This paper proposes the load-independent class-E Zero-Voltage Switching (ZVS) power oscillator. Conventional class-E power oscillator has a load-dependent phase shift, which hinders sustained oscillation under varying load conditions. Hence, we introduce the class-E power amplifier that ensures the load-independent phase shift in the output current. By incorporating it into an LCLC filter with a resonant-capacitor feedback network, the load-independent self-oscillation is realized. The proposed power oscillator satisfies a phase-shift requirement for sustained oscillation regardless of the load resistance. Furthermore, the proposed circuit exhibits load independence in ZVS, output current, and gate-drive voltage. This paper provides a thorough analysis of the proposed power oscillator, covering its operating principle, power loss prediction, design, and limitations. This paper also gives two design examples with 0.8 MHz and 6.78 MHz oscillation frequencies. In the experiment, the prototype power oscillators achieved 93.8 % and 87.4 % power-conversion efficiencies, respectively. The validity and effectiveness of the proposed power oscillator are demonstrated from the experimental verifications.

The integration of the Internet of Things (IoT) with blockchain technology offers a promising solution to tackle interoperability, privacy, and security issues in IoT applications. However, maintaining low latency is a significant challenge in IoT-blockchain systems, as blockchain relies on an underlying communication network to create an overlay network for transmission and synchronization. The random nature of blockchain’s broadcast mechanism results in an overlay network topology that is not optimized for low-latency transmission. This study examines the impact of overlay network topologies on latency performance within an Ethereum-based IoT system. We developed a novel method for generating various overlay topology configurations and implemented Ethereum clients to establish neighboring connections based on these configurations. Our findings reveal that the overlay network significantly influences latency metrics, specifically the delays in transmitting transactions or blocks. We observed that the trade-off between delays caused by network congestion and latency reduction from fewer hops in the overlay network is dependent on the number of connections. To investigate further, we implemented three models for overlay networks. None of these network models consistently exhibited superior latency performance, indicating that latency is affected by network dynamics. In response, we developed an efficient peer selection method for blockchain nodes to reduce latency in dynamic environments. Drawing inspiration from the Perigee algorithm’s success in peer selection for transaction propagation, we propose Dual Perigee, an extension of Perigee that also optimizes block propagation latency. Through experiments in an emulated 50-node IoT-blockchain system, we compared the effectiveness of Dual Perigee against Ethereum’s default peering method and the Perigee algorithm. The results demonstrate that Dual Perigee reduces block latency by 48.5 %. These latency reductions enable more real-time responsiveness in IoT-blockchain systems and support their deployment in latency-sensitive applications.

A hybrid wired/wireless SDN switch enables unified control across mixed-access networks but faces challenges in real-time traffic classification and flow management due to the lack of intelligence. This paper presents an intelligent hybrid SDN switch augmented with embedded machine learning (ML) models for accurate traffic classification and low-latency prediction. We evaluated six supervised ML algorithms, Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, k-Nearest Neighbors, and Neural Network, under both single-flow and concurrent multiflow scenarios using real traffic traces, including DNS, Ping, Telnet, and interactive gaming protocols. The evaluation results show that the Random Forest model consistently outperforms others, achieving classification accuracies of 99.97% in single-flow and 99.86% in multi-flow scenarios, while accurately predicting the traffic in millisecond-level inference latency. These findings demonstrate the feasibility of embedding ML-driven intelligence into hybrid SDN switches to support real-time, application-aware traffic control in emerging network infrastructures.

This paper proposes a machine learning (ML)–based design for a load-independent (LI) wireless power transfer (WPT) system. The fundamental concept of the proposed design strategy is to solve an optimization problem for achieving high-frequency LI operation by full numerical computations. An evaluation function for optimization is formulated, taking into account output voltage regulation against load variations and high power-delivery efficiency. The developed optimization software provides the best parameter set of coupling coil parameters and circuit-component values. In addition, the ML-based optimization finds the optimal parameter set from parameters outside the range that it had considered to be common sense, which is an unexpected but valuable result. The WPT system, designed through the proposed method, achieved ideal LI operation in the experiments. The quantitative agreements between experimental and numerical waveforms substantiate the validity of the proposed design.

In this paper, a WPT system that can switch between constant current (CC) and constant voltage (CV) in a single circuit is proposed. In the proposed WPT system, the CC and CV modes are achieved by varying the on-duty ratio of the buck converter. In addition, the proposed system also keeps the zero-voltage switching against coil misalignment and load variations without any information feedback from the rectifier to the inverter. This is because the load-independent behavior is built into the proposed system. The experimental results are consistent with the analytical predictions, which show the validity of the proposed circuit design strategy and the derived analytical expressions. Under rated conditions, the output power was 36.9 W and the power conversion efficiency was 86 % at the operating frequency of 6.78 MHz.

This paper proposes a machine learning (ML)-based design for a load-independent (LI) wireless power transfer (WPT) system. The proposed design strategy formulates the WPT design as an optimization problem and solves it by fully-numerical computation to achieve high-frequency LI operation. A multi-objective evaluation function is given to achieve the LI operation, which evaluates output voltage, power-delivery efficiency, and total harmonic distortion. The class-EF WPT system designed using the proposed method achieved narrower output voltage variations and higher power-delivery efficiency than the analysis-based design system. Additionally, the proposed design algorithm identifies parameter sets that were never found through the analysis-based design. Namely, only a small amount of current flows through the previous harmonic resonant filter of the class-EF inverter. These results demonstrate the potential of ML-based design in the field of power electronics.

Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.

The fusion of IoT and Blockchain (i.e., IoT-Blockchain) promisingly revolutionizes numerous domains. However, IoT-Blockchain systems may face scalability challenges due to a large number of IoT devices and blockchain performance. Sharding, which divides the blockchain into smaller groups, offers a solution to enhance blockchain scalability. However, most previous sharding research typically considers even node distribution in a shard (i.e., a fixed node number). Hence, it may not capture the characteristics of IoT- Blockchain systems well, where nodes are diversely distributed. This paper investigates a new scenario of shards having unevenly distributed nodes, aiming to see the impacts of the scenario on sharding protocols. More specifically, we evaluate the BrokerChain protocol on the sharding-based emulation platform (i.e., BlockEmulator) in scenarios with even and uneven node distributions. The results show that the protocol performance degrades with the uneven node distribution, suggesting the need for a dynamic sharding protocol.

This paper presents the design of the class-Φ3power oscillator. The topology of the proposed power oscillator features the feedback network from the resonant capacitance, which allows for the application of wireless power transfer (WPT) systems. Furthermore, the required phase shift necessary for applying the proposed feedback network is achieved by employing the class-Φ3inverter, which has a finite input inductance. Closed-form design equations for the proposed power oscillator are provided, which allows a quick and intuitive design. An experimental verification is conducted with the prototype power oscillator. The measured power-conversion efficiency was 92.2 % with 70 W output power at 1 MHz operation. The experimental results agreed quantitatively with the analysis, which shows the validity of the design.

This study explores the intersection of neuroscience and computer science, focusing on the use of spiking neural networks (SNNs) to simulate the behavior of biological neurons. A neural network model based on the Izhikevich neuron model is proposed to simulate the local auditory network of crickets. The parameters of the neuron model are optimized based on evaluation functions and identified by Particle Swarm Optimization (PSO), aligning its input-output relationships with the observed cricket neuron responses. The results showed that the network successfully simulated the behavior of individual neurons, promising applications in fields like neural prosthetics.

Blockchain holds significant potential in addressing the security, privacy, decentralization, and interoperability challenges prevalent in the Internet of Things (IoT), However, this advancement often comes at the expense of scalability. Therefore, enhancing the scalability of IoT blockchain systems while preserving other essential blockchain attributes is imperative. This paper aims to mitigate network latency in the blockchain network, a factor directly linked to blockchain scalability. Achieving this goal requires implementing an efficient peer selection method, moving beyond the reliance on default selection (i.e., the one in Bitcoin, Ethereum, etc.). In existing literature, Perigee has been introduced as a method that nearly optimizes the delay in the transaction transmission process. However, Perigee has not comprehensively addressed the complete transaction life cycle, which includes a crucial process-block transmission. In response to this limitation, we propose Dual Perigee, a solution that thoroughly considers and optimizes both transaction-oriented latency (TOL) and block-oriented latency (BOL). To show the effectiveness of Dual Perigee, we implemented and evaluated it within an emulated IoT-Blockchain system, comparing its performance with Perigee and the default peering method in Ethereum. The results reveal that Dual Perigee excels in reducing BOL compared to Perigee. Moreover, Dual Perigee exhibited a latency that was 43% and 80% lower than the default peering method and Perigee, respectively.

With the increasing demand for video streaming services, efficient video streaming with MPQUIC in mobile wireless networks is gaining interest. Although MPQUIC can leverage multiple network connections (e.g., Wi-Fi, 5G) for simultaneous transfer, improving bandwidth and resilience, its performance is heavily dependent on the MPQUIC scheduler. In mobile scenarios, network fluctuations challenge the scheduler to adapt to dynamic conditions while maintaining good video streaming quality. Addressing this issue, this paper proposes a novel MPQUIC scheduler named LoGra (i.e., LSTM-DDPG integrated MPQUIC Scheduler) with two advanced features. First, LoGra utilizes Long Short-Term Memory (LSTM) to model the temporal correlation of network conditions over time and handle the challenges posed by mobility patterns. Second, it leverages the Deep Deterministic Policy Gradient (DDPG), with its self-learning capabilities and the strength of deep neural networks, to analyze the informative temporal feature vector to influence scheduling decisions. We have implemented the proposed scheduler and compared it to existing ones. The results show that the LoGra scheduler effectively manages multipath communication in heterogeneous wireless networks under mobility scenarios. Moreover, compared to existing schedulers, LoGra achieves significant improvements in data transmission, both in general metrics (loss, goodput) and streaming-related metrics (bitrate, freezing time).

Due to global air pollution , there is a growing demand for accurate and large-scale air quality monitoring systems. Consequently, low-cost air monitoring devices have emerged as a potential alternative to expensive conventional ones. However, the low-cost devices’ major drawback is their insufficient level of accuracy. This work investigates the problem of calibrating the sensory data, especially PM2.5 concentration, collected by low-cost sensor-based air quality monitoring devices. Recently, deep learning has emerged as a potential solution for data calibration instead of using traditional methods, whose accuracy is relatively low. Nevertheless, it generally incurs significant costs. Moreover, it is necessary to employ a dedicated calibration model for each device to increase precision, resulting in additional expenditures. To address the issue, this study provides a novel approach named GAMMA, which entails the development of a deep learning-based model capable of accurately calibrating data for multiple devices simultaneously. The proposed method leverages the multitask learning paradigm to solve the challenge of concurrently processing several devices’ data. This involves capturing common features across all devices’ data while also distinguishing the device-specific characteristics. Furthermore, GAMMA also employs the adversarial training approach to augment the accuracy of predictions. This method has been implemented and integrated into an air quality monitoring system in Hanoi, Vietnam. Comprehensive experiments are conducted on real-world data to demonstrate the superiority of GAMMA against the comparison benchmarks in terms of various metrics. Notably, GAMMA reduces MAE from 60.19% to 74.09% compared to the best comparison baseline. The source code is available at https://github.com/anhduy0911/FimiCalibIdea/tree/multi_attention .

This paper proposes the load-independent (LI) class-E frequency multiplier along with a unified circuit analysis method with the LI class-E amplifier. A circuit-parameter determination strategy is presented to achieve LI operation and maximum power output capability at the rated condition. We designed the class-E amplifier and frequency doubler using the unified analytical expressions. Both the implemented circuits achieved the LI operation, namely constant output voltage amplitude and zero-voltage switching against load variations without any control. The experimental results showed quantitative agreements with the analysis results, namely waveforms and power conversion efficiency, which indicates the validity of the derived analytical expressions and design procedure.

In the era of 5G and beyond, mobile devices usually can access several heterogeneous wireless networks (e.g., Wi-Fi and 5G). To simultaneously and efficiently utilize the accessible network resources, muli path transport protocols, such as MPTCP and MPQUIC, have shown much potential. In these protocols, scheduling is one of the critical processes to ensure the performance of the multipath transmission. Although there have been many proposed multipath schedulers in the literature, they have not performed well in heterogeneous networks, especially when the network conditions vary (i.e., dynamicity). In this paper, we propose a novel Q-learning-based Multipath scheduler for data transmission optimization (Q-SAT), aiming to bypass the existing limitation. By leveraging the self-learning ability of reinforcement learning, Q-SAT can instantly observe environmental changes and deploy appropriate path selection to optimize data transmission time. As a result, Q-SAT efficiently schedules multipath communication in heterogeneous wireless networks with different dynamicity levels. We have implemented Q-SAT with MPQUIC and extensively evaluated Q-SAT in an emulated environment and a real network. The evaluation results show that Q-SAT improves the data transmission time by at least 10% in the emulation and 26% in the actual deployment compared to the state-of-the-art schedulers.

This paper proposes a load-independent high-frequency wireless power transfer (WPT) system for multiple receivers with a single transmitter. The proposed system achieves zero-voltage switching (ZVS) at the transmitter and constant output voltages at all the receivers against load variations without any control. Moreover, the proposed system keeps the load-independent operation even when the number of receivers changes. The circuit analysis for the proposed WPT system is given. Experimental verifications for the proposed WPT systems with two receivers were conducted, which showed the effectiveness. The implemented WPT system achieved a peak efficiency of 83.4% at 6.78 MHz and total output power of 50 W.

This paper presents a simulator-based design-optimization software for high-frequency power-electronics circuits, which consists of the heuristic algorithm, magnetic-component design tool, and commercial simulator, SPICE. Because we can use the SPICE component library, the software suggests high-accurate design results, and the experimental adjustments can be reduced. Besides, by using the heuristic algorithm, the optimization algorithm converges stably. The magnetic-component design tool provides a precise estimation of the power losses, which is important for high-frequency power-electronics circuit designs. As an example, we design the class-DE inverter by applying the developed software, which shows the usefulness of the software.

This study proposes a self-tuned series resonant power oscillator capable of load-independent operation. The proposed power oscillator has a self-tuning feedback loop that provides a gate-driving voltage with a constant phase shift and amplitude. Therefore, it is robust against component tolerances in a series resonant filter. It is also robust against load variations owing to its load-independent design. Consequently, it maintains a high-efficiency and constant AC output against load variations and component tolerances. Experimental results demonstrated the effectiveness of the proposed power oscillator.

In recent IEEE 802.11 WLANs, an Access Point (AP) can use the Channel Bonding (CB) technique to increase the transmission bandwidth. Given limited bandwidth resources, it is not trivial to allocate channels for densely deployed APs to fairly meet their diverse traffic demands. In general, the non-overlapping channel allocation approach has been proven to be an ideal solution for maximizing the achievable throughput of WLANs using CB. However, we argue that the approach is no longer efficient because of two common situations: i) insufficient non-overlapping channels and ii) imbalanced traffic demands. Instead, the overlapping channel allocation is more suitable in such situations. In this study, we first apply a continuous-time Markov Chain (CTMC) model to analyze the effect of overlapping channel allocations for neighboring APs under unsaturated traffic conditions. Based on the analysis results, we propose a novel channel allocation algorithm based on the Bin-Packing Problem (BPP) that maximizes the fairness of the throughput-to-demand satisfaction ratio between APs. Through simulations, we demonstrated that the proposed algorithm can achieve near-optimal performance in various network settings. The algorithm also outperformed existing algorithms with the same overlapping channel approach.

This paper introduces a host-based method for investigating IPv6 adoption and performance in academia that targets universities in Japan and Vietnam. Unlike other works, the investigation has been utilized on a host native IPv6 host with a standard tool (i.e., curl). We first probe the IPv6 capabilities of the universities’ websites. Second, within the IPv6-supported websites, we compare the performances of IPv4 and IPv6 when letting the two IP versions access the websites concurrently. We find that, despite the popularity of IPv6 in the two countries, a significant number of academic websites are not yet IPv6 capable. The native IPv6 client can only access fifty of the more than one thousand websites in Japan. Furthermore, there are no IPv6-supported websites at Vietnamese universities. Among the accessible IPv6 websites in Japan, we observe that the web access performances of IPv4 and IPv6 are similar.

In this research, a novel load-independent Class-E/F inverter with constant voltage (CV) and constant current (CC) output modes is proposed. By adding one auxiliary switch to the class-E/F inverter, the proposed inverter can change into the class-E topology. A components design method is proposed to achieve load-independent conditions for both topologies by merging the design conditions of the load-independent class-E and class-E/F inverters together. Therefore, the proposed inverter can switch between CV and CC output modes against load changes at the same switching frequency. Additionally, zero-voltage switching (ZVS) can be maintained for both modes despite load variation. The proposed inverter has a simplified topology, making it suitable for minimized wireless power transfer applications as the transmitter part. A 1 MHz experimental prototype circuit was designed and implemented. A circuit experiment was conducted, which confirmed the usefulness of the proposed inverter.

Blockchain technologies have been emerging with the potential to disrupt many fields (e.g., cryptocurrencies replacing the traditional ones, enabling trustworthy voting, etc.). The Internet of Things (IoT) has been predictably strengthened when integrating to the private blockchain, such as Ethereum. In an IoT deployment with private Ethereum, a thorough understanding of the latency is a critical issue that has not been adequately understood in the literature. Motivated by that, this work aims to comprehend the latency performance in the IoT Ethereum with two popular consensus algorithms: Proof of Work (PoW) and Proof of Authority (PoA). Initially, we clarify different latency segments from transaction submission to execution, namely, the transaction lifecycle in a private blockchain. We then consider the three related latency metrics: 1) transaction-oriented latency; 2) mining time; and 3) block-oriented latency in the PoW case. With PoA, the mining time’s consideration is omitted since the mining process is not necessary. After that, we construct a realistic private Ethereum IoT network (i.e., using a laptop and seven Raspberry Pi 3b+ nodes) and a large-scale emulated one with 30 nodes. We write and deploy a smart contract to read and write data to the blockchain and measure the latencies in various scenarios. The measurement results reveal the values of transaction-oriented and block-oriented latency with PoW and PoA in both the actual and emulated networks. Moreover, we derive the expected value for the PoW’s mining time by fitting the probabilities to an exponential curve.

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.

This paper tackles the problem of using and paying a network service with guaranteed quality of service (QoS) at a fine granularity in the future wireless network. We propose the IOTA-based QoS guaranteed Flow system (IQF), where a user and service provider can exchange the IOTA cryptocurrency for the guaranteed network resources, such as latency and bandwidth. First, IQF uses the IOTA Tangle, a lightweight, efficient distributed ledger technology, for its payment and transaction record. Second, IQF adopts Software Defined Networking to realize the guaranteed delay and bandwidth provision. We have implemented the IQF systems using the mininet-wifi emulator, an SDN controller (i.e., POX), and IOTA clients. Moreover, we have evaluated the service provisioning algorithms with a micropayment of IQF with the test Tangle (i.e., comnet). The evaluation results show that IQF has successfully achieved the delay and bandwidth provision with all the transactions stored in comnet.

Air pollution is a critical issue in cities in developing countries like Hanoi, Vietnam. An efficient and comprehensive air quality monitoring system may reduce the harmfulness and improve the cities' sustainability. This paper presents a novel approach to realize such a system in which the air monitoring sensors are mobile. More specifically, we introduce a three-tier architecture for the air quality system, including sensing, communication, and application layers. Initially, we discuss each layer concept to bypass the limitation of the traditional stationary monitoring system. We then describe our design and implementation of air quality monitoring devices installed on vehicles, such as buses. The device is carefully designed to satisfy the conditions of impedance matching and power integrity. Besides, it fully functions in measuring parameters from the ambient environment. The device is aware of its location (using GPS) and uses Wi-Fi and 4G (LTE) to transmit sensing data on the Internet. We have conducted various experiments, including a trial deployment of the devices on a vehicle running in Hanoi. The results show our device achieves sensing data transmission with high-reliability levels (i.e., 97%, 100% on Wi-Fi, 4G (LTE), respectively). Moreover, the trial deployment confirms the feasible operation of our device in actual condition.

In a three-tier Vehicle to X (V2X) network, a vehicle can offload the computational tasks to the edge computing component at a roadside unit (RSU) or a base station with cloud computing (gNB). Moreover, an RSU can also offload to gNB, forming three offloading paths: vehicle-to-RSU, vehicle-to-gNB, and RSU-to-gNB. This paper aims to minimize the offloaded tasks' average latency while dealing with the network dynamic. Note that the existing works assume the fixed network parameters, hence have failed to address the dynamic. As a solution, we use the multi-agent multi-armed bandits (MBA) learning for offloading that can adapt to the network dynamic and optimize the latency. More importantly, we propose a new MBA offloading scheme with an exploration mechanism based on the Sigmoid function. We conduct an extensive evaluation to evaluate and show the superiority of our proposal. First, the proposed Sigmoid exploration mechanism reduces the tasks' average latency by 35% compared to a basic MBA using negative rewarding. Second, the simulation results show our proposed offloading algorithm shortens the task latency by 18.5% on average and 56.9% in the best case, compared to the state-of-the-art.

This paper presents a 2-hop wireless power transfer (WPT) system for the robot-arm application. The proposed WPT system consists of the class-E inverter on the transmitter side and a class-D rectifier on the receiver side. We obtain the analytical model of the WPT system for obtaining the class-E ZVS/ZDS conditions. The model includes the detailed physical information of the coil, namely coil diameter, height, wire thickness, winding turn number, and so on. Therefore, it is possible to optimize the WPT system for the theoretical highest power-delivery efficiency under the robot-arm coil-space restriction. The circuit experiment showed quantitative agreements with the theoretical predictions, which proved the validity of the proposed design method. The experimental prototype achieved 78.0 % power-delivery efficiency at 6.78 MHz operating frequency and 39.3 W output power.

Advances in communication and sensing technologies have enabled low-cost air quality monitoring devices that are easy to deploy. Moreover, the diverse deployment of the devices, which share a huge amount of sensing data, may help monitor and predict air quality at a fine grain granularity. In such context, valuing the data and introducing micropayment may encourage more people to install monitoring devices and share their data. More specifically, the micropayment, a small amount of electronic currency, will be paid for each portion of shared sensing data. To this end, IOTA cryptocurrency shows potential due to its high-speed transactions without transaction fees. This paper introduces a novel IOTA-based micropayment system for air quality monitoring applications. Our system allows IoT devices (i.e., Raspberry Pi) running IOTA clients to exchange the data on the public IOTA network (i.e., the Tangle). We also implement IOTA nodes, which can join the public IOTA or form a private IOTA. Our system has been proven to work well with real air quality monitoring devices. We have also evaluated various system performance parameters, including latency, jitter, and throughput.

The fifth generation of mobile wireless networks (5G) will provide an infrastructure with abundant and reliable connectivity for innovative and complicated applications. In 5G, 5G mobile devices, which will have improved computing resources for such applications, play an essential role. However, the network stack of 5G devices may continue to be borrowed from 4G legacy operating systems, thereby degrading user experience. In this paper, we posit that 5G users should have flexibility in utilizing networks and gain more awareness of network selection. To this end, we exploit network softwarization technologies to empower 5G devices. We then devise 5GSoft, a novel softwarized networking stack on each 5G mobile device. 5GSoft includes wireless virtualization to relax the dependence on hardware, thereby enabling sharing and multiple access. The 5GSoft device can concurrently exploit surrounding wireless networks using software-defined networking. Finally, the 5GSoft device is aware of network selection for each application process by applying network namespace. Qualitative evaluation of 5GSoft, in comparison to other approaches, highlights its effectiveness in terms of awareness and flexibility provision. Moreover, real experiments show that the virtualization in 5GSoft has negligible overhead.

Leveraging mobile cloud computing (MCC) and mobile edge computing (MEC) for offloading computational tasks is a promising approach to enabling delay-sensitive applications executing vehicles. Despite MCC and MEC's ability and complementary characteristics, most of the existing works on offloading focus on only either MCC or MEC. In this paper, we study their cooperation in a three-tier offloading model of a V2X network where a vehicle can offload computational tasks to cloud computing and MEC. Specifically, we investigate the optimal offloading probabilities of three offloading paths, including Vehicle-to- Infrastructure, Vehicle-to-Cloud, and Infrastructure-to-Cloud. Our contribution is twofold. First, we derive a mathematical model of task execution latency and a formulation to find an optimal solution for the minimum latency problem. Second, we propose an approximation algorithm based on the genetic algorithm toward the optimum. The experiment results show that by exploiting both MCC and MEC's complementary advantages, our proposed algorithm in the three-tier model can shorten the delay significantly compared to existing two-tier models. Depending on the traffic load and the number of Road Side Units, our proposal can reduce the delay by 93.75% on the average, and 99.9% in the best case.

In this paper, we focus on broadening the monitoring area of a mobile air quality monitoring system, in which the sensors mounted on buses. In particular, we investigate the optimal buses to place the sensors and the optimal monitoring timings to maximize the number of monitored critical regions. We mathematically formulate the targeted problem. Then, we leverage the greedy approach to propose a polynomial-time$\frac{e-1}{2e-1}$approximation algorithm. We use the data of real bus routes in Hanoi, Vietnam, for the experimentation and show that the proposed algorithm guarantees an average performance ratio of 63.87%.

This paper introduces a novel charging strategy for wireless rechargeable sensor networks (WRSNs), in which a mobile charger (MC) moves and wirelessly transfers the power to the sensor nodes. The first distinct point of this work is designing the MC's charging algorithm under the consideration of target coverage and connectivity. As a solution, we introduce a novel on-demand charging scheme for WRSNs that optimize the charging time at each MC's charging location. Moreover, we take advantage of the Q-learning technique (i.e., hence named our algorithms Q-charging) to maximize the number of monitored targets. Q-charging can prioritize the sensor nodes, which play a more critical role in the network. Hence, Q-charging can select a suitable charging location aiming to provide sufficient power for the prioritized sensors. We have evaluated our proposal in comparison to the previous works. The evaluation results show that Q-charging can prolong the time until the first target is not monitored by 5.2 times on the average, and 14.3 times in the best case, compared to existing algorithms.

Thanks to the advancements in wireless power transfer technologies, a new paradigm of wireless sensor network (WSNs) called wireless rechargeable sensor networks (WRSNs) has recently emerged. For a WRSN, designing an efficient charging schedule is a challenging issue due to the inherent constraints of WSNs. Although there have been many efforts to optimize the charging schedule, the existing works suffer from several critical problems. Firstly, they rarely tackle the dead node minimization problem, which is the ultimate objective of wireless charging. Secondly, most of the existing works assume impractical conditions, which include the unlimited battery capacity of the charger, and a fully charging scheme at the sensors. In this paper, aiming at minimizing the number of dead nodes, we propose a novel charging scheme based on the genetic algorithm. Our scheme works when the mobile charger has only limited capacity, and the sensors are charged partially at each charging round. The experiment results show that our proposed algorithm reduces the number of dead nodes significantly compared to other existing studies.

The dawn of 5G is rising with emerging wireless and network technologies that promisingly transform various aspects of human lives. The 5G mobile devices are going to tightly engage with the 5G infrastructure for future applications such as virtual reality, AI-enabled applications, etc. However, we argue that the tight engagement with convenience may bring downside that is a user may not correctly control/own her/his device. In particular, the user may not have the flexibility in utilizing networks, as well as, be aware of the network selection. This paper introduces a novel networking stack for 5G mobile devices (namely 5GVir) that leverages virtualization techniques for flexibility and awareness. The 5GVir device can concurrently exploit surrounding wireless networks by using Software Defined Networking (SDN). SDN provides a rich set of flexibility feature that will meet user expectation. Moreover, 5GVir includes wireless virtualization that can relax the dependence on hardware as well as provides a wireless link for the SDN's control channel. Last but not least, the 5GVir device has an additional awareness feature aware that drives each application process to predetermined networks (i.e., applying network namespace). Our prototype shows the potential of realizing 5GVir. Moreover, the initial experiments show that the virtualization in 5GVir has negligible overhead.

Geographic routing is one of the most popular routing protocols in wireless sensor networks (WSNs) due to its simplicity and efficiency. However, with the occurrence of holes, geographic routing incurs with the so-called local minimum problem that may lead to a long hole detour path as well as the traffic concentration around the hole boundary. In consequence, the network lifetime is shortened. In this paper, we aim at proposing a lightweight distributed geographic routing protocol, which can prolong the lifetime of WSNs under the hole occurrence. Our main idea is to exploiting Q-learning technique to estimate the distance from a node to the holes. The routing decision is then determined based on the residual energy of the nodes, their estimated distance to the holes, and their distance to the destination. The simulation experiments show that our protocol strongly outperforms state-of-the-art protocols in terms of the network lifetime, packet latency and energy consumption. Specifically, our proposed protocol extends the network lifetime by more than 12% compared to the existing protocols.

This paper considers wireless string-topology multi-hop networks, in which each network node operates autonomously following the IEEE 802.11 Distributed Coordination Function (DCF). The IEEE 802.11 DCF defines multiple transmission rates. In general, the transmission rate is inversely proportional to the transmission distance. The carrier-sensing distance, however, is independent of the transmission rate under the condition of identical transmission power. Therefore, when the transmission rate amplifies, the number of nodes within the carrier-sensing range may increase; hence affect the network performance. The previous works either simplify or have no consideration of the effect. Addressing the issue, we newly propose an analytical expression of end-to-end throughputs of IEEE 802.11 string-topology multi-hop networks, taking into account transmission rates. The validity of the proposed analytical model is confirmed from the quantitative agreements with the simulation results.

The IEEE 802.11ad technology, which allows wireless devices to communicate in the unlicensed 60 GHz ISM band, promisingly provides multi-Gbps data rates for bandwidth-intensive applications. After years of research and development, we are now observing an increasing number of commodity IEEE 802.11ad radios that motivate researchers to exploit the IEEE 801.11ad capability for applications. This work first conducts an empirical study on the IEEE 802.11ad performance. In particular, we characterize the performance of IEEE 802.11ad links considering the variation of network parameters and interference. Secondly, we investigate the possibility of introducing IEEE 802.11ad to an evolving Wi-Fi network. The evaluation results show that our off-the-shelf IEEE 802.11ad hardware can achieve the Gbps level throughput of the transmission control protocol (TCP) and user datagram protocol (UDP). However, the evolvement is not trivial since the hardware can not well maintain the 60 GHz link. The main reason is lacking the fast switchover function between an IEEE 802.11ad and a legacy Wi-Fi link. We then seek the potential of multipath TCP (MPTCP) for the expected switchover. The default MPTCP, which enables data transmissions on both the IEEE 802.11ad and Wi-Fi links, is harmful to the IEEE 802.11ad throughput. Meanwhile, the backup mode of MPTCP, in which the Wi-Fi link acts as a backup for IEEE 802.11ad one, can maintain the comparable performance. Therefore, we propose to adopt MPTCP with the backup mode in the evolving Wi-Fi networks. The efficiency of MPTCP-based switchover is confirmed by conducting real experiments.

The 3rd generation partnership project (3GPP) has recently specified the non-standalone 5G New Radio (NR), in which the dual connectivity (DC) feature plays an essential role. A User Equipment (UE) with DC is able to concurrently connect to an evolved NodeB (eNB) (e.g., using LTE in a macro cell) and a next generation NodeB (gNB) (e.g., using 5G NR in a small cell) aiming to satisfy the 5G's required Key Performance Indicators (KPIs). In this work, we focus on the issue of concurrent transmissions on the 5G small cell, which potentially exploit the benefits of DC. With DC, an application is able to spread its traffic over different two paths (via eNB and gNB) between the UE and the LTE core network. The traffic over diverse paths may experience different delay since the conditions are generally different (e.g., due to wireless channel, background traffic). Therefore, one of the most important problems in efficient operation with DC is minimizing the end-to-end delay considering all the connections. To address that problem, we adopt the deterministic network calculus to first characterize the delay experienced by each connection; then to model the overall end-to- end delay in the 5G small cell. We then formulate an optimization problem that attempts to find the optimal traffic splitting over the connections while minimizing the delay. We derive the solution for the problem, as well as, present the numerical results.

The shared resource access wireless communication system is regarded as a paradigm that allows the mobile network operators (MNOs) to have extended coverage and allows them to satisfy their subscribers' high capacity demands while keeping the capital and operational expenditure in check. On the other hand, dual connectivity (DC), a small cell enhancement feature, allows the subscribers to have two simultaneous connections increasing throughput and enhancing mobility robustness. For the DC mechanism, the user needs to be under the coverage of multiple BSs concurrently. Because of random nature of the wireless links and distribution of BSs in a network, the practicability/operability of DC for a typical user under such random operating conditions (both topological and wireless link randomness) needs to be properly evaluated. In this paper, we perform stochastic analysis of DC in shared resource access high capacity wireless networks. We consider a fading-averaged signal propagation loss process, where the selection of the serving BS is perturbed shadowing, not by fading. The DC coverage probability is analyzed in terms of tractable, integral expressions. We have observed that the DC coverage probability is greatly affected by the density of the small cell BSs and the variance of the log-normal shadowing.

To make WiGig/IEEE 802.11ad backward compatible with the legacy Wi-Fi, a multi-band device that is capable of WiGig and Wi-Fi is necessarily equipped a function of fast switchover between the WiGig and Wi-Fi link. It is expected that the switching action is done as quickly as possible in order to minimize negative effects on an ongoing application. The IEEE 802.11ad standard defines the switching operation within the scope of fast session transfer (FST) protocol. To properly run FST, the device needs extra layer-2 entities that simultaneously manage the PHY/MAC states of multiple radios. Although introducing WiGig radios is a trivial task, installing those entities on an existing legacy Wi-Fi network is unfortunately a challenging one. This work presents an experimental feasibility study of achieving the backward compatibility without FST. We initially analyze the feasible capability of multipath transmission control protocol (MPTCP). We then setup a real- world testbed and evaluate different operational modes of MPTCP, aiming to find the most suitable one. Our experimental results show that the fast switchover between a multi-Gigabit WiGig and a legacy Wi-Fi link is achievable with the MPTCP's backup mode.

We investigate an advanced two-phase shared spectrum access communication scheme as an efficient approach to enhance the spectral utilization of a network. In the first phase, we devise a spectrum-sharing policy based on demands, fairness, and so on, which utilizes a priority scheme in fulfilling operators' demands, and envision a secure operator-specific information sharing policy where no critical information is exchanged between the operators. In the second phase, a macro cell network (MCN) benefits through offloading services offered by small cell network (SCN). This allows the MCN to satisfy its users' capacity demands, improve its quality of services and coverage under Nakagami fading channel. As a repayment, the SCN is rewarded with licenses to share and operate on the spectrum originally owned by the MCN. We devise a density division-based shared spectrum access model, where the density of the licensee's SCN deployment is exploited as network resources. A fair division of the densities of the licensee operator's small cell base stations into fractions of licensed small cell base stations serving its own users and offloading small cells is presented. Unlike most of the previous research works that considered Poisson point process (PPP) to model the distribution of the network entities even when PPP modeling is not accurate for the networks, where the number of MCN/SCN base stations is definite and the number of MCN/SCN base stations in disjoint areas is not independent, we employ a more realistic network model known as binomial point process to perform an analytical analysis of the cumulative interference and performance of the system. Furthermore, we analyze the rate coverage and outage performances considering a wide range of values for path-loss exponent and fading severity parameter of Nakagami fading.

In this paper, we conceive an advanced small cells wireless network deployment framework within a managed space under shared spectrum access paradigm. We also conceive a complementary business model, referred to as neutral host micro operator (NH-μO), that leverages a single shared wireless infrastructure to mutually benefit μO (a third party service provider), the owner of the space/facility, and mobile network operators (MNOs). The model is composed of a μO slice, which delivers a venue with customized wireless services tailored to the its local service requirements, and an MNO slice, which facilitates improved wireless coverage to visitors/end-users with subscriptions to several different MNOs. The NH-μO is not biased to favor any specific client, which can be enforced through a concrete commercial agreement. A radio access network slicing concept is exploited to support and optimize both the slice instances (SIs) efficiently in a shared manner on a single physical network infrastructure. In addition, we devise an efficient architecture for the NH-μO small cell base station and dynamic spectrum assignment control unit, and their required functionalities supporting sustainable coexistence of different SIs in shared spectrum. We also devise both interSI and intra-SI dynamic spectrum allocation policies considering time-varying requirements of different SIs. These policies take care of application level priority by providing a proper mixture of users with guaranteed quality of service and best-effort users, while ensuring a healthy SI competition. The advantages of the proposed framework are twofold. It enables the venue owner to manage its wireless networks and consider its very specific requirements while capitalizing from the MNO slice. This, in turn, reduces the need for deployment of new infrastructure while providing improved wireless coverage and savings to MNOs. Finally, our proposed framework leverages efficient utilization of spectrum, physical infrastructure, and computational resources. The simulation results exhibit various important features of the proposed shared spectrum access policies.

In the software defined networks (SDNs), the Open-Flow protocol is typically used as the southbound API in manipulating OpenFlow switches. However, the OpenFlow control messages are in a low abstraction level. Therefore, even a single application-level operation requires many OpenFlow messages, which consume the bandwidth of the control network and reduce the SDN's scalability. One potential solution is to use high level domain specific northbound APIs in the control network. In this paper, we explore the possibility of adopting this solution by implementing and evaluating a new SDN framework, Ryuo. In Ryuo, we introduce Local Service, which runs directly on each SDN switch (hardware/software). In operations, Local Service provides northbound APIs to the SDN applications while it can use different southbound APIs for different switches. Ryuo eliminates unnecessary control messages, hence it decreases the volume of control traffic. Our evaluation of Ryuo on Mininet with example applications shows that Ryuo reduces the volume of control traffic at least 50% compared to the standard OpenFlow, and up to 40% compared to the local controller approach. We also evaluate the performance of running Local Services directly on physical switches. The results show that we can achieve lower event handling latency in large networks, but with the trade-off of a lower event handling throughput due to the computing power limitation on physical switches. In summary, we have shown that using high level northbound API in the control network can make the control network more efficient, and leads to better scalability.

In recent years, there has been an increasing number of Software Defined Wireless Access Network (SDWAN) proposals aiming to cope with the proliferation of mobile devices, as well as, the novel Quality of Service (QoS) demands. One of the most important issues in the SDWANs is to maintain a scalable and robust OpenFlow channel through which the network control and update information (i.e., OpenFlow traffic) are exchanged. However, the standard channel does not well support those characteristics nor does it properly conform to the SDWAN environment. This paper seeks to remedy these problems by analyzing the requirements of SDWAN's OpenFlow channel. We then propose a novel OpenFlow channel named mOpenFlow, which is not only compatible with the SDWANs but also evolved with the OpenFlow standard. The advantage of mOpenFlow is using multipath communication for conveying OpenFlow traffic. Hence, both the robustness and the achievable throughput of the OpenFlow channel is significantly enhanced (i.e., scalable). Combining with the compatibility requirement, the multipath TCP is adopted in the mOpenFlow's design. We extensively evaluate the channel's performance in SDWANs including emulated SDN devices and a real controller. The results show that mOpenFlow outperforms the standard channel both in terms of robustness and scalability.

In recent years, there has been an increasing interest in Software Defined Networking (SDN)/OpenFlow, which is a novel network architecture splitting control and data planes. The SDN-enabled products have also been widely deployed in many production networks aiming to bypass the limitations of current Internet architecture. There are several efforts on building Software Defined Routing Platform (SDRP), which integrates SDN/OpenFlow within IP routing. However, to the best of our knowledge, there are a few successful works on realizing SDRP despite of the flexibility and rich features of SDN. In this paper, we investigate state-of-the-art works towards SDRP. We have evaluated the performance of a mainstream SDRP named RouteFlow. Our evaluations have been conducted using both the emulator Mininet and a real testbed with physical switches. Different to other works, we focus on the resilience aspect of the routing platform. The evaluation results show that RouteFlow is resilient against both single and multiple link failures. Additionally, OSPF provided by RouteFlow achieves a shorter failover time than the legacy OSPF. In the case of RIPv2, RouteFlow's protocol has a comparable performance value with the distributed one.

In wireless sensor networks (WSNs), the multi-hop MAC protocol combines duty cycling radio and forwarding packet via multiple hops (i.e., multi-hop forwarding) to achieve a good balance between energy and latency efficiency. The multi-hop MAC protocol has been proven to outperform other traditional duty cycling protocols, which allow forwarding a packet at most one hop in an operational cycle. Among state-of-the-art multi-hop protocols, M AC2, which additionally leverages packet concatenation technique, achieves the best performance in normal environments (i.e., without considering noisy). The concatenation technique, which lets several small packets be concatenated into a bigger one before sending out at a node, effectively saves control overhead and shortens delivery latency in WSNs. However, the packet concatenation may downgrade the performance of the network in the noisy environment since the cost for retransmission is high. In this paper, we investigate the negative effect of the noisy level of environment on the efficiency of M AC2. We observe that in the noisy environment, M AC2still keeps the reasonable performances, which are better than the one without concatenation (i.e., the demand-wake up MAC DW-MAC). The simulation results using ns-2 confirm our observations.

This paper introduces a cross-layer implementation, named WiPoMu, that aims to improve efficiency and resilience of the traditional WiFi model. WiPoMu is developed based on three key technologies: Wireless virtualization, Policy routing, and Multipath TCP (MPTCP). Specifically, WiPoMu adopts the wireless virtualization to create multiple virtual WiFi interfaces on a single physical one, hence WiPoMu enables concurrent connections with different access points (APs). In addition, WiPoMu leverages the policy routing and MPTCP in order to efficiently direct and schedule traffic flows over multiple virtual interfaces. On the other hand, WiPoMu is transparent to users since it requires no modification in the physical and application layers. We have conducted evaluations to validate the efficiency of WiPoMu on an indoor testbed and a real home network. The evaluation results show that a WiFi client equipped WiPoMu is able to establish multiple active paths to Internet across different APs. Besides that, WiPoMus is resilient to path failure by achieving seamless handover between the active paths. Furthermore, WiPoMu improves up to 300% aggregated throughput comparing to the traditional WiFi model using TCP in the testbed.

Wireless access network is an appropriate approach providing Internet connection in disasters where communication infrastructures might completely be damaged. This paper clarifies essential requirements for a resilient disaster recovery multihop access network (DRAN). Based on those requirements, which could not be satisfied by existing access network technologies, a novel approach, thereby multihop access networks can be established quickly using commodity mobile devices (laptops, tablets, smart phones), has been proposed. Users can connect to the proposed wireless access network as easily as connecting to conventional access points (APs) and unconsciously contribute to the network extension. As a result, the network is extended incrementally providing more Internet access opportunities to far apart nodes. Experimental evaluations reveal the feasibility as well as the scalability of the proposed approach.

Information and communication technology (ICT) is indispensable for today's society and business environment. The continuous usage of ICT is an essential requirement of the society, especially during a disaster. Besides that, resilient ICT is desirable not only for almost any business environment but also for business continuity planning of such businesses in order to make their internal ICT resilient. We first discuss the requirements of resilient ICT based on surveys of the latest technologies that aim to improving resiliency, such as virtualization of servers and networks. We then argue that it is necessary to consider two levels of ICT resiliency. Additionally, we propose how to select evaluation means of the technologies for facilitating resiliency, and operational frameworks for achieving resiliency.

Fast recovering backbone networks from failures caused by unexpected disasters is critical to enhance the Internet resilience. The major challenge is fast switchover traffic from a faulty path to an alternative one. In this paper, we present an approach toward achieving a zero switchover time (i.e., optimal) by using two emerging technologies: Multipath TCP (MPTCP) and Software-Defined Networking (SDN). The conceptual idea is keeping multiple concurrent paths in an end-to-end communication, which is continuously alive unless all the paths get failure. Besides that, when a disaster occurs, traffic engineering is adopted to quickly balance the traffic on available paths. In order to establishes the concurrent paths, MPTCP divides an application's byte stream into multiple TCP subflows, each of which is forwarded in an end-to-end route. The routes are normally determined by IP routing protocols, such as OSPF or BGP, which have two disadvantages. The first one is the protocols always take long time to reestablish new routes in disasters (i.e., long convergence time). The second one is they are originally designed without traffic engineering. On the other hand, SDN enables the use of central controllers with global view of dynamic network state. In addition, the controllers can remotely controls both direction and capacity of traffic flows in many flexible ways (i.e., using OpenFlow). Therefore, SDN potentially solves the disadvantages of IP routing protocols.

The current technology of Internet provides an acceptable level of resilience in normal operation. However, the Internet may get catastrophic impacts when unexpected disasters such as earthquakes, tsunami, etc. happen. Specifically, the Internet infrastructure including Wide Area Networks (WANs) face many challenges to normal operation including power outage, link, device failures, rerouting packets, traffic engineering, etc. In this paper, we follow a systematic approach for realizing disaster-resilient WANs using Software-Defined Networking (SDN) technology. SDN enables the network control plane to be decoupled from the network forwarding hardware, and moves the control plane to a programmable component, i.e., the network controller. The network management and operation therefore increase flexibility. To confirm the feasibility of SDN-based resilient network towards fast disaster recovery, we have constructed two evaluations under the real large scale network topology. One is to investigate the latencies between controllers and switches in order to find the appropriate number and locations of controllers. Another one is to simulate a reactive switch-over from a faulty link to an alternative link, assuming a realistic scenario. The results show that the switch-over time depends on the latencies between networking devices and a controller. Additionally, even those latency values equal the worst-case latency, the fast rerouting of TCP traffic is achievable.

Disasters may destroy everything including communication infrastructures isolating people in the disaster-stricken areas. Recovery of these infrastructures is often prolonged which is not suitable for disastrous fast-responses. This paper proposes a wireless multihop access network virtualization (WMANV) approach thereby users/victims are provided Internet access transparently. Concretely, users are provided Internet connection via multihop wireless access networks as if they are connecting to conventional access points (APs) using their commodity mobile devices. A tree-based architecture was proposed to realize the concept of WMANV. This approach allows participating mobile nodes (MNs) to contribute on growing up access networks, thus provide the Internet connection means to further disconnected MNs. The feasibility and the scalability of the proposed approach have been verified by real deployments and experimental evaluations.

It is increasingly popular that there are multiple accessible access points (APs) surrounding a WiFi client. In an ideal case, the client would simultaneously connects to all the APs and maximize the connections' utilization, for example, aggregating the bandwidth of APs' backhauls, load balancing among the connections, etc. In this paper, we present WM (Wireless virtualization with Multipath TCP) a cross-layer approach that aims to improve performance of mobile WiFi users. The demonstration shows that aWiFi client equipped WM can keep multiple concurrent connections to APs by using wireless virtualization. Moreover, WM enhances the aggregated bandwidth and achieves seamless handover by adopting Multipath TCP.

In wireless sensor networks (WSNs), MAC protocols utilize duty cycling to extend the network lifetime. Receiver-Initiated MAC (RI-MAC) is an asynchronous duty cycling protocol, which schedules transmissions based on receivers. By letting a receiver initiate the rendezvous for the transmissions between senders and the receiver, the protocol achieves good latency performance at the cost of high energy consumption at the senders. On the other hand, different types of traffic are very popular in WSNs. The MAC protocols are necessarily to be equipped QoS (quality of service) mechanisms to handle with the variation of traffic. In this paper, we propose AQ-MAC; a new asynchronous MAC protocol with QoS awareness. AQ-MAC adopts the receiver-initiated manner and provides QoS service per packet following the priority imposed. When a node has an incoming packet with a high priority, it immediately turns on the radio in order to wait for a transmission initiated by a receiver. Otherwise, the node keeps a low priority packet in a queue and sends out in a burst until a high priority packet comes or after a timeout value. In addition, AQ-MAC utilizes a packet concatenation scheme to improve the energy efficiency by reducing control overhead. We have evaluated AQ-MAC in multiple scenarios using ns-2. The results show that AQ-MAC adapts well with different types of traffic as well as achieves good performance in terms of energy efficiency and latency.

Achieving low latency and high energy efficiency are critical requirements in many of sensor network applications. Combining the duty cycling mechanism and multi-hop transmission is one of the solutions to this problem. Multi-hop protocols such as demand wakeup MAC (DW-MAC) schedule data transmissions during the sleep period. In so doing, a data packet can traverse multiple hops within a single operational cycle. By introducing a scheduling parameter, called proportional mapping function, DW-MAC can provide better performance in terms of both latency and energy efficiency, while avoiding data collision. In this paper, we derive the minimum value Rminof the scheduling function by numerically analyzing the delivery latency. Using that value, a DW-MAC-like protocol not only guarantees no collisions at the intended receivers but also achieves the minimum latency in the multi- hop scenario. In addition, we also show the upper and lower bounds of the delay. We evaluated the accuracy of our findings by running ns-2 simulations in multiple scenarios.

Sensor network MAC protocols use a duty cycling mechanism and a multi-hop transmission to create a good trade-off between their energy efficiency and latency. The nodes in duty cycling multi-hop MAC protocols such as RMAC periodically sleep/listen during the operational cycle to reduce their energy consumption by idle listening; and the listening period is usually long in order to support packets in reaching a multi-hop destination in a single cycle (multi-hop transmission). The traffic network in the wireless sensor network is otherwise very scarce, and keeping the radio on during such long listening periods when there is no data transmission in the network wastes energy. In addition, these protocols incur a large amount of control overhead, which is one of the main energy wasted sources. This paper proposes an adaptive low overhead MAC protocol we call AM-MAC (Adaptive Multi-hop MAC). It can reduce the wastage caused by long listening period and minimizes the control overhead. The nodes in AM-MAC use an adaptive method, and thus they can adjust the duration of the listening period according to the traffic load. To reduce the control overhead, a single packet has more than one role. During a multi-hop transmission, the new control packet replaces the RTS/CTS pair, and one DATA packet can play both DATA/ACK roles. An extended evaluation of AM-MAC has been conducted through simulation, against RMAC. The results illustrate that AM-MAC significantly reduces the energy consumption and notably lessens the end-to-end latency.

In wireless sensor networks (WSNs), a duty-cycling scheme is typically applied to the medium access control (MAC) protocol to reduce energy consumption due to idle listening. However, this scheme introduces huge end-to-end latency and still suffers from a large control packet overhead. We propose a new MAC protocol with low latency and low control overhead for WSNs use (the LCO-MAC). In our protocol, a DATA packet can be transmitted through multiple hops in a single duty cycle to shorten end-to-end latency. To reduce energy consumption caused by control packet overhead, we force one packet to play more than one role. In the initial transmission period, a control packet acts as an RTS (request to send) for a downstream node and a CTS (clear to send) for an upstream node. In the actual data transmission period, a DATA packet keeps its original DATA role for the downstream node, but also plays an ACK (acknowledgment) role for the upstream node. Our simulation using ns-2 has shown that LCO-MAC enables a notable improvement in energy efficiency and decreases end-to-end latency compared to those of RMAC.

Quality of Service (QoS) in Layer 3 is essential for satisfying various types of Internet-application requirements by making the best use of limited bandwidth. A large number of such applications still use TCP/IP in order to connect various types of computer nodes. This paper investigates QoS performance in a network equipment testbed. We examine the major Class of Service (CoS) functions provided by the Juniper T320 router, and measure their performance. In addition to fundamental analysis of the QoS behavior, we show the impact of QoS operations on a parallel system distributed in multi-domain networks as a practical case study of grid environments.