Acknowledgement Research Articles

Acknowledgment Mechanisms for Reliable File Transfer Over Highly Asymmetric Deep-Space Channels

Deep-space network channels are characterized “in part” by asymmetric channel rates. The effect of the asymmetric channel rates is that the slow acknowledgement (ACK) channel cannot effectively support transmission of the ACK segments generated at the data receiver. Increment of data block size and extension of the retransmission timeout (RTO) timer are widely used transmission mechanisms for eliminating the effect of highly channel-rate asymmetry. The aggregate custody signal (ACS) is recently proposed to resolve the effect of asymmetric channel rates in deep-space networks. In this article, these three acknowledgment mechanisms are studied, with a focus on a comparison of their effectiveness evaluation over highly asymmetric deep-space network channels accompanied by data loss. The study is conducted through realistic file delivery via an experimental testbed infrastructure. It is found that the ACS mechanism shows significant performance advantages over other two mechanisms for both the “normalized” goodput and total data amount in successful file delivery. In particular, the ACS mechanism makes it a better choice to operate with small data bundles without increasing the RTO timer length.

Resilient Control Under Quantization and Denial-of-Service: Codesigning a Deadbeat Controller and Transmission Protocol

This article is concerned with the problem of stabilizing continuous-time linear time-invariant (LTI) systems subject to quantization and denial-of-service (DoS) attacks. In this context, two DoS-induced challenges emerge in the design of resilient encoding schemes, namely, the coupling between encoding strategies of different signals, and the synchronization between the encoder and decoder. These challenges are addressed by a novel proposed structure based on a deadbeat controller as well as a delicate transmission protocol for the input and output channels, and codesigned leveraging the controllability index. When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals. This property is further corroborated by designing encoding schemes as well as conditions ensuring exponential stability of the closed-loop system. On the other hand, when only the output channel is subject to network attack phenomena, the proposed structure can achieve exponential stabilization without acknowledgment (ACK) signals, in contrast to existing ACK-based results. Finally, a numerical example is given to demonstrate the practical merits of the proposed theoretical and practical approach.

Rollout approach to sensor scheduling for remote state estimation under integrity attack

We consider the sensor scheduling problem for remote state estimation under integrity attacks. We seek to optimize a trade-off between the energy consumption of communications and the state estimation error covariance when the acknowledgment (ACK) information, sent by the remote estimator to the local sensor, is compromised. The sensor scheduling problem is formulated as an infinite horizon discounted optimal control problem with infinite states. We first analyze the underlying Markov decision process (MDP) and show that the optimal scheduling without ACK attack is of the threshold type. Thus, we can simplify the problem by replacing the original state space with a finite state space. For the simplified MDP, when the ACK is under attack, the problem is modeled as a partially observable Markov decision process (POMDP). We analyze the induced MDP that uses a belief vector as its state for the POMDP. We investigate the properties of the exact optimal solution via contractive models and show that the threshold type of solution for the POMDP cannot be readily obtained. A suboptimal solution is then obtained via a rollout approach, which is a prominent class of reinforcement learning (RL) methods based on approximation in value space. We present two variants of rollout and provide performance bounds of those variants. Finally, numerical examples are used to demonstrate the effectiveness of the proposed rollout methods by comparing them with a finite history window approach that is widely used in RL for POMDP.

Deep Learning in Network-Level Performance Prediction Using Cross-Layer Information

Wireless communication networks are conventionally designed in model-based approaches through utilizing performance metrics such as spectral efficiency and bit error rate. However, from the perspectives of wireless service operators, network-level performance metrics such as the 5%-tile user data rate and network capacity are far more important. Unfortunately, it is difficult to mathematically compute such network-level performance metrics in a model-based approach. To cope with this challenge, this work proposes a data-driven machine learning approach to predict these network-level performance metrics by utilizing customized deep neural networks (DNN). More specifically, the proposed approach capitalizes on cross-layer information from both the physical (PHY) layer and the medium access control (MAC) layer to train customized DNNs, which was considered impossible for the conventional model-based approach. Furthermore, a robust training algorithm called weighted co-teaching (WCT) is devised to overcome the noise existing in the network data due to the stochastic nature of the wireless networks. Extensive simulation results show that the proposed approach can accurately predict two network-level performance metrics, namely user average throughput (UAT) and acknowledgment (ACK)/negative acknowledgment (NACK) feedback with great accuracy.

Real-Time Status Updates in Wireless HARQ With Imperfect Feedback Channel

We study the impact of the erroneous wireless control feedback channel on the Age of Information (AoI) performance. We consider a point-to-point communication setup employing packet combining strategies to transmit status update packets over an erroneous wireless data channel. The sender receives the positive acknowledgment (ACK) or negative acknowledgment (NACK) of packet reception over an error-prone wireless feedback channel. To mitigate the impact of the imperfect feedback channel on the system performance, we adopt an asymmetric signal detection model to control the detection accuracy of ACK and NACK signals. We then compute the explicit expressions for the average AoIs under preemptive and non-preemptive service management policies. We show the optimum parameter design for the control channel model in order to minimize the average AoI. The numerical results validate the analysis and provide detailed perspectives on the optimal signal detection setup minimizing the average AoI, and the possible trade-off between AoI and resource utilization. Generally, the analysis for a preemption setting illustrates that a better protection for the NACK messages compared to the ACK messages can preserve the minimum AoI performance. Especially, under a high noisy feedback channel setup, we show that the viable solution minimizing the average AoI is a blind transmission mechanism at the cost of increasing unnecessary utilization of the channel resources. Moreover, the analysis for a non-preemptive policy reveals the dependence of the optimal feedback signal detection design on the status packet generation rate at the sensor. Such a dependency makes the feedback signal detection approach to provide a more reliable ACK detection compared to NACK messages under the condition of more frequent packet arrival, whereas the opposite holds under the condition of less frequent packet arrival.

A Probabilistic Spray-and-Wait Routing Algorithm Based on Node Interest Preference in Delay Tolerant Networks

How to select proper relay nodes to ensure the successful delivery of messages is still a hot topic in delay tolerant networks (DTN). In this paper, we propose a probabilistic Spray-and-Wait routing algorithm based on node interest preference (called NIP-PSW). Firstly, considering the influence of the social attributes of nodes, we define a metric called node interest preference (NIP) to measure the probability of nodes becoming friends. Secondly, in view of the influence of node quality and connection time between nodes on message forwarding, we define the delivery probability (DP). Finally, according to the historical information of nodes, the node interest similarity (NIS) is proposed. In spray phase, NIP and DP are used to select the relay node and allocate the number of message copies adaptively. In wait phase, it is judged whether to forward the message to the encountering nodes again according to the NIS and the DP. In addition, the concept of message storage value (MSV) and the acknowledgment (ACK) mechanism are introduced to manage the buffer of nodes. The simulation results show that the NIP-PSW not only can significantly improve the delivery rate and reduce the average delay but also shows good performance in network overhead and average number of hops.

Evaluation of Receiver-Feedback Techniques for Fragmentation Over LPWANs

The Internet engineering task force (IETF) has standardized a new framework for IPv6 support over low-power wide-area networks (LPWANs), called static context header compression and fragmentation (SCHC). SCHC includes acknowledgment (ACK)-based mechanisms for reliable fragmented packet transmission. For the latter, SCHC defines a receiver-feedback technique (RFT), called compressed bitmap (CB), by which a receiver reports to the sender whether the fragments carrying a packet have been received or not. Such information is carried as ACK payload. Considering the extraordinary frame size and message rate constraints of LPWANs, ACK payload size becomes crucial. In this article, we compare the performance of CB with that of several alternative RFTs, namely, list of lost fragments (LLF), List of Deltas (LoD), and uncompressed bitmap (UB), where the latter is used as a benchmark. We evaluate the considered RFTs in terms of ACK size, number of Layer 2 (L2) frames needed to carry an ACK, and ACK Time on Air. Our analysis shows that the use of RFTs different from CB offers significant performance improvement in many scenarios. Furthermore, we provide guidance on which RFT should be used for different packet sizes, error rates, and error patterns.

BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker

In next-generation wireless communication systems, non-orthogonal multiple access (NOMA) has been recognized as essential technology for improving the spectrum efficiency. NOMA allows multiple users transmit data using the same resource block simultaneously with proper user pairing. Most of the pairing schemes, however, require prior information, such as location information of the users, leading to difficulties in realizing prompt user pairing. To realize real-time operations without prior information in NOMA, a bandit algorithm using chaotically oscillating time series, which we refer to as the laser chaos decision-maker, was demonstrated. However, this scheme did not consider the detailed communication processes, e.g., modulation, error correction code, etc. In this study, in order to adapt the laser chaos decision-maker to real communication systems, we propose a user pairing scheme based on acknowledgment (ACK) and negative acknowledgment (NACK) information considering detailed communication channels. Furthermore, based on the insights gained by the analysis of parameter dependencies, we introduce an adaptive pairing method to minimize the bit error rate of the NOMA system under study. The numerical results show that the proposed method achieves superior performances than the traditional using pairing schemes, i.e., Conventional-NOMA pairing scheme (C-NOMA) and Unified Channel Gain Difference pairing scheme (UCGD-NOMA), and ϵ-greedy-based user pairing scheme. As the cell radius of the NOMA system gets smaller, the superior on the BER of our proposed scheme gets bigger. Specifically, our proposed scheme can decrease the BER from 10−1 to 10−5 compared to the conventional schemes when the cell radius is 400 m.

Realizing Opportunistic Routing in Multi-Channel Environments

Opportunistic Routing (OR), which utilizes the broadcast nature of wireless communication to route packets in a dynamic way, significantly improves the transmission quality. Most of the researches focus on the single channel scenario; however, multi-channel (MC) is widely adopted over wireless communication systems. How to apply OR over MC environments is quite challenging and still an unsolved issue. OR operating over single channel environments collects acknowledgements mainly through overhearing data packets. In contrast, MC environments increase the performance by reducing interference and providing more bandwidth. Since users scatter over multiple channels, the chance of overhearing is reduced. Without overhearing acknowledgements (ACKs), the performance of OR degrades due to duplicate transmissions. To better understand the behavior of OR over MC environments, this paper first surveys current OR methods and constructs a simplified scenario to observe how they behave as the number of interfaces and channels increases. Then, analyze and compare their costs and penalties to find out a potential method. Finally, refine the potential method for MC environments. It is observed that token passing is the most potential method for its high efficiency of utilizing control packets. To apply token passing to OR in MC environments, this paper proposed multi-channel token passing (MCTP) to resolve the hazards of token passing paths and dynamic rate for multiple flows. Simulation results show that MCTP has high performance with low and consistent overheads. In addition, MCTP is scalable with increasing interface and channel numbers, which makes it a prototype for the future OR research in MC environments.

Joint Power Allocation for Remote State Estimation With SWIPT

In this paper, we consider remote state estimation with simultaneous wireless information and power transfer (SWIPT). A remote estimator receives the state estimate transmitted by a sensor and sends an acknowledgment (ACK) signal to the sensor via a feedback wireless channel. The sensor has no other energy supply except that it can harvest wireless energy from the received ACK signal, which carries information and energy simultaneously. Since the symbol error rate of the state estimate transmitted by the sensor depends on the sensor’s transmission power allocation, there exists a tradeoff between the accuracy of the state estimate received by the remote estimator and the transmission power consumption of the sensor. Moreover, the sensor’s transmission power is restricted by the remaining energy level in its battery, which is decided by the amount of the harvested wireless energy. Therefore, we jointly optimize the transmission power allocation of the remote estimator and the sensor and formulate the optimization problem into an infinite time-horizon average cost Markov decision process (MDP). We first prove the existence of the optimal stationary power allocation policy, and then present the monotonic structures of the optimal policy. Simulation results are provided to verify and illustrate the main results.

Trade-Offs in Reliability, Latency, and Energy for Random Network Coding-Enabled Networks

In random network coding (RNC)-enabled networks, a sufficient number of linearly independent encoded packets, i.e., generations, is guaranteed at the cost of high latency and energy consumption at the transmitter. This letter aims to reduce the weighted sum of latency and energy while guaranteeing a targeted successful transmission probability, which is defined as the probability that the RNC-enabled network has sufficient generations when the transmitter receives a required number of acknowledgements (ACKs) feedback. An analytical framework and a deep learning-based method are developed. Results show that the latency and energy consumption are reduced by jointly optimizing the required number of ACKs and the transmit power at the transmitter.

TCP Acknowledgment Optimization in Low Power and Embedded Devices

Paper investigates transport control protocol (TCP) acknowledgment (ACK) optimization in low power or embedded devices to improve their performance on high-speed links by limiting the ACK rate. Today the dominant protocol for interconnecting network devices is the TCP and it has a great influence on the entire network operation if the processing power of network devices is exhausted to the processing data from the TCP stack. Therefore, on high-speed not congested networks the bottleneck is no longer the network link but low-processing power network devices. A new ACK optimization algorithm has been developed and implemented in the Linux kernel. Proposed TCP stack modification minimizes the unneeded technical expenditure from TCP flow by reducing the number of ACKs. The results of performed experiments show that TCP ACK rate limiting leads to the noticeable decrease of CPU utilization on low power devices and an increase of TCP session throughput but does not impact other TCP QoS parameters, such as session stability, flow control, connection management, congestion control or compromises link security. Therefore, more resources of the low-power network devices could be allocated for high-speed data transfer.

Acknowledgment on Demand for Transport Control

The dependence on frequent acknowledgments (ACKs) is an artifact of current transport protocol designs rather than a fundamental requirement. Frequent ACKs waste resources when the overhead of ACKs is nonnegligible. However, reducing the number of ACKs may hurt transport performance. “Tame ACK” is an on-demand ACK mechanism that seeks to minimize ACK frequency, which is exactly what is required by transport.

Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning

Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating between multiple PUs or multiple SUs in a real DSA environment. Considering these factors, the deep multi-user reinforcement learning (DMRL) is proposed by introducing the cooperative strategy into dueling deep Q network (DDQN). With no demand of prior information about the system dynamics, DDQN can efficiently learn the correlations between channels, and reduce the computational complexity in the large state space of the multi-user environment. To reduce the conflicts and further maximize the network utility, cooperative channel strategy is explored by utilizing the acknowledge (ACK) signals without exchanging spectrum information. In each time slot, each user selects a channel and transmits a packet with a certain probability. After sending, ACK signals are utilized to judge whether the transmission is successful or not. Compared with other popular models, the simulation results show that the proposed DMRL can achieve better performance on effectively enhancing spectrum utilization and reducing conflict rate in the dynamic cooperative spectrum sensing.

An Accelerated End-to-End Probing Protocol for Narrowband IoT Medical Devices

In this study, a narrowband internet of things (NB-IoT) medical device (MD) was presented. We consider a wireless communication system where the multi-hop sources shared the downlink and uplink resources to probe and control hospital patients’ MDs.A multicast data packet was distributed over the multi-hop MD networks, and a random linear network coding approach was applied in the source instead of the intermediate nodes. We evaluated the MD probe cycle between a master station and remote terminal units, which were connected with several critical devices in a medical room. Furthermore, an accelerated end-to-end (E2E) probing protocol was established in the wireless mesh network to minimize the probing cycle of the health devices. In particular, a faster protocol was developed to address the probe and control subproblem.The proposed approach could assist in realizing the accelerated E2E probing for NB-IoT MDs, reducing the number of retransmissions, recovering lost packets, and providing a prompt overview of the medical network performance,based on several basic parameters such as modulation types, Quadrature amplitude modulation (QAM) and Frequency-shift keying (FSK), acknowledgment (ACK), average message sizes AMS, subcarrier spacing (SCS). Furthermore, in a comparative study of the contemporary probing cycle techniques, the proposed protocol notably outperformed several widely used protocols,with the reduction of the number of transmissions as well as the improvement of round trip time (RTT), and E2E probing cycle exceeding 89%, 71%, and 60%, respectively.

Tradução como (re)conhecimento: lições de Trotula para a história das mulheres e da medicina

O percurso que assinala a evolução do saber médico e botânico se entrelaça, de modo imprescindível, com a história da cultura das mulheres. De fato, desde os tempos mais remotos, as funções da mulher, voltadas predominantemente à gestão da dimensão interna da casa e ao domínio do espaço fechado, deram-lhe o domínio da esfera da geração da vida, da fertilidade e do mundo natural, dentro do qual se fazia portadora do conhecimento e dos segredos da cura (MADERNA, 2017, p. 09). Dentre os nomes que se destacam na história das mulheres e da medicina, Trotula de Ruggiero – que, no século XI, desempenhou suas atividades de médica e professora na Escola de Medicina de Salerno, sul da Itália, e que teve a sua presença histórica negada por longo período – parece alcançar cada vez mais notoriedade nos séculos XX e XXI, a considerar estudos e traduções advindos de diferentes partes do mundo e em diferentes línguas. O presente estudo pretende evidenciar como a tradução é uma importante ferramenta e meio para o (re)conhecimento da obra de mulheres como Trotula. Será realizada uma reflexão a partir do tratado De passionibus mulierus [Sobre as doenças das mulheres], escrito por Trotula, no qual trata de elementos que permeiam a saúde, a doença e a beleza das mulheres, visando analisar em que medida a cura pelas plantas, os preceitos de Hipócrates e Galeno, a defesa da higiene e da boa alimentação e o cuidado do organismo em sua relação com o social podem se constituir em lições de Trotula para as mulheres do seu e do nosso tempo.

Enhanced Reliable ACK (ERACK) Scheme for Detecting Misbehaving Nodes in MANETs

In Mobile Ad Hoc Networks (MANETs), the process of sending end-to-end acknowledgement (ACK) for each packet by the destination, causes unnecessary overhead. This paper proposes an Enhanced Reliable ACK (ERACK) scheme for detecting misbehaving attacks in MANET. Initially, reliable multiple paths are established by measuring the path reliability. For defending the data transferred from the dispatcher, one-way hash chain based Message Authentication Code (MAC) is utilised. If the packet loss count is above a threshold, then Secure ACK (S-ACK) mode is activated which identifies the misbehaving node. The source then generates a Misbehavior Report Authentication (MRA) message which is transmitted to the destination using the path with next higher reliability. By experimental results it is shown that the ERACK method decreases the packet loss and delay while ensuring the successful delivery of MRA to the destination.

On stability and convergence of suboptimal estimation for systems over lossy networks without acknowledgement

This paper concentrates on the suboptimal state estimation problem for systems without acknowledgement (ACK). The ACK signal is used for informing the estimator of whether control-input packets have been lost during the transmission or not. For such systems, which are usually regarded as NACK systems, the optimal estimator has been proved to be beset by exponentially increasing computational burden so that it is scarcely possible to be implemented in practice and then the suboptimal estimation becomes a necessary need. Inspired by the idea of multiple-model approaches, we first design an efficient suboptimal estimator for NACK systems to significantly improve computational efficiency. More importantly, we obtained a sufficient and necessary condition to characterise the stability of this proposed suboptimal estimator, which depends only on the packet loss rate (PLR) of the observation regardless of the control-input PLR. We also prove that its estimation error will converge to that of the optimal estimator under a certain condition. A distinguishing feature of the suboptimal estimator proposed in this paper is that not only its stability and convergence can be determined theoretically, but also they are proved to be the same as those of the optimal one for NACK systems.

Playback experience driven cross layer optimisation of APP, transport and MAC layer for video clients over long‐term evolution system

In traditional communication system, information of APP (Application) layer, transport layer and MAC (Media Access Control)layer has not been fully interacted,which inevitably leads to inconsistencies among TCP congestion state, clients'requirements and resource allocation. To solve the problem, we propose a joint optimization framework, which consists of APP layer, transport layer and MAC layer, to improve the video clients'playback experience and system throughput. First, a client requirement aware autonomous packet drop strategy, based on packet importance, channel condition and playback status, is developed to decrease the network load and the probability of rebuffering events. Further, TCP (Transmission Control Protocol) state aware downlink and uplink resource allocation schemes are proposed to achieve smooth video transmission and steady ACK (Acknowledgement) feedback respectively. For downlink scheme, maximum transmission capacity requirement for each client is calculated based on feedback ACK information from transport layer to avoid allocating excessive resource to the client, whose ACK feedback is blocked due to bad uplink channel condition. For uplink scheme, information of RTO (Retransmission Timeout) and TCP congestion window are utilized to indicate ACK scheduling priority. The simulation results show that our algorithm can signficantly improve the system throughput and the clients'playback continuity with acceptable video quality.

Machine learning based lightweight interference mitigation scheme for wireless sensor network

The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference.