Time-critical Data Research Articles

A Security Aware Channel Assignment Protocol for Software Defined Networks

Software Defined Networks (SDNs) have a great potential in supporting time-critical data delivery among the Internet of Things (IoT) devices and for emerging applications such as smart cities and automation. A wireless channel (radio) about which we have information is called a software defined radio. However, Software defined radio Networks share common resources such as bandwidth or spectrum among several users or stations. Due to continued sharing of resources, cognitive networks often come under security attacks, most common of which are jamming attacks. In the case of jamming attacks, deliberately designed random jamming signals are added to the channel. These jamming signals along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. Moreover, this also reduces the effects of noise in the channel. It has been shown that the proposed system achieves higher throughput compared to previous techniques for low, moderate and high jamming activity. Keywords:- Software defined radio, Internet of Things (IoT), Jamming Activity, Energy Detection, Equalization, Throughput.

An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks

The dynamic characteristics of vehicular ad hoc networks (VANETs) demand reliable and secure communication over wireless media. However, there are significant contradictions in autonomous vehicular systems related to security and privacy. Furthermore, VANETs require seamless data transmission to exchange time-critical data about accidents and traffic. Therefore, VANETs require secure seamless data transmission mechanisms to provide secure reliable communication without communication interruption to exchange time-critical data between vehicles. This paper proposes an Enhanced Location-Aided Ant colony Routing (ELAACR) for VANETs to achieve seamless secure data transmission. The proposed ELAACR is a two-fold scheme in which Phase 1 implements Location-Aided Key Management (LAKM) and Phase 2 implements Ant Colony Routing (ACR). An ACR combined with an LAKM pattern for offering secure and reliable communication. The LAKM scheme works based on the group signature concept to construct a secure network, and an ACR is used to find an efficient secure shortest path for seamless data transmission. The proposed work is simulated in Network Simulator (NS 2.35). By using an efficient and secure shortest path with secure seamless data transmission, the proposed ELAACR achieves an average throughput (T) of 640 Kbps, packet loss rate (PLR) of 0.018%, packet delivery ratio (PDR) of 98.5%, overhead (OH) of 0.157%, and 0.16 s end-to-end delay (EED) compared to Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) and Fuzzy Based Ant Colony Optimization (F-ANT). The simulation results show that the proposed ELAACR registers better performance in all aspects compared to the existing EHACORP and F-ANT.

Time, causality, and realizability: Engineering interactive, distributed software systems

Today, software systems are distributed, concurrent, interactive, embedded, and often time-critical. They communicate and cooperate by data exchange. Introducing notions of time, causality, and realizability into interface-based data flow models results into a powerful model for engineering cyber-physical and, generally, distributed software systems. Architectures and their specification are designed by the concurrent composition of systems. This approach provides high expressive power supporting explicitly specification of interface behavior, encapsulation, abstraction, refinement, verification, concurrent composition, and modularity. The key idea is a formal, semantically coherent modelling, and specification framework that integrates the specification of time-critical and non-time-critical software embedded into cyber-physical systems. The approach supports the refinement of interface specifications of non-time-critical systems into interface specifications of time-critical systems and vice versa. This leads to an integrated specification and design approach for time-critical data flow architectures with soft, firm as well as hard time-critical requirements.

A camera-based vehicular visible light communication system using modulated taillights in public road scenarios

This paper proposes a camera-based vehicular visible light communication system using modulated LED taillights for vehicle-to-vehicle communication. The taillights are modulated using on-off keying with a carrier frequency above the critical flicker frequency threshold to appear steady to the human eye and only a camera with a short exposure time can perceive the signal. For receiving the signal, a common camera with a recording frame rate of 30 frames per second was used for the experiments. This limits the throughput of the communication system as only one bit per frame and per individually modulated light source can be transmitted, thus the system is not suitable for transmitting time-critical data. However, the main advantage is that the transmitted data as well as the transmitting vehicle are visible in the camera image, hence we can associate the message and its origin.For the communication between two cooperative autonomous vehicles, e.g., members of a platoon, a secure wireless channel with low latency is needed. To establish an encrypted connection in this main channel our system can be used as an out-of-band channel to verify the identity of the vehicle in front by transmitting a verification code.In this paper we evaluate how the communication quality in terms of bit error rate of the raw data transmission without any error correction methods is influenced by various environmental factors, e.g., communication distance, traveling speed, weather, brightness, etc. This is done by testing our system in controlled scenarios as well as on public roads. The results show that our system is capable of transmitting data with a raw bit error rate of 1.59% on average in platooning relevant situations on the highway in dry conditions. Rain affects the system due to raindrops on the windshield, spray thrown up by vehicles in front, and the windshield wipers occluding the modulated taillights of the transmitting car. In light to medium rain conditions the mean bit error rate is between 2.7% and 6.2%, depending on the intensity.

Enhancing Perceptual Experience of Video Quality in Drone Communications by Using VPN Bonding

Recently, drones are increasingly being used as innovative IoT sensors. The application contexts of UAV systems where there is often a need to transmit time-critical data between the drone and the Internet are increasingly numerous and varied. Keeping this in mind and assuming that most professional UAVs are equipped with multiinterface routers and use virtual private networks (VPNs) to ensure reliable, robust, and secure transmission to the operation room, this letter presents a technique to enhance real-time and time-critical communications such as real-time video streaming. An embedded system, Raspberry-based gateway prototype, was used to create a testbed using a professional drone that captured and transmitted real-time video during a 30-min flight over a rural area characterized by poor mobile radio coverage. A perceptual video quality assessment tool, video multimethod assessment fusion (VMAF), has been employed to evaluate the improvement in terms of video quality perceived by the operator.

A dual-mode MAC protocol with service differentiation for industrial IoT networks using wake-up radio

The Internet of Things (IoT) has successfully revolutionized our society and all aspects of our daily lives. Wake-up radio (WuR) technology on its part has contributed to the success of IoT by significantly reducing latency and power consumption. Moreover, emerging mission critical Industrial IoT (IIoT) applications are time sensitive and generate more stringent delay and reliability constraints. Therefore, WuR-based MAC protocols must be carefully designed to meet these requirements. In this paper, a Dual-Mode MAC protocol (D2MAC) is proposed to fulfill QoS requirements of both delay-tolerant and real-time IoT applications by using service differentiation in the context of WuR-based IoT networks. Our protocol adopts two operation modes: normal mode, used in the case of normal traffic conditions, and priority mode for time–critical data. A Markov chain model is used to evaluate the performance of D2MAC. A simulation model based on MiXiM/ OMNet++ framework is also proposed to validate this analytical model. We illustrate the benefits of using the priority mode (PCA-WuR) to fulfill the requirements of IIoT applications. We also demonstrate that the Performance of PCA-WuR strongly depends on the acceptable delay limit for packet transmission.

Reconfigurable Intelligent Surfaces for 6G IoT Wireless Positioning: A Contemporary Survey

The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time critical, ultrareliable, and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On the one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This article first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then, we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.

Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network

IEEE 802.1 Time-Sensitive Networking (TSN) assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data in handling time-critical traffic. TSN handles different quality of service (QoS) requirements and frame preemption is one of the key features of TSN. In the healthcare sector networking technology preferred by large organizations uses an enormous number of nodes, and thereby, the complexity of the network increases. Since the priority of the medical data varies at times based on the patient's health, dynamic traffic scheduling mechanisms are preferred. To improve the efficiency of the network, the software-defined access mechanism is used to control the network switches and bridges in the time-sensitive network. This work uses reinforcement learning to identify and eliminate the bridges dropping packets, and the alternative path is used to schedule the real-time data traffic. It is perceived that it performs well for the time-critical data in congestion network, increases the throughput, and reduces latency.

A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques

Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.

A New Explainable Deep Learning Framework for Cyber Threat Discovery in Industrial IoT Networks

Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among manufacturing processes, industrial machines, and utility services. The time-critical data collected from heterogeneous sensing devices are usually communicated to processing points for analysis and aggregation as the basis of IIoT. The IIoTs’ service quality typically depends on data integrity and accuracy, which could be exploited by injecting malicious events, such as false data injection and data poisoning attacks. Thus, effective anomaly recognition and explanation are critical for ensuring quality services and empowering security administrators to interpret the causal reasoning of prediction decisions and underlying data evidence. This study proposes an autoencoder-based detection framework using convolutional and recurrent networks to discover cyber threats in IIoT networks and explain the model. A two-step sliding window (SW) is applied to learn the latent representations of data features better. Malicious points from the raw time series are transformed into fixed-length series through the first-step SW. Every series is converted into continuous-time-reliant subseries via another smaller SW to learn latent representations of malicious events. Fully connected networks use the extracted temporal and spatial features for the classification and explanation of attack events. The empirical results revealed that this framework effectively extracts features that include contexts of malicious patterns. This demonstrated that the proposed framework is robust in detecting malicious events using multiple evaluation metrics and outperforming the contemporary state-of-the-art methods, indicating its suitability as an operative application method in real-world IIoT-based networks.

Abstract 4092: Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining

Abstract Detection of gene fusions is important for discovery of cancer drivers and clinical oncology testing, but existing software tools for fusion detection usually take hours to run and may fail to find lowly expressed fusions. To overcome these limitations, we developed the Fuzzion2 program, which uses pattern matching to detect known gene fusions in unmapped paired-read RNA-Seq data. Given a set of patterns representing fusion transcript breakpoints, Fuzzion2 finds every read pair matching any of the patterns. Both exact and inexact (fuzzy) matches are detected; the fuzzy matching tolerates variations caused by sequencing errors, SNVs, and indels. By employing a novel index of frequency minimizers, Fuzzion2 needs only minutes to process a sample. We have also developed pipelines to produce patterns for Fuzzion2, from fusion contig sequences, from genomic breakpoints in DNA and RNA, and from fusion protein sequences. To evaluate its applicability in clinical testing, we ran Fuzzion2 on ~2,000 RNA-seq samples profiled by the St. Jude clinical genomics program and confirmed its sensitivity in identifying lowly expressed fusions, such as KIAA1549-BRAF in low-grade glioma, which are frequently missed by commonly used fusion detection programs. Notably, Fuzzion2 detected a subclonal BCR-ABL1 fusion expressed at 1% and 6% of the wild-type BCR and ABL1 transcription level, respectively, in a B-lineage ALL sample that also has an IGH-CRLF fusion. Processing RNA-seq data from BCR-ABL1 cell lines, K562 with p210 fusion and OP1 with p190 fusion, diluted at 1:10, 1:100, and 1:1000 showed that Fuzzion2 can detect the fusion at 1:10-1:100 dilution, achieving a sensitivity 10 times greater than that of other fusion detection programs. We also evaluated the performance of Fuzzion2 for large-scale data mining in a study to compare the prevalence of gene fusions in pediatric versus adult cancers. We assembled a set of 15,474 patterns representing 5,480 fusions identified in the Pediatric Cancer Genome Project, NCI TARGET, clinical sequencing, and the COSMIC database. Fuzzion2 was deployed to the NCI Cancer Genomics Cloud and analyzed 9,464 TCGA RNA-seq samples from adult solid and brain tumors. Processing took an average of 6 minutes at a cost of only US$0.16 per sample. Among the 105 recurrent fusions identified in pediatric cancers, only 11 were also found in adult cancers. These shared fusions can be classified into two categories: 1) gene fusions present in cancers that occur in both children and young adults, e.g., synovial sarcoma, papillary thyroid cancer, and fibrolamellar hepatocellular carcinoma; and 2) kinase fusions involving ABL1, NTRK, and FGFR. Our experience with Fuzzion2 demonstrates that it is a powerful tool for time-critical clinical application and large-scale data mining. It is publicly available at https://github.com/stjude/fuzzion2. Citation Format: Stephen V. Rice, Michael N. Edmonson, Liqing Tian, Michael Rusch, David A. Wheeler, Jennifer L. Neary, Scott Newman, Lu Wang, Patrick R. Blackburn, Michael Macias, Andrew Thrasher, Jian Wang, Mark R. Wilkinson, Xin Zhou, Jinghui Zhang. Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4092.

Design of FFNN architecture for power quality analysis and its complexity challenges on FPGA

As we all know, power quality (PQ) issues are a major concern these days. Field programmable gate array (FPGA) are essential in PQ analysis, particularly in smart meters for data processing, storage, and transmission. One of the most significant advantages of FPGA is its reconfigurability, with vast hardware resources that can be used to implement complex as well as time-critical data processing units. Because the FPGA architecture supports fixed point arithmetic, data loss occurs in the data path unit, necessitating the realization of the PQ event detection module, and classification model to be more accurate than software implementation algorithms. The majority of the work reported, with feed forward neural network (FFNN) structure occupying large number of multipliers and adders for classification, most of the work reported has not addressed to minimize the data path resources for FFNN instead have addressed in improving classification accuracy. Based on these issues, this paper addresses the implementation challenges in FFNN architecture design by proposing improved and fast architectures. The proposed FFNN architecture design using optimum resources. The FFNN based classifier are designed to perform PQ event detection and classification with 99.5% accuracy. The FFNN processor operates at maximum frequency of 238 MHz.

Control and Implementation of Positioning System with Symmetrical Topology for Precision Manufacturing

In recent years, the needs of modularized controller for the multiaxes servo system increase significantly since traditional controller still exists many drawbacks such as limited control axes, low speed data acquisition, or heavy weight. In this paper, we present the design and implementation of both hardware and software for real-time express-based motion controller. This controller can meet the demand for high-speed motion control and high performance which conventional fieldbus controllers cannot realize. With modular design, the controller brings many benefits such as low-cost, expandable ability, multiaxes control, or small physical size. Experimental results for an industrial motion system indicate that the proposed modular controller can perform well in time critical data transmission and is feasible and applicable in various fields.

CONGESTION FREE CHANNEL ALLOCATION FOR HIGH TRAFFIC USING MAC IN WSN

Slot Allocation Medium Access Channel (SAMAC), an efficient slot allocating MAC protocol is proposed for predicting the timely channel access for time-critical data operations and also to reduce data congestion. In this scheme the time of non-periodic critical traffic grabs the time slots allotted for the periodic non-critical data traffic. Additionally, Earliest Deadline First (EDF) scheduling algorithm is used to offer efficient channel access in emergency conditions where numerous sensor nodes are prompted concurrently to transmit the periodic time-critical data to the controller. SAMAC protocol is analyzed with the delay in data delivery rates for the time-critical traffic. Simulation analysis is carried out for proving the efficiency of SAMAC in terms of network throughput.

5G Emerging and Mission Critical Framework with Ultra‐Reliable Low Delay

This paper presents a new user-centric approach in establishing reliable communication for mission and time-critical data, being able to use multiple available heterogeneous data networks to deliver one or several multimedia services at once. The framework is using present and future networks, and the proposed architecture presents an emergency and mission critical data system model, based on the fundamental principles of 5G network architectures. Thus, the end-IoT devices communicate with IoT applications, hosted in cloud servers or edge computing servers. The novel simulation results and analysis present superior system performance with high level of end-to-end reliability depending on the link delays, and at the same time low average end to end outage, based on the link outage. The framework also provides an intelligent solution for high reliability and low end-to-end delay for emergency and mission critical data services and communications, emerging as essential for the security of the society and citizens, by simultaneously using multiple radio access technologies.

Load aware multipath data forwarding for enhanced lifetime of WSN

Modern Wireless sensor network (WSN) requires strict constraints on delay and energy consumption. These networks are internet of things (IoT), multimedia sensor network, and body area network. It requires huge amount of bandwidth to deliver the data. Since, time critical data must be reached at sink node without loss because sensor network has energy limitations and hardware constraints. In this paper, an efficient multipath routing algorithm is proposed to enhanced the lifetime of the network. The proposed scheme, rather than one route, discovered independent routes from source to destination. The Lagrangian relaxation method is used to model hop to hop distance for prolonging the network lifetime. The proposed routing technique selects the multiple paths by focusing on hop count and the residual energy of sensor nodes. Simulation results show that the proposed routing protocol improves the network performance in terms of packet delivery ratio, energy consumption, first dead node and average end to end delay.

An Internet of Things (IoT) Reference Model for an Infectious Disease Active Digital Surveillance System

Internet of Things (IoT) technological assistance for infectious disease surveillance is urgently needed when outbreaks occur, especially during pandemics. The IoT has great potential as an active digital surveillance system, since it can provide meaningful time-critical data needed to design infectious disease surveillance. Many studies have developed the IoT for such surveillance;however, such designs have been developed based on authors' ideas or innovations, without consideration of a specific reference model. Therefore, it is essential to build such a model that could encompass end-to-end IoT-based surveillance system design. This paper proposes a reference model for the design of an active digital surveillance system of infectious diseases with IoT technology. It consists of 14 attributes with specific indicators to accommodate IoT characteristics and to meet the needs of infectious disease surveillance design. The proof of concept was conducted by adopting the reference model into an IoT system design for the active digital surveillance of the Covid-19 disease. The use-case of the design was a communitybased surveillance (CBS) system utilizing the IoT to detect initial symptoms and prevent closed contacts of Covid-19 in a nursing home. We then elaborated its compliance with the 14 attributes of the reference model, reflecting how the IoT design should meet the criteria mandated by the model. The study finds that the proposed reference model could eventually benefit engineers who develop the complete IoT design, as well as epidemiologists, the government or the relevant policy makers who work in preventing infectious diseases from worsening. © 2021. All Rights Reserved.

Toward connectivity of a disconnected cluster in partitioned wireless sensor network for time-critical data collection

Reliable network connectivity is one of the major design issues in the context of wireless sensor networks. These networks have diverse communication patterns due to non-uniform sensing activities at various locations in the environment being monitored. In such scenarios, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This gives rise to a situation in which alive nodes are trapped in a disconnected cluster, and they do not have enough radio range to communicate their data to the destination (i.e. a sink or a relay node connected to the main part of the network). This phenomenon may deprive a large number of alive nodes of sending their important time-critical data to the sink. In this article, we propose a virtual antenna–based cooperative beamforming approach for retrieving valuable data from these disconnected nodes. In the proposed approach, the sensor nodes in an isolated partition work together to form a directional beam. This directional beam significantly increases their overall communication range to reach out to a distant relay node which is connected to the main part of the network. The proposed approach of cooperative beamforming–based partition connectivity is more effective when a cluster with a favorably large number of nodes gets partitioned. Furthermore, a beamforming-based mechanism is proposed for a disconnected cluster to locate the nearest relay node which is still connected to the sink and to reconnect itself to the main part of the network via the most adjacent relay node for time-critical data transmission. The proposed mechanism is then evaluated through simulation results.

Differentiated Service Model-Supported Cluster-Based Routing in Wireless Sensor Networks

Wireless Sensor Network finds its extensive use in healthcare applications for the transfer of time-critical data through wireless connectivity. The primary cause of network failure is the transfer of time-critical multimedia data. The article presents a new differentiated service modelsupported (DSM) cluster-based routing in wireless sensor networks (WSNs) that overcomes the above issue. DSM prioritizes the transfer of different flow types based on packet type and packet size. The employment of computational offloading minimizes delay for critical and small-sized data packets and by carrying out data reduction of large-sized packets at proxy server. It outperforms the existing protocols in terms of energy efficiency, throughput, and reliability by prioritizing the transfer of time-critical health application data

A scalable hybrid MAC strategy for traffic-differentiated IoT-enabled intra-vehicular networks

The increasing popularity of Internet of Things-enabled Intra-Vehicular Wireless Sensor Networks (IoT-IVWSNs) relying on IEEE 802.15.4 standard has generated a massive amount of wireless data traffic and put a great pressure in the network functionalities. Along this trend, the existing medium-access control (MAC) protocol struggles to keep up with the unprecedented demand of vehicle monitoring sensors simultaneously emitting data, which can lead to packet collisions, severe network congestion and lost of time-critical data, due to the inflexible characteristics of the protocol. In order to mitigate these issues, this work proposes an enhanced MAC scheme that is scalable to account for diverse sensor-traffic quality of services. The hybrid scheme aims to effectively combine two procedures, namely history- and priority-based MAC, to allocate appropriate network resources for smooth transmission flow from multiple sensors. History-based MAC exploits historical contention data to optimize a near-future contention window that aims to minimize packet collision and expedite the average data delivery. Priority-based MAC assigns priority based on the time-criticality of the sensing data, which is subsequently being used to schedules network resources. Numerical results show the desirable performance of the hybrid scheme for IoT-IVWSNs in comparison to the existing MAC and sole history-based or priority-based strategies in the context of packet delivery ratio and transmission delay.