Payload Length Research Articles

Optimized Payload Length and Power Allocation for Generalized Superimposed Pilot in URLLC Transmissions

Optimized Payload Length and Power Allocation for Generalized Superimposed Pilot in URLLC Transmissions

USteg-DSE: Universal quantitative Steganalysis framework using Densenet merged with Squeeze & Excitation net

Carrying concealed communication via media is termed as steganography and unraveling details of such covert transmission is known as steganalysis. Extracting details of hidden message like length, position, embedding algorithm etc. forms part of forensic steganalysis. Predicting length of payload in camouflaged interchange is termed as quantitative steganalysis and is an indispensable tool for forensic investigators. When payload length is estimated without any prior knowledge about cover media or used steganography algorithm, it is termed as universal quantitative steganalysis.Most of existing frameworks on quantitative steganalysis available in literature, work for a specific embedding algorithm or are domain specific. In this paper we propose and present USteg-DSE, a deep learning framework for performing universal quantitative image steganalysis using DenseNet with Squeeze & Excitation module (SEM). In deep learning techniques, deeper networks easily capture complex statistical properties. But as depth increases, networks suffer from vanishing gradient problem. In classic architectures, all channels are equally weighted to produce feature maps. Presented USteg-DSE framework overcomes these problems by using DenseNet and SEM. In DenseNet, each layer is directly connected with every other layer. DenseNet makes information and gradient flow easier with fewer feature maps. SEM incorporates content aware mechanism to adaptively regulate weight for every feature map. Presented framework has been compared with existing state-of-the-art techniques for spatial domain as well as transform domain and show better results in terms of Mean Absolute Error (MAE) and Mean Square Error (MSE).

Formation Planning for Tethered Multirotor UAV Cooperative Transportation With Unknown Payload and Cable Length

This study investigates the formation planning problem of tethered multirotor unmanned aerial vehicle (UAV) cooperative transportation with unknown payload and cable length. Normally, the transportation formation and trajectory are given in advance or designed based on the coupled system model. It is challenging to dynamically generate flexible formations in response to changing environments when the payload and cable length are unknown. This paper proposes an online formation planning method for multirotor UAVs. First, by analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. Then, desired trajectories/formations that can reduce the cost functions are generated by using the admittance model. Next, an estimation-based formation tracking control is designed, which ensures that multirotor UAVs follow the desired trajectories/formations. Finally, numerical simulations and experiments are conducted to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the formation planning problem of tethered multirotor UAV cooperative transportation. In industry and production applications, a team of multirotor UAVs has a larger load capacity than a single one. Nevertheless, the formation planning of the tethered cooperative transportation is challenging, especially when the payload and cable length are unknown. Rather than give a predefined formation or trajectory, this paper suggests an online formation planning method for multirotor UAVs in case of unknown payload and cable length. The method is implemented through the following three parts: 1) By analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. 2) By using the admittance model, we generate desired trajectories/formations that can minimize the proposed cost function. 3) By estimating cable tension, we design formation tracking control laws for multirotor UAVs to follow the desired trajectories/formations. The proposed formation planning method does not rely on the knowledge of the payload and length of cables, which makes it can be easily applied to extensive industry, production, and military practice. Finally, numerical simulations and experiments are conducted to demonstrate the feasibility of the proposed method.

MVF: A Novel Technique to Reduce the Voip Packet Payload Length

Abstract The adoption of the Voice over Internet Protocol (VoIP) system is growing due to several factors, including its meagre rate and the numerous contours that can be joined with VoIP systems. However, the wasteful utilisation of the computer network is an inevitable problem that limits the rapid growth of VoIP systems. The essential explanation behind this wasteful utilisation of the computer network bandwidth (BW) is the considerable preamble length of the VoIP packet. In this study, we invent a technique that addresses the considerable preamble length of the VoIP packet. The designed technique is known as the manikin voice frame (MVF). The primary idea of the MVF technique is to utilise the VoIP packet preamble tuples that are not essential to the voice calls, particularly client-to-client calls (voice calls between only two users). Specifically, these tuples will be utilised for reserving the data of the VoIP packet. In certain instances, this will make the VoIP packet data manikin or even make it empty. The performance assessment of the introduced MVF technique demonstrated that the utilisation of the computer network BW has enhanced by 33%. Along these lines, the MVF technique indicates potential progress in resolving the inefficient usage of the computer network BW.

The IAI Robot Control System in Researching Phone Speaker Production Lines

This article presents research on the IAI Robot control system in a phone speaker production line being operated in Vietnam. In assembly and handling technology, electric actuators are used for linear, rotary, or clamping movements. Single-axis IAI robots become flexible when paired together. IAI robots (Intelligent Actuators) meet significantly high requirements in terms of payload, speed, and travel length. The IAI Robot's control program is programmed on Keyence PLC and uses expansion modules to ensure the IAI Robot's stable operation, high accuracy, and fast processing speed. The results of research, construction, and direct experiments at the factory show that the system operates stably with high accuracy.

Dynamic transmission policy for enhancing LoRa network performance: A deep reinforcement learning approach

Long Range (LoRa) communications, operating through the LoRaWAN protocol, have received increasing attention from the low-power and wide-area network communities. Efficient energy consumption and reliable communication performance are critical aspects of LoRa-based applications. However, current scientific literature tends to focus on minimizing energy consumption while disregarding channel changes affecting communication performance. Other works attain appropriate communication performance without adequately considering energy expenditure. To fill this gap, we propose a novel solution to maximize the energy efficiency of devices while considering the desired network performance. This is done using a maximum allowed Bit Error Rate (BER) that can be specified by users and applications. We characterize this problem as a Markov Decision Process and solve it using Deep Reinforcement Learning to dynamically and quickly select the transmission parameters that jointly satisfy energy and performance requirements over time. Moreover, we support different payload sizes, ensuring suitability for applications with varying packet lengths. The proposed selection of parameters is evaluated in three different scenarios by comparing it with the traditional Adaptive Data Rate (ADR) mechanism of LoRaWAN. The first scenario involves static nodes with varying BER requirements. The second one realistically simulates urban environments with mobile nodes and fluctuating channel conditions. Finally, the third scenario studies the proposed solution under dynamic frame payload length variations. These scenarios cover a wide range of operational conditions to ensure a comprehensive evaluation. The results of our experiments demonstrate that our proposal achieves a 60% improvement in performance metrics over the default ADR mechanism.

A shortening pendulum: Hoisting control for medical evacuation rescues

Strategies to vary the length of a pendular payload maneuvered by a helicopter in an aerial medical evacuation scenarios and similar situations are discussed. The aim is to rapidly stabilize the swinging payload to decrease effects of cable swing and decrease rescue time. A variable length procedure is proposed for small changes to the line length as the pendulum swings. The 2D nonlinear equation of motion of the pendulum is implemented in MATLAB, and for a proof of concept, manual intervention is used to identify values of the time interval and line parameters to achieve payload stabilization. Three time-varying line procedures are proposed. Conservation of energy is used to determine expressions for the initial angle and angular velocity that decrease the energy of the pendular payload when it swings through its highest and lowest points. Results show that the proposed stabilization strategies are able to control the swing angle due to variable length reel-in and reel-out at precise timing intervals. Further, validation of the variable length idea is demonstrated with energy calculations determined from Mathematica simulations in our prior work.

Low Latency TOE with Double-Queue Structure for 10Gbps Ethernet on FPGA.

The TCP protocol is a connection-oriented and reliable transport layer communication protocol which is widely used in network communication. With the rapid development and popular application of data center networks, high-throughput, low-latency, and multi-session network data processing has become an immediate need for network devices. If only a traditional software protocol stack is used for processing, it will occupy a large amount of CPU resources and affect network performance. To address the above issues, this paper proposes a double-queue storage structure for a 10G TCP/IP hardware offload engine based on FPGA. Furthermore, a TOE reception transmission delay theoretical analysis model for interaction with the application layer is proposed, so that the TOE can dynamically select the transmission channel based on the interaction results. After board-level verification, the TOE supports 1024 TCP sessions with a reception rate of 9.5 Gbps and a minimum transmission latency of 600 ns. When the TCP packet payload length is 1024 bytes, the latency performance of TOE's double-queue storage structure improves by at least 55.3% compared to other hardware implementation approaches. When compared with software implementation approaches, the latency performance of TOE is only 3.2% of the software approaches.

F-ACCUMUL: A Protocol Fingerprint and Accumulative Payload Length Sample-Based Tor-Snowflake Traffic-Identifying Framework

Tor is widely used to protect users’ privacy, which is the most popular anonymous tool. Tor introduces multiple pluggable transports (PT) to help users avoid censorship. A number of traffic analysis methods have been devoted to de-anonymize these PT. Snowflake is the latest PT based on the WebRTC protocol and DTLS encryption protocol for peer-to-peer communication, differing from other PT, which defeat these traffic analysis methods. In this paper, we propose a Snowflake traffic identification framework, which can identify whether the user is accessing Tor and which hidden service he is visiting. Rule matching and DTLS handshake fingerprint features are utilized to classify Snowflake traffic. The linear interpolation of the accumulative payload length of the first n messages in the DTLS data transmission phase as additional features are extracted to identify the hidden service. The experimental results show that our identification framework F-ACCUMUL can effectively identify Tor-Snowflake traffic and Tor-Snowflake hidden service traffic.

A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment

The advantages of LoRaWAN over conventional networks (GSM, 4G, 5G) in terms of investment and operating costs have been proven for network coverage in urban and rural areas. However, the theoretical coverage compared to the reality on the ground and the quality of service (QoS) provided remain very relative and depend on several technical factors, subject to increased research. Several recent approaches and hardware specifications recommended adding gateways as a solution to improve the LoRaWAN QoS indicators, mainly for high-traffic situations. However, such a solution will not work in all real-life scenarios since many factors must be considered. This article presents a study of the factors impacting the LoRaWAN QoS in the case of the usage of multiple gateways by exploring different scenarios to show how the payload length impacts the whole network’s packet delivery ratio (PDR) and how it interacts when enhancing the GW number with and without confirmed traffic. Based on the simulation results, increasing the number of gateways can negatively impact the network’s ability to support higher payload packets, especially in a high-traffic scenario. More precisely, we can say that for a low number of GWs, it is more appropriate to use a high payload length since we can achieve a high PDR. Nevertheless, with a high number of GWs, it would be more appropriate to use a low payload length to achieve a good PDR. Similarly, our analyses show that increasing the number of gateways ensures a better PDR but with a significant packet loss at the gateways, which is synonymous with higher energy consumption.

Adding Redundancy to LoRaWAN for Emergency Communications at the Factory Floor

Wireless communications may be the key solution to satisfy mobility, flexibility, and scalability requirements of the Internet of Things. Particularly, the low power wide area networks, operating in unlicensed bands, are promising because they permit both private and public backends. Unfortunately, their target applications are situations where sporadic transmissions are tolerated. They could not fit scenarios in which high reliability and short round trip time are required. In this article, the LoRaWAN solution is considered for managing emergency and alarm communications. Reliability and timeliness limits have been overcome by jointly using a message replication scheme with variable payload length together with low-cost repeaters. Experiments have been carried out in both fixed and low-speed mobility scenarios (with an interfering traffic of 95 message/h, generated by regular LoRaWAN colocated network). Results, in the former case, show that the success probability of confirmed messages is 100%, the 99.6% of the available data are delivered to the backend, and the average delay decreases by 150 ms. When mobility is considered (e.g., for an automatic guided vehicle use case) results confirm significant improvements for both the transaction success ratio and the data extraction rate that significantly increase up to 95% and 87%, respectively. On the contrary, the overall average delay is not always reduced.

GRAIN: Granular multi-label encrypted traffic classification using classifier chain

Granular traffic classification categorizes traffic into detailed classes like application names and services. Application names represent parent applications, such as Facebook, while application services are the individual actions within the parent application, such as Facebook-comment. These granular classes are still insufficient to keep pace with modern applications that offer various services. Accordingly, this paper further divides the application service class into inter-application and intra-application services to provide more insights. Inter-application service refers to a similar service between different parent applications, such as Facebook-comment and Youtube-comment, whereas intra-application service differentiates services within the same parent application, such as Facebook-comment and Facebook-post. Most studies focus on classification at the application name and inter-application service levels. In contrast, classification at the intra-application service level receives far less attention due to its complexity despite providing the highest flexibility. Therefore, this paper presents GRAIN, a granular multi-label approach to classify encrypted traffic at all three levels of granular classification: application name, inter-application and intra-application service levels using a classifier chain. GRAIN chains two random forest classifiers to produce a multi-label classification using seven novel statistical features based on packet payload length. The utilized features are independent of the packet payload content, thus unaffected by packet encryption and preserving user privacy. Our performance evaluation showed that GRAIN achieved an average F-measure of 99% at the application name level, 93% at the inter-application service level and 88% at the intra-application service level. To test for robustness, we compared GRAIN against four baseline classifiers and the ISCX VPN-nonVPN public dataset in which GRAIN maintained its comparable performance across all tests.

Underwater to above water LoRaWAN networking: Theoretical analysis and field tests

The aim of this paper is to demonstrate the exploitability of Long Range Wide Area Network (LoRaWAN) networking to set up a wireless transmission channel allowing the real time data collection from underwater depths up to more than 1 m. To this aim, a theoretical analysis is carried out to determine the actual feasibility of this link taking into account the technical features of the LoRa channel. Following this analysis, field tests in a swimming pool have been performed analyzing the transmission performance in terms of Received Signal Strength Indicator (RSSI) and Signal to Noise Ratio (SNR) for different transmission parameters and at increasing depths. Moreover, two test campaigns varying the antenna’s sensor node and the packet payload length have been performed. Experimental results have seen a good corroboration of the theoretical analysis with efficient underwater LoRaWAN data transmission at depths up to 110 ±5 cm, considering 14 dBm transmitted power.

Estimating cover image for universal payload region detection in stego images

With the increase in digital communication, images have become indispensable media for carrying obscured communication these days. Unraveling the presence or details of obscured transmission also known as steganography is termed as steganalysis. Passive steganalysis focuses only on classification of object as clean or adulterated. Finding the length of payload and regions where the message is hidden, in a stego object is a challenging task and forms a part of Active or Forensic Steganalysis. Many researchers have worked towards finding the length of message, but not much work has been done on finding region where message is hidden. This motivated us to work towards finding the region of payload in stego images. Hence this paper proposes a novel technique for detection of payload regions in a stego image combining Denoising Autoencoder with Local Binary Pattern (LBP) Operator. Denoising Autoencoder is used to learn inter relationships between pixels of an image for estimating cover image from the given stego image. LBP operator capable of capturing local inter-relationships within neighborhood coordinates, have been used to compare corresponding blocks in input stego image and estimated cover image for unraveling payload locations. The major advantage of the proposed technique is that it is universal and it does not require cover image or knowledge about steganographic algorithm used. Accuracy of 96.5% and 97.7% respectively was achieved in the experimental study conducted with spatial and JPEG domain images from BOSSBase dataset.

Safety Architecture Proposal for Low-Latency Sensor/Actuator Networks using IO-Link Wireless

In the field of production automation, IO-Link Wireless (IOLW) offers energy-efficient and cost-effective solutions for networking wireless sensors and actuators close to the machines on the industrial shop-floor. In this paper, a concept is presented to enhance IOLW with security-for-safety and safety features in order to make safety critical systems in industrial environments with performance characteristics dedicated to demanding applications feasible. As data security is of paramount importance, security mechanisms already implemented in other wireless protocols are investigated and security-for-safety mechanisms for IOLW are introduced. Potential cryptographic algorithms are evaluated for IOLW with respect to energy consumption and timing. Taking performance parameters into account, which are crucial for industrial manufacturing processes, a safety protocol data unit (SPDU) is described and evaluated for different payload length and cycle times. Finally, an outlook towards the implementation of a demonstrator setup completes this work.

Apply machine learning techniques to detect malicious network traffic in cloud computing

Computer networks target several kinds of attacks every hour and day; they evolved to make significant risks. They pass new attacks and trends; these attacks target every open port available on the network. Several tools are designed for this purpose, such as mapping networks and vulnerabilities scanning. Recently, machine learning (ML) is a widespread technique offered to feed the Intrusion Detection System (IDS) to detect malicious network traffic. The core of ML models’ detection efficiency relies on the dataset’s quality to train the model. This research proposes a detection framework with an ML model for feeding IDS to detect network traffic anomalies. This detection model uses a dataset constructed from malicious and normal traffic. This research’s significant challenges are the extracted features used to train the ML model about various attacks to distinguish whether it is an anomaly or regular traffic. The dataset ISOT-CID network traffic part uses for the training ML model. We added some significant column features, and we approved that feature supports the ML model in the training phase. The ISOT-CID dataset traffic part contains two types of features, the first extracted from network traffic flow, and the others computed in specific interval time. We also presented a novel column feature added to the dataset and approved that it increases the detection quality. This feature is depending on the rambling packet payload length in the traffic flow. Our presented results and experiment produced by this research are significant and encourage other researchers and us to expand the work as future work.

NB-IoT and Wi-Fi Technologies: An Integrated Approach to Enhance Portability of Smart Sensors

The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, particularly if connected to a cloud service for data sharing. There are several ways to connect sensors to the cloud: wearable or portable devices often lean on a smartphone that acts as a gateway, while other sensors, such as smart sensors for continuous monitoring (e.g. fall detectors) are connected through wireless networks covering a limited area (e.g. ZigBee or Wi-Fi). Their functionality can be improved using them in both outdoor and indoor environments without other devices. NB-IoT is a recently introduced wide-range protocol with a good compromise between low power, low deployment costs, payload length, and data rate. Traditionally, sensor nodes rely on only one type of radio: an innovative solution could be a sensor node exploiting a combination of different transmission technologies with the aim of achieving higher portability. In this paper, a hybrid solution based on NB-IoT/Wi-Fi is presented. The Wi-Fi connection is primarily selected due to its lower power consumption (compared to NB-IoT), while NB-IoT is activated only when a Wi-Fi network is not available. This study aims to evaluate the power consumption of the proposed solution with respect to single radio NB-IoT technology. Test boards have been implemented, and several data transmission tests have been carried out with both NB-IoT and Wi-Fi radios. Different received powers and payload lengths have been considered to analyze the impact on the energy profile of smart sensors. It has been demonstrated that using NB-IoT for both indoor and outdoor leads to an acceptable battery discharge time, with a strong dependence on the payload length. Under certain conditions, the proposed hybrid solution results in a battery duration up to two times higher than single-radio NB-IoT.

Power Adjustment Algorithm for Higher Throughput in Mobile Ad-Hoc Networks

In Mobile Ad Hoc Networks (MANETs), power control is necessary in order to reduce power consumption rates, avoid collisions within packets, increase spatial throughput of the system and to reduce contention among flows. The MANET nodes which are outside the transmission range are energy constraint and they consume more power for packet transmission. In this paper, we propose to develop a power adjustment algorithm to provide higher throughput and consume less power in the Mobile Ad hoc Networks. An optimal transmission power is calculated at the receiver based upon the data payload length and the interference amount. This power is given to the transmitter which increments or decrements the power with respect to the number of neighboring nodes. The adjusted power is retransmitted to the receiver so that the power level can be adjusted between the transmitter and receiver. Since the optimal transmission power is determined based upon the interference amount the possibility of collision among the nodes is reduced effectively. From our simulation results, we show that this algorithm provides higher throughput and lower energy consumption in ad hoc networks.

Fast Packet Inspection for End-To-End Encryption

With the recent development and popularization of various network technologies, communicating with people at any time, and from any location, using high-speed internet, has become easily accessible. At the same time, eavesdropping, data interception, personal data leakage, and distribution of malware during the information transfer process have become easier than ever. Recently, to respond to such threats, end-to-end encryption (E2EE) technology has been widely implemented in commercial network services as a popular information security system. However, with the use of E2EE technology, it is difficult to check whether an encrypted packet is malicious in an information security system. A number of studies have been previously conducted on deep packet inspection (DPI) through trustable information security systems. However, the E2EE is not maintained when conducting a DPI, which requires a long inspection time. Thus, in this study, a fast packet inspection (FPI) and its frame structure for quickly detecting known malware patterns while maintaining E2EE are proposed. Based on the simulation results, the proposed FPI allows for inspecting packets approximately 14.4 and 5.3 times faster, respectively, when the inspection coverage is 20% and 100%, as compared with a DPI method under a simulation environment in which the payload length is set to 640 bytes.

Robust localized cyber-attack detection for key equipment in nuclear power plants

Most nuclear power plants (NPPs) are looking deploying digital instrumentation and control (I&C) systems, which allow for more precise control and more economical operation. However, both the quantity and capability of industrial control system (ICS)-targeted cyber-attacks have grown dramatically over recent years. Therefore, one of the most significant challenges that digital I&C systems bring is the issue of cybersecurity, which should be enhanced before their deployment.Several different types of cyber-attacks can be introduced to NPPs; a false data injection attack on key equipment is the focus of this research due to the potential severe consequences associated with such an attack. In false data injection, the attackers may alter the reading of control sensors or commands to change the operation of an NPP. Current cybersecurity efforts focus on intrusion prevention by firewalls or data-flow direction control and use commercial intrusion detection systems, which usually focus on monitoring Internet Protocol (IP) addresses, ports, and payload length. However, attention should be given to conditions where these approaches can fail, such as an insider attack. Previous research based on process data shows the potential of a last defenseâ, line using online monitoring of the process data in concern with cyber data analysis. However, existing models involve different subsystems across the whole NPP, which has a wide attack surface and may require high computing cost. This holistic approach may not meet the time-sensitive requirements imposed upon I&C systems. This paper proposes a localized kit for key equipment in a process as a complementary detection method to improve the robustness of key equipment under cyber-attacks. Compared to existing models, this reduces the number of variables used in the model and significantly improves the computational speed. It also reduces the attack surface by limiting the data acquisition locally. This localized kit includes a cyber-attack detection model to detect anomalies within key components, such as the control system actuator, and an inference model to potentially reconstruct a compromised signal to allow the safe shut down.To develop and demonstrate the localized cybersecurity kit, a hardware-in-the-loop (HIL) testbed was built with a pressurized water reactor (PWR) simulator and a programmable logical controller (PLC). The PLC was programmed to control the steam generator (SG) water level at a specified set point, and the PWR simulator was utilized to simulate the nuclear system and response for parameters outside of the SG. Three false data injection attacks were conducted towards the testbed to generate the data needed for the localized kit development and evaluation. The results show the cyber-attack detection model is effective under false data injection scenarios and the inference model is promising as a signal reconstruction method.