Maintain Network Connectivity Research Articles

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

Network connectivity contributes to native small‐bodied fish assemblages in the upper Mississippi River system

Abstract Effective management and conservation of fishes requires understanding habitat use across multiple life stages while ensuring necessary habitats are both available and accessible. Tributary habitats may play an important role in recruitment and dispersal of fishes in anthropogenically modified rivers such as the Mississippi and Illinois Rivers of the Midwest U.S.A. Identifying source locations that contribute to recruitment of fish populations can determine the importance of connectivity within river networks and pinpoint critical habitats that sustain fish populations. In the Upper Mississippi River System (UMRS), the environments that fish use in early life stages (i.e., natal origin) can be identified using otolith trace element analysis due to stability and distinctness in water chemistry (strontium: calcium [Sr:Ca]) among water bodies that is reflected in otoliths. Here, we used trace element analysis to determine natal origin of six small‐bodied fishes including bullhead minnow (Pimephales vigilax), emerald shiner (Notropis atherinoides), gizzard shad (Dorosoma cepedianum), bluegill (Lepomis macrochirus), orangespotted sunfish (Lepomis humilis), and yellow perch (Perca flavescens) across six reaches of the UMRS (Pools 4, 8, 13, 26, the Open River of the Middle Mississippi River, and the La Grange Pool of the Illinois River). Otolith core Sr:Ca for fishes was quantified using laser ablation inductively coupled plasma mass spectrometry. Using the resulting Sr:Ca chemical signatures of otolith cores, natal origin (tributary, immigrant, or potential resident) was determined for individual fish based on family‐specific relationships between otolith and water chemistries. We found that all species originated from tributaries and other reaches (i.e., were immigrants) to varying extents, which acted as evidence for network connectivity. Specifically, tributaries contributed up to 48% of individuals at a given reach. In certain reaches, Pool 26 and the Open River reach, up to 80% of individuals in a species immigrated from another mainstem river reach. Network connectivity was also important in both upstream and downstream directions. Contributions from network connectivity varied among species: bullhead minnow used less whereas orangespotted sunfish used more network connectivity than when all species were combined. Further, the use of network connectivity varied spatially where individuals captured in Pool 8 and the La Grange Pool less often and those from Pool 26 and the Open River more often originated from network connectivity compared to the whole assemblage across reaches. These results indicate that species' life history traits probably interacted with the physical environment, which differs spatially, to yield observed recruitment source patterns. Our results show that network connectivity contributes to established assemblages of native small‐bodied fishes throughout the UMRS and underscore the role of interjurisdictional management in maintaining network connectivity to sustain fish populations.

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles.

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

A Novel SDWSN-Based Testbed for IoT Smart Applications

Wireless sensor network (WSN) environment monitoring and smart city applications present challenges for maintaining network connectivity when, for example, dynamic events occur. Such applications can benefit from recent technologies such as software-defined networks (SDNs) and network virtualization to support network flexibility and offer validation for a physical network. This paper aims to present a testbed-based, software-defined wireless sensor network (SDWSN) for IoT applications with a focus on promoting the approach of virtual network testing and analysis prior to physical network implementation to monitor and repair any network failures. Herein, physical network implementation employing hardware boards such as Texas Instruments CC2538 (TI CC2538) and TI CC1352R sensor nodes is presented and designed based on virtual WSN- based clustering for stationary and dynamic networks use cases. The key performance indicators such as evaluating node (such as a gateway node to the Internet) connection capability based on packet drop and energy consumption virtually and physically are discussed. According to the test findings, the proposed software-defined physical network benefited from “prior-to-implementation” analysis via virtualization, as the performance of both virtual and physical networks is comparable.

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy.

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph's s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

Nonsmooth Control Barrier Function design of continuous constraints for network connectivity maintenance

This paper considers the problem of maintaining global connectivity of a multi-robot system while executing a desired coordination task. Our approach builds on optimization-based feedback design formulations, where the nominal cost function and constraints encode desirable control objectives for the resulting input. Our solution uses the algebraic connectivity of the multi-robot interconnection topology as a control barrier function and critically embraces its nonsmooth nature. We take advantage of the understanding of how Laplacian eigenvalues behave as their multiplicities change, in combination with the flexibility provided by the concept of control barrier function, to carefully design additional constraints that guarantee the resulting optimization-based controller is continuous and maintains network connectivity. The technical treatment combines elements from set-valued theory, nonsmooth analysis, and algebraic graph theory to imbue the proposed constraints with regularity properties so that they can be smoothly combined with other control constraints. We provide simulations and experimental results illustrating the effectiveness and continuity of the proposed approach in a resource gathering problem.

Identifying influential nodes by leveraging redundant ties

Structure-based influential nodes identification is a long-term challenge in the study of complex networks. While global centrality-based approaches are generally considered to be more accurate and reliable, the requirements of complete network information and high computational complexity are hard to meet, limiting their applications in many practical scenarios. In addition, recent studies have highlighted the effect of cyclic structures introducing redundant paths in network connectivity and exaggerating the importance of traditional centrality measures. In this work, we develop a new centrality metric, called Multi-Spanning Tree-based Degree Centrality (MSTDC), to quantify node importance with linear complexity by leveraging redundant ties. MSTDC is calculated using the aggregation of degrees of a small number of spanning trees constructed with a few randomly selected root nodes. Experiments on synthetic and empirical networks reveal that MSTDC obtains superior performance than other benchmark network centralities in identifying influential nodes from the perspective of both maintaining network connectivity and maximizing spreading capacity. In addition, we find that MSTDC is extraordinarily effective in networks with high clustering coefficients. Our study provides novel insights into the role of redundant ties in network structural and functional analyses.

Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs.

Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs' reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs' reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs' reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks.

Energy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor Networks

In recent years, Wireless Sensor Networks (WSNs) have received increased international attention due to advancements in the communication, electronics, and information fields. These sensor networks integrate a huge number of sensor nodes to track rapidly changing physical events. The key improvement is that these nodes can be easily positioned in any environment due to their small size. Therefore, maintaining network connectivity is crucial in WSNs. If some nodes become unavailable, the connectivity of the routing path fails, resulting in significant packet loss in the WSN. Therefore, many types of research in WSNs have focused on energy efficiency, where energy consumption is minimized to improve the network lifetime. Here, Energy and Distance-aware Multi-Objective Firebug Swarm Optimization (ED-MOFSO) is projected to achieve an energy efficient process. Furthermore, ED-MOFSO minimizes delays to enhance performance measures. From the overall simulation, it shows that ED-MOFSO achieves improved metrics, including residual energy (14.29 J), delay (11.1 ms), packet delivery ratio (0.994), routing overhead (0.10), and throughput (1.233 Mbps) when compared to conventional Elephant Herding Optimization (EHO) greedy and Ant Colony Optimization Integrated Glow-worm Swarm Optimization (ACI-GSO).

Enhancing Heterogeneous Wireless Sensor Networks Using Swarm Intelligence –Based Routing Protocols

The design of efficient communication protocols for wireless sensor networks has aroused great interest in the research community, especially in the face of the limited energy of sensor nodes and the frequent change in network topology. Routing remains a challenging problem in wireless communications, as deploying or replacing sensor nodes in hazardous environments is difficult. Many studies have been devoted to alleviate certain limitations, such as clustering to maintain network connectivity, injecting heterogeneity to avoid the rapid death of nodes, or incorporating evolution-based optimization methods to find the best network configuration. This work combined heterogeneity and swarm-based optimization to efficiently balance energy consumption between nodes to increase network reliability. Specifically, this work employed the binary particle swarm optimizer and the binary artificial bees colony optimizer to find approximately the optimal set of cluster heads (CHs) with their optimal number. Based on the probabilistic principle of the heterogeneous protocols: SEP, EDEEC, and BEENISH, a new refined formulation of CHs selection using swarm optimization is proposed. The swarm flight is guided towards the best CHs with an objective function representing a good balance between the initial and residual energy of nodes. Compared to the standard heterogeneous protocols SEP, EDEEC, and BEENISH, the developed protocols significantly perform better in terms of stability (FND), the round of half nodes' death (HND), the network lifetime (LND), and energy saving. Indeed, the BABC-SEP was found 31,66% better than SEP in terms of remaining energy percentage, and CHs selection in EDEEC and BEENISH using BABC improved them by more than 20% in the percentage of remaining energy.

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

A Low Complexity Persistent Reconnaissance Algorithm for FANET

In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs' high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC.

Species assemblage networks identify regional connectivity pathways among hydrothermal vents in the Northwest Pacific.

The distribution of species among spatially isolated habitat patches supports regional biodiversity and stability, so understanding the underlying processes and structure is a key target of conservation. Although multivariate statistics can infer the connectivity processes driving species distribution, such as dispersal and habitat suitability, they rarely explore the structure. Methods from graph theory, applied to distribution data, give insights into both connectivity pathways and processes by intuitively formatting the data as a network of habitat patches. We apply these methods to empirical data from the hydrothermal vent habitats of the Northwest Pacific. Hydrothermal vents are "oases" of biological productivity and endemicity on the seafloor that are imminently threatened by anthropogenic disturbances with unknown consequences to biodiversity. Here, we describe the structure of species assemblage networks at hydrothermal vents, how local and regional parameters affect their structure, and the implications for conservation. Two complementary networks were formed from an extensive species assemblage dataset: a similarity network of vent site nodes linked by weighted edges based on their pairwise assemblage similarity and a bipartite network of species nodes linked to vent site nodes at which they are present. Using these networks, we assessed the role of individual vent sites in maintaining network connectivity and identified biogeographic sub-regions. The three sub-regions and two outlying sites are separated by their spatial arrangement and local environmental filters. Both networks detected vent sites that play a disproportionately important role in regional pathways, while the bipartite network also identified key vent sites maintaining the distinct species assemblages of their sub-regions. These regional connectivity pathways provide insights into historical colonization routes, while sub-regional connectivity pathways are of value when selecting sites for conservation and/or estimating the multivent impacts from proposed deep-sea mining.

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus, the detection and elimination of attacks in a timely manner become an important factor to maintain network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. The proposed conventional trust models are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of the network achieved from them is low. To improve survivability, selfish and malicious nodes present in the network are required to be treated separately. Hence, in this paper, an improved Hidden Markov Trust (HMT) model is developed, which accurately detects the selfish and malicious nodes that illegally intercept the network. The proposed model comprises the Learning Module which aims to understand the behavior of nodes and compute trust using HMT with the expected output. The probability parameters of the HMT model are derived from the data flow rate and the residual energy of the nodes. Next, in Decision-Module, the actual nature of the node is obtained with the help of the evaluated node’s likelihood functions. If the node is selfish and is close to crashed state then, is isolated from the routing function, while the selfish node with sufficient energy is immediately destroyed from the network. On the other hand, malicious nodes are provided with a time-based opportunity to reset themselves before being knocked down. Finally, if the node is legitimate, then the function continues smoothly. At last, the Path-Formation-Module establishes the trusted optimal routing path. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power consumption, delay, and false-alarm-rate, for different network sizes and vulnerability. Simulation result on average provides a 10% higher PDR, 29% lower overhead, and 17% higher detection rate when compared to a Futuristic Cooperation Evaluation Model, Futuristic Trust Coefficient-based Semi-Markov Prediction Model, Opportunistic Data Forwarding Mechanism, and Priority-based Trust Efficient Routing using Ant Colony Optimization trust models presented in the literature.

Novel group handover mechanism for Cooperative and Coordinated Mobile Femtocells technology in railway environment

Recently, the Mobile Femto (MF) Technology has been debated in many research papers to be a promising solution that will dominate future networks. This small cell technology plays a major role in supporting and maintaining network connectivity, enhancing the communication service as well as user experience for passengers in High-Speed Trains (HSTs) environments. Within the railway environment, there are many MF Technologies placed on HSTs to enhance the train passengers’ internet experience. Those users are more affected by the high penetration loss, path loss, dropped signals, and the unnecessary number of Handovers (HOs). Therefore, it is more appropriate to serve those mobile users by the in-train femtocell technology than being connected to the outside Access Points (APs) or Base Stations (BSs). Hence, having a series of MFs (called Cooperative and Coordinated MFs -CCMF) installed inside the train carriages has been seen to be a promising solution for train environments and future networks. The CCMF Technologies establish Backhaul (BH) links with the serving mother BS (DeNB). However, one of the main drawbacks in such an environment is the frequent and unnecessary number of HO procedures for the MFs and train passengers. Thus, this paper proposes an efficient Group HO mechanism that will improve signal connection and mitigate the impact of a signal outage when train carriages move from one serving cell to another. Unlike most work that uses Fixed Femtocell (FF) architecture, this work uses MF architecture. The achieved results via Matlab simulator show that the proposed HO scheme has achieved less outage probability of 0.055 when the distance between the MF and mobile users is less than 10 m compared to the signal outage probability of the conventional HO scheme. More results have shown that the dropping calls probability has been reduced when mobile users are connected to the MF compared to the direct transmission from the eNB. That is in turn has have improved the call duration of mobile UEs and reduced the dropping calls probability for mobile users who are connected to the MF compared to eNB direct connection UEs.

Internet-of-Things-Enabled Smart Bed Rail for Application in Hospital Beds.

This article presents an atypical offline based LoRaWAN application for use in hospital settings, where the ability to maintain network connectivity during internet connection disruption is paramount. A prototype bed rail is demonstrated, providing advanced functionality compared to traditional bed rails. The manufactured prototype provides data to a nurses station reliably and operates under battery backup. The power consumption of the system under different transmission intervals was tested, allowing appropriate battery sizing for different applications to be specified accurately. It is expected that a single LoRaWAN gateway will be able to cover bed rails across an entire modern hospital, allowing minimal infrastructure cost to implement the device or application in a rapidly deployed field hospital.

An Adaptive Topology Management Scheme to Maintain Network Connectivity in Wireless Sensor Networks.

The roots of Wireless Sensor Networks (WSNs) are tracked back to US military developments, and, currently, WSNs have paved their way into a vast domain of civil applications, especially environmental, critical infrastructure, habitat monitoring, etc. In the majority of these applications, WSNs have been deployed to monitor critical and inaccessible terrains; however, due to their unique and resource-constrained nature, WSNs face many design and deployment challenges in these difficult-to-access working environments, including connectivity maintenance, topology management, reliability, etc. However, for WSNs, topology management and connectivity still remain a major concern in WSNs that hampers their operations, with a direct impact on the overall application performance of WSNs. To address this issue, in this paper, we propose a new topology management and connectivity maintenance scheme called a Tolerating Fault and Maintaining Network Connectivity using Array Antenna (ToMaCAA) for WSNs. ToMaCAA is a system designed to adapt to dynamic structures and maintain network connectivity while consuming fewer network resources. Thereafter, we incorporated a Phase Array Antenna into the existing topology management technologies, proving ToMaCAA to be a novel contribution. This new approach allows a node to connect to the farthest node in the network while conserving resources and energy. Moreover, data transmission is restricted to one route, reducing overheads and conserving energy in various other nodes’ idle listening state. For the implementation of ToMaCAA, the MATLAB network simulation platform has been used to test and analyse its performance. The output results were compared with the benchmark schemes, i.e., Disjoint Path Vector (DPV), Adaptive Disjoint Path Vector (ADPV), and Pickup Non-Critical Node Based k-Connectivity (PINC). The performance of ToMaCAA was evaluated based on different performance metrics, i.e., the network lifetime, total number of transmitted messages, and node failure in WSNs. The output results revealed that the ToMaCAA outperformed the DPV, ADPV, and PINC schemes in terms of maintaining network connectivity during link failures and made the network more fault-tolerant and reliable.

Destabilization of the Bacterial Interactome Identifies Nutrient Restriction-Induced Dysbiosis in Insect Guts.

ABSTRACTStress-associated dysbiosis of microbiome can have several configurations that, under an energy landscape conceptual framework, can change from one configuration to another due to different alternating selective forces. It has been proposed—according to the Anna Karenina Principle—that in stressed individuals the microbiome are more dispersed (i.e., with a higher within-beta diversity), evidencing the grade of dispersion as indicator of microbiome dysbiosis. We hypothesize that although dysbiosis leads to different microbial communities in terms of beta diversity, these are not necessarily differently dispersed (within-beta diversity), but they form disrupted networks that make them less resilient to stress. To test our hypothesis, we select nutrient restriction (NR) stress that impairs host fitness but does not introduce overt microbiome selectors, such as toxic compounds and pathogens. We fed the polyphagous black soldier fly, Hermetia illucens, with two NR diets and a control full-nutrient (FN) diet. NR diets were dysbiotic because they strongly affected insect growth and development, inducing significant microscale changes in physiochemical conditions of the gut compartments. NR diets established new configurations of the gut microbiome compared to FN-fed guts but with similar dispersion. However, these new configurations driven by the deterministic changes induced by NR diets were reflected in rarefied, less structured, and less connected bacterial interactomes. These results suggested that while the dispersion cannot be considered a consistent indicator of the unhealthy state of dysbiotic microbiomes, the capacity of the community members to maintain network connections and stability can be an indicator of the microbial dysbiotic conditions and their incapacity to sustain the holobiont resilience and host homeostasis.IMPORTANCE Changes in diet play a role in reshaping the gut microbiome in animals, inducing dysbiotic configurations of the associated microbiome. Although studies have reported on the effects of specific nutrient contents on the diet, studies regarding the conditions altering the microbiome configurations and networking in response to diet changes are limited. Our results showed that nutrient poor diets determine dysbiotic states of the host with reduction of insect weight and size, and increase of the times for developmental stage. Moreover, the poor nutrient diets lead to changes in the compositional diversity and network interaction properties of the gut microbial communities. Our study adds a new component to the understanding of the ecological processes associated with dysbiosis, by disentangling consequences of diets on microbiome dysbiosis that is manifested with the disruption of microbiome networking properties rather than changes in microbiome dispersion and beta diversity.

Joint Optimisation of Real-Time Deployment and Resource Allocation for UAV-Aided Disaster Emergency Communications

In this work, we consider a joint optimisation of real-time deployment and resource allocation scheme for UAV-aided relay systems in emergency scenarios such as disaster relief and public safety missions. In particular, to recover the network within a disaster area, we propose a fast K-means-based user clustering model and jointly optimal power and time transferring allocation which can be applied in the real system by using UAVs as flying base stations for real-time recovering and maintaining network connectivity during and after disasters. Under the stringent QoS constraints, we then provide centralised and distributed models to maximise the energy efficiency of the considered network. Numerical results are provided to illustrate the effectiveness of the proposed computational approaches in terms of network energy efficiency and execution time for solving the resource allocation problem in real-time scenarios. We demonstrate that our proposed algorithm outperforms other benchmark schemes.

Distributed herding of multiple robots in cluttered environments

This article introduces distributed herding controllers to make multiple robots (agents) follow a leader in environments with many unknown obstacles. The proposed herding controllers are developed considering agents which can only measure the bearing of a nearby agent using bearing sensors, such as camera. In addition, an agent uses Received Signal Strength Indicator (RSSI) sensors to detect the moment when it meets another agent. Every agent, except for the leader, is not equipped with sensors to localize itself. Every agent can only detect nearby agents and moves utilizing local sensing measurements. Since every agent moves in an unknown cluttered environment, communication or sensing between agents may be lost blocked by obstacles. Therefore, this article presents distributed herding controllers so that multiple agents follow a leader while maintaining network connectivity in unknown cluttered environments. The leader changes its velocity adaptively so that it can reach the goal while maintaining network connectivity with its followers. The proposed herding controllers are developed to overcome a network faulty situation. As far as we know, this paper is novel in presenting distributed herding controls of multiple agents, such that the network connectivity is maintained while agents move in cluttered environments. Under MATLAB simulations, we verify the effectiveness of the proposed herding approach in cluttered environments.