Network Topology Control Research Articles

Photochemical Control of Network Topology in PEG Hydrogels.

Hydrogels are often synthesized through photoinitiated step-, chain-, and mixed-mode polymerizations, generating diverse network topologies and resultant material properties that depend on the underlying network connectivity. While many photocrosslinking reactions are available, few afford controllable connectivity of the hydrogel network. Herein, a versatile photochemical strategy is introduced for tuning the structure of poly(ethylene glycol) (PEG) hydrogels using macromolecular monomers functionalized with maleimide and styrene moieties. Hydrogels are prepared along a gradient of topologies by varying the ratio of step-growth (maleimide dimerization) to chain-growth (maleimide-styrene alternating copolymerization) network-forming reactions. The initial PEG content and final network physical properties (e.g., modulus, swelling, diffusivity) are tailored in an independent manner, highlighting configurable gel mechanics and reactivity. These photochemical reactions allow high-fidelity photopatterning and 3D printing and are compatible with 2D and 3D cell culture. Ultimately, this photopolymer chemistry allows facile control over network connectivity to achieve adjustable material properties for broad applications.

CHEABC-QCRP: A novel QoS-aware cluster routing protocol for industrial IoT

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

Distributed active vibration control for helicopter based on diffusion collaboration

The active vibration control technology has been successfully applied to several helicopter types. However, with the increasing of control scale, traditional centralized control algorithms are experiencing significant increase of computational complexity and physical implementation challenging. To address this issue, a diffusion collaboration-based distributed Filtered-x Least Mean Square algorithm applied to active vibration control is proposed, drawing inspiration from the concept of data fusion in wireless sensor network. This algorithm distributes the computation load to each node, and constructs the active vibration control network topology of large-scale system by discarding the weak coupling secondary paths between nodes, achieving distributed active vibration control. In order to thoroughly validate the effectiveness and superiority of this algorithm, a helicopter fuselage model is designed as the research object. Firstly, the excellent vibration reduction performance of the proposed algorithm is confirmed through simulations. Subsequently, specialized node control units are developed, which utilize STM32 microcontroller as the processing unit. Further, a distributed control system is constructed based on multi-processor collaboration. Building on this foundation, a large-scale active vibration control experimental platform is established. Based on the platform, experiments are carried out, involving the 4-input 4-output system and the 8-input 8-output system. The experimental results demonstrate that under steady-state harmonic excitation, the proposed algorithm not only ensures control effectiveness but also reduces computational complexity by 50%, exhibiting faster convergence speed compared with traditional centralized algorithms. Under time-varying external excitation, the proposed algorithm demonstrates rapid tracking of vibration changes, with vibration amplitudes at all controlled points declining by over 94%, proving the strong robustness and adaptive capability of the algorithm.

Cluster-based fusion detection of soft and hard decisions for underwater non-cooperative targets

Underwater target detection performs the essential contribution in marine exploration. Unfortunately, the little energy available in the underwater environment makes detection difficult. To address the aforementioned obstacles, this paper proposed a detection scheme based on soft and hard decision fusion. The detection scheme consists of two stages: network topology control and the fusion of local and global detection. The first stage, an improved low energy adaptive clustering hierarchy (LEACH) algorithm is introduced into the underwater sensor network to choose the most suitable cluster heads and to introduce relay nodes to establish multi-hop routes between clusters, effectively balancing node energy consumption and extending network lifetime. With the network topology, the generalized likelihood ratio rule (GLRT) is employed by each sensor to detect the underwater non-cooperative target and make the corresponding decision during the second phase. Specifically, the member nodes make the multi-bit local soft decision, and then the cluster head node fuses the decision information from each member nodes to make the one-bit hard decision and transmits it to the fusion center, and finally the fusion center makes the global decision, such that the tradeoff between detection accuracy and energy consumption may be balanced by generating the soft and hard decision fusion technique. Simulations show that the strategy maintains detection performance extremely comparable to that of centralized and conventional multi-bit soft decisions while eliminating communication overhead.

Reinforcement learning-based resilient power maximization and regulation control for large-scale wind turbines under cyber actuator attacks

Large-scale wind turbine systems (LS-WTSs) are vulnerable to cyber threats due to their flat control network topology, fragile protocols, and remote locations. To solve these security problems and improve electrical efficiency, this study aims to present a reinforcement learning (RL)-based attack-resilient coordinated pitch and torque control for LS-WTS under actuator attacks. The proposed scheme utilizes the RL technique to reduce the number of tuning parameters and computational complexity. To do this, a model-free agent-environment framework is first proposed with an aperiodic denial-of-service (ADoS) attack model to enable the RL features. Then, an RL-based resilient sliding-mode pitch and torque control scheme is proposed to optimize maximum power extraction and regulation by incorporating reward updates and adaptive learning rates for optimal control input selection under ADoS attacks with varying wind conditions. At the same time, the adaptive learning-rule-based gain fluctuations term is presented to mitigate the uncertain and disruption parameters in the control system. Finally, the effectiveness and robustness of the proposed method are verified through the evaluation of a 4.8MW benchmark and a 20MW PMSG-based LS-WTS, showing distinct advantages over conventional approaches. Theoretical insights are practically validated with experimental prototypes to confirm the resiliency of the presented method against network and physical impacts caused by cyber actuator attacks in the LS-WTS in various wind cases.

A topology control algorithm for fusion networks based on link quality

To extend the network life, an ordinal potential game is introduced into network topology control, and a network topology control algorithm based on the Theil entropy measure is designed by improving the revenue function. The revenue function is based on the Theil entropy measure, whose factors are in the residual energy of the nodes and their neighbors. By using a primary payoff function that considers all initial network factors and a secondary one that addresses connectivity in case of reduced residual energy, a segmentation function can calculate the model's payoff. Simulation experiments show that compared to the existing game algorithms of 3DK-RNG, DEBA, and EFPC, the proposed algorithm can effectively eliminate redundant links, reduce node degree and network link length, balance node energy consumption, enhance network load, and extend the network life cycle.•A Network Topology Control Algorithm Based on the Fusion of Thiel Entropy and Ordinal Potential Games.•A network topology control method for extending the network lifecycle is successfully established.•It proposes that the performance of the fusion topology control algorithm is significantly superior to other single algorithms in the paper.

The Upper Bounds of Cellular Vehicle-to-Vehicle Communication Latency for Platoon-Based Autonomous Driving

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols.

Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control

The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads. We present Duo, a novel demand-aware reconfigurable rack-to-rack datacenter network design realized with a simple and efficient control plane. Duo is based on the well-known de Bruijn topology (implemented using a small number of optical circuit switches) and the key observation that this topology can be enhanced using dynamic (''opportunistic'') links between its nodes. In contrast to previous systems, Duo has several desired features: i) It makes effective use of the network capacity by supporting integrated and multi-hop routing (paths that combine both static and dynamic links). ii) It uses a work-conserving queue scheduling which enables out-of-the-box TCP support. iii) Duo employs greedy routing that is implemented using standard IP longest prefix match with small forwarding tables. And iv) during topological reconfigurations, routing tables require only local updates, making this approach ideal for dynamic networks. We evaluate Duo in end-to-end packet-level simulations, comparing it to the state-of-the-art static and dynamic networks designs. We show that Duo provides higher throughput, shorter paths, lower flow completion times for high priority flows, and minimal packet reordering, all using existing network and transport layer protocols. We also report on a proof-of-concept implementation of Duo's control and data plane.

Метод оцінки зв’язності вузлів бездротових епізодичних мереж за умови застосування безпілотних літальних апаратів

The paper considers a method for assessing the connectivity of nodes of wireless episodic networks (WEN) under the condition of using unmanned aerial vehicles (UAVs). It is proposed to perform an evaluation of the connectivity of a pair of nodes according to a method, which is based on: an evaluation of geometric connectivity, which is limited to the maximum range of radio visibility at the physical level and the vulnerability interval of a given multiple access protocol at the channel level; assessment of information connectivity, which takes into account the presence of not only a physical connection of a given reliability, but also the presence of a free channel resource, a given amount of transmission delay at a given traffic limit value; assessment of the duration of connectivity taking into account the mobility of network nodes. On the basis of analytical mathematical models, the duration of connectivity of mobile subscribers (nodes) (MS) of the WEN consisting of MS and UAVs in direct radio visibility conditions and taking into account relaying was investigated. The connectivity of a pair of nodes is determined by the characteristics of different levels of OSI information interaction, such as radio range, channel bandwidth, information transmission delay, etc. It is shown that the duration of connectivity is directly proportional to the size of the coverage zone and inversely proportional to the speed of movement of nodes. The nature (scenario) of node mobility also affects the duration of connectivity. The simulation of the movement of nodes was carried out under 4 scenarios: «march», «skirmish», «random wandering in the field» and «random wandering in the city». The largest values of the duration of connectivity correspond to the third scenario, and the smallest to the second (with a fixed radius of the coverage area and the speed of movement of nodes). Thus, the average connection duration of the UAV-pedestrian connection in the event of a «difference» will be of the order of 36 minutes, and the UAV-car connection of the order of 5 minutes. The obtained results can be used in the UAV network topology control method.

Energy efficient grid based k‐means clustering algorithm for large scale wireless sensor networks

SummaryHierarchical based routing is a kind of group‐based routing that consists in creating of a virtual hierarchy among network sensor nodes. This class of routing technique is generally designed for large‐scale networks. It aims to efficiently increase network lifetime by cutting the whole network into clusters. However, traditional clustering techniques show some limitations and do not take into consideration the self‐organized with dynamic topology inherent in wireless sensor networks (WSNs). These limitations can lead to an unbalanced cluster head distribution that affects the whole network energy consumption. In this paper, we propose a grid‐based k‐means clustering protocol (named GBK), which combines grid‐based routing with k‐means algorithm in order to overcome the above mentioned weaknesses. From the supervised zone area size parameter, the base station determines the optimal grid size based on our optimization study. Afterwards, the k‐means algorithm is executed in every grid cell generating a cluster head per cell grid where the nearest node to the grid cell centroid is elected. An enhancement of this proposed GBK algorithm named GBK‐R is also proposed to extend network stability of the GBK algorithm by node scoring calculation that take as parameters the node remaining energy in addition to the distance to centroid. Our proposed GBK and GBK‐R algorithms allow for an enhanced network stability and increase the network lifetime as demonstrated by our performance evaluation study. In addition, this GBK clustering algorithm provides a better network topology control and a better control of the random nature of node distribution by generating cluster heads with bounded localization.

A unified model for transient flow analysis of the integrated electric power and natural gas system with multiple time scales

The electric-gas coupling integrated energy system provides an effective way to improve the operation flexibility of IES and the consumption capacity of renewable energy resources. It is very hard to achieve due to the different characteristics between the power and gas system in aspects of control mechanism, network topology and dynamic process. To solve this problem, both a unified model of the transient energy flow and an algorithm for solving this unified model are proposed for the first time in this paper, combining the dynamic modeling of the electric-gas network and the singular perturbation theory. Moreover, in the unified model, an improved model of the gas turbine is designed for capturing the energy interactions of electric-gas networks and meeting the calculation need of multiple time scales simultaneously. Simulation results show that the transient information can be acquired under disturbances of any time by solving the unified model.

ASHEED: Attention-shifting mechanism for depolarization of cluster head energy consumption in the smart sensing system

The sensing node clustering algorithm is a network topology control method that can effectively extend the lifetime of smart sensing systems (SSSMs). However, the traditional topology algorithms suffer from the excessively early death of cluster heads. Hence, the attention-shifting mechanism for energy consumption based on hybrid energy-efficient distributed clustering (HEED) is proposed in this paper, called attention-shifting hybrid energy-efficient distributed clustering (ASHEED). The energy consumption of the cluster heads are reduced by shifting data reception and fusion energy consumption of the cluster heads to other cluster member nodes (Agents) within its competitive radius. Agent selection is performed by communication energy consumption comparison to ensure the rationality of cluster heads and agent positions with the aim of reducing the communication energy consumption for re-clustering. Experiment results demonstrated that the proposed approach could maximize the balance of energy consumption of cluster heads and common nodes, maintain the integrity of the network, and extend the optimal operation time of SSSMs.

Target Controllability of Networked LTI Systems

A generalized-eigenvector-based condition is derived for the target controllability, which is necessary and sufficient. It is then applied to networked MIMO LTI systems with some lower-dimensional conditions established. These conditions are easily-verified, which explicitly show how the node-system dynamics, network topology, inner couplings, target-node positions, and external control inputs affect the target controllability of the network. Then, the target controllability of multi-agent systems is revisited. The omission of the criterion given in [1] is revealed. As a remedy, a modified criterion is suggested. Several examples show the effectiveness of the conditions.

Consensus-Based Distributed Connectivity Control in Multi-Agent Systems

This paper addresses a distributed connectivity control problem in networked multi-agent systems. The system communication topology is controlled through the algebraic connectivity measure, the second smallest eigenvalue of the communication graph Laplacian. The algebraic connectivity is estimated locally in a decentralized manner through a trust-based consensus algorithm, in which the agents communicate the perceived quality of the communication links in the system with their set of neighbors. In the presented approach, link qualities represent the weights of the communication graph from which the adjacency matrix is estimated. The Laplacian matrix and its eigenvalues, including the algebraic connectivity, are then calculated from this local estimate of the global adjacency matrix. A method for network topology control is proposed, which creates and deletes communication links based on the Albert-Barabási probabilistic model, depending on the estimated and referenced connectivity level. The proposed algebraic connectivity estimation and connectivity maintenance strategy have been validated both in simulation and on a physical robot swarm, demonstrating the method performance under varying initial topology of the communication graph, different multi-agent system sizes, in various deployment scenarios, and in the case of agent failure.

Comparison principle and synchronization analysis of fractional-order complex networks with parameter uncertainties and multiple time delays

<abstract><p>This paper investigates the global synchronization problems of fractional-order complex dynamical networks with uncertain inner coupling and multiple time delays. In particular, both internal time delays and coupling time delays are introduced into our model. To overcome the difficulties caused by various delays and uncertainties, a generalized delayed comparison principle with fractional-order and impulsive effects is established by using the Laplace transform. Based on the Lyapunov stability theory and mixed impulsive control technologies, some new synchronization criteria for concerned complex dynamical networks are derived. In addition, the synchronization criteria are related to the impulsive interval, network topology structure, fractional-order, and control gains. The theoretical results obtained in this paper can enhance the value of previous related works. Finally, numerical simulations are presented to show the correctness of our main results.</p></abstract>

Network for AI and AI for Network: Challenges and Opportunities for Learning-Oriented Networks

The “data pipe” model used by the existing Internet protocol stack is no longer ideal for many emerging applications, due to multimedia, multicast, mobility, machine learning, and network management challenges. A new learning-oriented network architecture is required to deal with these challenges and serve learning-centric applications in data centers, around network edges, and on mobile devices. This article focuses on the network for AI and AI for network for learning-oriented network architecture. This is done by leveraging, improving, and creating new learning techniques to determine and optimize protocol mechanisms and control policies. The new network architecture can provide ample research opportunities in network topology control, protocol design, and performance evaluation, aiming to network a truly dependable cyber-infrastructure. The learning-oriented network can also learn from applications and communications automatically and continuously while running on different infrastructures to support diverse requirements. In addition, the network can keep evolving its protocol mechanisms and control policies in an online manner. It does this while maintaining protocol security and preserving user privacy, to learn and perform more effectively and efficiently. Finally, the main challenges and opportunities of learning-oriented network are discussed, encouraging further research.

Multi‐Scale Lipid Membrane Flow by Electron Beam‐Induced Electrowetting

AbstractThis study proposes a new technique for control of lipid membrane flow using an electron beam (EB)‐induced virtual cathode (VC). A spot 2.5 keV EB is indirectly irradiated onto supported lipid bilayers (SLBs) which are formed on a 100‐nm‐thick silicon nitride (SiN) membrane, and then a focused electric field around the EB spot (that is, the VC) causes membrane flow of the SLBs due to a voltage drop across them and electrowetting effects between them and the SiN membrane. This VC technique is shown to be able to change membrane shapes by small‐ and large‐scale controls of lipid membrane flow. For SLBs consisting of a single component, a spot VC can achieve small‐scale control of membrane flow; and the edge of the SLBs is demonstrated to change only around the spot VC. It is also shown that the spot VC can cause large‐scale deformation of SLBs consisting of ternary components by inducing a surface instability phenomenon known as Saffman–Taylor instability. The obtained results indicate that the proposed VC technique can achieve multi‐scale control of lipid membrane flow, which will contribute to on‐demand control of network topology in biomolecular devices based on artificial lipid membranes.

Design and implementation of a smart elderly positioning management system based on wireless communication network

Wireless communication refers to long-distance transmission communication between multiple nodes without propagation through conductors or cables, and wireless communication can be carried out by radio and electromagnetic waves, etc. Judging from the current situation, the home space positioning design of the medical and elderly care communities is not perfect, which affects the traditional home care. Wireless communication can give wisdom, coordination, and development space in the smart elderly care space positioning design, allowing the elderly to interact, contact, and know their precise location and behavior. This article mainly studies the design and implementation of smart pension space positioning under BIM technology. This paper introduces the frontier of Internet-of-things technology and the theory of sensor network topology control. It uses basic IoT technology to implement the "point, line, and surface" combination of personnel placement algorithms, sensor network communication protocols, topology checks, security models, sensor network wireless communication units, and wireless communication technology. The basic technology and communication technology of personnel deployment based on BIM technology are introduced, the development strategy of wireless communications node is studied, the installation of equipment radio personnel and communication equipment is designed, and the system management platform for the design and development of staff placement in pension institutions is provided. The Web site's information service management, standardized procedures, optimized services, provision of information, intelligent management, and pension services. The experimental results of this paper show that the smart pension space positioning under the BIM technology solves the management loopholes in nursing homes, shortage of nursing staff, inefficiency, lack of services, and family conflicts. It improves the efficiency of the elderly by 25% and the safety index by 23–27%. The development of wireless communication technology promotes the development of intelligent pension space. It will be an important future development direction for the smart community pension.

A new algorithm of clustering AODV based on edge computing strategy in IOV

In the vehicular ad hoc network (VANET), due to the particularity of high-speed movement of vehicle nodes, there are higher challenges in link stability and network topology control overhead. In this paper, a new algorithm of clustering AODV based on edge computing strategy is proposed. Considering the vehicle node energy and speed, the AODV routing protocol based on the minimum hop number is optimized, which divided the communication mode into vehicle to vehicle (V2V) and vehicle to road (V2R) mode. Adding edge server in the road side unit (RSU) and using the idea of clustering, that is, the nodes in the cluster use V2V communication mode, and the nodes between clusters use V2V and V2R combined communication mode to select routes. The algorithm improves the routing efficiency in the high-speed mobile. Experiments show that the algorithm is feasible, reducing the network topology control overhead, lowering the end-to-end delay and improving the packet delivery rate comparing with others in different environment.

Improved design of low energy clustering algorithm for mobile sensor network

LEACH class clustering algorithms have been widely used in the topology control of mobile sensor networks because of their simplicity, practicality and good energy efficiency. Aiming at the common problems of LEACH clustering algorithm, such as large energy consumption of nodes, short network life cycle, easy loss of data transmission, etc., according to the set mobile sensor network model, based on LEACH-Mobile algorithm, a low-energy clustering algorithm LEACH-MII is designed. LEACH-MII clustering algorithm assigns tasks according to the working conditions of sensor nodes, combines the activity, location and energy consumption of nodes to select and rotate cluster heads; and uses dynamic time slot allocation mechanism to avoid time slot reallocation caused by frequent join-cluster and leave-cluster of sensor nodes, so as to ensure the stability and connectivity of the network structure, reduce the energy consumption of nodes, and extend the life cycle of the network. In order to verify the feasibility and effectiveness of LEACH-MII clustering algorithm, the simulation experiment of LEACH-MII clustering algorithm is carried out. The simulation results show that The data transmission efficiency, average node energy consumption and network life cycle of LEACH-MII network are 30%, 20% and 15% better than LEACH-Mobile network, respectively. In addition, the performance of LEACH-MII network is better than that of LEACH-Mobile network, such as the load balance degree, the scalability of network monitoring range, and the speed effect of network life cycle.