model. Based on the obtained results, it was shown that the FISSER model achieved better results in both the network throughput and the channel switching time.

CsPb(Br/I)3 Perovskite Nanocrystals for Hybrid GaN-Based High-Bandwidth White Light-Emitting Diodes

The modulation bandwidth of white light emitting diodes (LEDs) is an important factor in visible light communication (VLC) system, which is mainly limited by the down-conversion materials. The broad spectrum and long lifetime of conventional light conversion materials represent an obstacle to future technological developments. Here, we show that inorganic semiconductor perovskite nanocrystals offer a promising alternative nanomaterial. Anion exchange between different perovskite nanocrystals by post-synthesis is a highly efficient protocol to tune the chemical composition and optoelectronic properties of lead halide perovskite nanocrystals. The fine-tuning of the nanocrystal fluorescence is achieved by 2 blending colloidal solutions of CsPb(Br/I)3 lead halide perovskites with different content of halide. The tunable optical emission and short fluorescence lifetime (< 5 ns) of the nanocrystals are exploited to realize white LEDs with a high modulation bandwidth (0.7 GHz), offering a potential route towards fast, energy efficient visible light communication.

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs

Trust-based secure routing schemes are more effective than cryptographic routing protocols to convey energy-efficient data in WSNs since cryptographic protocols require high computation, more convergence time as well as storage space. The paper presents a well-organized trust estimation-based routing scheme (ETERS) that consists multi-trust (communication trust, energy trust, data trust) approach to alleviate several internal attacks like badmouthing, Sybil, selective forwarding, on–off,​ black hole, and gray-hole attacks for clustered WSN. The proposed multi-trust approach is used to analyze the credibility of sensitive monitored data. A novel and efficient cluster head selection algorithm (ECHSA) is employed to improve the performance of the cluster head (CH) selection process in clustered WSN. ECHSA allows the facility to elect a robust CH after a certain period to perform an equal load balance on all CHs. ETERS utilizes the Beta distribution-based trust function because recovery of trust values under attacks is faster in Beta distribution than Gaussian and Dirichlet distribution. ETERS also incorporates an irregular attenuation factor during the evaluation of communication trust to reflect the effect of various external factors such as natural calamity (earthquake), network congestion, etc., based on the trust values. However, a trust-based attack detection algorithm (TADA) assesses the reliability of SNs to detect internal attacks. TADA employs IDs, locations, and triple trust to detect internal attacks. Additionally, the proposed trust system employs an adjustable dynamic sliding length logical timing window to eradicate the limitations of existing trust models. Furthermore, a trust-based secure routing algorithm is incorporated to dynamically detect misbehavior in terms of packet forwarding for energy-efficient communication among sensor nodes. ETERS is compared with QEBSR, ATRP, ELPC, and SQEER to examine its performance. Experimental results on the MATLAB simulation platform exhibit excellent performance in terms of severity analysis, attack detection, and prevention to protect WSN, energy consumption under (i) normal (ii) attack scenario, latency (in sec.), packet delivery ratio (PDR), and throughput (in %) in the existence of spiteful nodes.

A Comparative Study of Machine Learning Models for Spreading Factor Selection in LoRa Networks

Low power wide area networks (LPWAN) technologies offer reasonably priced connectivity to a large number of low-power devices spread over great geographical ranges. Long range (LoRa) is a LPWAN technology that empowers energy-efficient communication. In LoRaWAN networks, collisions are strongly correlated with spreading factor (SF) assignment of end-nodes which affects network performance. In this work, SF assignment using machine learning models in simulation environment is presented. This work examines three approaches for the selection of the SF during LoRa transmissions: 1) random SF assignment, 2) adaptive data rate (ADR), and 3) SF selection through machine learning (ML). The main target is to study and determine the most efficient approach as well as to investigate the benefits of using ML techniques in the context of LoRa networks. In this research, a library that enables the communication between ML libraries and OMNeT++ simulator was created. The performance of the approaches is evaluated for different scenarios using the delivery ratio and energy consumption metrics.

Wearable energy-efficient fitness tracking system for sports person health monitoring application

The high potential of wearable physiological sensors in regenerative medicine and continuous monitoring of human health is currently of great interest. In measuring in real-time and non-invasively highly heterogeneous constituents, have a great deal of work and therefore been pushed into creating several sports monitoring sensors. The advanced engineering research and technology lead to the design of a wearable energy-efficient fitness tracking (WE2FT) system for sports person health monitoring application. Instantaneous accelerations are measured against pulses, and specific walking motions can be tracked by this system using a deep learning-based integrated approach of an intelligent algorithm for gait phase detection for the proposed system (WE2FT). The algorithm’s effects are addressed, and the performance has been evaluated. In this study, the algorithm uses a smartphone application to track steps using the Internet of Things (IoT) technology. For this initiative, the central node’s optimal location is measured with the antenna reflectance coefficient and CM3A path loss model (IEEE 802.15.6) among the sensor nodes for energy-efficient communication. The simulation experiment results in the highest performance in terms of energy efficiency and path loss.

Energy Efficiency Hybrid Precoding for UAV Inspection Communication System

In order to reduce the communication power consumption during UAV power line inspection, a energy efficiency hybrid recoding scheme based on one phase shifter is proposed in its communication subsystem. The solution is realized by using a partially connected hybrid precoder architecture in the UAV communication terminal, and using a beam combiner at the wireless network access point. At the same time, in order to reduce the complexity of hybrid precoding and combiner beam optimization design, convolutional neural networks are used to dynamically design and select the optimized beam required for UAV inspection and communication. Under the premise of efficiency, the communication energy efficiency can be improved as high as possible, thereby effectively improving the battery life cycle and cruise time of the UAV. The simulation results show that, compared with the manifold optimization and orthogonal matching pursuit algorithms, the proposed algorithm can significantly improve the energy efficiency of communication coding and battery life cycle.