Packet Size Research Articles

Energy Efficient Protocol for Lifetime Prediction of Wireless Sensor Network using Multivariate Polynomial Regression Model

The sensor network performs gathering, monitoring, and tracking of objects in the given area. The sensor nodes are normally distributed randomly in the network area for collecting the information. The major issues in Wireless Sensor Networks (WSN) are coverage, energy, and limited resources. Sensor Nodes’ (SN) performance depends on so many parameters but normally depends on Residual Energy (RE) and Distance from the base station. The Cluster Head (CH) cooperatively communicates with Base Station (BS) via routing protocols. The proposed Energy Efficient Multilevel Region Based (EEMRB) protocol performs the task by partitioning the entire network area into multiple levels and sub-levels. The sub-levels are partitioned to perform clusters to communicate the sensor via CH (s) using single/multi-hop communication to BS. The proposed protocol is compared with the Stable Election Protocol and shows improvement in network lifetime. Based on the proposed protocol data set, a Multivariate Polynomial Regression (MPR) Model is proposed to predict network lifetime. The model uses packet size and node density as network design parameters. The simulation results show that the size of the packet and network area play a major role in network lifetime. Therefore, the lifetime of the predicted model and EEMRB protocol are close to each other. This prediction model is suitable for the prediction of any network area's lifetime.

Encrypted Traffic Classification Based on a Convolutional Neural Network

To resolve the issues of low accuracy, weak universality, and easy invasion of privacy in traditional encryption traffic classification methods, an encryption traffic classification method based on a convolutional neural network is offered. Firstly, according to the packet size and time message of the net traffic, the original traffic is transformed into a two-dimensional picture to avoid relying on the packet payload to violate privacy, and then the model is embedded. The Inception module performs feature fusion to improve the classification accuracy. Finally, the average pooling layer and the convolution layer are used to replace the fully connected layer, increasing the calculation speed and avoiding overfitting. Experimental results show that the algorithm achieves an accuracy of more than 95% for application traffic classification tasks.

Message queue telemetry transport and lightweight machine-to-machine comparison based on performance efficiency under various scenarios

<span>Internet of things (IoT) is been advancing over a long period of time in many aspects. For data transfer between IoT devices in a wireless sensor network, various IoT protocols are proposed. Among them, the most widely used are constrained application protocol (CoAP) and message queue telemetry transport (MQTT). Overcoming the limitations of CoAP, lightweight machine-to-machine (LwM2M) framework was designed above CoAP. Recent statistics show that LwM2M and MQTT are the widely used, but LwM2M is still less used than MQTT. Our paper is aimed at comparing both MQTT and LwM2M on the basis of performance efficiency, which will be achieved by sending same file through both protocols to the server. Performance efficiency will be calculated in two scenarios, i) when the client makes a connection with the server i.e., while initial connection and ii) while sending data file to server i.e., while data transfer. Both the protocols will be tested on the number of packets sent and the variability of packet size throughout the session. Experimental results indicated that LwM2M outperformed MQTT in both above scenarios by almost 69%. Therefore, we concluded by stating that LwM2M is best choice over MQTT, but MQTT can still be used in some situations if necessary.</span>

Top 12 layer-wise security challenges and a secure architectural solution for Internet of Things

Digitalization, intelligent virtual assistants, and smart consumer products prepare us for life at every stage. Smart IoT, Sensor nodes, software communicate with one another via voice and emotions, simplifying our lives. Smart IoT is becoming more prevalent in our daily lives. Even though the use of Smart IOTs has grown in Geometric progression, the security of these devices remains traditional. Technically, most security discussions revolve around the implementation of better Network Intrusion Prevention/Detection Systems (NIPS/NIDS), the installation of a traditional firewall, or the use of proxy scanners, which is the standard recommendation of security vendors. All architectures are rooted in the same zeal that drives cyber warfare. However, when considering the Narrowband Internet of Things, the packet size is too small, and the memory and processors are so small that they cannot hold the packets for inspections, and the lucrative protocols will not work. Malicious hackers, BOTs, Trozens, and Honeypots may tamper with and virtually control these smart Internet of Things (IoT) to steal/tamper sensitive information for filthy lucre, financial reward, or, to put it simply, money in this cyber warfare. All of these issues stem from a lack of proper security design controls, architectural security flaws, and a failure to apply Secure by Design principles. Fixing these security controls earlier in the process will save us a significant amount of time, energy, and money spent on repairing broken IoT security flaws and performance aesthetics. If we consider the Shift Left Secure by Design/Architecture Journey in the Internet of Things Security Architecture, these Architectural/Design flaws can be identified in earlier stages and will help the delivery teams to plan necessary security controls in the IoT use cases.In this paper, the author discusses Layer-wise Security Challenges, Attack Vectors, and Architectural Flaws, and proposes a secure architectural solution for the Internet of Things (IoT) that assists delivery teams in securely designing/architecting resource-intensive smart Internet of Things (IoT)/Narrowband (NIoT) use cases earlier in the Life cycle by employing the Secure Design Shift Left approach. In this smart digital IoT era, the author also suggests future research directions for future generations to increase security.

Traffic-Aware Mean-Field Power Allocation for Ultradense NB-IoT Networks

The narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the third-generation partnership project (3GPP) to provide connectivity to a large number of low-cost Internet of Things (IoT) devices with strict energy consumption limitations. However, in an ultradense small cell network employing NB-IoT technology, intercell interference can be a problem, raising serious concerns regarding the performance of NB-IoT, particularly in uplink transmission. Thus, a power allocation method must be established to analyze uplink performance, control and predict intercell interference, and avoid excessive energy waste during transmission. Unfortunately, standard power allocation techniques become inappropriate as their computational complexity grows in an ultradense environment. Furthermore, the performance of NB-IoT is strongly dependent on the traffic generated by IoT devices. In order to tackle these challenges, we provide a consistent and distributed uplink power allocation solution under spatiotemporal fluctuation incorporating NB-IoT features, such as the number of repetitions and the data rate, as well as the IoT device’s energy budget, packet size, and traffic intensity, by leveraging stochastic geometry analysis and mean-field game (MFG) theory. The effectiveness of our approach is illustrated via extensive numerical analysis, and many insightful discussions are presented.

Toward a Data-Driven Cognitive Framework for Adaptive QoS in IoT

Quality of service (QoS) is an increasingly important, stringent, and challenging requirement in the Internet of Things (IoT). Domain and context-dependent circumstances bring forth the need for a cognitive design to facilitate QoS that can learn and adapt as the requirements change and deployments evolve. The performance data that measure QoS metrics based on variations in different communication stack parameters/protocols is becoming available. The research literature highlights the impact of various configurable parameters, such as transmission power (TP) and packet size (PS) on metrics, such as reliability and delay. In this article, we propose the design of a cognitive framework for adaptive QoS in the IoT. The proposed approach is based on performance data from deployments of different types of communication networks forming IoT. The aim is to be able to adaptively configure communication parameters/protocols ensuring that the QoS constraints in evolving network scenarios are met.

Convergence of a Packet Routing Model to Flows over Time

Mathematical approaches for modeling dynamic traffic can be roughly divided into two categories: discrete packet routing models and continuous flow over time models. Despite very vital research activities on models in both categories, their connection was poorly understood so far. We build this connection by specifying a (competitive) packet routing model, which is discrete in terms of flow and time, and proving its convergence to the intensively studied model of flows over time with deterministic queuing. More precisely, we prove that the limit of the convergence process when decreasing the packet size and time step length in the packet routing model constitutes a flow over time with multiple commodities. In addition, we show that the convergence result implies the existence of approximate equilibria in the competitive version of the packet routing model. This is of significant interest as exact pure Nash equilibria cannot be guaranteed in the multicommodity setting. Funding: This work was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy—The Berlin Mathematics Research Center MATH+ [Grant EXC-2046/1, project ID: 390685689] and the Research Training Group [Grant 2236 UnRAVeL].

Подход к проектированию нейронной сети для формирования индивидуальной траектории тестирования знаний

The paper discusses the issues of implementing an adaptive testing system based on the use of artificial neural network (INS) modules, which should solve the problem of intelligent choice of the next question, forming an individual testing trajectory. The aim of the work is to increase the accuracy of the INS to form the level of complexity of the next test question for two types of architectures – direct propagation (FNN – Feedforward Neural Network) and recurrent with long-term short-term memory (LSTM – Long-Short Term Memory). The data affecting the quality of training are analyzed, the architectures of the input layer of the direct propagation INS are considered, which have significantly improved the quality of neural networks. To solve the problem of choosing the thematic block of the question, a hybrid module structure is proposed, including the INS itself and a software module for algorithmic processing of the results obtained from the INS. A study of the feasibility of using direct propagation ANNs in comparison with the LSTM architecture was carried out, the input parameters of the network were identified, various architectures and parameters of the ANN training were compared (algorithms for updating weights, loss functions, the number of training epochs, packet sizes). The substantiation of the choice of a direct distribution network in the structure of the hybrid module for selecting a thematic block is given. The above results were obtained using the Keras high-level library, which allows you to quickly start at the initial stages of research and get the first results. Traditionally, learning has taken place over a large number of eras.

HeteroTiC: A robust network flow watermarking based on heterogeneous time channels

Tracking back stealthy attackers is still a great challenge in face of complicated Internet environments and increased attacker capabilities. As a possible solution, network flow watermarking has been developed to embed attack labels actively in data flows, trying to identify the attack source accurately in time. However, due to the influences of uncertain network noises, the robustness of watermarking cannot be ensured efficiently in case of non-cooperative network scenarios currently. Meanwhile, it is difficult to enhance robustness and covertness simultaneously when suffering from severe network noises. Thus, a robust network flow watermarking based on Heterogeneous Time Channels (HeteroTiC) is proposed, for which packet order, packet timing and packet size constitute heterogeneous time channels to carry watermarks. The beginning positions are located by packet order to support accurate synchronization, the long watermark sequences are carried by inter-packet delays to improve capabilities against packet dropping and time jitter, the mapping relationships for the watermark sequences on heterogeneous time channels are mixed by packet size of the flow itself to enhance covertness. With multiple layers design of watermarking, HeteroTiC is designed to accomplish fast detection and accurate extraction on watermarks. By experiments, the average detection accuracy reaches 100% and the average extraction accuracy is validated with 94.2%. Furthermore, the same mapping pattern on multiple flows only accounts for 8.11%, which validates the covertness against multiple flow similarities. The extraction accuracy exceeds 9.76%∼18.1% in comparison with classical watermarking methods, which validates the robustness under network noises in Internet.

Effect of Traditional and Software-Defined Networking on Performance of Computer Network

Purpose: Computer networks and the Internet are changing the way we communicate, learn, work, and even play. Conventional computer networks are not smart enough towards processes that contribute to improving online control transaction of services and demand for unlimited communication services. Hence, computer networking has to go smart.Methods: This paper explores the effect of different computer networking types - traditional computer networking (D0) and Software-Defined Networking (D1). The paper combined traditional computer networking (D0) with Software-Defined Network (D2) running applications (A1, A2, A3, A4 and A5) with the host sending 5 packets (P1, P2, P3, P4 and P5) across the networks emulated using Mininet network emulation to observe various performance parameters on the network.Result: It was observed that Application A1 recorded the highest bandwidth, throughput and latency. The least bandwidth, throughput and latency were observed in A4. The result showed that below 80% of the IPv4 packet size (65,507 bytes) of running application, the higher the bandwidth the higher the throughput. Also, the lower the latency the more statistically similar the jitter experienced. Packet P1 has the highest bandwidth and throughput usage with high latency. The results indicate that the higher the bandwidth and throughput, the higher the latency observed in the packet sent across the network. Traditional computer networking (D1) recorded the highest bandwidth and throughput with the highest jitter. The correlation result showed that the jitter decreases with increasing bandwidth and throughput.Novelty: This study provides information on traditional computer networking and Software-Defined Networking. The result validates studies that observed significant F-value and stability in the SDN application-awareness experiment.

Side-Channel Security Analysis of Connected Vehicle Communications Using Hidden Markov Models

This paper investigates side-channel vulnerabilities of a wireless communication application in vehicular environments (DSRC/WAVE) protocol implementation of a traffic intersection application. A prototype roadside unit (RSU) was implemented using real DSRC devices. The functionality of the WAVE short message (Wsm)-channel is extended to include an implementation of WAVE short message protocol (WSMP) for broadcasting GPS data and RSU instructions in vehicular communications. In the example used, DSRC is used to replace an intersection stoplight. Denial of service attacks are executed that leverage DSRC RSU timing and packet size side-channels to selectively disable the stoplight. Simulations are implemented to determine our ability to stealthily drop packets so as to force two vehicles to collide. Hidden Markov models (HMM) and Support Vector Machines (SVM) are constructed from sniffed side-channel information. We use inter-packet delay time and packet size side-channel information to design our attackes. In operational networks, packets should be encrypted in order to hide the contents of the packet payloads, but packet sizes and timing are not affected by encryption. HMMs were inferred using only side-channel information. The inferred HMMs track the protocol status over time. The SVM classifier was inferred using both side-channel data and packet payloads. At run-time, though, the SVM only had access to side-channel information. Simulation experiments were implemented to test HMM and SVM ability to identify packets used to signal vehicles to stop and yield right-of-way. Timing HMM side-channel attack caused collision with 2.5% false positive rate (FPR), while the packet size one resulted 9.5% FPR.

Neutrino decoherence and the mass hierarchy in the JUNO experiment

The finite size of a neutrino wavepacket at creation can affect its oscillation probability. Here, we consider the electron antineutrino wavepacket and decoherence in the context of the nuclear reactor based experiment JUNO. Given JUNO's high expected statistics [$\sim$100k IBD events ($\bar{\nu}_e p \rightarrow e^+ n$)], long baseline ($\sim$53\,km), and excellent energy resolution [$\sim$$0.03/\sqrt{E_{\mathrm{vis}}~\mathrm{(MeV)}}$], its sensitivity to the size of the wavepacket is expected to be quite strong. Unfortunately, this sensitivity may weaken the experiment's ability to measure the orientation of the neutrino mass hierarchy for currently allowed values of the wavepacket size. Here, we report both the JUNO experiment's ability to determine the hierarchy orientation in the presence of a finite wavepacket and its simultaneous sensitivity to size of the wavepacket and the hierarchy. We find that wavepacket effects are relevant for the hierarchy determination up to nearly two orders of magnitude above the current experimental lower limit on the size, noting that there is no theoretical consensus on the expectation of this value. We also consider the effect in the context of other aspects of JUNO's nominal three-neutrino oscillation measurement physics program and the prospect of future enhancements to sensitivity, including from precise measurements of $\Delta m^2_{3l}$ and a near detector.

Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator

With the expansion of network scales, the B/S architecture of monolithic applications is gradually being replaced by microservices. The unbundling of services has led to exponential growth in the size of APIs. When handling massive microservice requests, the commercial NIC shows limitations in three aspects: deterministic, programmability, and data copy. To ensure that each microservice node handles requests efficiently, flexibly, and precisely, this paper proposes a programmable deterministic multi-queue FPGA Accelerator. The Accelerator relies on the instantiated 1000 queues and the queue management unit to extend the rule-based RSS algorithm for the serverless-friendly programmability of packet distribution. A PTP hardware clock is added to collaborate with the queue management unit to control the deterministic delivery. To improve the sending and receiving efficiency of network node data, a driver adapted to the FPGA accelerator is designed to realize zero-copy. Experiments conducted on a 100 Gbps FPGA show that the Accelerator can support the multi-queue transmission with various packet sizes, define the forwarding behavior, and almost approach the line rate on an 8-core FPGA device. In addition, it can forward packets with low latency close to that of the current state-of-the-art ovs-DPDK. This Accelerator overcomes, to some extent, the limitations of commercial NICs when oriented to microservice architectures.

Teletraffic analysis of energy-efficient intruder detection using hash function techniques in images for remote monitoring in Wireless Sensor Networks

Many surveilling applications that use Wireless Sensor Networks (WSNs) are based on image transmissions to monitor relevant events like intruder detection. However, the continuous transmission of such images promptly drains the energy supply of nodes due to the large size of packets compared to other WSN-based applications such as temperature, gas leakages, and humidity, where packets are relatively small.In this work, we propose two techniques to reduce the packet size in image transmission data for outdoor surveillance. These techniques are based on obtaining a hash function of the histograms of images in different formats to transmit small-sized packets containing relevant information about the surveilled environment. If an event is detected, the complete images are transmitted to perform an in-depth inspection of the region of interest. Additionally, we propose an ON/OFF scheme to reduce energy consumption further. A teletraffic analysis is provided to evaluate the efficiency of such schemes for different environments. The derived mathematical model allows finely tuning the system variables to achieve both performance goals, which are usually in contraposition to each other, namely, increasing system lifetime and high detection probability.

Data Aggregation in Regular Large-Scale IoT Networks: Granularity, Reliability, and Delay Tradeoffs

This article studies data aggregation in large-scale regularly deployed Internet of Things (IoT) networks. The data granularity, in terms of information content and temporal resolution, is parameterized by the sizes of the generated packets and the average interpacket generation time. The generated data packets at the devices are aggregated through static terrestrial gateways. Universal frequency reuse is adopted across all gateways and randomized scheduling is utilized for the IoT devices associated with each gateway. Such network model finds applications in environmental sensing, precision agriculture, and geological seismic sensing to name a few. To this end, we develop a novel spatiotemporal mathematical model to characterize the interplay between data granularity, transmission reliability, and delay. The developed model accounts for several IoT design parameters, which include packet sizes, average generation duty cycle, devices and gateways spatial densities, transmission rate adaptation, power control, and antenna directivity. For tractable analysis, we propose two accurate approximations, based on the Poisson point process (PPP), to characterize the signal-to-interference-plus-noise-ratio (SINR)-based transmission reliability. For the delay analysis, we propose a phase-type arrival/departure (PH/PH/1) queueing model that accounts for packet generation, transmission scheduling, and rate-sensitive SINR-based packet departure. The developed model is utilized to obtain the optimal transmission rate for the IoT devices that minimizes delay. The numerical results delineate the joint feasibility range of packet sizes and interarrival times for data aggregation and reveal significant gains when deploying directional antennas.

Effects of different silicon content on irradiation defects and hardening in 9Cr ferritic/martensitic steel

In order to explore the effect of silicon (Si) content on the irradiation behavior of ferritic/martensitic (F/M) steel, 9Cr F/M steel with Si of 0, 0.4%, 0.7%, and 1.0% in weight percent were prepared. All specimens were irradiated with 2.4 MeV iron ions at 550 °C to the fluence of 6.41 × 1016 ions cm−2, corresponding to a peak damage dose of 70 dpa. Transmission electron microscopy (TEM) was used to characterize the microstructure of the alloys before and after irradiation, and nanoindentation tests were used to assess the irradiation hardening. The addition of Si inhibited the growth of both M23C6 and MX precipitates and promoted the nucleation of MX precipitates, and the nucleation of the M23C6 precipitates was inhibited when the added Si was less than 0.7 wt.%, indicating that the precipitates can be regulated by Si content. The martensite lath width and the packet size of 0.4 wt.% Si content alloys are the largest and then decrease with the increase of Si content. As the Si content increases, the average size of dislocation loops decreases while the number density increases. Alloys without Si have the smallest void number density, followed by alloys with 0.7 wt.%, 0.4 wt.%, and 1.0 wt.% Si content. The hardness of the unirradiated alloy increased with the increase of Si content. After irradiation, the most severe radiation hardening occurred in the 0.4 wt.% Si alloy and the lowest in the 0.7 wt.% Si alloy.

LoRa network reconfiguration with Markov Decision Process and Fuzzy C-Means clustering

Long Range (LoRa) is a proprietary modulation technique that uses Chirp Spread Spectrum (CSS) modulation for low power and wide area communications. Despite the advantages of LoRa technology, the reconfiguration of transmission parameters such as Spreading Factor (SF) and Transmission Power (Ptx), remains limited to maximize the uplink traffic. In this paper, we look upon additional parameters such as the Bandwidth (BW) and the Coding Rate (CR). We apply Fuzzy C-Means (FCM) algorithm to acquire knowledge about the quality of each transmission setting. Then, we use this knowledge in Q-learning and Markov Decision Process (MDP) algorithms as a state transition matrix to converge better and faster to the set of transmission settings that maximize the uplink data rate. As the solution should cope with different scenarios, we vary the number of End Devices (EDs), Base Stations (BSs), Packet Sizes (PSs) and Packet Rates (PRs). In addition, we compare our solution with many algorithms such as EXP3, ADR and EXPLoRaTS. Simulation results show that MDP with FCM clustering preprocessing improves better several Quality of Service (QoS) metrics including the Data Rate (DR), Packet Delivery Ratio (PDR), Time on Air (ToA) and Transmission Energy (Etx). Thus, the PDR and the DR were improved by 25%, the ToA was reduced by 40% and Etx was reduced by 20%.

Takeaways from an experimental evaluation of eXpress Data Path (XDP) and Data Plane Development Kit (DPDK) under a Cloud Computing environment

The popularization of the Linux kernel at data center servers became a significant factor to consider when developing or deploying network applications. New “packet processing frameworks” emerged to ensure fast packet processing inside these servers that rely on the Linux kernel, XDP and DPDK being the two main ones. Alongside this, Cloud Computing environments dominated the data center scenario, bringing virtualization to the center of these servers. This research compares DPDK and XDP packet processors when subject to a cloud computing environment with I/O, CPU, and network load. We first describe an architecture that implements frameworks and emulates a cloud environment. We measure throughput and packet loss while varying the number of competing VMs, CPU usage by the frameworks, and packet size and present our results in the form of takeaways. Furthermore, we conclude that the virtual environment can impact the performance of both frameworks depending on the received packet size and the type of workload the cloud environment performs. A significant case to highlight is when the cloud environment performs network load, and the servers receive small-sized packets, creating both throughput degradation and increasing packet loss. We conclude our experiments by conducting statistical non-parametric hypothesis tests to enhance our conclusions and have a closer look at the results.

Design and implementation of congestion aware router for network-on-chip

Network-on-Chip (NoC) is the state of the art on-chip interconnection network for packet based communication. NoCs can offer low packet latency, high bandwidth, high throughput with minimum area, better energy efficiency and fault tolerance. Routers are the basic building blocks of the NoCs. In this paper, we present the design of a Congestion Aware Router for NoC which is then implemented using Vivado HLS. The router is then used to develop a scalable NoC based on mesh topology. Using the NoC as a test bed we carry out simulations and estimate performance metrics like latency, waiting time and total packets handled for various configurations of NoC. Provisions to alter parameters like buffer depth, packet size, packet injection interval and traffic are also added. Further, we propose a simple mechanism for detecting congestion at the router. The congestion metric is then used to adapt the XY dimension order routing into a Congestion Aware minimal adaptive X/Y routing strategy with very low hardware overhead. The proposed routing method is compared against conventional XY DOR, GCA routing and RCS based routing algorithms for different parameter variations. The results show that the proposed routing method can reduce packet latency for different traffic patterns at medium packet injection rates.

A Quality of Service Aware Source Routing Based Protocol for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) handle many underwater applications such as environment monitoring, surveillance and navigation. These applications generate varied types of traffic such as continuous bit rate, sporadic and different packet sizes, leading to additional QoS requirements that are traffic and application dependent. This paper presents the development of a Quality of Service Aware Source Routing (QASR) protocol. QASR discovers multiple paths from the sources to the sinks and selects the most QoS compatible route among them. QASR is distinctive because it incorporates multiple QoS parameters such as Signal to Noise Ratio (SNR), latency and residual energy. Depending on which of these parameters are chosen, QASR has three variants, namely, QASR-Latency (QASR-L), QASR-Residual Energy (QASR-RE) and QASR-Signal to Noise Ratio (QASR-SNR). The performance of QASR protocol is compared against traditional source routing protocols, with simulations showing a reduction of about 10% to 20% in latency and about 5% to 10% lesser energy consumption than source routing. QASR protocol exhibits comparable performance to classic source routing protocols while simultaneously adhering to the QoS requirements of the application. It is also worth noting that the performance profile of all the three variants of QASR do not have sudden and drastic variations, with the performance profiles showing consistent trend-lines.