Data Collision Research Articles

Data Collision Avoidance based on TDMA in LoRa Wireless Sensor Networks

Data Collision Avoidance based on TDMA in LoRa Wireless Sensor Networks

Asynchronous nonfragile guaranteed performance control for singular switched positive systems: An event‐triggered mechanism

SummaryThis paper addresses the guaranteed performance control problem for a class of singular switched positive systems, where the switching signal is subject to a state‐dependent process. Firstly, the causality, regularity, positivity, and asymptotical stability of the considered systems are discussed. In order to prevent the occurrence of data collisions and reduce the consumption of communication resources, the event‐triggered (E‐T) mechanism is applied. This is one of the initial attempts to introduce the E‐T scheme for such special systems. Besides, an asynchronous nonfragile controller under the E‐T scheduling scheme is designed to make certain that the resulting closed‐loop systems are causal, regular, positive, asymptotically stable, and have a guaranteed performance value . Through the application of the co‐positive Lyapunov function method and the min‐projection strategy, the corresponding sufficient conditions are given in the form of linear programming (LP). Finally, the effectiveness of the controller proposed in this paper is verified via two simulation examples.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

Master–slaves synchronization of teleoperation systems with the Try‐Once‐Discard protocol under event‐triggered communication

AbstractIn this paper, a class of single‐master‐multiple‐slaves (SMMS) bilateral teleoperation systems with the limitation of communication bandwidth is investigated. The signals are exchanged between the master and the slaves through delayed communication. Besides, to avoid data collisions, the Try‐Once‐Discard (TOD) scheduling protocol is utilized to decide which slave gets the network access at each triggered instant. A control algorithm with the event‐triggered mechanism is proposed to ensure the master–slaves synchronization. Furthermore, the closed‐loop system's stability criterion associated with the controller gains, the varying time delays' bounds, and the maximum transmission interval is established on the basis of the Lyapunov–Krasovskii analysis. Finally, the presented algorithm's performance is demonstrated by the simulation and experiment results.

Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET.

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.

Event-based filter design for singular positive Markov jump systems with parameter uncertainty and measurement delay

This paper is concerned with the filter design problem for a class of singular positive Markov jump systems with parameter uncertainty and measurement delay. Considering the limitation of communication bandwidth, the mode-dependent event-triggered (E-T) mechanism is introduced when designing the filter. In general, the phenomena of data collision and congestion can be reduced by employing this form of filter. On the basis of stochastic co-positive Lyapunov function technique, the regularity, causality, positivity, stochastic stability, and weighted l1-gain performance of the resulting filtering error system are discussed. In addition, the corresponding design method applied to filter parameters are also provided in detail. Finally, two numerical simulations are given to demonstrate the effectiveness and feasibility of the results proposed in this paper.

Confronting combined cyber-attacks on collaborative DC microgrids using a communication protocols-based distributed unscented Kalman filter

The functioning of microgrids (MGs), which represent a contemporary arrangement of distribution networks featuring local control over generation and consumption, necessitates telecommunication infrastructure for transmitting and receiving measurement data and control commands. This reliance on telecommunication infrastructure renders MGs vulnerable to the threat of cyber-attacks. The existing methods for identifying cyber-attacks have been restricted solely to MG modeling. However, identifying and restoring a cyber-attack lack dependability and feasibility in a practical application without the concurrent modeling of MGs and their communication protocols. Therefore, this study aims to identify hybrid cyber-attacks (simultaneous false data injection and time delay attacks) to flow sensors and cyber links in direct flow MGs by considering the communication protocols between them, as well as extracting and reconstructing real measurement data. In order to achieve this objective, a generalized unscented Kalman filter (UKF) is introduced in conjunction with communication protocols incorporating an augmented state space to estimate the power of nonlinear loads. For this purpose, two measurement models have been developed to prevent data collisions and reduce the communication load. These models are based on the round-robin scheduling protocol and the try-once-discard weighted protocol. They aim to establish a specific order for data transfer from sensors to the filter. The article concludes by presenting a stability analysis of the protocol-based generalized UKF, which aims to ensure the convergence and limitation of the estimation error. The study presents simulation results on the resilient reconstruction of hybrid cyber-attacks involving unknown constant power loads and noise. The proposed approach utilizes the protocol-based generalized UKF and exhibits superior performance.

Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

Designing Iterative Learning Schemes for Cooperative-Antagonistic Systems With Random Access Communication Protocols.

In this article, the design issue for an iterative learning controller is investigated for the cooperative-antagonistic system under the scheduling effects of random access protocol (RAP). In order to reflect the heterogeneous characteristic of the underlying system, the dynamics of each node in the cooperative-antagonistic system are described by a two-time-scale system. For the purpose of avoiding data collisions in signal transmissions, the so-called RAP is introduced to schedule the data exchanges among nodes, where the transmission opportunities of nodes are modeled by a sequence of random variables with certain transition probabilities. Considering that the mutual relationships may be cooperative and also competitive among nodes in many real-world networks, a novel cooperative-antagonistic-based iterative learning controller is developed to handle the tracking problem of the system dynamics. Sufficient conditions are obtained for the design of the controller parameters. Furthermore, the derived results are extended to the case that the transition probabilities for the RAP are partially unknown. Finally, a numerical example is presented to illustrate the effectiveness of the proposed iterative learning control (ILC) scheme.

Optimized Distributed Filtering for Saturated Systems With Amplify-and-Forward Relays Over Sensor Networks: A Dynamic Event-Triggered Approach.

In this article, the optimized distributed filtering problem is studied for a class of saturated systems with amplify-and-forward (AF) relays via a dynamic event-triggered mechanism (DETM). The AF relays are located in the channels between sensors and filters to prolong the transmission distance of signals, where the transmission powers of sensors and relays can be described by a sequence of random variables with a known probability distribution. With the purpose of alleviating the communication burden and preventing data collision, the DETM is used to schedule the transmission cases of nodes by dynamically adjusting the triggered threshold according to the practical requirements. An upper bound matrix (UBM) of the filtering error (FE) covariance is first provided under the sense of variance constraint and the proper filter gain is further constructed via minimizing the proposed UBM. In addition, the boundedness evaluation regarding the trace of the UBM is provided. Finally, simulation experiments are used to illustrate the usefulness of the developed distributed recursive filtering scheme.

Formula omitted] containment control for multi-unmanned aerial vehicle systems: A self-triggered control scheme

This paper is concerned with the containment control problem for multi-unmanned aerial vehicle (multi-UAV) systems with data transmission among agents during a limited-bandwidth network. A good deal of data transmitted during a limited-bandwidth network may result in data collision, which brings negative effects on controller design and containment tracking implementation. To reduce the network load during data transmission, a consensus condition and a triggering subsidiary condition are first proposed to realize the containment tracking with H∞ performance by using the triggered states. Then a self-triggered scheme is established under a multi-agent framework, in which the next triggering instant is calculated beforehand by using the past states. Combined with the self-triggered scheme, a containment controller is obtained via solving the derived consensus condition, which ensures that follower UAVs are driven into a convex set surrounded by leader UAVs. Finally, the validity of the self-triggered containment control method is proved through a simulation experience.

Distributed Recursive Filtering Over Sensor Networks Under Random Access Protocol: When State Saturation Meets Censored Measurement.

In this article, a new distributed filtering problem is studied for a class of state-saturated time-varying systems over sensor networks under measurement censoring, where the censored measurements are described by the Tobit measurement model. To curb the data collision and ease communication burden, a random access protocol (RAP) is implemented onto the sensor-to-filter channels to orchestrate the transmission sequence of multiple sensor nodes. The purpose of the addressed problem is to construct a state-saturated distributed filter such that upper bounds (on filtering error covariances) are guaranteed and filter parameters are determined to accommodate both measurement censoring and state saturation under the RAP. By means of matrix difference equations, the desired upper bounds are first acquired and later minimized through appropriately designing filter parameters. Particularly, the sparsity issue with respect to the network topology is tackled via the employing certain matrix simplification technique. A simulation example is finally presented to showcase the applicability of the proposed state-saturated distributed filtering algorithm.

Decentralized piggybacking-based dissemination of Cooperative Awareness Messages in vehicular ad-hoc networks

Vehicle-to-Vehicle (V2V) communication has shown great promise to improve road safety and traffic efficiency via the periodic broadcasting of Cooperative Awareness Messages (CAMs), in which traffic information could be exchanged among adjacent vehicles. Since each CAM contains local-observed traffic information such as geolocation, speed, and safety alerts, frequent CAM losses will decrease the reliability on avoiding crashes and injuries on the road. Existing solutions tend to recover the lost CAMs through either source-based re-transmission or neighbor-based forwarding, which may suffer from significant performance degradation due to the fast movement of vehicles and high dynamic network topology. In this paper, we develop a decentralized cooperative broadcasting protocol for CAM dissemination, where each vehicle can piggyback some received CAMs in its routine broadcasting to help others recover their lost CAMs. To promote cooperation of CAM piggybacking, a new data structure called bitmap is introduced to record the packet receiving status at each vehicle, based on which time slots could be allocated to vehicles with low data collisions, lost CAMs could be detected only using each vehicle’s local information, and the qualities of wireless links could be estimated dynamically. Then, we propose two piggybacking schemes, Benefit-based Piggybacking (BP) and Suggestion-based Piggybacking (SP), aiming to maximize the average CAM delivery ratio. We evaluate the proposed schemes in simulations with realistic vehicle mobility traces and channel models. Simulation results show that our bitmap-based lost CAM detection has superior performance than traditional request-based solutions. In comparison with forwarding-based schemes, our schemes can achieve much higher CAM broadcast reliability with short time delay and small communication overhead.

Improving Worst-case TSN Communication Times of Large Sensor Data Samples by Exploiting Synchronization

Higher levels of automated driving also require a more sophisticated environmental perception. Therefore, an increasing number of sensors transmit their data samples as frame bursts to other applications for further processing. As a vehicle has to react to its environment in time, such data is subject to safety-critical latency constraints. To keep up with the resulting data rates, there is an ongoing transition to a Time-Sensitive Networking (TSN)-based communication backbone. However, the use of TSN-related industry standards does not match the automotive requirements of large timely sensor data transmission, nor it offers benefits on time-critical transmissions of single control data packets. By using the full data rate of prioritized IEEE 802.1Q Ethernet, giving time guarantees on large data samples is possible, but with strongly degraded results due to data collision. Resolving such collisions with time-aware shaping comes with significant overhead. Hence, rather than optimizing the parameters of the existing protocol, we propose a system design that synchronizes the transmission times of sensor data samples. This limits network protocol complexity and hardware requirements by avoiding tight time synchronization and time-aware shaping. We demonstrate that individual sensor data samples are transmitted without significant interference, exclusively at full Ethernet data rate. We provide a synchronous event model together with a straightforward response time analysis for synchronous multi-frame sample transmissions. The results show that worst-case latencies of such sample communication, in contrast to non-synchronized approaches, are close to their theoretical minimum as well as to simulative results while keeping the overall network utilization high.

Event-triggered state estimation for complex networks under deception attacks: a partial-nodes-based approach

This paper addresses the issue of state estimation for a kind of complex network (CN) with distributed delays and random interference through output measurements. In the data transmission, the deception attacks are taken into account by resorting to a sequence of Bernoulli random variables with a given probability. Considering the complexity of the network, the fact that only partial output measurements are available in practical environments presents a new challenge. Therefore, the partial-nodes-based (PNB) state estimation problem is proposed. For the sake of data collision avoidance and energy saving, a general event-triggered scheme is adopted in the design of the estimator. A novel estimator is constructed to consider both cyber attacks and resource limitations, filling the gap in previous results on PNB state estimation. By using the Lyapunov method and several stochastic analysis techniques, a few sufficient conditions are derived to guarantee the desired security and convergency performance for the overall estimation error. The estimator gains are obtained by solving a set of matrix inequalities with nonlinear constraints. At last, two examples and simulations are presented to further show the efficiency of the proposed method.

Future transportation computing model with trifold algorithm for real-time multipath networks

<p><strong>Purpose:</strong> In the past ten years, research on Intelligent Transportation Systems (ITS) has advanced tremendously in everyday situations to deliver improved performance for transport networks. To prevent problems with vehicular traffic, it is essential that alarm messages be sent on time. The truth is that an ITS system in and of itself could be a feature of a vehicular ad hoc network (VANET), which is a wireless network extension. As a result, a previously investigated path between two nodes might be destroyed over a short period of time. <strong>Design:</strong> The Time delay-based Multipath Routing (TMR) protocol is presented in this research which efficiently determines a route that is optimal for delivering packets to the target vehicle with the least amount of time delay. Using the TMR method, data flow is reduced, especially for daily communication. As a result, there are few packet retransmissions. <strong>Findings:</strong> To demonstrate how effective the suggested protocol is, several different protocols, including AOMDV, FF-AOMDV, EGSR, QMR, and ISR, have been used to evaluate the TMR. Simulation outcomes show how well our suggested approach performs when compared to alternative methods. <strong>Originality:</strong> Our method would accomplish two objectives as a consequence. First, it would increase the speed of data transmission, quickly transfer data packets to the target vehicle, especially warning messages, and prevent vehicular issues like automobile accidents. Second, to relieve network stress and minimize network congestion and data collisions.</p>

Parameterized SDRAM-based content-addressable memory on field programmable gate array

Contents-addressable memory (CAM) is a special memory that searches the input data with the entire pre-loaded database and generates corresponding address information. CAMs are advancing to be a core technology in computer networking systems. As field programmable gate array (FPGA) is recently being used for network acceleration applications, the demand to integrate CAM on FPGA is increasing. FPGA-based CAMs are divided into three categories of implementation: register-based, block RAM (BRAM)-based, and distributed RAM-based CAM. However, they come with a cost of excessive resource usage. Besides, the collision ratio is high in FPGA-based CAMs, leading to data loss and failure to produce accurate addresses. Synchronous dynamic random-access memory (SDRAM)-based CAMs, benefiting from the features of high density and low price of SDRAM, solve the limitations of FPGA’s on-chip resources. This paper proposes a data collision CAM hardware implementation using modern FPGA’s off-chip SDRAM for data storage. The hardware architecture is customized for massive lookup tables and resource-saving. Furthermore, the architecture is parameterized, which is better for integration. The synthesis results and comparisons show significant advancement compared to other FPGA-based CAM implementations by total reduction of on-chip RAM. The novel architecture shows remarkable improvement in the memory depth and width with the capacity of 128 Mbyte lookup table.

Dynamic output feedback model predictive control for asynchronous Markovian jump systems under try‐once‐discard protocol

AbstractThis paper is concerned with the dynamic output feedback robust model predictive control (RMPC) problem for a class of discrete‐time asynchronous Markovian jump systems (MJSs) subject to polytopic parameter uncertainties and hard constraints on states and inputs. A hidden Markov model is employed to describe the asynchronous phenomenon between the detected modes and the system modes. For the purpose of improving the network utilization as well as reducing the transmission burden and avoiding data collisions, the try‐once‐discard (TOD) protocol is deployed in the shared communication network between sensor nodes and the controller node. Considering the difficulty of obtaining the system state in the practice, the dynamic output‐feedback control in the framework of RMPC is adopted, and the worst‐case optimization problem over the infinite moving horizon is formulated for the performance analysis and control synthesis. By means of the Lyapunov‐like function approach, free weighting matrices and inequality techniques such as the ‐procedure are introduced to deal with the non‐convexity caused by couplings between decisive variables and the negative influence resulting from the data orchestration is mitigated. Based on the delicate establishments, a certain upper bound of the objective function is employed to construct an auxiliary optimization problem with solvability to find the desired controllers, and sufficient conditions are obtained to guarantee that the MJSs under the proposed RMPC‐based controllers are mean‐square stable. Finally, an illustrative example is used to demonstrate the validity of the proposed methods.

An Efficient Geo-Routing-Aware MAC Protocol Based on OFDM for Underwater Acoustic Networks

Performing an effective Media Access Control (MAC) protocol suffers from strong dependencies between Underwater Acoustic Networks’ upper and lower layers: 1) the network layer frequently uses geo-routing protocols, which do not provide the specific next-hop for MAC protocols, resulting in serious data collisions. 2) in such scenarios with the unknown next-hop, fixed Orthogonal Frequency Division Multiplexing (OFDM) resource does not adapt to the changing environment, and degrades network performance (OFDM is a mature modulation technology in the physical layer). However, there is scant research on MAC protocols considering the network layer and the physical layer simultaneously, to solve data collisions and resource allocation. To this end, we present a cross-layer MAC protocol to integrate Geo-routing protocols and OFDM technology (GO-MAC) at the same time. GO-MAC employs a handshake scheme to allocate optimal communication resources and select the next-hop concurrently. Firstly, we formulate the OFDM resource allocation as a joint optimization problem based on transmission mode, subcarrier spacing, guard interval, and transmission power, to decrease transmission delay and energy consumption. Then a Karush-Kuhn-Tucker conditions based Heuristic algorithm (KKT-H) is proposed to solve this problem. Finally, we consider node congestion and channel quality to assist geo-routing protocols with the next-hop selection, and decrease packet collisions. Simulation results show that our protocol matches geo-routing protocols and OFDM technology better than the state-of-the-art protocols, providing higher end-to-end reliability with lower costs.