Extensive Simulation Results Research Articles

Cooperative Localization in Hybrid Active and Passive Wireless Sensor Networks With Unknown Tx Power

Hybrid active and passive wireless sensor networks (HWSNs) gain advantages in extending the network lifetime and reducing the overall cost. Because the passive sensors without battery harvest the energy from distributed active sensor signal beam, and only a few active sensors can maintain a large-scale network. Thus, how to track the passive sensor’s location is essential for network management. Since the active sensors are sparsely deployed, cooperative localization which employs passive sensors to locate themselves together is a promising solution. In this paper, we analyze the energy beam generated by the active sensors on the cooperative localization accuracy of the passive sensors. We consider the spatial-temporal cooperative localization based on the received signal strength (RSS) model with unknown Tx power information of each sensor due to the limited processing capabilities, circuit complexity and energy constraints. We formulate the Fisher information matrix (FIM) and the corresponding Cramér-Rao lower bound (CRLB) for the static full connected network and dynamic spatial-temporal recursive network. Accordingly, energy beamforming schemes are proposed to optimize localization accuracy and energy efficiency problems. For the optimal localization problem, we derive the closed-form solution of the optimal energy beamforming wave. For the optimal energy efficiency problem, we propose a semi-definite programming (SDP) solution to achieve optimal energy consumption with a self-calibration method, which can address the over-relax problem. Extensive simulation results indicate that our proposed beamforming schemes have high localization accuracy and lower power consumption compared with the existing power allocation-based schemes.

A Novel Carrier Based Precharging Technique for Single-Phase Odd-Level Flying Capacitor Multilevel Converters (FCMCs)

Flying Capacitor Multilevel Converter(FCMC) needs all its flying-capacitors charged to the appropriate voltage levels during stat-up. This paper presents a novel carrier based precharging technique for single-phase odd-level FCMCs. A new set of modified phase-shifted carrier signals are derived for precharging. These carrier signals when compared to zero modulation signal, ensure the synthesisation of sufficient number of zero output voltage switching modes (zero-modes) of the converter. The proposed precharging technique is detailed using a five-level FCMC and then generalized to higher odd-level FCMCs. State-space averaging models are derived to validate the proposed technique mathematically. Extensive simulation and experimental results are provided to validate the functionality of the proposed technique.

A MAC Protocol Design for Maximizing End-to-End Throughput and Fairness Guarantee in Chain-Based Multi-Hop Wireless Backhaul Networks

Chain-based multi-hop wireless backhaul networks (WBN) have attracted considerable attention in next-generation networks since it can cost-effectively extend the service coverage area. The major challenge pertaining to such networks is the designing of a high-performance medium access control (MAC) scheme to raise the end-to-end throughput and ensure fairness. In this paper, we propose a novel MAC protocol, that is aimed at resolving the well-known location-dependent unfairness problem of chain-based networks, maximizing spatial reuse, and most importantly, achieving exceptional end-to-end throughput. Our scheme provides an elaborate control mini-slots design that resolves the different relay-traffic load problem of nodes at various positions near or far from the gateway and further provides local-traffic fairness among all nodes. Moreover, based on the traffic analysis, we provide a four-node group-transmission methodology. By using the neighbor-nodes echoes (broadcasts) the reservation-signals design, the chain-based WBN is allowed to extend to numerous nodes for concurrent and collision-free data transmission through distributed scheduling. Extensive simulation and analytic results also demonstrate that our proposed MAC scheme achieves exceptional system throughput which is approximately 1.6 times better than previous works for a four-node-chain network and also attains high system performance under a wide range of traffic loads and various system parameters.

CoOR: Collaborative Task Offloading and Service Caching Replacement for Vehicular Edge Computing Networks

In vehicular edge computing networks, edge service caching has emerged as a promising technology that supports delay sensitive applications. When the vehicles pass the coverage areas of roadside units (RSUs), the vehicles can offload part/all of their tasks to the RSUs that had cached the related service data in advance. However, it is difficult to utilize the service caching resources in RSUs efficiently, for the high mobility, variable task computing requests of vehicles and the limited storage and computation capacity of RSUs. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co</u> llaborative task <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> ffloading and service caching <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> eplacement scheme from a vehicle perspective, named CoOR, the task processing cooperation between the adjacent RSUs and the service caching replacement for vehicle are mainly considered. We formulate the system computing cost and delay minimization as a mixed integer programming problem, it faces challenges of task offloading and service caching coupling, the heterogeneous computation requests and dynamic data transmission conditions. Thus, we develop an iterative algorithm combined with the Gibbs sampling and deep reinforcement learning (DRL) to find the optimal decisions. Extensive simulation results show that the proposed schemes have good convergence and better performance than the traditional baselines.

Lightweight Per-Flow Traffic Measurement Using Improved LRU List

Per-flow traffic measurement is extremely important for various applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , flow size distribution, flow cardinality and DDos detection. Previous works, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , Elastic Sketch and FlowRadar, incur massive per-packet processing overhead and/or large memory consumption. In this paper, we present a novel least-recently-used (LRU) list based solution, called LRU-TX, for lightweight and accurate per-flow traffic measurement. Different from the classic LRU list with one exit at the tail, we maintain another exit at a certain position <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> (not the tail) in the list. Many terminated flows will be removed from the exit at position <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> as soon as possible with their counters less than a given threshold, which helps to decrease both the amortized processing overhead and the memory consumption. We also formally analyze the impact of different parameters ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , the exit's position, the threshold) on the measurement performance. Experimental results and extensive simulation results show that LRU-TX can increase the throughput by 30% at least, and reduce the memory cost by 50%, compared with the state-of-the-art solutions under the same measurement error conditions.

Fault diagnosis of 5G networks based on digital twin model

Fault diagnosis of 5G networks faces the challenges of heavy reliance on human experience and insufficient fault samples and relevant monitoring data. The digital twin technology can realize the interaction between virtual space and physical space through the fusion of model and data, providing a new paradigm for fault diagnosis. In this paper, we first propose a network digital twin model and apply it to 5G network diagnosis. We then use an improved Average Wasserstein GAN with Gradient Penalty (AWGAN-GP) method to discover and predict failures in the twin network. Finally, we use XGBoost algorithm to locate the faults in physical network in real time. Extensive simulation results show that the proposed approach can significantly increase fault prediction and diagnosis accuracy in the case of a small number of labeled failure samples in 5G networks.

Charger and receiver deployment for trajectory coverage with delay constraint in mobile wireless rechargeable sensor networks

Wireless Rechargeable Sensor Networks (WRSNs) powered by radio-frequency radiation attract a lot of attention for their sustainability and convenience, etc. One focus is how to rationally deploy the key devices, such as receiver and charger, to improve the network performance and deployment budget. This paper considers a novel deployment problem for WRSNs in a two-dimensional space with static trajectories, deploying the minimum number of wireless chargers and receivers for mobile nodes, subject to the non-overtime updating probability requirement for mobile node at any location of the trajectories. This timeliness communication problem is tricky and NP-hard when involving the influence of three practical delays (retransmission delay, charging delay and competition delay). Therefore, a performance controllable discretization strategy is proposed, based on which the Greedy Heuristic (GH) and Particle Swarm Optimization (PSO) based approximate solutions are introduced. The execution process and time complexity of these two solutions are analyzed, and then the numerical results of extensive simulations are introduced to verified the scalability, accuracy and efficiency of the two solutions. As a result, the GH solution has better deployment success rate and efficiency, and is the recommended solution for the novel problem. Finally, two real-life scenarios (indoor exhibition hall and hospital interior road) are introduced to further verify the robustness of the two solutions under the practical challenges of uneven density of mobile nodes, restricted deployment locations and occlusion of wireless links.

Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT

Recent years have witnessed a renewed interest in reinforcement learning (RL) due to the rapid growth of the Internet-of-Things (IoT) and their associated intelligent information processing and decision-making demands. As the slow learning speed is one of the major stumbling blocks of the classic RL algorithms, substantial efforts have been devoted to developing faster RL algorithms. Among them, post-decision state (PDS) learning is a prominent one, which can often improve the learning speed by orders of magnitude by exploiting the structural property of the underlying Markov decision processes (MDPs). However, conventional PDS learning requires prior information about the PDS transition probability, which may not be always available in practice. To lift this limitation, a novel blind PDS (b-PDS) learning algorithm is proposed in this work by leveraging the generic two-timescale stochastic approximation framework. By introducing an extra estimating procedure about the PDS transition probability, b-PDS learning can achieve a similar improvement of learning speed as conventional PDS learning while excluding the need for prior information. In addition, by analyzing the globally asymptotically stable equilibrium of the corresponding ordinary differential equation (o.d.e.), the convergence and optimality of b-PDS learning are established. Moreover, extensive simulation results are provided to validate the effectiveness of the proposed algorithm. Over the considered random MDPs, it has been observed that, to reach 90% of the best possible time average reward, the proposed b-PDS learning can reduce the learning time by 70% compared to Q-learning and 30% compared to Dyna.

Two-Timescale Learning-Based Task Offloading for Remote IoT in Integrated Satellite–Terrestrial Networks

In this paper, we propose an integrated satellite-terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state-action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor-critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially in scenarios with scarce spectrum resources and heavy traffic loads.

Online Learning for Joint Energy Harvesting and Information Decoding Optimization in IoT-Enabled Smart City

In this study, we first present a framework that jointly optimize energy harvesting and information decoding for Internet of Things (IoT) devices, which are capable of simultaneous wireless information and power reception, in a smarty city. In particular, a generalized power splitting receiver for IoT devices is designed, where each antenna in the receiver has an independent power splitter, unlike the existing works in which only one power splitter is employed regardless of the number of antennas in the receiver. Such a receiver design can provide a great degree of freedom to improve the network performance. Based on the presented framework, for each IoT device, we formulate an optimization problem whose objective is to maximize the harvested energy of each IoT device while satisfying its data rate requirement. To solve this problem, we propose a double-deep deterministic policy gradient based online learning algorithm which enables each IoT device to jointly determine receive beamforming and power splitting ratio vectors in real-time. Further, each IoT device can implement the proposed algorithm in a distributed manner using only its local channel state information. As such, cooperation and information exchange among the base stations and IoT devices are not necessary when performing the proposed algorithm at IoT devices. The extensive simulation results show the validity of the proposed algorithm.

Quality of Service Management for Fast Data Transmission in Industrial Mobile Communications Using PSO-Based Computer Vision Technique

Mobile ad hoc networks (MANETs) have rapidly expanded in recent years, mostly as a result of the mobile devices’ obvious low cost, heterogeneity, and flexibility. When communication networks are down or inaccessible, sensors can quickly create a reliable network that can be used as a rescue data system. Industrial mobile communication has developed into a significant study area for both industry and academia in recent years. The need for data interchange between various smart devices with various latency flows is enormous. Nevertheless, there has not been as much research done in this area. The suggested work suggests a fuzzy-based improved PSO optimized in MANET to alleviate the drawbacks of the conventional routing approach. The suggested study offers a numerical modelling that can be used to carry out adaptive transmission optimization with a variety of programmable module structures and guarantee cost-effective route establishment with greater throughput, goodput, and lowest delay requirements. To find the best route, the proposed approach combines energy-optimized route construction with data-driven cluster head (CH) selection based on swarm intelligence. Particle swarm optimization- (PSO-) based clustering achieves improved delay, goodput, throughput, and path difference degree as compared to other conventional approaches, according to the extensive simulation results. The energy efficiency of a network that is decentralized is more important. The MANET device’s energy efficiency helps to extend battery life and improve network performance. This research demonstrates how the fuzzy-based improved PSO optimized in MANET helps to raise the network’s energy efficiency. As a result, network energy conservation improves network performance and battery life. Additionally, this method enhances the quality of service methodology. End-to-end delay, energy consumption, packet delivery ratio, and normalized routing overhead are measured and compared between the simulation and conventional routing protocols.

Task-oriented Energy Scheduling in Wireless Rechargeable Sensor Networks

In recent years, the flourishing of Wireless Power Transfer (WPT) technology brings Wireless Sensor Networks (WSNs) a renewable, reliable, and controllable energy source. To achieve high efficiency, WPT and sensors both adopt directional antennas. Most previous works about directional charging optimize overall charging efficiency, but neglect energy scheduling on nodes for optimizing tasks. This disadvantage may cause a serious imbalance between the energy supply and task loads. This article takes both aspects into consideration. To achieve an overall performance improvement, we jointly schedule rotatable chargers and allocate tasks to nodes in a WSN powered by directional chargers. As far as we know, this is the first work to propose the Task-oriented Energy Scheduling (TOES) problem, i.e., given a set of rotatable directional chargers and some wireless rechargeable sensor nodes, scheduling chargers and nodes to maximize the total utility gained from a set of tasks. We prove the NP-Hardness of TOES. Then we design a 4-approximation algorithm for TOES by jointly solving two subproblems of the original one. We further extend this problem to the windowed task model and propose a \(\frac{4}{1-\gamma }\) approximation algorithm for it, where γ is the proportion of the rotating period. Finally, extensive simulation results show that our algorithms outperform baselines by at least 40.3%.

A Blockchain-Based Method for Optimizing the Routing of High-Frequency Carbon-Trading Payment Channels

Carbon trading is an effective way to achieve carbon neutrality. It is a market mechanism aimed at reducing global greenhouse gas emissions and carbon dioxide emissions. Blockchain technology can be applied to the carbon-trading scenario using characteristics that guarantee the security, decentralization, data immutability, and data traceability of the carbon-trading process. It would be difficult to implement carbon trading on blockchains for all enterprises and individuals, as the current performance of blockchains does not meet the requirements. There has been extensive research conducted on blockchain performance optimization, and the off-chain payment channel is one of the more mature solutions. This approach involves the transfer of transactions to off-chain transactions, thus avoiding high transaction fees. Existing research has addressed the problem of routing security and efficiency, with less emphasis on factors such as routing transaction costs, node reputation, and routing path considerations. This paper researches the optimization of payment routing in Payment Channel Networks (PCNs) and proposes the Multi-Factor Routing Payment Scheme (MFPS), which integrates factors such as the node reputation, transaction fee cost, and distance to select the route for payment transactions. In order to improve the success ratio of routing transactions, the transaction-splitting algorithm is proposed. To ensure the security and privacy of the transaction process, the Asymmetric Time-Lock Contract (ATLC) protocol is proposed. The results of extensive experimental simulations show that the MFPS proposed in this paper outperforms the ShortestPath, Cheapest, and SplitDistance algorithms. It achieves an approximately 13.8%∼49% improvement in the transaction success ratio and reduces the average transaction processing cost. The security and privacy measures can defend against wormhole and double-flower attacks and exhibit better performance in terms of computational verification and message overhead.

Categorization of Arabic posts using Artificial Neural Network and hash features

Sentiment analysis is an important study topic with diverse application domains including social network monitoring and automatic analysis of the body of natural language communication. Existing research on sentiment analysis has already utilised substantial domain knowledge available online comprising users’ opinion in various areas such as business, education, and social media. There is however limited literature available on Arabic language sentiment analysis. Furthermore, datasets used in majority of these studies have poor classification. In the present study, we utilised a primary dataset comprising 2122 sentences and 15,331 words compiled from 206 publicly available online posts to perform sentiment classification by using advanced machine learning technique based on Artificial Neural Networks. Unlike lexicon-based techniques that suffer from low accuracy due to their computational nature and parameter configuration, Artificial Neural Networks were used to classify people opinion posts into three categories including conservative, reform and revolution, accompanied by multiple hasher vector size to benchmark the performance of the proposed model. Extensive simulation results indicated an accuracy of 93.33%, 100%, and 100% for the classification of conservation, reform, and revolutionary classes, respectively.

Engram-Driven Videography

Sensing and understanding large-scale dynamic scenes require a high-performance imaging system. Conventional imaging systems pursue higher capability by simply increasing the pixel resolution via stitching cameras at the expense of a bulky system. Moreover, they strictly follow the feedforward pathway: that is, their pixel-level sensing is independent of semantic understanding. Differently, a human visual system owns superiority with both feedforward and feedback pathways: The feedforward pathway extracts object representation (referred to as memory engram) from visual inputs, while, in the feedback pathway, the associated engram is reactivated to generate hypotheses about an object. Inspired by this, we propose a dual-pathway imaging mechanism, called engram-driven videography. We start by abstracting the holistic representation of the scene, which is associated bidirectionally with local details, driven by an instance-level engram. Technically, the entire system works by alternating between the excitation–inhibition and association states. In the former state, pixel-level details become dynamically consolidated or inhibited to strengthen the instance-level engram. In the association state, the spatially and temporally consistent content becomes synthesized driven by its engram for outstanding videography quality of future scenes. The association state serves as the imaging of future scenes by synthesizing spatially and temporally consistent content driven by its engram. Results of extensive simulations and experiments demonstrate that the proposed system revolutionizes the conventional videography paradigm and shows great potential for videography of large-scale scenes with multi-objects.

Outage Performance and Ergodic Capacity of User Pairing in Downlink MIMO-NOMA systems with Imperfect SIC Over Nakagami-m Fading Channels

Non-orthogonal multiple access (NOMA) has been envisaged as a key driving scheme for future wireless communication systems because of its huge connecting ability and superior spectrum efficiency. Integrating the concepts of Multiple-input-multiple-output (MIMO) into NOMA enhance the system capacity at the cost of hardware complexity. In this article, we consider an MIMO-NOMA-based system in which the users are partitioned to form a cluster. A beamforming vector that helps to maximize the channel gain has been designed to remove the inter-cluster interference. This work presents the effect of user pairing in downlink MIMO-NOMA systems. User pairing has a significant impact on the performance of MIMO-NOMA systems. Outage probability and Ergodic Capacity (EC) are analyzed for MIMO-NOMA systems. Exact outage probability expressions are provided for user pairing to reveal some useful insights that pairing two users with dissimilar channel conditions increases the probability that NOMA achieves a larger sum rate than traditional orthogonal multiple access (OMA) systems. This mathematical investigation is carried out by considering Nakagami-m fading channel. Furthermore, this notion of user pairing is presented for imperfect successive interference cancellation (SIC). Diversity order is also derived for the strong and weak users. Extensive simulation results are provided to validate the derived analytic expressions. Furthermore, it is also demonstrated that the EC of MIMO-NOMA systems is improved significantly when compared with traditional OMA systems.

DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver

In this paper, we propose a novel deep learning-based spectrum sensing scheme using a multi-antenna receiver. Our main idea is constructing a correlation matrix composed of not only auto-correlation functions per each antenna but also cross-correlation functions between antennas. By using the rich informative matrix, with a simple convolutional neural network (CNN) structure, our model, DS2MA (Deep Spectrum Sensing with Multiple Antennas), can efficiently learn to detect the presence of a primary user (PU). In our extensive simulation results, DS2MA using only auto-correlation functions for each antenna can outperform previous related works. By adding cross-correlation functions together, DS2MA can significantly improve the detection performance. We also show the impact of impulsive noise and correlation coefficient. The simulation results show that the proposed DS2MA provides a higher detection probability compared to the existing works regardless of the impulsive noise impact and correlation coefficient.

Optimization of drug scheduling for cancer chemotherapy with considering reducing cumulative drug toxicity

An improved optimal drug scheduling model with considering two control drugs is proposed and the Gauss pseudospectral-based optimization method is studied to decrease the tumor size and drug toxicity in this work. Firstly, the Dexrazoxane drug, which has significant clinical effect to reduce the toxicity of the anticancer drug, is introduced. By analyzing the growth kinetics model of cancer chemotherapy, the toxicity reduction drug is regarded as the second input in the cancer dynamic equations. Correspondingly, the drug scheduling optimization problem with particular optimization goal and necessary constraints is established. Next, a model transformation technique is proposed to reduce the complexity of dynamic equations. With deriving the Gaussian time grid discretization detailly, the Gauss pseudospectral method (GPM)-based cancer chemotherapy drug scheduling algorithm is presented to test the performance of the proposed model within different rates. Finally, the implementation structure of drug scheduling optimization is given in detail. To test and validate the performance of proposed chemotherapy model, extensive simulation results and comparative evaluation are carried out on a specific mathematical model. Simulation results show that the improved optimization model is superior to other literature studies, resulting in the average improvement of performance index by 66.54% and revealing the significant guiding property for cancer chemotherapy.

Ergodic Capacity of Intelligent Omni-Surface-Aided Communication Systems With Phase Quantization Errors and Outdated CSI

In this article, we study the performance of an intelligent omni-surface (IOS)-aided communication system, considering the impacts of actual phase quantization errors and outdated channel state information (CSI). Different from the conventional reflective reconfigurable intelligent surface that can only reflect the incident signals, IOS can offer effective communication service to mobile users via the reflective or the transmissive operation. As for the transmissive scenario, we derive the accurate closed-form ergodic capacity (EC) expressions taking into account the phase quantization errors in both the near- and far-fields. To further obtain practical physical insights, tight upper- and lower-bound expressions for EC are provided. It is found that when the transmit power and the quantization bits are sufficiently large, the EC performance would become saturated due to the existence of outdated CSI. Also, the impacts of the phase quantization errors, the outdated CSI, and channel parameters on the considered system performance are revealed. Furthermore, the correctness of our derived expressions is validated by extensive Monte-Carlo simulation results.

Routing and Resource Scheduling for Air-Ground Integrated Mesh Networks

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.