Packet Selection Research Articles

A robust multi-key authority system for privacy-preserving distribution and access control of healthcare data

Innovation in medical technology and communication has rapidly empowered the development of smart healthcare devices. This has led to privacy breaches, threats and vulnerabilities to sensitive patient data that result in unwanted or targeted advertising. Previous research has focused on protecting access to sensitive patient data from unauthorized entities, especially by defining roles of healthcare entities in the overall system with their access privileges. However, such efforts need to be further robust due to the involvement of a single key authority that may lead to a critical point of failure. In this paper, this vulnerability has been addressed by developing a novel approach to crucially increase the number of key authorities using homomorphic encryption. The proposed approach ensures genuine access to the verified entity by forming a subsystem of t key authorities from a total of n authorities (t<n). This creates rigorous challenge to a malicious attacker, obfuscating the selection and functioning of key access packets in a multi-key authority setup. The results of the proposed approach achieve medical data confidentiality, entity authentication, and strategic data sharing. The security of the proposed approach is assessed for different vulnerabilities of the overall system using a challenge–response game model. Moreover, the proposed approach is found to be better and secure as compared to existing schemes.

DeX: Deep learning-based throughput prediction for real-time communications with emphasis on traffic eXtremes

Recent years have witnessed a remarkable upsurge in the global proliferation of Real-Time Communications (RTC) applications, a trend propelled by the flourishing advancement of network technologies and further amplified by the COVID-19 pandemic. Within this context, there is a burgeoning interest in the innovation of sophisticated and intelligent network infrastructures and technologies. Positioned as a promising candidate for this purpose, real-time throughput prediction emerges as a key enabler to foster network observability and offer proactive functions, upholding advanced system management, including but not limited to, bandwidth allocation and adaptive streaming. Nonetheless, existing methodologies struggle with predicting extreme conditions of throughput, notably peaks, valleys, and abrupt changes, that are critical in RTC traffic. To surmount these obstacles, we introduce DeX, a Deep Learning (DL)-based framework, designed to predict short-term throughput, with a dexterous proficiency and dedicated focus on navigating the complexities of traffic eXtremes. In particular, DeX leverages solely packet-level information as features and is composed of three integral components: a packet selection module that opts for an optimal subset of input features, a feature extraction block that partially incorporates the Transformer architecture, and a multi-task learning pipeline that improves the proficiency in handling traffic extremes. Moreover, our work is anchored in extensive traffic traces garnered during actual video-teleconferencing calls, and we formulate a time-series regression problem, rigorously evaluating a spectrum of technologies ranging from an adaptive filter to diverse Machine Learning (ML) and DL approaches. Initially, we aim at predicting throughput within 500-ms time windows using historical 1024 packets out of 2048, and consequently, our methodology exhibits exceptional efficacy, especially in forecasting traffic extremities. Conclusively, we conduct a series of ablation experiments and thorough analyses to showcase the enhanced performance of various scenarios, further validating the effectiveness and robustness of DeX.

Optimizing 5G network performance with dynamic resource allocation, robust encryption and Quality of Service (QoS) enhancement.

The International Telecommunication Union (ITU) predicts a substantial and swift increase in global mobile data traffic. The predictions suggest that this growth will vary from 390 EB (exabytes) to 5,016 EB (exabytes) from 2024 to 2030, accordingly. This work presents a new maximum capacity model (MCM) to improve the dynamic resource allocation, robust encryption, and Quality of Service (QoS) in 5G networks which helps to meet the growing need for high-bandwidth applications such as Voice over Internet Protocol (VoIP) and video streaming. Our proposed MCM model enhances data transmission by employing dynamic resource allocation, prioritised traffic management, and robust end-to-end encryption techniques, thereby guaranteeing efficient and safe data delivery. The encryption procedure is applied to the header cypher, while the output parameters of the payload are altered. This indicates that only the sender and recipient will possess exclusive knowledge of the final outcome. In result, the comparative analyses clearly show that the MCM model outperforms over conventional models in terms of QoS packet planner, QoS packet scheduler, standard packet selection, traffic management, maximum data rate, and bandwidth utilisation.

Weighted packet selection for rechargeable links in cryptocurrency networks: Complexity and approximation

We consider a natural problem dealing with weighted packet selection across a rechargeable link, which e.g., finds applications in cryptocurrency networks. The capacity of a link (u,v) is determined by how many nodes u and v allocate for this link. Specifically, the input is a finite ordered sequence of packets that arrive in both directions along a link. Given (u,v) and a packet of weight x going from u to v, node u can either accept or reject the packet. If u accepts the packet, the capacity on link (u,v) decreases by x. Correspondingly, v's capacity on (u,v) increases by x. If a node rejects the packet, this will entail a cost affinely linear in the weight of the packet. A link is “rechargeable” in the sense that the total capacity of the link has to remain constant, but the allocation of capacity at the ends of the link can depend arbitrarily on the nodes' decisions. The goal is to minimise the sum of the capacity injected into the link and the cost of rejecting packets. We show that the problem is NP-hard, but can be approximated efficiently with a ratio of (1+ε)⋅(1+3) for some arbitrary ε>0.

A Simulink model for modified fountain codes

This paper introduces a Simulink model design for a modified fountain code. The code is a new version of the traditional Luby transform (LT) codes. The design constructs the blocks required for generation of the generator matrix of a limited-degree-hopping-segment Luby transform (LDHS-LT) codes. This code is especially designed for short length data files which have assigned a great interest for wireless sensor networks. It generates the degrees in a predetermined sequence but random generation and partitioned the data file in segments. The data packets selection has been made serialy according to the integer generated from both degree and segment generators. The code is tested using Monte Carlo simulation approach with the conventional code generation using robust soliton distribution (RSD) for degree generation, and the simulation results approve better performance with all testing parameter.

Robust filtered Smith predictors for networked control systems with packet-based data transmission

Filtered Smith predictors are well established for controlling linear plants with constant time delays. Apart from this classical application scenario, they are also employed within networked control loops, where the measurements are sent in separate packets over transmission channels that are subject to time-varying delays. Consequently, classical tools for the stability analysis, e.g., for time delay systems, have to be extended to facilitate an explicit consideration of network-induced effects such as packet skipping and disordering of sent packets. The present paper illustrates that the time-varying delays and, particularly important, the packetized character of the transmissions have to be explicitly taken into account for the stability analysis. Three network protocols, which use different packet selection and hold mechanisms, are considered to state criteria for the robust stability of the networked feedback loops with filtered Smith predictors. They are based on the small gain theorem and allow a computationally inexpensive way to check stability for the case with uncertain plant models and bounded time-varying packet delays in the communication channels.

Analysis of average consensus of multi-agent systems with time delay using packet selection and synchronization protocol

A stabilization approach for the average consensus of homogeneous multi-agent systems (MASs) entailing non-uniform, asymmetric, and time-varying delays in communication networks is proposed. Agents are modeled using a continuous-time linear dynamical system with a local discrete-time controller. To address communication delays, we propose two logic functions, namely, a packet selection algorithm that constantly selects and updates the latest received control signal packets and a synchronization algorithm that asymmetrically synchronizes these packets to preserve the average value of state variables. Based on the Lyapunov theory, we establish a linear matrix inequality (LMI) condition that sufficiently guarantees the stability and consensus of the overall system. To establish this condition, only the time delay upper bound is required, without any special assumptions on the delay pattern. We thoroughly discuss the proposed algorithm and evaluate its effectiveness via numerical simulations.

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast multiple packets with heterogeneous quality-of-service (QoS) requirements, such as safety-related packets (e.g., accident reports) that require very low latency communication, while raw sensor data sharing (e.g., high-definition map sharing) requires high-speed communication. To ensure heterogeneous service requirements for different packets, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface (i.e., sidelink communication). In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we first provide a mathematical programming formulation and a thorough NP-hardness analysis of the problem. Then, we model it as a multi-agent Markov decision process. Finally, to solve it efficiently, we use a deep reinforcement learning (DRL) approach and specifically propose a deep Q learning (DQL) algorithm. The proposed DQL algorithm is practical because it can be implemented in an online and distributed manner. It is based on a cooperative learning strategy in which all agents perceive a common reward and thus learn cooperatively and distributively to improve the resource allocation solution through offline training. We show that our approach is robust and efficient when faced with different variations of the network parameters and compared to centralized benchmarks.

Dual channel transmission for reliable V2X broadcasting messages

A Vehicle to Anything (V2X) packet data is used for a vehicle safety and automated driving applications. It is broadcasted periodically to provide a warning message or share the vehicular sensor's data among vehicles. This paper proposes IEEE 802.11p based dual channel transmission and packet selection method to improve the Packet Detection Rate (PDR) for V2X broadcasting messages. The PDR gain of the proposed scheme over the single channel transmission is given theoretically by the packet error rate of a single radio channel. And the PDR improvement is evaluated by a computer simulation in a highway or urban multipath fading channel.

A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11ax WLANs

The latest IEEE 802.11ax protocol has been launched to provide efficient services by adopting multi-user (MU) transmission technology. However, the MU transmissions in the aggregation-enabled wireless local area networks (WLANs) face two drawbacks when adopting the existing automatic repeat request (ARQ) schemes. (1) The failed packets caused by the channel noise can block the submission of subsequent packets and the transmission of queued ones. (2) When the lengths of aggregate media access control protocol data units (A-MPDU) transmitted by different users are varied, dummy bits should be added to the shorter frames to align the transmission duration. These drawbacks degrade the quality of service (QoS) performances, such as throughput, latency, and packet loss rate. In this paper, a novel QoS-aware backup padding ARQ (BP-ARQ) scheme for MU transmissions in the IEEE802.11ax WLANs is proposed to address these problems. The proposed scheme utilizes backups of selected packets instead of dummy bits to align the duration and to supress the influence of channel noise. An optimization problem that aims to improve the blocking problem of the failed packets is derived to determine the selection of packets. The promotion of the proposed scheme is well demonstrated by the simulations in NS-3.

A Stability Criterion for Networked Control Systems With Packetized Transmissions

A stability criterion for networked feedback loops with time-varying measurement delays is proposed. It allows to check stability under the presence of different packetized network transmission policies where, e.g., newer packets may arrive at the same time or before older packets. The criterion is based on the small gain theorem and explicitly takes into account a packet selection and hold mechanism at the receiver side. It can easily be checked in frequency domain and does not involve any computationally expensive optimization algorithms.

Joint Delay Reduction and Power of Device for Differentiated Data Transmission in D2D Network

In wireless communication, Device-to-Device (D2D) networks will become more and more common with the popularity of mobile terminals. However, in a D2D network, the processing capabilities of different terminals may be different, and the energy of different terminals is different due to battery life. In order to improve the quality of experience (QoE) of terminals with different processing capabilities and energy, and realize the transmission of differentiated data streams, this paper combines the existing base station retransmission and terminal retransmission algorithms in D2D network. In the transmission process, the data packet is divided into a basic layer and several enhancement layers according to the definition of the data. Based on the link quality, the basic layer data packet is transmitted firstly, then the enhancement layer data packet is transmitted. Moreover, not only the base station retransmits the data packet, but also the terminal that has received the corresponding data packet also participates in the retransmission. In order to extend the average life of the network, we propose the maximum weight packet selection algorithm based on channel quality and terminal energy (MWP-QE). The simulation results show that compared with the existing retransmission algorithm, this algorithm can realize the transmission of differentiated data, reduce the retransmission delay and extend the life of the network. A post-publication change was made to this article on 17 Jun 2020 to amend the author list.

Analysis of Packet Diversity in Buffer-Aided Relaying over Symmetric and Asymmetric Rayleigh Fading Channels

In this paper, we propose innovative schemes for relay selection that jointly explore packet selection and relay selection for buffer-aided amplify and forward (AF) cooperative relaying networks. The first proposed scheme chooses the most suitable channel based on link quality from all active channels, i.e., channels with neither empty nor full corresponding buffers. In the second proposed scheme, the most suitable channel is chosen based on buffer status. When the source-relay channel is determined, the corresponding relay collects data in the buffer. Likewise, when the relay-destination channel is picked, the most suitable packet is dispatched from the buffer. The most suitable packet is one that provides the highest end-to-end equivalent signal-to-noise ratio. We simulated the outage probability, average throughput and packet delay and analyzed the proposed protocol for both symmetric and asymmetric channel conditions. Comparison is made against the existing buffer-aided schemes. The results show that the proposed relay and packet selection systems help to reduce the outage probability, diversity gain and delay.

Adaptive QoS constraint-based service differentiated routing in wireless sensor network

Achieving the best quality of service (QoS) as per user requirement is one of the important challenges. The time-critical applications in wireless sensor networks (WSNs) demand energy efficient transmission of data with limited resource availability. To resolve these issues, AQSDR has been proposed. The proposed protocol support packet differentiation and selection of sensor node based on energy, delay, and congestion for path establishment to transfer normal data packets providing energy conservation. The multipath is chosen for transmission of emergency packets satisfying delay requirements of non-delay tolerant applications. AQSDR support adaptive path selection, according to application requirement. The proposed technique in this work outperforms when compared with the existing protocol in terms of minimised energy consumption, delay, control overhead, packet drop ratio, and high throughput.

Decoding CMOS Rolling-Shutter Pattern in Translational or Rotational Motions for VLC

We propose and demonstrate a CMOS rolling-shutter pattern decoding scheme based on two newly introduced schemes, named row grayscale redistribution and differential grayscale packet selection to improve the performance in decoding the CMOS rolling-shutter pattern in translational or rotational motions. Results show that even the CMOS image sensor is in translational or rotational motions, no significant signal degradation is observed at wireless transmission distance up to 150 cm. The maximum visible light communication data rate at different wireless transmission distances is also analyzed.

Adaptive Carrier Aggregation with Differentiating Cloud Services for Maximizing Radio Resource Efficiency and Reward Toward 5G Cellular Network

Toward 5G cellular network, to achieve an extremely high data rate and Ultra Reliable Low Latency Communication by using a limited radio frequency spectrum bands becomes a big challenge. 3GPP LTE-A/LTE-A Pro thus specifies the technologies of Carrier Aggregation (CA) and Network Function Virtualization (NFV) to increase the frequency spectrum efficiency and to dynamically allocate the virtualized network component for different classes of requests, respectively. CA can aggregate multiple contiguous or non-contiguous Component Carriers (CCs) and to improve frequency spectrum utilization and signal quality. NFV can dynamically allocate network (virtualized) resource for different classes of services, e.g., human-driven Cloud Computing, machine-driven Internet of Vehicles (IoVs) (e.g., Autonomous Self Driving Vehicle) and sensing-based Internet of Things (IoTs), etc. However, different SINRs of different frequency spectrum bands suffer from the exiting radio nature of CCs. The CA effect and system capacity are thus limited obviously. Additionally, in CC accesses via IoV/IoT, human-driven and machine-driven types communications exhibit different arrival requests and traffic characteristics. Various QoS requirements and traffic distributions of Human and Machine transmissions are certainly different. This paper thus proposes the Cross-Layer scheduling with CC Aggregation (CLCA) toward 5G. CLCA consists of three mechanisms: (1) Markov Decision Process-based cost reward Packet Selection (MDP-PS), (2) Adaptive Packet Scheduling (APS) and (3) Adaptive Component Carrier scheduling (ACC). Numerical results demonstrate that the proposed CLCA approach outperform the compared approaches in system capacity, network reward and packet failure rate.

A Packet Scheduling Scheme for Improving Real-time Applications Performance in Downlink LTE–advanced

Quality of Service based packet scheduling is a key-feature of LTE-A mandating selection and transmission of individual user packets based on their priority. HARQ Aware Scheduling, Retransmission Aware Proportional Fair, Chase Combining Based Max C/I Scheduling and Maximum- Largest Weighted First (M-LWDF) are popular Packet Scheduling Algorithms (PSAs) developed to meet QoS requirements. In highly erroneous LTE-A cannel, M-LWDF is considered to be one of best PSA. To validate the performance of M-LWDF for the LTE-A channel, Mean User Throughout, and Fairness performance measures were evaluated for 3 different PSAs designed based on M-LWDF algorithm in this paper. A C++ based simulation results indicate the superiority of the PSA3 algorithm within the threshold of the performance measures against benchmarks. It has shown more efficiency and the performance of RTA traffic was enhanced. Results show that PSA3 is superior to its benchmark PSA2 by 12% in Mean User Throughput and 11% in Fairness. PSA2 performed the worst because it prioritizes new users and it allocated all available RBs to the scheduled user leaving the rest to wait in the buffer. PSA3 maintians good Mean User Throughput and fairnessdue to scheduling each user on its RB which leads to multi-user diversity.

A Packet Scheduling Scheme for Improving Real-time Applications Performance in Downlink LTE–advanced

Quality of Service based packet scheduling is a key-feature of LTE-A mandating selection and transmission of individual user packets based on their priority. HARQ Aware Scheduling, Retransmission Aware Proportional Fair, Chase Combining Based Max C/I Scheduling and Maximum- Largest Weighted First (M-LWDF) are popular Packet Scheduling Algorithms (PSAs) developed to meet QoS requirements. In highly erroneous LTE-A cannel, M-LWDF is considered to be one of best PSA. To validate the performance of M-LWDF for the LTE-A channel, Mean User Throughout, and Fairness performance measures were evaluated for 3 different PSAs designed based on M-LWDF algorithm in this paper. A C++ based simulation results indicate the superiority of the PSA3 algorithm within the threshold of the performance measures against benchmarks. It has shown more efficiency and the performance of RTA traffic was enhanced. Results show that PSA3 is superior to its benchmark PSA2 by 12% in Mean User Throughput and 11% in Fairness. PSA2 performed the worst because it prioritizes new users and it allocated all available RBs to the scheduled user leaving the rest to wait in the buffer. PSA3 maintians good Mean User Throughput and fairnessdue to scheduling each user on its RB which leads to multi-user diversity.

Deep Packet Flow: Action Recognition via Multiresolution Deep Wavelet Packet of Local Dense Optical Flows

Action recognition with dynamic actor and scene has been a tremendous research topic. Recently, spatio temporal features such as optical flows have been utilized to define motion representation over sequence of time. However, to increase accuracy, deep decomposition is necessary either to enrich information under location or time-varying actions due to spatio temporal dynamics. To this end, we propose algorithm consists of vectors obtained by applying multi-resolution analysis of motion using Haar Wavelet Packet (HWP) over time. Its computation efficiency and robustness have led HWP to gain popularity in texture analysis but their applicability in motion analysis is yet to be explored. To extract representation, a sequence of bin of Histogram of Flow (HOF) is treated as signal channel. Deep decomposition is then applied by utilizing Wavelet Packet decomposition called Packet Flow to many levels. It allows us to represent action’s motions with various speeds and ranges which focuses not only on HOF within one frame or one cuboid but also on the temporal sequence. HWP, however, has translation covariant property that is not efficient in performance because actions occur in arbitrary time and various sampling location. To gain translation invariant capability, we pool each respective coefficient of decomposition for each level. It is found that with proper packet selection, it gives comparable results on the KTH action and Hollywood dataset with train-test division without localization. Even if spatiotemporal cuboid sampling is not densely sampled like of baseline method, we achieve lower complexity and comparable performance on camera motion burdened dataset like UCF Sports that motion features such as HOF do not perform well.

Simulation of Tsunami Effect by Seismic Wave Propagation in Hypoplastic Medium at Vicinity of Free Boundary

A theoretical study of seismic waves propagation in a soil layer with a free surface has a great importance for a prediction in engineering decisions. Wave packets are radiated from an earthquake source and transfer energy. A transformation and a selection of wave packets occur in a process of wave propagating that why waves which arrive in a layer have a length considerably greater than a variation scale of heterogeneity in a medium in a layer near free surface. In the case, when the properties of different layers affect a relatively small degree on a behavior of the waves, an approximation of effective medium gives a fairly good solution. A model of a hypoplastic medium is used for a describing of some effects, which are observed in the time of seismic wave propagation. The model of hypoplastic medium allows describing many effects which are observed in granular soils. We consider a successive application of effective medium and ray methods in order to receive of approximate analytical solutions wishing to describe shear wave propagation in stratified layer, which lies on a half-space.