Networks In Dynamic Environments Research Articles

Sum Rate Optimization for Multi-IRS-Aided Multi-BS Communication System Based on Multi-Agent

Intelligent reflecting surface (IRS) is a revolutionizing technology for improving the spectral and energy efficiency of future wireless networks. In this paper, we consider a downlink large-scale system empowered by multi-IRS to aid communication between the multiple base stations (BSs) and multiple user equipment (UEs). We target maximizing the sum rate by jointly optimizing the UE association, the transmit powers of BSs, and the configurations of the IRS beamforming. Due to the applicability restrictions of conventional optimization methods and their high complexity with large-scale networks in dynamic environments, deep reinforcement (DRL) learning is adopted as an alternative approach to finding optimal solutions. First, we model the optimization problem as a multi-agent Markov decision problem (MAMDP). Then, because large-scale wireless networks are naturally complex and changeable, and because many entities interact and affect how the whole system works, it is important to use a multi-agent approach to understand the complex dependencies and relationships between the different parts. In order to solve the problem, we propose a cooperative multi-agent deep reinforcement learning (MADRL)-based algorithm that works for both continuous and discrete IRS phase shifts. Simulation results validate that the proposed algorithm surpasses iterative optimization benchmarks regarding both sum rate performance and convergence.

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Wireless sensor networks, have drawn a lot of interest because of their adaptability and range of uses in different industries. WSNs face significant challenges when it comes to energy efficiency because sensor nodes are usually battery-powered and have limited resources. Several energy-efficient methods and protocols, such as duty cycling, data aggregation, and topology management, have been put forth to address this problem. Moreover, new potential for mobile wireless sensor networks are presented by the integration of WSNs with mobile and static devices, such as drones, tablets, and smartphones. In this study, we suggest utilizing Zigbee technology to establish a robust and flexible monitoring system for both stationary and mobile sensors. Numerous industries, including healthcare, smart agriculture, asset tracking, energy management, smart home automation, and industrial monitoring and control, have made extensive use of Zigbee. By leveraging Zigbee's capabilities, we hope to improve the protocol's performance while establishing dependable communication links between nodes, analyzing the communication range, and assessing the influence of environmental conditions. In this study, a system model for Zigbee deployment in mobile robots will be presented. It will address the basics of Zigbee, communication difficulties, networking with Zigbee, and simulations or real-world outcomes. We will learn about the strengths and weaknesses of Zigbee-based systems in terms of creating reliable communication links in mobile wireless sensor networks by looking at their architecture and functionality. The results of this study will help us comprehend Zigbee's potential to improve monitoring systems and make better decisions across a range of industries. The study's emphasis on mobile monitoring systems signifies a step forward in addressing the evolving needs of wireless sensor networks in dynamic environments.

Robust energy efficient maximization in wireless powered CRNs based on power splitting

In this paper, we study the optimal design for simultaneous wireless information and power transfer (SWIPT) in cognitive radio networks (CRNs), where each secondary receiver (SR) can harvest energy and decode information using the signals received from the secondary transmitter (ST). It is assumed that the ST and SR adopt orthogonal frequency division multiplexing (OFDM) modulation. Each SR uses the power splitting (PS) to coordinate the process of energy harvesting (EH) and information decoding (ID). The optimization problem of maximizing the energy efficiency of the secondary system is formulated. In particular, in order to accurately analyze the uncertain channel state information (CSI) and improve the performance of wireless networks in dynamic environments, the probability constraints are introduced. To settle this non-convex problem, the nonlinear fractional programming is utilized. Then we decompose the equivalent optimization problem into two convex subproblems, and the probability constraints of signal-to-interference-plus-noise ratio (SINR) are converted to deterministic ones by integral transformation. An efficient iterative algorithm for robust resource allocation is presented. Finally, simulation results have verified the effectiveness of the proposed algorithm.

GloDyNE: Global Topology Preserving Dynamic Network Embedding

Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step. The idea of most existing DNE methods is to capture the topological changes at or around the most affected nodes (instead of all nodes) and accordingly update node embeddings. Unfortunately, this kind of approximation, although can improve efficiency, cannot effectively preserve the global topology of a dynamic network at each time step, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To tackle this challenge, we propose a novel node selecting strategy to diversely select the representative nodes over a network, which is coordinated with a new incremental learning paradigm of Skip-Gram based embedding approach. The extensive experiments show GloDyNE, with a small fraction of nodes being selected, can already achieve the superior or comparable performance w.r.t. the state-of-the-art DNE methods in three typical downstream tasks. Particularly, GloDyNE significantly outperforms other methods in the graph reconstruction task, which demonstrates its ability of global topology preservation. The source code is available at https://github.com/houchengbin/GloDyNE

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

Millimeter-wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. Thus, the communicating devices need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes an efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this paper, we discuss the above-mentioned challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at a lower frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.

Swarm Intelligence-Inspired Autonomous Flocking Control in UAV Networks

The collaboration of multiple unmanned aerial vehicles (UAVs) has stimulated the emergence of a novel wireless network paradigm named UAV network. UAV network, compared with uncoordinated UAV systems could provide wider coverage, better monitoring, and understanding of the interested area, and smarter decision-making. However, realizing the full potential of UAV network in dynamic environments poses great challenges in topology/flocking control, energy conservation, and quality of service guarantee. In this backdrop, this paper proposes a swarm intelligence-inspired autonomous flocking control scheme for UAV networks. First, based on the concept of intelligent emergence of swarm agents, a swarm intelligence-inspired multi-layer flocking control scheme is built for the flocking control problem. Second, an integrated sensing and communication method is put forward to regulate how a UAV can calculate its distances to its neighbors and its deflection angle. Finally, a series of experiments are conducted on our simulator developed on OMNeT++ and the flocking prototype to evaluate the effectiveness of the proposed scheme. The simulation and experimental results have shown that the proposed scheme could realize efficient flocking control with low energy consumption and satisfied the quality of service.

Research on backbone node deployment for Wireless Mesh Networks in dynamic environments

Wireless Mesh Network is a type of wireless networks in which demands of bandwidth for users has mobility. The backbone node placement of wireless mesh networks in a dynamic scenario is investigated, and the TSDPSO algorithm is used to adapt the dynamic environment, which updates node deployment location to adapt to changes in demand if it detects environmental changes at the beginning of the cycle time. In order to meet the demands of bandwidth for users and network connectivity, particle swarm optimization algorithm is employed to select the gateway location, then nodes to the backbone network is added constantly until all requirement is covered. The experimental results show that algorithm could get effective solution in dynamic environment.

The complete coverage for the vacuum cleaner robot using pulse-coupled neural network in dynamic environments

The complete coverage for the vacuum cleaner robot using pulse-coupled neural network in dynamic environments

Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.

Solving the Towers of Hanoi – how an amoeboid organism efficiently constructs transport networks

Many biological systems require extensive networks to transport resources and information. Biological networks must trade-off network efficiency with the risk of network failure. Yet, biological networks develop in the absence of centralised control from the interactions of many components. Moreover, many biological systems need to be able to adapt when conditions change and the network requires modification. We used the slime mould Physarum polycephalum (Schwein) to study how the organism adapts its network after disruption. To allow us to determine the efficiency of the constructed networks, we used a well-known shortest-path problem: the Towers of Hanoi maze. We first show that while P. polycephalum is capable of building networks with minimal length paths through the maze, most solutions are sub-optimal. We then disrupted the network by severing the main connecting path while opening a new path in the maze. In response to dynamic changes to the environment, P. polycephalum reconstructed more efficient solutions, with all replicates building networks with minimal length paths through the maze after network disruption. While P. polycephalum altered some of its existing network to accommodate changes in the environment, it also reconstructed large sections of the network from scratch. We compared the results obtained from P. polycephalum with those obtained using another distributed biological system: ant colonies. We hypothesise that network construction in ants hinges upon stronger positive feedback than for slime mould, ensuring that ants converge more accurately upon the shortest path but are more constrained by the history of their networks in dynamic environments.

Autonomic and Distributed Joint Routing and Power Control for Delay-Sensitive Applications in Multi-Hop Wireless Networks

Multi-hop wireless networks can provide flexible network infrastructures at a low cost. However, most existing wireless networking solutions are designed for delay-insensitive applications, thereby resulting in poor performance when handling delay-sensitive applications. Traditionally, network design problems are formulated as static optimizations, by assuming the network characteristics remain static. However, these solutions are not optimal when the environments are dynamic. Recently, several research works apply machine learning to maximize the performance of multi-hop wireless networks in dynamic environments, but they either only focus on determining policies at the network layer, without considering the lower-layers' actions, or use centralized learning approaches, which are inefficient for delay-sensitive applications, due to the large delay when propagating messages throughout the network. We propose in this paper a new solution that enables the nodes to autonomously determine their routing and transmission power to maximize the network utility, in a dynamic environment. We formulate the problem as a Markov Decision Process, and propose a distributed computation of the optimal policy. Moreover, we use reinforcement-learning to find the optimized policy when the dynamics are unknown. We explicitly consider the impact of the information overhead on the network performance, and propose several novel algorithms to reduce the information overhead.

Self-organizing broadband hybrid wireless networks

Feature Issue on Optical Wireless Communications (OWC) We describe what we believe to be a new paradigm for autonomous configuration and self-organization of directional broadband wireless networks. These networks use combined free-space optics (FSO) and directional radio-frequency (RF) wireless communication modalities, along with agile transceivers that can intelligently direct their narrow beams. Autonomous hardware and software control of network topologies is used to set up links or reconfigure them in response to performance degradation. We call our new paradigm for dynamic network operation and self-organization "topology control." We describe research in the design, development, and evaluation of novel, fully optical wireless pointing, acquisition, and tracking (PAT) techniques. We also describe the modeling and simulation of hybrid FSO-RF networks in dynamic environments, which include changing traffic and atmospheric conditions, and node mobility.

Survivability of Multiagent-Based Supply Networks: A Topological Perspective

Supply chains involve complex Webs of interactions among suppliers, manufacturers, distributors, third-party logistics providers, retailers, and customers. Although fairly simple business processes govern these individual entities, real-time capabilities and global Internet connectivity make today's supply chains complex. Survivability is a critical factor in supply network design. Specifically, supply networks in dynamic environments, such as military supply chains during wartime, must be designed more for survivability than for cost effectiveness. We present a methodology for building survivable large-scale supply network topologies that can extend to other large-scale MASs. To create survivable-and hence dependable-multiagent systems, we must also consider the interplay between network topology and node functionalities.

A Flexible Dynamic Traffic Model for Reverse Link CDMA Cellular Networks

In this paper, we focus on the reverse link traffic analysis of a code-division multiple-access (CDMA) cellular network in dynamic environments. In this respect, we propose a new and flexible traffic model, which takes into account the interference-limitedness attribute of CDMA capacity as well as its soft-handoff feature. This new traffic model is developed according to an interference-based call admission control (ICAC) method and a geographical structure with three regions. The main advantage of this traffic model is in its flexibility when we consider different traffic conditions including time-varying status of traffic in the neighboring cells.