Request-service scheduling in dynamic communication networks

In this paper, we propose a hybrid evolutionary algorithm (EA) for the optimization of efficient monitoring in dynamic communication networks. The first step towards improving communication infrastructures is gathering information about the current situation. One part of collecting this information is to implement an adequate monitoring in the network, i.e., the optimal positions and amount of monitoring devices, in order to analyze communication flows.Solving the general monitor selection problem using evolutionary computation has already been done in the past. Our approach focuses on the efficient optimization of monitors having a dynamic search landscape, i.e., having recurring substantial changes of the underlying network model in order to simulate bulks of entering or leaving nodes and edges.Here, we compare the steady optimization versions of a common genetic algorithm (GA), the proposed hybrid EA, and a local search based EA, in conjunction with a total restart version of the hybrid EA. Empirical results are obtained using multiple well-known real-world problem instances. We show that we can achieve reliably fast high quality results using the proposed hybrid EA.

Semi-supervised anomaly detection in dynamic communication networks

We have been looking at the properties of physical configurations that occur in nature in order to characterize, predict, and control network robustness in dynamic communication networks. Our framework is based on the definition of a potential energy function to characterize robustness in communication networks and the study of first- and second-order variations of the potential energy to provide prediction and control strategies for network-performance optimization. This paper describes novel investigations within this framework that draw from molecular system dynamics. The Morse potential, which governs the energy stored in bonds within molecules, is considered for the characterization of the potential energy of communication links in the presence of physical constraints such as the power available at the transmitters in a network. The inclusion of the Morse potential translates into improved control strategies, where forces on network nodes drive the release, retention, or reconfiguration of communication links based on their role within the network architecture. The performance of the proposed approach is measured in terms of the number of source-to-destination connections that have an end-to-end communications path. Simulation results show the effectiveness of our control mechanism, where the physical topology reorganizes tomaximize the number of source-to-destination communicating pairs. The algorithms developed are completely distributed, show constant time complexity and produce optimal solutions from local interactions, thus preserving the system's self-organizing capability.

Slide—The Key to Polynomial End-to-End Communication

Coordinated Path Following of Multiple UAVs for Time-Critical Missions in the Presence of Time-Varying Communication Topologies

In this chapter, the regulation of an Unmanned Aerial Vehicle (UAV) communication network is investigated in the presence of dynamic changes in the UAV lineup and user distribution. We target an optimal UAV control policy which is capable of identifying the upcoming change in the UAV lineup (quit or join-in) or user distribution, and proactively relocating the UAVs ahead of the change rather than passively dispatching the UAVs after the change. Specifically, a deep reinforcement learning (DRL)-based UAV control framework is developed to maximize the accumulated user satisfaction (US) score for a given time horizon which is able to handle the change in both the UAV lineup and user distribution. The framework accommodates the changed dimension of the state-action space before and after the UAV lineup change by deliberate state transition design. In addition, to handle the continuous state and action space, deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic based DRL method, is exploited. Furthermore, to promote learning exploration around the timing of the change, the original DDPG scheme is adapted into an asynchronous parallel computing (APC) structure which leads to better training performance in both the critic and actor networks. Finally, extensive simulations are conducted to validate the convergence of the proposed learning approach, and demonstrate its capability in jointly handling the dynamics in UAV lineup and user distribution as well as its superiority over a passive reaction method.KeywordsUnmanned aerial vehicle (UAV)UAV-based communication networksDeep reinforcement learningDynamic user spatial distributionUAV trajectory design

Coordination in a large number of networked robots is a challenging task, especially when robots are constantly moving around the environment and there are malicious attacks within the network. Various approaches in the literature exist for detecting malicious robots, such as message sampling or suspicious behavior analysis. However, these approaches require every robot to sample every other robot in the network, leading to a slow detection process that degrades team performance. This paper introduces a method that significantly decreases the detection time for legitimate robots to identify malicious robots in a scenario where legitimate robots are randomly moving around the environment. Our method leverages the concept of "Dynamic Crowd Vetting" by utilizing observations from random encounters and trusted neighboring robots' opinions to quickly improve the accuracy of detecting malicious robots. The key intuition is that as long as each legitimate robot accurately estimates the legitimacy of at least some fixed subset of the team, the second-hand information they receive from trusted neighbors is enough to correct any misclassifications and provide accurate trust estimations of the rest of the team. We show that the size of this fixed subset can be characterized as a function of fundamental graph and random walk properties. Furthermore, we formally show that as the number of robots in the team increases the detection time remains constant. We develop a closed form expression for the critical number of time-steps required for our algorithm to successfully identify the true legitimacy of each robot to within a specified failure probability. Our theoretical results are validated through simulations demonstrating significant reductions in detection time when compared to previous works that do not leverage trusted neighbor information.

This paper investigates the consensus problem for a team of agents with inconsistent information, which is a core component for many proposed distributed planning schemes. Kalman filtering approaches to the consensus problem have been proposed, and they were shown to converge for strongly connected networks. However, it is demonstrated in this paper that these previous techniques can result in biased estimates that deviate from the centralized solution, if it had been computed. A modification to the basic algorithm is presented to ensure the Kalman filter converges to an unbiased estimate. The proof of convergence for this modified distributed Kalman consensus algorithm to the unbiased estimate is then provided for both static and dynamic communication networks. These results are demonstrated in simulation using several simple examples

Despite great advances in research and treatment, lung cancer is still one of the most leading causes of cancer-related deaths worldwide. Evidence is mounting that dynamic communication network in the tumor microenvironment (TME) play an integral role in tumor initiation and development. Cancer-associated fibroblasts (CAFs), which promote tumor growth and metastasis, are the most important stroma component in the tumor microenvironment. Consequently, in-depth identification of relevant molecular mechanisms and biomarkers related to CAFs will increase understanding of tumor development process, which is of great significance for precise treatment of lung cancer. With the development of sequencing technologies such as microarray and next-generation sequencing, lncRNAs without protein-coding ability have been found to act as communicators between tumor cells and CAFs. LncRNAs participate in the activation of normal fibroblasts (NFs) to CAFs. Moreover, activated CAFs can influence the gene expression and secretion characteristics of cells through lncRNAs, enhancing the malignant biological process in tumor cells. In addition, lncRNA-loaded exosomes are considered to be another important form of crosstalk between tumor cells and CAFs. In this review, we focus on the interaction between tumor cells and CAFs mediated by lncRNAs in the lung cancer microenvironment, and discuss the analysis of biological function and molecular mechanism. Furthermore, it contributes to paving a novel direction for the clinical treatment of lung cancer.

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.

The author considers the problem of packet routing in a dynamic communication network as a problem of autonomous agent navigation in an uncertain environment, and proposes a novel method for network routing. The basic idea is that, instead of using an estimated constant, one uses a probability distribution to model the delay of link in a computer network, and computes routing tables based on such a probabilistic model of the network. Since the new model is no longer just a snapshot of the network at a particular time, but represents a probabilistic pattern which treats the changes of link delays as normal rather than as an exception, it is reasonable to expect that the routing tables based on the new model can be very robust against the dynamic changes of link delays. A computer network with such probabilistic information can be represented by a U-graph and the network routing problem is quite similar to mobile agent navigation in uncertain environments. >

The proposed work illustrates a novel technique for cooperative spectrum sensing in a cognitive radio (CR) network. The work includes an approach of identifying secondary users (SUs) based on Hierarchical Maximum Likelihood (HML) technique followed by Vector Quantization. Initially, the arrangement of the SUs are been observed using HML with respect to a spatial domain and then the active SUs among them are identified using VQ. The approach will not only save the energy, but the decision of the real-time and dynamic cooperative communication network becomes more accurate as we can predict the behavior of SUs movement and spectrum sensing by each individual SU at that particular place. The results and simulations of the real-time experiment justifies with the proposed approach.

The authors consider the problem of traveling with least expected delay in networks whose link delays change probabilistically according to Markov chains. This is a typical routing problem in dynamic computer communication networks. They formulate several optimization problems, posed on infinite and finite horizons, and consider them with and without using memory in the decision making process. It is proved that all these problems are, in general, intractable. However, for networks with nodal stochastic delays, a simple polynomial optimal solution is presented. This is typical of high-speed networks, in which the dominant delays are incurred by the nodes. For more general networks, a tractable epsilon -optimal solution is presented. The performance of a regular shortest-path algorithm in such an environment is considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The aim of the paper is to present suitable mobility management and call routing protocols for a non-GEO based satellite personal communications network. These new protocols take into account the dynamic connectivity and power limitation of satellites. The impact of these protocols on a GSM integration scenario is highlighted and solutions proposed. Two future satellite constellations are used as examples in this paper; these are Globalstar and ICO-like systems.

BackgroundRenal-resident macrophages (RMs) are essential regulators of kidney homeostasis and repair, yet the cellular and molecular mechanisms governing RM niche regeneration after acute depletion remain poorly defined. How epithelial-immune interactions coordinate RM repopulation is particularly unclear.MethodsWe employed an inducible hCD59 intermedilysin (ILY) ablation system to achieve rapid and specific depletion, and subsequent replenishment of RMs, followed by longitudinal single-cell RNA sequencing (scRNA-seq) of kidneys at baseline and days 1, 3, and 7 post-ablation. Integrated transcriptomic, pathway, transcription factor, and cell-cell communication analyses were combined with functional validation using clodronate-mediated macrophage depletion in Spp1 (Opn)-deficient mice.ResultsAcute ILY-mediated ablation resulted in rapid and selective RM depletion, followed by robust regeneration reaching ~ 75% of baseline by day 7. scRNA-seq faithfully captured RM loss and recovery and revealed a sustained epithelial-derived chemotactic response, with proximal tubule epithelial cells identified as the dominant source of CX3CL1 driving RM recruitment and maintenance. Regenerating macrophages adopted a transient injury-adaptive transcriptional program characterized by metabolic activation, proliferation, and stress-response pathways, with relative attenuation of canonical inflammatory signaling. Cell-cell communication analysis identified macrophages as dominant signaling hubs, coordinating immune and epithelial responses through temporally regulated Spp1, Fn1, Ccl, and App-mediated networks. Functional studies demonstrated that Spp1/osteopontin is required for efficient RM regeneration following depletion. SCENIC–STRING analysis connected 18 upregulated transcription factors (TFs) to IL-1 signaling, myeloid differentiation, and tissue remodeling, indicating a coordinated transcriptional program driving RM regeneration. Sub-clustering uncovered five RM subsets and ten proximal tubule cell states with dynamic, time-dependent shifts, revealing a hierarchical macrophage-epithelial communication program that orchestrates niche restoration and tubular repair.ConclusionOur study defines RM regeneration as a transcriptionally regulated, communication-driven process orchestrated by epithelial-derived chemokines, macrophage metabolic reprogramming, and subtype-specific signaling networks. These findings revealed a hierarchical macrophage-epithelial communication program coordinating RM niche restoration and tubular repair.