Packet Length Research Articles

Joint Packet Length and Power Optimization for Covert Short-Packet D2D Communications

This paper proposes a joint optimization mechanism for packet length and power in the scenario of covert short packet D2D communication, so as to effectively improve the communication performance between D2D pairs subject to the covert constraint. Specifically, we construct a short-packet D2D communication model assisted by covert communication, and further propose the notion of effective covert throughput (ECT) to quantitatively characterize the trade-off between the covertness and reliability of IoT state monitoring information transmission. Secondly, in the constructed communication scenario, we analyze the detection error probability of the warden and clarify that the existing equal power transmission of pilot and data signals can minimize the detection performance of the warden. However, this strategy is achieved by compromising the transmission performance of the system, which means that the ECT of D2D pair may not be optimal. Thirdly, we aim to maximize the ECT of D2D pair and construct a joint optimization problem for pilot transmission power, data transmission power, and packet length. Furthermore, a joint optimization algorithm based on the 2D search is adopted to obtain the optimal solution of the established optimization problem. Simulation results demonstrated that the transmission performance of the joint optimization algorithm is better than that of the scheme of the equal power scheme on the premise of ensuring the covertness.

XTS: A Hybrid Framework to Detect DNS-Over-HTTPS Tunnels Based on XGBoost and Cooperative Game Theory

This paper proposes a hybrid approach called XTS that uses a combination of techniques to analyze highly imbalanced data with minimum features. XTS combines cost-sensitive XGBoost, a game theory-based model explainer called TreeSHAP, and a newly developed algorithm known as Sequential Forward Evaluation algorithm (SFE). The general aim of XTS is to reduce the number of features required to learn a particular dataset. It assumes that low-dimensional representation of data can improve computational efficiency and model interpretability whilst retaining a strong prediction performance. The efficiency of XTS was tested on a public dataset, and the results showed that by reducing the number of features from 33 to less than five, the proposed model achieved over 99.9% prediction efficiency. XTS was also found to outperform other benchmarked models and existing proof-of-concept solutions in the literature. The dataset contained data related to DNS-over-HTTPS (DoH) tunnels. The top predictors for DoH classification and characterization were identified using interactive SHAP plots, which included destination IP, packet length mode, and source IP. XTS offered a promising approach to improve the efficiency of the detection and analysis of DoH tunnels while maintaining accuracy, which can have important implications for behavioral network intrusion detection systems.

Intelligent Reflecting Surface-Aided Full-Duplex Covert Communications: Information Freshness Optimization

This work investigates the covert information freshness in intelligent reflecting surface (IRS)-aided communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). In particular, with the help of Alice’s undisguised signal transmission, Bob can establish covert communications such that his transmission can be shielded from Willie. Considering both the non-retransmission protocol and the automatic repeat-request (ARQ) protocol for Bob’s transmission, we study the resource allocation design. By exploiting the channel statistics, the joint design of active beamforming at Alice and Bob, the passive beamforming at the IRS, and the packet length of the confidential data packet is formulated as a nonconvex optimization problem which minimizes the age of information (AoI) at Alice for the two considered protocols taking into account the quality of service in terms of the maximum tolerable AoI at Bob and communication covertness. To circumvent the non-convexity of the design problem, we propose alternating optimization algorithms to find effective solutions. Numerical results demonstrate the superiority of our proposed optimization algorithms over various benchmarks and unveil the decrease of the optimized packet length with the improved covert channel quality.

Study performance indicators covert channels of steganographic systems in telecommunication networks

Composite performance indicators of steganographic systems are investigated, on the basis which a new approach to the construction covert channel models in telecommunication communication networks is proposed. Analytical expressions are obtained for estimating the maximum value covert channel bandwidth when introducing a counteraction method and when changing packet lengths, which allow determining the performance characteristics of the steganographic data hiding system.

Energy-Efficient Multiple Network-on-Chip Architecture With Bandwidth Expansion

As technology feature sizes diminish to the nanometer regime, the leakage power crisis has become a major challenge in network-on-chip (NoC) design. Power gating (PG) is used to mitigate growing leakage power as an effective static power-saving technique. Applying PG in a multiple NoC (Multi-NoC) rather than a traditional NoC is a promising solution. However, limited by the channel width of the subnets, the increase in packet length will bring a severe serialization issue and performance loss. Previous Multi-NoC schemes have to wake up more subnets to minimize the performance loss, which also sacrifices their energy efficiency. In this article, we introduce an architecture, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BandExp</i> , which allows subnets to expand their bandwidth by utilizing the idle physical links of other subnets. More bandwidth helps subnets mitigate the serialization issue and reduce the performance loss. Meanwhile, other subnets gain longer sleep cycles and thus save more energy. Evaluation results indicate that compared to the state-of-the-art <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Catnap</i> , the proposed architecture reduces the average packet latency and execution time of different benchmarks by 19.3% and 3.2%, respectively. Also, the net static energy of the network is reduced by 23.2% on average, while the incurred area overhead is only 1.3%.

Real-Time Detection System for Data Exfiltration over DNS Tunneling Using Machine Learning

The domain name system (DNS) plays a vital role in network services for name resolution. By default, this service is seldom blocked by security solutions. Thus, it has been exploited for security breaches using the DNS covert channel (tunnel). One of the greatest current data leakage techniques is DNS tunneling, which uses DNS packets to exfiltrate sensitive and confidential data. Data protection against stealthy exfiltration attacks is critical for human beings and organizations. As a result, many security techniques have been proposed to address exfiltration attacks starting with building security policies and ending with designing security solutions, such as firewalls, intrusion detection or prevention, and others. In this paper, a hybrid DNS tunneling detection system has been proposed based on the packet length and selected features for the network traffic. The proposed system takes advantage of the outcome results conducted using the testbed and Tabu-PIO feature selection algorithm. The evolution of the proposed system has already been completed using three distinct datasets. The experimental outcome results show that the proposed hybrid approach achieved 98.3% accuracy and a 97.6% F-score in the DNS tunneling datasets, which outperforms the other related works’ techniques using the same datasets. Moreover, when the packet length was added into the hybrid approach, the run-time shows better results than when Tabu-PIO was used when the size of the data increases.

A Traffic-Aware Fair MAC Protocol for Layered Data Collection Oriented Underwater Acoustic Sensor Networks

Underwater acoustic channels are characterized by long propagation delay, limited available bandwidth and high energy costs. These unique characteristics bring challenges to design media access control (MAC) protocol for underwater acoustic sensor networks (UASNs). Especially in data-collection-oriented UASNs, data generated at underwater nodes are transmitted hop-by-hop to the sink node. The traffic loads undertaken by nodes of different depths are different. However, most existing MAC protocols do not consider the traffic load imbalance in data-collection-oriented UASNs, resulting in unfairness in how the nodes transmit their own generated data. In this paper, we propose a novel traffic-aware fair MAC protocol for layered data-collection-oriented UASNs, called TF-MAC. TF-MAC accesses a medium by assigning time slots of different lengths to each layer via different traffic loads to achieve traffic fairness of nodes. To improve throughput and avoid collision in the network, an overlapping time slot division mechanism for different layers and multi-channel allocation scheme within each single layer is employed. Considering the time-varying traffic loads of the nodes, an adaptive packet length algorithm is proposed by taking advantage of the spatial-temporal uncertainty of underwater channels. A sea experiment was conducted to prove the spatial-temporal uncertainty of UASNs, which provides a feasibility basis for the proposed algorithm. Simulation results show that TF-MAC can greatly improve the network performance in terms of throughput, delay, energy consumption, and fairness in the layered data-collection-oriented UASNs.

Areion: Highly-Efficient Permutations and Its Applications to Hash Functions for Short Input

In the real-world applications, the overwhelming majority of cases require hashing with relatively short input, say up to 2K bytes. The length of almost all TCP/IP packets is between 40 to 1.5K bytes, and the maximum packet lengths of major protocols, e.g., Zigbee, Bluetooth low energy, and Controller Area Network (CAN) are less than 128 bytes. However, existing schemes are not well optimized for short input. To bridge the gap between real-world needs (in future) and limited performances of state-of-the-art hash functions for short input, we design a family of wide-block permutations Areion that fully leverages the power of AES instructions, which are widely deployed in many devices. As its applications, we propose several hash functions. Areion significantly outperforms existing schemes for short input and even competitive to relatively long message. Indeed, our hash function is surprisingly fast, and its performance is less than 3 cycles/byte in the latest Intel architecture for any message size. Especially, it is about 10 times faster than existing state-of-the-art schemes for short message up to around 100 bytes, which are most widely-used input size in real-world applications, on both the latest CPU architectures (IceLake, Tiger Lake, and Alder Lake) and mobile platforms (Pixel 6 and iPhone 13).

Bayesian Over-the-Air Computation

As an important piece of the multi-tier computing architecture for future wireless networks, over-the-air computation (OAC) enables efficient function computation in multiple-access edge computing, where a fusion center aims to compute a function of the data distributed at edge devices. Existing OAC relies exclusively on the maximum likelihood (ML) estimation at the fusion center to recover the arithmetic sum of the transmitted signals from different devices. ML estimation, however, is much susceptible to noise. In particular, in the misaligned OAC where there are channel misalignments among received signals, ML estimation suffers from severe error propagation and noise enhancement. To address these challenges, this paper puts forth a Bayesian approach by letting each edge device transmit two pieces of statistical information to the fusion center such that Bayesian estimators can be devised to tackle the misalignments. Numerical and simulation results verify that, 1) For the aligned and synchronous OAC, our linear minimum mean squared error (LMMSE) estimator significantly outperforms the ML estimator. In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86.4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator. Moreover, the SP-MAP estimator is computationally efficient, the complexity of which grows linearly with the packet length.

Short Packet-Based Status Updates in Cognitive Internet of Things: Analysis and Optimization

In the cellular Internet of Things (IoT), some machine type communication devices (MTCDs) are used to sample and send status information of monitored targets to help base station (BS) perform monitoring or controlling tasks. However, due to limited spectrum resources, the MTCDs often cannot occupy separate spectrum resources. This paper studies a cognitive IoT where the MTCDs exploit the spectrum resource vacated by cellular users to send status packets to the BS. The timeliness of status updates is characterized by the recently proposed metric, age of information (AoI). In the IoT, the packets sent by the MTCDs are often short, which means it is necessary to analyze the status updates under short packet communication theory (SPC). In this case, we adopt the automatic repeat request protocol to retransmit the erroneous packets, and the preemption policy is exploited to improve the status update performance. We first carefully analyze the evolution of AoI in the cognitive IoT, and adopt a recursive method to obtain the closed-form expression of the average peak AoI. Then, we analyze age-energy tradeoff, and propose a scheme of jointly optimizing transmit power, packet length, and spectrum sharing to reduce the average peak AoI of the network. The simulation results verify the correctness of the theoretical analysis, and show that the proposed scheme outperforms the benchmark schemes.

Second-Order Statistics for IRS-Assisted Multiuser Vehicular Network With Co-Channel Interference

In this paper, we consider a downlink multi-user vehicular communication network empowered by an intelligent reflecting surface (IRS) with the base station (BS) employing the orthogonal frequency division multiple access (OFDMA) scheme. The performance of the considered system is evaluated in terms of second-order statistics (SOS) in the presence of multiple co-channel interferers with varying powers and speeds. In particular, we develop the analytical expressions of level crossing rate (LCR) and average outage duration (AOD) of the received signal envelope at a desired vehicle by considering a generalized fading model as - distribution. The impact of various system and channel parameters such as number of reflecting elements, amplitude of reflection coefficients, number of interferers, Doppler frequencies of desired and interfering vehicles, and different channel fading environments has been shown on the LCR and AOD performances. Moreover, we derive an expression for asymptotic LCR under high transmit power conditions. It has been shown that the asymptotic LCR is independent of the threshold and transmit power. In addition, we consider a stop and wait automatic repeat request (SW-ARQ) protocol for reliable data packet transmission and derive the expressions for packet error rate (PER) and throughput by utilizing the finite-state Markov channel (FSMC) model. The optimal packet length is also determined which maximizes the throughput under SW-ARQ protocol. The impact of various system parameters is also shown on the PER and throughput performances.

A Compression Router for Low-Latency Network-on-Chip

Network-on-Chips (NoCs) are important components for scalable many-core processors. Because the performance of parallel applications is usually sensitive to the latency of NoCs, reducing it is a primary requirement. In this study, a compression router that hides the (de)compression-operation delay is proposed. The compression router (de)compresses the contents of the incoming packet before the switch arbitration is completed, thus shortening the packet length without latency penalty and reducing the network injection-and-ejection latency. Evaluation results show that the compression router improves up to 33% of the parallel application performance (conjugate gradients (CG), fast Fourier transform (FT), integer sort (IS), and traveling salesman problem (TSP)) and 63% of the effective network throughput by 1.8 compression ratio on NoC. The cost is an increase in router area and its energy consumption by 0.22mm2 and 1.6 times compared to the conventional virtual-channel router. Another finding is that off-loading the decompressor onto a network interface decreases the compression-router area by 57% at the expense of the moderate increase in communication latency.

Method and Algorithm for Determining the Type of Traffic in an Encrypted Communication Channel

The article proposes a method for determining the composition of protocols used in IPsec communication channel, based on the regularities of the chronology and the lengths of encrypted load packets. The characteristic informative features of the protocols are considered. An algorithm is given to obtain the length values of ESP packets containing arbitrary user data for common modes of IPsec tunnel operation.

Signature identification and user activity analysis on WhatsApp Web through network data

WhatsApp messenger is a popular instant messaging application that employs end-to-end encryption for communication. WhatsApp Web is the browser-based implementation of WhatsApp messenger. Users of WhatsApp communicate securely using SSL protocol. Encryption and use of common port for communication by multiple applications poses challenge in traffic classification for application identification. It is highly needed to analyze the network traffic for the purpose of QoS, Intrusion Detection and application specific traffic classification. In this paper, we have done traffic analysis on the network packets captured through data transfer in whatsApp web. In the result, we have explored the user activities such as message texting, contact sharing, voice message, location sharing, media transfer and status viewing. Packet level traffic analysis of user activities reveal patterns in the encrypted SSL communication. This pattern is identified across SSL packet lengths for WhatsApp media transfer and voice message communication. Other important features WhatsApp is the ability to view the status of the message being sent. We have identified the read and unread message status in these data packets by exposing signatures in the network layer. These signatures are identified with the help of the SSL lengths in the TLS header information of WhatsApp Web network traffic traces. Various other information on WhatsApp traffic presented in our study is relevant to the version of WhatsApp Web v0.3.2386. Also in this work machine learning based classifier is trained to classify the encrypted malicious, normal network traffic. From the results it is clear that SVM classifier gives the highest accuracy of 0.9666.

Optimized MAC Protocol Using Fuzzy-Based Framework for Cognitive Radio AdHoc Networks

Cognitive radio networks (CRNs) have been widely used in various applications for effective radio spectrum utilization in recent years. It is essential to fend off the growing demand for this finite natural resource for next-generation communications. In CRNs, detecting the activity of the primary user requires opportunistic spectrum sensing for efficient usage of the available radio spectrum, which is a limited and exquisite resource. Thus, CRNs are the key component in solving the spectrum scarcity issue in the presence of primary user bands through secondary users. Cognitive radio AdHoc networks (CRAHNs) are a unique kind of CRNs where infrastructures less cognitive radio (CR) nodes are furnished. In CRAHN, the CR-MAC protocol works slightly differently from the traditional wireless network MAC protocols. Particularly, the proposed method includes a high traffic scenario under contention-based IEEE 802.11 DCF MAC protocol. Accordingly, it can be observed that both, throughput and delay, increase as the CW size and packet length of the 802.11 (DCF) MAC protocol for CRAHN varies. Therefore, this paper proposed a fuzzy-based optimization framework for the 802.11 (DCF) MAC protocol in CRAHNs. Furthermore, it optimized throughput and delay by training a database of input parameters, contention window, and packet length for the Mamdani and Sugeno fuzzy inference system (FIS) models of the 802.11 (DCF) protocol simultaneously. The experimental result of the proposed framework for CRAHN with FIS shows that altering the contention window increases throughput by 25% and reduces the delay by 38% compared to the IEEE802.11 (DCF) protocol for CRAHNs without FIS. Moreover, it is also revealed that the throughput is increased by and 7% and the delay is reduced by 40% to 50% due to altering the packet length.

Road Safety on the Example of Wireless Services – Case Study

The paper considers organising wireless access in vehicular environments. Such environments are normally affected by Doppler effects, so the IEEE 802.11p standard is expected to ensure an appropriate quality of service for moving objects. Theoretically, the IEEE 802.11p standard compensates for Doppler effects, but it should be ascertained whether 802.11p is still efficient at tiny Doppler shifts and when an object moves at higher speeds. The 802.11p link provides a data rate which is twice as low for 802.11a. Thus, an end-to-end simulation is carried out for the links at wide ranges of signal-to-noise ratio by varying the Doppler shift from 0 Hz to 100 Hz. The simulation also involves 8 modulation types for 128-, 512-, and 1024-bit packet transmissions to cover all possible study cases. The efficiency criterion is the packet-error rate, to which the data rate is additionally considered. The main simulation result is that the 802.11p link is efficient only at not high speeds. The packet length should be shortened to suppress the influence of the object’s speed. Therefore, to enable high-quality wireless access in vehicular environments, a combination of the 802.11p and 802.11a links should be used, where phase shift keying is more effective for 802.11a and quadrature amplitude modulation is more effective for 802.11p. The trade-off herein is a data rate versus margin speed.

Optimal Frequency Regulation Using Packetized Energy Management

Packetized Energy Management (PEM) is a demand dispatch scheme that can be used to provide ancillary services such as frequency regulation. In PEM, distributed energy resources (DERs) are granted uninterruptible access to the grid for a pre-specified time interval called the packet length. This results in a down ramp-limited response in PEM for DERs that can only consume power from the grid. In this work, a linearized virtual battery model of PEM is provided that is capable of predicting the down ramp-limited output of PEM and is used in a model predictive control (MPC) framework to improve the performance of PEM in tracking an automatic generation control (AGC) signal. By performing statistical analysis on the AGC regulation signal, PJM Reg-D, an autoregressive moving average (ARMA) model is derived as a predictor for the MPC-based precompensator. Finally, as an alternative to MPC, it is shown that by varying the packet length as a function of time, for example through packet randomization, frequency regulation can be improved under PEM.

Energy efficient machine learning based SMART-A-BLE implemented Wireless Battery Management System for both Hybrid Electric Vehicles and Battery Electric Vehicles

Electric vehicles have a larger battery pack with more than 100's cells connected in series or parallel combinations to form many battery modules. A Battery Management system (BMS) continuously monitors Current, Voltage, and Temperature data to trigger control algorithms like cell balancing and thermal management using wired communication which is then sent to the cloud for real-time analytics [1]. Wired communication restricts the flexibility in battery module assembly and makes it difficult for battery swapping. The proposed wireless communication, along with the SMART-A-BLE algorithm for BMS in the electric vehicle, uses Bluetooth Low Energy [2], [3], [4], and [5]. SMART-A-BLE algorithm predicts the best connection interval using the Decision tree model based on the link quality factor captured based on real time latency. The model uses threshold latency, expected retransmission factor, data packet length, and the number of peripherals as input features. A look-up table-based algorithm is also proposed for resource constraint devices to achieve the optimal operating point where the slaves could transmit at both low latency and low current consumption with less congestion. SMART-A-BLE control algorithm outperforms the mathematical model-based algorithm in both ideal and interference conditions within a metallic container by maintaining robust connectivity.

Bidirectional Bluetooth Backscatter with Edges

Bidirectional links are widely adopted in many active radios to provide efficient data exchanges, e.g., WiFi and Bluetooth. However, they are underexplored for general-purpose backscatter communications. The downlinks of state-of-the-art Bluetooth backscatter systems are based on packet length modulation, which is inefficient and unreliable. In this paper, we propose BiBlue, a bidirectional Bluetooth backscatter system that uses an edge server to enable fast and reliable downlinks. Specifically, our edge server acts like a bridge that can translate commercial Bluetooth signals to Amplitude Shift Keying (ASK) signals. In addition, we design a reliable edge-to-tag link that adaptively decodes ASK signal under variance. Finally, we propose a low-power uplink design that enables connection-based bidirectional communication with commodity devices. We prototype BiBlue using FPGA and Software Defined Radio (SDR). Extensive experimental results demonstrate that BiBlue achieves more than 62x downlink throughput gains over FreeRider and meanwhile supports an 80 cm communication range of edge-to-tag with BER at around 1%.

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper developed a trust management technique called Enhanced Beacon Trust Management with Hybrid Optimization (EBTM-Hyopt) for efficient cluster head selection and malicious node detection. It tries to build trust among connected nodes and may improve security by requiring every participating node to develop and distribute genuine, accurate, and trustworthy material across the network. Specifically, optimized cluster head election is done periodically to reduce and balance the energy consumption to improve the lifetime network. The cluster head election optimization is based on hybridizing Particle Swarm Optimization (PSO) and Gravitational Search Optimization Algorithm (GSOA) concepts to enable and ensure reliable routing. Simulation results show that the proposed EBTM-HYOPT outperforms the state-of-the-art trust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13% of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay, and 16.34% of packet length.