Communication Load Research Articles

Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity

To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs. In the online stage, all the agents build their sensitivity equations based on new information. Three variants of sensitivity equation are developed based on the level of communication load capacity, and the corresponding computation strategies are proposed. After solution, the background inputs are corrected and implemented. The optimality and robustness of the proposed algorithm are rigorously derived. Finally, the superiority of this DMPC scheme is demonstrated in the multi-vehicle system with two different topologies.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Performance Evaluation of Routing Algorithm in Satellite Self-Organizing Network on OMNeT++ Platform

Self-organizing networks of small satellites have gradually gained attention in recent years. However, self-organizing networks of small satellites have high topological change frequency, large transmission delay, and complex communication environments, which require appropriate networking and routing methods. Therefore, this paper, considering the characteristics of satellite networks, proposes the shortest queue length-cluster-based routing protocol (SQL-CBRP) and has built a satellite self-organizing network simulation platform based on OMNeT++. In this platform, functions such as the initial establishment of satellite self-organizing networks and cluster maintenance have been implemented. The platform was used to verify the latency and packet loss rate of SQL-CBRP and to compare it with Dijkstra and Greedy Perimeter Stateless Routing (GPSR). The results show that under high load conditions, the delay of SQL-CBRP is reduced by up to 4.1%, and the packet loss rate is reduced by up to 7.1% compared to GPSR. When the communication load is imbalanced among clusters, the delay of SQL-CBRP is reduced by up to 12.7%, and the packet loss rate is reduced by up to 16.7% compared to GPSR. Therefore, SQL-CBRP performs better in networks with high loads and imbalance loads.

Dynamic Event-Triggered Control for Sensor–Controller–Actuator Networked Control Systems

We consider the problem of output feedback stabilization of LTI systems under event-triggering implementation. In particular, we assume that both the plant output and the control input are both transmitted over the network in an asynchronous manner. To that end, two independent event-triggering rules are constructed to generate the transmission instants of the submitted signals. The proposed approach is dynamic in the sense that the triggering rules involve internal dynamical variables to allow for further reduction in the communication load. Moreover, the inter-transmission times for both sides of the channel are lower bound by enforced dwell times to prevent the occurrence of Zeno phenomena. The problem is challenging due to mutual interactions between the sampling errors of the plant output and the control input, which requires careful handling to ensure closed-loop stability. The triggering mechanisms are designed by emulation as we first ignore the effect of the network and stabilize the plant in continuous-time. Then, the communication constraints are taken into account to derive the triggering conditions such that the stability of the networked control system is preserved. The required conditions are formulated in terms of a linear matrix inequality. The effectiveness of the technique is demonstrated by numerical simulations.

Privacy-preserving distributed frequency control for AC microgrids with constrained communication

Distributed control of AC microgrids is one of the most popular methods in the islanded operation mode. Whereas, most of the existing studies either do not consider the potential threat of privacy security or rely on the assumption of ideal communication networks. To this end, this paper presents a novel privacy-preserving distributed secondary frequency control strategy for the privacy protection problem of an islanded AC microgrid with constrained communication. The key contributions of this paper are threefold. (1) Different from the existing privacy-preserving approaches used in AC microgrids, a time-varying function is introduced to mask interactive information such that the frequency cannot be reconstructed by malicious attackers. (2) An event-triggered communication scheme is employed to cope with the constrained communication environment. (3) A privacy-preserving distributed event-triggered control strategy with communication delay is developed such that the frequency restoration and active power sharing of the microgrid are guaranteed. Moreover, the maximum communication delay that the proposed control can withstand is analyzed. Simulation results show the properties of the privacy preservation, the decrease of communication load, and the bounded communication delay allowed in the proposed control strategy.

Dynamic event-triggered quantized secure output consensus of heterogeneous multi-agent systems under DoS attacks.

This paper investigates secure output consensus problem of heterogeneous multi-agent systems (HMASs) under denial-of-service (DoS) attacks. First, an adaptive dynamic compensator depending on local measurement information is designed for tracking the signal of the virtual exosystem. Second, for mitigating network communication load, a novel adaptive control protocol is developed by introducing dynamic event-triggered control and quantized control strategies. Moreover, Zeno behavior can be eliminated under the proposed scheme. Third, the secure output consensus of HMASs under DoS attacks can be guaranteed by satisfying the designed sufficient conditions. Finally, simulation is shown to verify reliability of the proposed control protocol and theory.

Robust adaptive control for dynamic positioning vessels with actuator faults via the multiport event-triggered mechanism

This paper presents a robust adaptive neural control algorithm for dynamic positioning tasks of full-actuated vessels using a novel multiport event-triggered mechanism. The proposed mechanism only updates control signals and feedback error signals upon violation of the multiport event-triggered rule. Therefore, the communication loads between the sensors and controller, as well as between the controller and actuators, are significantly mitigated. Furthermore, a fault-adaptive method is adopted by estimating the uncertain actuator faults, based on the proposed event-triggered mechanism, the fault-adaptive parameters will be updated at the triggering instant, eliminating the destabilizing effects of the closed-loop system. Besides, the dynamic surface control (DSC) and the robust neural damping technique are employed to address the problems of “complexity explosion” and system uncertainties. Based on Lyapunov stability theory, the closed-loop system is proved to satisfy semi-globally uniform ultimate bounded (SGUUB). Finally, a simulation experiment validates the effectiveness of the proposed algorithm.

Cubature Kalman fusion filtering under stochastic communication protocol: Tackling dynamical bias, scheduling probability and channel noise

The paper addresses the problem of cubature Kalman fusion filtering (CKFF) for nonlinear systems subject to dynamical bias and stochastic communication protocol (SCP). To account for the engineering practice of networked environment, the channels from sensors to filters, affected by Gaussian channel noise, are considered in the system model incorporating dynamical bias. With the purpose of reducing communication load and enhancing resource utilization efficiency, the SCP is adopted for information transmissions, where the scheduling probability is allowed to deviate within bounds from its designated value owing to possible implementation errors. The objective of this paper is to design a CKFF algorithm in spite of the presence of dynamical bias, channel noise and SCP with uncertain scheduling probability. Specifically, the local filter is constructed such that an upper bound on the filtering error covariance (FEC) is calculated through the resolution of matrix difference equations, and the filter gain is subsequently designed to minimize this upper bound. Then, the local filters are fused by utilizing the inverse covariance intersection fusion rule. Moreover, the boundedness of the FEC’s upper bound is also investigated using the matrix theory. The feasibility and effectiveness of the proposed CKFF approach are demonstrated through two simulation experiments.

Multi‐objective interval planning for 5G base station virtual power plants considering the consumption of photovoltaic and communication flexibility

AbstractLarge‐scale deployment of 5G base stations has brought severe challenges to the economic operation of the distribution network, furthermore, as a new type of adjustable load, its operational flexibility has provided a potential way to promote the consumption and utilization of photovoltaic. In this paper, a multi‐objective interval collaborative planning method for virtual power plants and distribution networks is proposed. First, on the basis of in‐depth analysis of the operating characteristics and communication load transmission characteristics of the base station, a 5G base station of virtual power plants participating in the cellular respiratory demand response model is constructed. In view of the inherent contradiction between system economy and environmental performance, a multi‐objective interval optimization model for collaborative planning of virtual power plants and distribution networks is established with the lowest system investment and operating costs and the lowest carbon emissions as the optimization goals. The established model is transformed into a deterministic optimization problem, which is solved by NSGA‐II algorithm. The modified IEEE‐33 node system is used in the case analysis to analyse the impact of different planning schemes and response characteristics on the system economy. The calculation results verify the effectiveness of the proposed method.

Adaptive event‐triggered safety control for multiplayer mixed zero‐sum game with partial inputs delay

AbstractThis paper proposes an adaptive safety control method applicable to a multiplayer mixed zero‐sum (MZS) game of nonlinear systems with partial inputs delay. Firstly, a framework is introduced involving N players, where player 1 and player N form a zero‐sum (ZS) game, and player 1 and players 2 to N‐1 form nonzero‐sum (NZS) games, with some players experiencing time delays. Subsequently, the system's value function is augmented with a control barrier function (CBF) to ensure that the system's state remains within a safe region. Secondly, to approximate Nash equilibrium solutions, the study employs adaptive dynamic programming (ADP) and utilizes a critic‐only neural network (NN) to approximate optimal solutions. Diverging from traditional time‐trigger methods, computational and communication load reduction is achieved by introducing a state‐related event trigger condition. The stability of the system is then meticulously analyzed using the Lyapunov theorem. Finally, to validate the effectiveness of the proposed method, the study provides a simulation example demonstrating its performance. In summary, this research introduces an efficient adaptive safety control method for addressing multiplayer MZS games with partial inputs delay, incorporating CBFs, ADP, and state‐related event triggering.

Event‐triggered asynchronous distributed MPC for multi‐quadrotor systems with communication delays

AbstractThis article investigates the formation composition and keeping problem for multi‐quadrotor systems under time‐varying communication delays. First, a distributed model predictive control (MPC) approach is employed to transform the formation composition and keeping of multi‐quadrotor systems into an online rolling optimization issue, accompanied by the event‐triggered mechanism to reduce solution frequency and communication load. Second, to address the asynchrony and time‐varying communication delays introduced by event‐triggered distributed MPC, a set of constraints is designed to restrict deviations between the current predictive state and previously broadcasted states. Consequently, based on the previously predicted states of neighbors, the cooperation of the multi‐quadrotor systems is achieved under asynchronous communication and time‐varying delays. This approach guarantees robust asymptotic stability and satisfactory formation performance for multi‐quadrotor systems under various delay scenarios. Finally, the numerical and software‐in‐the‐loop simulations validate the effectiveness of the proposed algorithm of multi‐quadrotors under communication delays.

The Influence Of Work Skills, Communication, Supervision, Work Quality, Support Facilities And Work Load On The Work Achievement Of Employees In The Protocol And Communication Section Of Lahat Regional Secretary Leaders

The research was carried out by the author with the aim of knowing and analyzing the influence of work skills, communication, supervision, work quality, supporting facilities and workload individually and collectively on the work performance of employees of the Protocol and Communication Section of the Lahat Regional Secretary. The research method used in this research is a quantitative method using questionnaires distributed to respondents. Test data quality using validity tests, reliability tests, normality tests. The analysis used is descriptive analysis and inferential analysis using statistical calculations with multiple regression formulas, partial simultaneous tests and coefficient of determination. The research results showed that work skills, communication, supervision, work quality, supporting facilities and workload had a significant effect individually and collectively on the work performance of employees of the Protocol and Communication Section of the Lahat Regional Secretary.

Dynamic event-triggered time-varying formation control for heterogeneous unmanned swarm systems with scaling attacks

This paper addresses the problem of time-varying formation control for heterogeneous unmanned swarm systems (USSs) consisting of multiple unmanned aerial vehicles (UASs) and unmanned surface vehicles (USVs) based on a directional switching topology. Firstly, a novel USSs model is established, involving the effect of multiple network attacks, network bandwidth constraints and input saturation. In particular, a scaling attack model is proposed to account for denial-of-service (DoS) and deception attacks. Secondly, a distributed controller based on a dynamic event triggering mechanism is designed to reduce the communication load by adjusting the communication frequency between individuals, and the Zeno phenomenon is effectively avoided. Finally, the Lyapunov function is used to obtain sufficient conditions for system stability and bounds on the frequency and duration of network attacks. The experiments and comparison results verify the proposed strategy's effectiveness and superiority.

Teleoperated Driving with Virtual Twin Technology: A Simulator-Based Approach

This study introduces an innovative Teleoperated Driving (ToD) system integrated with virtual twin technology using the MORAI simulator. The system minimizes the need for extensive video data transmission by utilizing text-based vehicle information, significantly reducing the communication load. Key technical advancements include the use of high-precision GNSS devices for accurate vehicle location tracking, robust data communication via the MQTT protocol, and the implementation of the Ego Ghost mode in the MORAI simulator for precise vehicle simulation. The integration of these technologies enables efficient data transmission and enhanced system reliability, effectively mitigating issues such as communication blackouts and delays. Our findings demonstrate that this approach ensures stable and efficient operation, optimizing communication resource management and enhancing operational stability, which is crucial for scenarios requiring high video quality and real-time response. This research represents a significant advancement in ToD technology, establishing a precedent for integrating virtual twin systems to create more resource-efficient and reliable autonomous driving backup solutions. The virtual twin-based ToD system provides a robust platform for remote vehicle operation, ensuring safety and reliability in various driving conditions.

Collaborative optimization of distribution network and 5G base stations considering its communication load migration and energy storage dynamic backup flexibility

5G base stations have experienced rapid growth, making their demand response capability non-negligible. However, the collaborative optimization of the distribution network and 5G base stations is challenging due to the complex coupling, competing interests, and information asymmetry among different stakeholders. In this paper, a distributed collaborative optimization approach is proposed for power distribution and communication networks with 5G base stations. Firstly, the model of 5G base stations considering communication load demand migration and energy storage dynamic backup is established. Afterward, a collaborative optimal operation model of power distribution and communication networks is designed to fully explore the operation flexibility of 5G base stations, and then an improved distributed algorithm based on the ADMM is developed to achieve the collaborative optimization equilibrium. Finally, the effectiveness of the proposed distributed collaborative optimization model is validated by a modified IEEE 33-bus power distribution and communication networks with 5G base stations.

Event-based secure control for cyber-physical systems against false data injection attacks

This paper mainly studies an event-based secure control problem for cyber-physical systems against false data injection (FDI) attacks. First, a dynamic event-triggered transmission protocol is established to schedule the transmitted data from the sensor to the controller. Subsequently, an imperfect FDI attack model is constructed considering the real network environment, which would bypass the traditional residual-based detection methods. To overcome this drawback, an attack detection strategy based on secure history information is designed, which effectively mitigates the negative impact of uncertainty on the detection results. Based on the detection results, an event-based secure linear quadratic Gaussian control scheme is designed, which can guarantee system performance and reduce the communication load effectively. Finally, a simulation experiment verifies that the proposed control method outperforms other algorithms in the literature, and the detection accuracy is increased by 91% to 98%.

Dynamic event-triggered adaptive neural control for MIMO nonlinear CPSs with time-varying parameters and deception attacks

This paper provides a dynamic event-triggered adaptive neural controller for the multi-input–multi-output (MIMO) non-strict feedback nonlinear cyber–physical systems (CPSs) with time-varying parameters encountering deception attacks (DAs). The single parameter learning method (SPLM) and the dynamic surface technology are combined under backstepping framework with adroitness, which not only resist the malicious sensor DA, but also reduce the complexity of the design scheme. Furthermore, a dynamic event-triggered strategy is added to update the threshold dynamically to further reduce the communication load. Theoretical analysis presents that the proposed control scheme can guarantee that all signals are bounded regardless encountering the DAs. Eventually, the validity of the developed methods is illustrated by a simulation case.

Integrated control strategy for 5G base station frequency regulation considering spatio-temporal variations in communication load

The decreasing system inertia and active power reserves caused by the penetration of renewable energy sources and the displacement of conventional generating units present new challenges to the frequency stability of modern power systems. Vast quantities of 5G base stations, featuring largely dormant battery storage systems and advanced communication technology, represent a high-quality fast frequency regulation resource for the power system. This paper proposes a double-layer clustering method for 5G base stations and an integrated centralized-decentralized control strategy for their participation in frequency regulation, considering the variability of available frequency regulation capacity due to spatial and temporal variations in base station communication loads. The proposed capacity model and control methods are evaluated using a case study of a two-machine test system with 10,000 real 5G base stations, demonstrating the effectiveness of the clustering method and the integrated control strategy in improving frequency stability.

Communicative linguistics through the lens of gestalt theory

Relevance. The relevance of this study is conditioned upon the importance of developing communicative competence, exploring communicative tactics and strategies used in dialogic speech, effective correlation of verbal and nonverbal codes, specifically the coexistence of various syntactic constructions within the text field. Purpose. The purpose of this study is to consider linguistic means, namely syntactic structures of a simple and complex sentence, using a gestalt approach with the allocation of the centre (figure) and periphery (ground). Methodology. The main method of research is theoretical understanding of problems related to communicative activity, using evidence from modern scientific studies, the use of transactional, structural, pragmatic, and linguistic analysis of verbal and nonverbal components during communicative activity. Results. This paper analyses the development of key gestalt structures using the examples of the most used syntactic constructions in communicative practice, specifically predicate and non-predicate simple and complex sentences. The study identified key gestalt structures in dialogic speech (interrogative, emotive, elliptical), with examples of basic syntactic constructions. Attention was focused on the functional use of key speech acts (expressiveness, representation) and communicative tactics (hyperbolisation, increasing and decreasing significance, self-presentation, juxtaposition). The role of nonverbal ways of transmitting information, social factors in the choice of linguistic means in the aspect of communicative load and communicative significance is indicated. Conclusions. Gestalt techniques in communication prove effective for addressing psychological issues and enhancing communicative impact. Keywords: simple sentence; syntactic structure; figure; ground; speech act

Aviation Operators’ Total Loads Analysis by Multi-Criteria Decision-Making

The aviation operator's role and duties undergo a dramatic change from direct engagement to information management and surveillance in today's advanced aviation environment. In this modern and highly automated working environment, operators must handle various information, data, and transmission during operations, particularly while making decisions in specific situations. The total load system of operators (information, communication, mental, task, and workload) has been fundamentally reshaped in response to these dynamic changes. The highly automated system is linked to unbalanced operator loads, unanticipated losses in situation awareness, inappropriate decision-making quality, and higher stress levels. This research employs the Analytical Hierarchy Process (AHP) as a foundational framework, integrating the Fuzzy Analytical Hierarchy Process (FAHP) on the AHP model to address uncertainties and applying the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) for ranking the aviation operators' total loads. online Survey distributed among 63 experts across four aviation operator groups: less experienced pilots, expert pilots, less experienced Air Traffic Controllers (ATCOs), and experienced ATCOs. serves to evaluate the pivotal characteristics of the operators' total loads model. The results highlighted the communication load's effect on the aviation operators specifically the level of the language as well the workload specifically both practicing referring to the experience and skills.