Preserving Network Connectivity Research Articles

Effect of molecular structure on the dynamics and viscoelasticity of vitrimers

Vitrimers are a class of dynamic polymer networks that perform bond-exchange reactions (BERs) under environmental stimuli while preserving network connectivity. The viscoelastic properties of vitrimers are not only dependent on the kinetics of BERs, but also the architectures of polymer matrix. Here, we investigate the influence of polymer architectures on the dynamics and linear viscoelasticity of vitrimers through varying the sticker (reactive bead) distribution in the precursor chain and the BERs energy barrier (Ub) by performing hybrid molecular dynamics-Monte Carlo simulations. Increasing Ub, vitrimers with block and gradient distributions exhibit considerably faster relaxations of chain ends, Rouse mode, and stress and lower zero-shear viscosity than the analogs with random and uniform distributions, in qualitative agreement with the predictions of the sticky Rouse model. In contrast, the distribution of stickers has a small influence on the relaxation of dynamic bonds regardless of the Ub value. By comparing the molecular structures of vitrimers with different distributions of stickers, we find that the molecular defects, especially the relatively longer dangling end in block and gradient distributions, impair the effect of dynamic bonds on the dynamics and viscoelasticity of vitrimers. This suggests that the dangling defects can be leveraged for further development of recyclable and healable materials with fast stress relaxation and improved flowability.

Connectivity preservation control for multiple unmanned aerial vehicles in the presence of bounded actuation

This paper proposes a novel multi-unmanned aerial vehicle (UAV) connectivity preservation controller, suitable for scenarios with bounded actuation and limited communication range. According to the hierarchical control strategy, controllers are designed separately for the position and attitude subsystems. A distributed position controller is developed, integrating an indirect coupling control mechanism. The innovative mechanism associates each UAV with a virtual proxy, facilitating connections among adjacent UAVs through these proxies. This structuring assists in managing the actuator saturation constraints effectively. The artificial potential function is utilized to preserve network connectivity and fulfill coordination among all virtual proxies. Additionally, an attitude controller designed for finite-time convergence guarantees that the attitude subsystem adheres precisely to the attitude specified by the distributed position controller. Simulation results validate the efficacy of this distributed formation controller with connectivity preservation under bounded actuation conditions. The simulation results confirm the effectiveness of the distributed connectivity preservation controller with bounded actuation.

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network’s physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem’s assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics.

Research on gaussian mixture model and its distributed data mining algorithm in wireless sensor networks

Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method’s effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN’s adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques.

Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network

Traffic on backbone communication networks is growing significantly every year. This results in an increase in both energy consumption and the carbon footprint they leave on the environment. As a response, research efforts are focused on reducing energy consumption in telecom networks. Wavelength division multiplexing (WDM) optical networks are key for addressing rising bandwidth demands in backbone networks, but this leads to a concurrent surge in energy usage. Additionally, regions with high seismic activity risk damage to backbone networks from earthquakes, causing significant bandwidth loss and service disruptions. This paper aims to reduce the energy consumption in a backbone network by implementing an algorithm that optimizes energy efficiency while preserving network connectivity and resistance to earthquake phenomena. The proposed algorithm redesigns and modifies a backbone network by deactivating the unnecessary links without affecting the network performance. The scheme is extensively evaluated through simulations using real seismic data from the Geodynamic Institute of the National Observatory of Athens, confirming earthquake resilience and energy efficiency goals, with an energy saving of up to 9% compared to existing solutions.

Distributed dynamic obstacle avoidance design to connectivity-preserving formation control of uncertain underactuated surface vehicles under a directed network

This paper presents a Lyapunov-based recursive design approach for unifying distributed connectivity-preserving formation control and dynamic obstacle avoidance problems of uncertain multiple underactuated surface vehicles (USVs) with heterogeneous range constraints. Compared with related works, the main contribution of this study is to develop a novel control design for preserving network connectivity under dynamic obstacle avoidance in the distributed formation tracking framework. Because of the limited communication ranges, it is difficult to maintain the network interactions among USVs during dynamic obstacle avoidance. To deal with this problem, a collision-avoiding function is defined as a local error surface for the recursive control design, and a distributed complementary signal vector is introduced in the connectivity-preserving formation error surface. The complementary signals are designed to prevent network disconnections when the formation error is increased by the avoidance action and to ensure the stability of the collision-avoiding error dynamics, which eventually lead to preventing network disconnections during dynamic obstacle avoidance. An adaptive formation tracking scheme is designed via the neural networks-based command-filtered backstepping. The stability of the overall closed-loop system with preserved network connectivity and obstacle avoidance is proved based on the Lyapunov stability theorem. Finally, the theoretical approach is demonstrated through simulations.

Fault‐tolerance based on augmenting approach in wireless sensor networks

SummaryA critical node is a sensor whose failure causes loss of connectivity and network fragmentation. In wireless sensor networks, the failure of a critical node, like the failure of all sensor nodes, can be caused by energy depletion or physical failure. To overcome the problem of failure of such nodes, this article proposes a fault‐tolerant strategy that allows a routing protocol to tolerate the failure of a critical node. The proposed strategy is carried out in two phases. In the first phase, a hybrid genetic algorithm with a local search heuristic called genetic algorithm‐critical node problem (GA‐CNP) is used to select the critical nodes and in the second phase, an algorithm called Aug‐CNP is involved to deal with the augmentation problem by deploying additional wireless edges to preserve network connectivity in case of critical node failure. Our proposal has been developed using the OMNET++ simulator, evaluated, and compared to the AODV protocol. The simulation results showed that the GA‐CNP algorithm selects the critical nodes whose failure can degrade the network lifetime with a rate of 40%. Moreover, the Aug‐CNP algorithm applied to the AODV protocol brings improvements in terms of network lifetime which reaches 22% compared to the traditional AODV protocol.

A universal error transformation strategy for distributed event-triggered formation tracking of pure-feedback nonlinear multiagent systems with communication and avoidance ranges

A universal error transformation method is proposed for designing a distributed event-triggered formation tracker with preserved network connectivity and collision avoidance for multiple pure-feedback nonlinear multiagent systems connected in a directed network. It is assumed that the communication and avoidance ranges of agents are heterogeneous and that all nonaffine nonlinear functions are unknown. The primary contribution of this study is to establish a universal nonlinear transformation of relative output errors among agents for dealing with the initial network interaction, collision prevention, and predefined-time tracking performance problems in a fully distributed manner. To this end, nonlinear relative output errors among agents are designed using performance functions dependent on communication and avoidance ranges. Local event-triggered adaptive control laws are recursively designed using nonlinear relative output errors and neural networks. It is proved that the boundedness of the presented nonlinear errors ensures the achievement of network interaction preservation, collision prevention, and formation tracking with predefined-time convergence. Furthermore, the existence of a minimum inter-event time is established for local event-triggered control laws. Finally, the effectiveness of the proposed theoretical strategy is validated by simulation examples.

Deep Reinforcement Learning-Based Effective Coverage Control With Connectivity Constraints

This work studies dynamic coverage control of a multi-agent system using deep reinforcement learning. Dynamic coverage control is a type of cooperative control which requires a multi-agent system to dynamically monitor an area of interest over time. To develop motion control laws, most of previous works highly rely on the knowledge of system models, such as the environment model and agent kinematics/dynamics. However, acquiring an accurate model can be restrictive and even impossible in many practical applications. Another challenge is that agent often has a limited communication capability in practice. Two agents may only exchange information when they are within a certain distance. To address these challenges, a multi-agent deep reinforcement learning (MADRL) based control framework is developed to enable agents to learn control policies directly from interactions with the environment to achieve dynamic coverage control while preserving network connectivity. The developed MADRL is model free and employs decentralized execution and centralized training, in which agents coordinate using only local information and do not need to know other agents' strategies at execution phase. Numerical simulations demonstrate the effectiveness of the developed control strategy.

Unentangled Vitrimer Melts: Interplay between Chain Relaxation and Cross-link Exchange Controls Linear Rheology

Vitrimers are polymer networks that engage in dynamic associative exchange reactions. Their covalent cross-links preserve network connectivity but permit topology fluctuations, making them both ins...

RETRACTED ARTICLE:Optimal coverage along with connectivity maintenance in heterogeneous wireless sensor network

Heterogeneous wireless sensor network (HWSN) is a network which contains sensor nodes with dissimilar capabilities, such as varying energy, sensing range etc. In Heterogeneous wireless sensor network, Coverage along with connectivity is one of the major issues. Coverage with no connectivity is meaningless in wireless sensor networks. In most of the existing work, coverage and connectivity is not provided efficiently for HWSN. The existing methods used in HWSN to attain coverage and connectivity are static and not applied in the dynamic environment. Optimal Coverage and connectivity maintenance not provided. Energy consumption should be concentrated to maximize the network lifetime. Preserving network connectivity at the same time maximizing coverage by using the less number of energy constrained sensor nodes is the most serious problem. To overcome these issues, in this work we put forward a technique called Coverage Scheduling and Power Aware Connectivity (CSPAC). Grouping the sensors into cluster by the strategy of Dynamic cluster formation. Identifying the minimum active set to provide Q-coverage by Artificial Bee Colony Optimization technique and maximize lifetime by mapping Power Aware Scheduling and Battery Discharge Value. Then Optimal Connectivity Management Technique for reachability to provide strong connectivity. By simulation results, we give you an idea that the proposed technique enhances the network coverage and connectivity in HWSN.

Binary grey wolf optimisation‐based topology control for WSNs

Wireless sensor networks (WSNs) are composed of a large number of sensor nodes that are deployed at target locations. Topology control (TC) is one of the significant fundamental challenges in WSNs because of node energy and computing power constraints. TC algorithms try to produce reduced topology by preserving network connectivity. This study presents a novel TC algorithm based on binary Grey wolf optimisation. It uses the active and inactive schedules of sensor nodes in binary format as well as introduces fitness function to minimise the number of active nodes (ANs) for achieving the target of lifetime expansion of the nodes and network. The proposed algorithm is compared with other TC algorithms. The result reduces a minimum of 10% of ANs and energy consumption by 6.84%. The proposed approach also gives maximum coverage and connectivity. The designed fitness function also benefits in the process of selecting a node with low residual energy to join the active topology. The standard deviation in the remaining energy for the proposed algorithms is lower than the other TC schemes.

Analysis of Security Mechanism in Adhoc Network with Machine Learning Techniques

In an ad hoc network, the routing protocol takes into account a variety of activities, including maintaining network connection, transmission scheduling, channel evaluation, and preserving network connectivity. Additionally, it determines network architecture. In addition, a number of different elements determines the performance of a routing protocol. These include node mobility, which is responsible for multiple link failures, support for quality of service (QoS), network size, the amount of traffic, and the level of security. The performance may occasionally also be affected by the manner in which the network is behaving in addition to the kinds of apps that are running in that environment. Selecting an appropriate protocol that is based on security is highly crucial in order to set up an effective network. A significant amount of effort has been put into improving the safety mechanisms that are built into routing protocols, most prominently in WSNs, MANETs, VANETs, and WMNs. Only MANET will be the topic of discussion here. Mobile Ad-Hoc Network is a wireless network that does not need infrastructure and is composed of mobile nodes. Mobile ad-hoc network, also known as MANET, is one of the most promising forms of next-generation wireless networking technology. It has garnered a significant amount of interest because it is self-organized and can be deployed at a cheap cost. In comparison to a traditional network, a MANET presents a number of challenges that are especially difficult to overcome when it comes to the duty of routing. The many difficulties that are inherent with MANET have made it an excellent subject for academic investigation. This provides a concise overview of security in MANETs as well as the issues that relate to maintaining them. Understanding the different routing mechanisms and the potential attacks that might be mounted against them is the first step in designing a reliable security mechanism. Within the scope of this study effort, we have provided specifics on the detection and prevention of various routing attacks, with the primary emphasis being placed on the network layer assaults that are unique to MANET. When compared to other study fields, MANET presents the greatest challenge in terms of maintaining network security. In recent years, a significant amount of research has been carried out to investigate several forms of assault; nevertheless, most of the surveys have been carried out without any kind of performance analysis. There is a paucity of research that seeks to find an all-encompassing study of the impact of the many different attacks that bring the overall performance of the Adhoc network down. On the other hand, secure routing in the face of a black hole attack can be difficult because preferences are often incomplete. The in-degree centrality and importance degree measurement applied to the collected consensus-based trust from decision-makers solves the issue of incomplete preferences and improves the accuracy of trust at the same time. Utilizing Network Simulator, we examine how well the suggested approach works. Based on the findings of the simulations, it has been demonstrated that the detection accuracy and throughput of the proposed CREDIT are both significantly higher than those of existing work, making the proposed CREDIT scheme superior.

Distributed Optimization of Multiagent Systems With Preserved Network Connectivity.

This paper deals with the problem of distributed optimization of a multiagent system with network connectivity preservation. In order to realize cooperative interactions, a connected network is the prerequisite for high-quality information exchange among agents. However, sensing or communication capability is range-limited, so it is impractical to simply make an assumption that network connectivity is preserved by default. To address this concern, a class of generalized potentials including discontinuities caused by unexpected obstacles or noises are designed. For a class of quadratic cost functions, based on the potentials, a new distributed protocol is proposed to formally guarantee the network connectivity over time and to realize the state agreement in finite time while the sum of local functions known to individual agents is optimized. Since the right-hand side of the proposed protocol is discontinuous, some nonsmooth analysis tools are applied to analyze system performance. In some practical scenarios, where initial states are unavailable, a distributed protocol is further developed to realize the consensus in a prescribed finite time while solving the distributed optimization problem and maintaining network connectivity. Illustrative examples are provided to demonstrate the effectiveness of the proposed protocols.

Connectivity and coverage based protocols for wireless sensor networks

A wireless sensor network (WSN) consists of a group of energy-constrained sensor nodes with the ability of both sensing and communication, which can be deployed in a field of interest (FoI) for detecting or monitoring some special events, and then forwarding the aggregated data to the designated data center through sink nodes or gateways. In this case, whether the WSN can keep the FoI under strict surveillance and whether the WSN can gather and forward the desired information are two of the most fundamental problems in wireless sensor networks that need to be solved. Therefore, preserving network connectivity while maximizing coverage by using the limited number of energy constrained nodes is the most critical problem for the deployment of WSNs. In this survey article, we classify and summarize the state-of-the-art algorithms and techniques that address the connectivity-coverage issues in the wireless sensor networks.

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer’s disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.

Energy Management Dynamic Control Topology In MANET

Topology management via per-node transmission power adjustment has been shown effective in extending network lifetime. The existing algorithms constructs static topologies which fail to take the residual energy of network nodes, and cannot balance energy consumption efficiently. To address this problem, a Light Weighted Distributed Topology Control algorithm EMDCT(Energy Management Dynamic Control Topology ) is proposed in this paper. Based on the link metric of the network, both the energy consumption rate level and residual energy levels at the two end nodes are considered. EMDCT generates a Dynamic Topology that changes with the variation of node energy without the aid of location information, each node determines its transmission power according to local network information, which reduces the overhead complexity of EMDCT greatly. The experiment results show that EMDCT preserves network connectivity and manitains minimum-cost property of the network also it can extend network lifetime more remarkably.

Formation tracking of multi-vehicle systems in unknown environments using a multi-region control scheme

ABSTRACTIn this paper, a multi-region control scheme is proposed for a formation of nonholonomic vehicles to track a reference trajectory while avoiding collisions and preserving network connectivity in unknown environments. The proposed control scheme defines three regions, safe region, dangerous region and transition region. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees bounded tracking of the reference trajectory for each vehicle. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to characterise constraints of obstacle and inter-vehicle collision avoidance as well as connectivity maintenance. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance control is achieved in transition region. It has been proved that each vehicle can track its reference trajectory while satisfying the constraints simultaneously with a bounded controller which means that the proposed control scheme satisfies input constraints by properly tuning parameters. Simulation results demonstrate the effectiveness of the proposed method.

Decentralized Rendezvous of Nonholonomic Robots With Sensing and Connectivity Constraints

A group of wheeled robots with nonholonomic constraints is considered to rendezvous at a common specified setpoint with a desired orientation while maintaining network connectivity and ensuring collision avoidance within the robots. Given communication and sensing constraints for each robot, only a subset of the robots are aware or informed of the global destination, and the remaining robots must move within the network connectivity constraint so that the informed robots (IRs) can guide the group to the goal. The mobile robots are also required to avoid collisions with each other outside a neighborhood of the common rendezvous point. To achieve the rendezvous control objective, decentralized time-varying controllers are developed based on a navigation function framework to steer the robots to perform rendezvous while preserving network connectivity and ensuring collision avoidance. Only local sensing feedback, which includes position feedback from immediate neighbors and absolute orientation measurement, is used to navigate the robots and enables radio silence during navigation. Simulation results demonstrate the performance of the developed approach.

Optimized and Smart Link Removal Topology Control for Unplanned Wireless Mesh Networks

Topology control of a network makes it possible for wireless nodes to reduce extra links while ensuring network connectivity and preserving at least one path for all pairs of the source and destination. The minimum edges should be constructed by allocating a single path to each flow and removing the edge, which do not affect the throughput of the network and ultimately removes all extra links. The resultant network topology then preserves network connectivity while also maintaining the desirable features like reduction in average node degree, quality of service, and throughput. To do so, we developed an algorithm called Link Removal that considers residue Capacity and Delay (LRCD) and removes the redundant links in a network by shifting the data load to suitable links that are responsible for making the path. Simulation results prove the effectiveness of LRCD and show that this not only produces good results, but is also beneficial for establishing a cost effective network.