Service In Wireless Networks Research Articles

Cognitive quality of service predictions in multi-node wireless sensor networks

Wireless sensor networks lead the way to the realization and penetration of the internet of things into daily life. As the sensors and communication devices interconnect things and people more, the quality of service demands become stringent and diverse. Optimizing conflicting quality of service goals is NP-hard. Moreover, the need for the communication systems to dynamically adapt has grown as factors like scale, application-specific performance demands, and deployment scenarios evolve. In this article, we intend to predict the quality of service with an aim to improve the performance of services in the internet of things. We conducted experiments using a real test-bed and collected performance data under a wide range of communication parameter configurations. Statistical analysis revealed a significant relationship between communication parameters and quality of service metrics. Based on the correlations, we trained deep learning models to assess the predictability of the metrics. The prediction results are encouraging. The outcomes of this research pave the way to lay the foundation for a data-driven design of adaptive quality of service in wireless sensor networks and the internet of things.

Utilizing Hybrid Machine Learning Models to Predict Quality of Service (QoS) in Multi-Channel Wireless Networks

Utilizing Hybrid Machine Learning Models to Predict Quality of Service (QoS) in Multi-Channel Wireless Networks

Hardware Implementation of Effective Framework for the Trade-off between Security and QoS in Wireless Sensor Networks

The trade-off between security and quality of service (QoS) in wireless networks has attracted great attention in recent years. This paper presents a hardware implementation of a novel framework for realizing the trade-off between security and QoS in wireless sensor networks (WSNs) regarding the hostility of the operating environment and the available resources of our objects. For evaluating the hostility of the operating environment, we developed an effective intrusion detection unit that sends an alarm signal to a control unit that also collects the data-carrying information about the available resources. As our work is concerned with WSNs, we focus on the power consumption of sensor nodes as the considered object resources. According to the collected data from the intrusion detection unit and resources status unit, the control unit can evaluate the situation of the object in the operating environment and sends control signals to a security unit to select the appropriate level of security among three levels (low, medium, and high) available in our developed framework. The three security levels consider different combinations of the cryptographic algorithms Hash-based Message Authentication Code (HMAC) and Advanced Encryption Standard (AES), with different key lengths, regarding the required security services (confidentiality, authentication and integrity) and available resources. To reinforce the QoS, we considered the advantages of hardware implementation in addition to maintaining the object resources. Our framework has been implemented using Field Programmable Gate Array (FPGA), Virtex-7 (xc7v585tffg1761–3), which helps in achieving reasonable performance with a maximum operating frequency of 206.838 MHz and throughput of 2.3 Gbps.

A Dynamic Task Scheduling Method for Multiple UAVs Based on Contract Net Protocol.

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method.

Smart Product Marketing Strategy in a Cloud Service Wireless Network Based on SWOT Analysis

Wireless network systems have multiplied, and mobile communication systems have grown. This article is aimed at developing a cloud-based service wireless network system to smartly market products online. It makes use of cloud services combined with various technologies of the wireless network system. An intelligent product is a device connected to the Internet via sharing information with its users. The usage of smart products has increased day by day, resulting in increased production numbers by various companies. Here, Arises a problem of marketing among huge competition all the manufactured products. So, a marketing strategy has to be designed to market the products. This marketing task can be done using cloud computing services. Cloud computing refers to accessing various computing applications online, such as storing and accessing data, servers, software, databases, and networking. Users can store and access data from a remote server, and there is no need to store it on one’s computer hard disc. Large clouds have data centers located at various locations. Data and computing services are available on-demand, whenever they are needed. With the help of a SWOT analysis, this article will investigate smart product marketing strategies in cloud service wireless network systems. The advantages and disadvantages of using the cloud service wireless network system are analyzed based on SWOT analysis. A technically detailed analysis is done by identifying its strengths (S), weaknesses (W), opportunities (O), and threats (T), considering various aspects. In this research, smart product marketing analysis with SWOT is performed to implement a novel hybrid algorithm with the Smart Product Marketing Crisis Prediction (SPMCP) method. For the Smart Product Marketing application, the proposed method is compared with the Ant Colony Optimization (ACO) and has obtained a higher accuracy of 99.54%.

6LoWPAN Over Optical Wireless Communications for IPv6 Transport in Internet of Things Networks

As an emerging technology for the Internet of Things (IoT) wireless connectivity, there have been a lot of research and standardization activities on Visible Light Communications (VLC) and Optical Wireless Communications (OWC) using Light Emitting Diode (LED) lights. In the meantime, the Internet Protocol version 6 (IPv6) over Low Power Wireless Personal Area Network (6LoWPAN) has been discussed to provide the IPv6-based IoT services in wireless networks. However, the study on IoT systems using 6LoWPAN over OWC networks has not been made so far. This letter proposes a new architectural model to effectively use 6LoWPAN between IoT gateway and IoT device in the OWC-based IoT networks. The proposed model is easy to implement and provides the performance enhancement in OWC-based IoT networks, compared to the general IPv6 model. From testbed experimentations, it is shown that the proposed model provides the delay gain up to 5% and the throughput gain up to 19.52%, compared to the conventional IPv6 transport model.

Fair and Energy-Efficient Coverage Optimization for UAV Placement Problem in the Cellular Network

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

Dynamic resource allocation schemes for eMBB and URLLC services in 5G wireless networks

The fifth generation (5G) of wireless networks features three core use cases, namely ultra-reliable and low latency communications (URLLC), massive machine type communications (mMTC), and enhanced mobile broadband (eMBB). These use cases co-exist in many practical scenarios and compete for the same set of time and frequency resources, resulting in a natural trade-off in their performance. In this paper, a network supporting both URLLC and eMBB modes of operation is studied. To guarantee the ultra low latency requirement of URLLC, a dynamic resource allocation scheme indicated by a two-dimensional bitmap is proposed. This approach is capable to achieve finer granularity as well as lower false cancellation rate compared to the state-of-the-art methods. A novel power control and indication method is also proposed to dynamically provide different power control parameters to the user equipment (UE), while guaranteeing the reliability requirement of URLLC and minimizing the impact to eMBB. In addition, we devise a dynamic selection mechanism (DSM) to accommodate diverse scenarios, which is empowered with load prediction to become more intelligent. Our extensive system-level simulation results for eMBB-URLLC co-existence scenarios showcase that the perceived throughput of eMBB UEs is increased by 45.3%, while about 13.3% more UEs are enjoying URLLC services with at most 84% transmit power savings compared to the state-of-the-art methods.

BlueSync: Time Synchronization in Bluetooth Low Energy With Energy-Efficient Calculations

Time synchronization is one of the important services in wireless sensor networks (WSN), and Bluetooth low energy (BLE) is one of the wireless protocols that has drawn a lot of attention recently, with applications in Internet of Things (IoT) and sensor fusion. Although, nonconnectable beacons are the most energy-efficient advertising mode of BLE, no synchronization service is defined for them in the BLE standard. Here, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlueSync</i> , a BLE compatible time synchronization protocol, which uses beacons for data transfer. As part of this study, we investigate the tradeoffs between energy efficiency, synchronization error, and speed by considering different drift-management methods and various synchronization packet configurations. Additionally, we propose a Discrete Adjustment technique that can improve energy efficiency in calculating timer ticks updates up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> without sacrificing accuracy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlueSync</i> is implemented on two commercial BLE system on chips and tested in 10-min long sessions without any resynchronization. Experimental results show that depending on the settings, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlueSync</i> can achieve average synchronization errors as low as 320 ns per 60 s (single hop) and is the most accurate time synchronization reported on BLE to date.

Performance of Predictive Indoor mmWave Networks With Dynamic Blockers

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams’ blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantees higher signal level stability and up to 82% data rate improvement with respect a detection-based method when blockers move at speed of 2 m/s.

A novel approach to fairness-aware energy efficiency in multi-RAT heterogeneous networks

Heterogeneous cellular networks are a viable solution in response to the growing demand for broadband services in the new-generation wireless networks. It is more challenging to manage resources (e.g., user association and power control) in multi-radio access technology (multi-RAT) HetNets than in conventional single-tier cellular networks due to the higher computational complexity and energy efficiency constraints. In addition, given the novel services and resource limitation of the user layer, energy efficiency and fairness assurance are critical issues in the uplink. Considering the uplink fairness criterion, this paper proposes a dynamic optimization model which maximizes the total uplink/downlink energy efficiency in addition to providing the essential coverage and capacity of heterogeneous cellular networks. Based on the non-convex characteristics of the energy efficiency maximization model, the mathematical model can be formulated into two subproblems, i.e., resource optimization and user association. In this approach, a subgradient method is applied for fair resource management and also successive convex approximation and dual decomposition methods are adopted to solve the proportional fairness problem. The simulation results exhibit a considerable throughput increase of 25% and 18% on average for random and hotspot user distributions, respectively. Based on the numerical results, the adaptive radio resource management method reduces the rate of vertical handoffs between RATs, which leads to more stable communications for the users. It also proved that the proposed approach managed to significantly improve the total network energy efficiency by up to 40%.

Fairness driven performance analysis for 3-D Poisson Voronoi cell

The requirement of excellent anywhere–anytime data transmission service in future wireless network encourages us to reconsider the fairness among different types of users. To this end, the three-dimensional Poisson point process (PPP) model-based closed-form expressions of coverage probability for both cell-edge user (CEU) and cell inner user (CIU) under multi-user multiple-input multiple-output (MIMO) are derived. It is found that the spectral efficiency of CEU is about 30% of that of CIU under single user scenario. Moreover, when the number of users equals to that of base station (BS) antennas, the difference of coverage probability between CIU and CEU decreases with the number of BS antennas for large signal-to-interference ratio threshold. In addition, for the fixed number of users case, an inverted U-shaped relationship (thereby resulting in a worst case) between the fairness among CEU and CIU, and the number of BS antennas is detected. The impact of massive MIMO on the fairness under the metric of spectral efficiency is also discussed.

Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives

&lt;span&gt;Multi-objective algorithms are used to achieve high performance for quality of service (QoS) in wireless sensor networks (WSNs) is an important field for researchers because these algorithms improve two or more conflicting objectives and present the best trade-off between the conflicting objectives to solve multi-objective problems (MOPs). Previous research proposed an algorithm that relies on non-dominated sorting genetic algorithm 3 (NSGA-III), namely enhanced non-dominated sorting genetic routing algorithm (ENSGRA). This algorithm is used to optimise three objectives, which include number of worked sensors, energy consuming and node covering area. The fourth objective, which is node load balancing, is added in the current research. Such an addition aims to improve node distribution around cluster heads and decrease network congestion, thus decreasing energy consumption and increasing network lifetime. The ENSGRA algorithm is compared with multi-objective multi-step particle swarm optimisation (MOMSPSO), non-dominated sorting genetic algorithm 2 (NSGA-II), and NSGA-III. The proposed algorithm ENSGRA exceed to MOMSPSO, NSGA-II, and NSGA-III in the proposed QoS model in the final outcomes, as the proposed approach achieved (38%) average combination (optimisation) percentage. Which is the highest percentage over other methods.&lt;/span&gt;

Guest Editorial Special Issue on Next Generation Multiple Access—Part II

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

A Novel Localization Approach for Irregular Wireless Sensor Networks Based on Anchor Segmentation

Source localization has been a crucial fundamental service in wireless sensor networks (WSNs). Existing algorithms assume a regular region or controlled deployment. In practice, however, irregular network topologies often occurs, which greatly downgrade the localization performance. In this paper, we propose a new distributed localization approach based on Anchor Segmentation and Projection for Irregular networks (ASPI). The new framework is composed of three phases: anchor segmentation boarder construction, convex hull identification and projection-based localization. An anchor based network approximate convex segmentation method is proposed to reduce the consumption of network resources in the first two phases and an improved giftwrapping based convex hull identification method is provided to reduce the complexity. In the localization phase, we formulate the localization as a convex feasibility problem to avoid the multimodality in Maximum likelihood techniques and an alternative procedure is provided for inconsistent situation in the projection-based scheme. Experiments are conducted and the results demonstrate that our algorithm outperforms other existing solutions in irregular-shaped networks in higher accuracy with low complexity.

Special Issue on Next Generation Multiple Access—Part I

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

QoS Assessment for Video and Audio Data based on a Testbed for Multimedia Wireless ad Hoc Networks

Currently, with the fast modernization of wireless networking technologies as well as the very complex needs of the network users, multimedia data takes for a large proportion in the types of network services, this type of data requires Quality of Service (QoS) differs from traditional data, by ensuring the listening and viewing elements of the user. In this paper, we analyse the effect of video and voice data on QoS of wireless network services based on the establishment of an experimental evaluation (emulation) system or testbed, thereby providing empirical analysis on the effect of multimedia data on mobile wireless QoS.

Scheduling and Optimization of Wireless Virtual Network Resources Based on Reinforcement Learning

With the booming and proliferation of 5G wireless network services in the future, a large number of wireless virtual network resources will emerge, and the densification and heterogeneity of the wireless communication networks that provide services for them will become the trend of development. To this end, a wireless virtual network resource scheduling model based on user satisfaction is constructed, and a reinforcement learning-based wireless virtual network resource scheduling mechanism, IRSUP, is proposed. IRSUP is designed with an intelligent optimization module for user service preferences to address the personalized needs of user service customization, and a reinforcement learning-based intelligent scheduling module is designed to address the challenge of joint optimization of multistar resources. Simulation results show that IRSUP can effectively improve resource scheduling rationality and link resource utilization and user satisfaction, among which service capacity is improved by 30% to 60% and user satisfaction is more than doubled.

Retracted: Lifetime maximization energy‐aware routing protocol for route optimization to improve quality of service in wireless sensor networks

AbstractWireless sensor networks (WSNs) are part of the short‐term networks, including sensitivity, computation, and Wi‐Fi connectivity capability. Many routing, range management, and log transfer protocols are specifically designed for WSN. The previous method showed less efficiency in routing management. The proposed lifetime maximization energy‐aware routing protocol (LTMEARP) protocol is known for its high routing efficiency, giving a higher lifetime and throughput performance. The requirement for a routing system approach is advanced with Internet service provider's protocols by recalculating the routing table after the link stage's substitution worldwide, leading to responses and connection failures by sharing important data after traffic. LTMEARP routing protocol has assured high availability routing performances during traffic conditions. LTMEARP support sends homogeneous and expanded nodes. Analyze a new approach to routing‐based selection algorithms for homogeneous node WSNs. The number of connections is limited and must be adjusted using separate paths and header packets to meet the user's network access location. The results show that the LTMEARP has achieved quality without introducing excessive network access program overhead, which deals with the study of communication and the benefits and problems with the performance of each routing technology.

QoE-driven Antenna Tuning in Cellular Networks With Cooperative Multi-agent Reinforcement Learning

Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This paper proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.