Global Network Topology Information Research Articles

Adaptive Bipartite Fixed-Time Time-Varying Output Formation-Containment Tracking of Heterogeneous Linear Multiagent Systems.

This study investigates the bipartite fixed-time time-varying output formation-containment tracking issue for heterogeneous linear multiagent systems with multiple leaders. Both cooperative communication and antagonistic communication between neighbor agents are taken into account. First, the bipartite fixed-time compensator is put forward to estimate the convex hull of leaders' states. Different from the existing techniques, the proposed compensator has the following three highlights: 1) it is continuous without involving the sign function, and thus, the chattering phenomenon can be avoided; 2) its estimation can be achieved within a fixed time; and 3) the communication between neighbors can not only be cooperative but also be antagonistic. Note that the proposed compensator is dependent on the global information of network topology. To deal with this issue, the fully distributed adaptive bipartite fixed-time compensator is further proposed. It can estimate not only the convex hull of leaders' states but also the leaders' system matrices. Based on the proposed compensators, the distributed controllers are then developed such that the bipartite time-varying output formation-containment tracking can be achieved within a fixed time. Finally, two examples are given to illustrate the feasibility of the main theoretical findings.

Fully Distributed Adaptive Finite-Time Consensus for Uncertain Nonlinear Multiagent Systems.

In this article, the adaptive finite-time consensus problem is discussed for uncertain nonlinear multiagent systems. In contrast with the correlative literature, the systems permit multiple uncertainties (not only in control coefficients but also in inherent nonlinearities), and typically the consensus protocol is pursued in a fully distributed fashion (independent from any global information of network topology). This essentially challenges the realization of the finite-time consensus. To overcome the challenge, a new continuous fully distributed protocol is proposed by combining adaptive techniques such that the finite-time consensus of the systems under investigation is achieved. Remarkably, a dynamic high gain, rather than multiple ones in the related literature, is adequate to resist two kinds of uncertainties and to guarantee the fully distributed fashion of the consensus protocol. Moreover, the adaptive finite-time consensus protocol is specified on the scenario of leader-following multiagent systems. Simulation results of three interaction topologies are acquired to illustrate the validity and the wider applicability of the proposed protocol.

Fully distributed bipartite time‐varying formation control for uncertain linear multi‐agent systems under event‐triggered mechanism

AbstractThis paper addresses the distributed bipartite time‐varying formation control for uncertain linear multi‐agent systems under limited communication resources. First, the distributed static bipartite control protocol with sampled data is proposed to address the time‐varying formation issue. It is noteworthy that in both controller updating and triggering condition monitoring, the communication is only occurred at sampling instants, which saves communication resources and reduces communication burden effectively. Moreover, the nonlinear term in static controller is used to suppress the effect of system uncertainty. Then the fully distributed adaptive bipartite event‐triggered control protocol is put forward such that the time‐varying formation control can be achieved. Different from the existing results on formation control, the proposed adaptive controller has the following four highlights: (1) it is fully distributed since the global information of network topology and the upper bound of uncertainty are not required; (2) the communication between neighbor agents is conducted at intermittent triggering instants, not real‐time, which reduces the communication bandwidth significantly; (3) the communication between neighbors can not only be cooperative, but also be antagonistic, which is more realistic; and (4) the effect of system uncertainty can be suppressed. Furthermore, it is proven that there is no Zeno behavior in both triggering mechanisms. Finally, some numerical examples are presented to demonstrate the feasibility of the main theoretical findings.

Distributed fault-tolerant output regulation for heterogeneous linear multi-agent systems under actuator faults

This study investigates the distributed fault-tolerant output regulation for heterogeneous linear multi-agent systems in the presence of actuator faults. For the systems which are not the neighbors of exosystem, the distributed fixed-time observer is put forward to observe the state of exosystem. Note that it is dependent on the global information of network topology. To address this issue, the fully distributed adaptive fixed-time observer is further proposed. It can estimate not only the state of exosystem, but also the system matrix of exosystem. Based on the proposed observer, a novel fault-tolerant controller is developed to compensate for actuator faults. Moreover, it is proven that the proposed controller is effective to address the fault-tolerant output regulation problem by the Lyapunov stability theory. Finally, two illustrative examples are given to illustrate the feasibility of the main theoretical findings.

Data Collection Mechanism Based on Wavelet Multi-Resolution for Opportunistic Social Networks

With the shaping of universal computing concept and the development of microelectronics technology, the mobile terminal devices have the strong functions of computing, storage and communication. The opportunistic social networks composed of a large number of terminal devices can be widely used in various scenarios by deploying them anytime and anywhere. One of its most important tasks is to collect data in order to communicate between people and things. The existing researches mainly focus on routing strategies that aim to improve the performance of data collection by optimizing the routing algorithm. However, the inherent characteristics of the opportunistic social networks such as the intermittent communication opportunities make it difficult to improve routing performance because nodes can not get global network topology information. In order to solve this problem, we should establish an efficient data collection mechanism based on wavelet multi-resolution to improve the efficiency of data collection from the source, which mainly studies the multi-resolution compression storage method of node data, the spatial multi-resolution data hierarchical storage framework, and the multi-resolution data management mechanism of mobile node. The experimental results show that the multi-resolution communication mode based on integer wavelet transform can greatly reduce the amount of data in the network and the energy consumption of nodes, and it is beneficial to the data collection of opportunistic social networks.

Fixed-time leader-following/containment consensus for a class of nonlinear multi-agent systems

This study focuses on the fixed-time leader-following consensus problem for a class of multi-agent system with multi-dimensional nonlinear dynamics. First, the distributed static fixed-time control protocol is put forward to settle this issue. Different from the existing fixed-time control methods, the proposed control protocol is continuous such that the chattering phenomenon can be avoided. Note that the proposed static fixed-time controller is dependent on the global information of network topology. To address above issue, the fully distributed adaptive fixed-time controller is further proposed. Moreover, the results in fixed-time leader-following consensus are extended to containment consensus. And the case of directed graph is also considered. Finally, several examples are provided to illustrate the feasibility of the theoretical results.

Distributed optimization via primal and dual decompositions for delay-constrained FANETs

This paper aims to optimize the different network parameters in a distributed manner for delay-constrained flying ad hoc networks (FANETs) without the global network topology information. To this end, each Unmanned Aerial Vehicle (UAV) calculates the average interference level during a certain time period to indicate the channel states. Next, we formulate the distributed optimization problem as a utility maximization problem, which jointly optimizes power control, rate allocation and delay-constrained routing. To obtain a distributed solution, a dual method is proposed to eliminate the link capacity constraint, and a primal decomposition method is employed to decouple the end-to-end delay constraint. Built on these two methods above, a distributed optimization algorithm is proposed in this work by considering the estimated one-hop delay for each transmission, which only uses the local channel information to optimize the sub-problems and limit the end-to-end delay. Finally, we deduce the relationship between the primal and dual solutions to underpin the advantage of the proposed algorithm. Experiments on simulate (and real) data demonstrate that the proposed algorithm effectively can improve network performances in terms of energy efficiency, packet timeout ratio and network throughput.

Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association

CircRNA is a special type of non-coding RNA, which is closely related to the occurrence and development of many complex human diseases. However, it is time-consuming and expensive to determine the circRNA-disease associations through experimental methods. Therefore, based on the existing databases, we propose a method named RWRKNN, which integrates the random walk with restart (RWR) and k-nearest neighbors (KNN) to predict the associations between circRNAs and diseases. Specifically, we apply RWR algorithm on weighting features with global network topology information, and employ KNN to classify based on features. Finally, the prediction scores of each circRNA-disease pair are obtained. As demonstrated by leave-one-out, 5-fold cross-validation and 10-fold cross-validation, RWRKNN achieves AUC values of 0.9297, 0.9333 and 0.9261, respectively. And case studies show that the circRNA-disease associations predicted by RWRKNN can be successfully demonstrated. In conclusion, RWRKNN is a useful method for predicting circRNA-disease associations.

Dissipative Consensus Tracking of Fuzzy Multi-Agent Systems Via Adaptive Protocol

This article considers the design problems of adaptive consensus protocol for nonlinear multi-agent systems under directed topology and external disturbance. Considering external disturbance, we propose a new polynomial fuzzy modeling approach. Based on the monotonically increasing functions, novel centralized adaptive protocol and distributed consensus protocol are designed respectively to guarantee that all agents can reach agreement. The distributed protocol is independent of global information of network topology and thereby is fully distributed. By constructing Lyapunov functions, SOS-based relaxed sufficient criteria are presented to achieve consensus. In addition, the time-varying coupling weights approach finite steady-state values. A simulation example is provided to demonstrate the effectiveness of the proposed adaptive consensus techniques.

On constructing contention aware connected dominating sets for inter-connectivity among internet of things devices

The heterogeneous IoT devices are often equipped with wireless interfaces capable of creating self-organising multi-hop networks. Any device in such networks often requires broadcasting of packets for data dissemination and route discovery. The straightforward flooding has serious drawback on network throughput due to redundant traffics, contention, and collision. Several methods for creating connected dominant sets (CDS) have been proposed to overcome such bottleneck where only the members of the CDS participate in packet forwarding. However, the state-of-the-art CDS construction algorithms aim at minimising number of forwarding and overlook contention minimisation. In this paper, we provide a novel mathematical analysis of contention and show that contention is mostly dependent on node density and the transmission radius. Then a new centralised algorithm is devised which intellectually selects member nodes while creating a CDS. This helps to reduce contention. Since collecting global network topology information is very difficult to achieve, a distributed algorithm and a hybrid distributed algorithm have also been devised. Finally, the proposed algorithms have been implemented in the state-of-the-art NS-2 simulator where their performance has been captured under realistic settings. The results show a significant reduction in contention. Interestingly, the proposed algorithms' contention and delay performance outperform some other state-of-the-art algorithms' performance although the number of forwarding is increased marginally.

On constructing contention aware connected dominating sets for inter-connectivity among internet of things devices

The heterogeneous IoT devices are often equipped with wireless interfaces capable of creating self-organising multi-hop networks. Any device in such networks often requires broadcasting of packets for data dissemination and route discovery. The straightforward flooding has serious drawback on network throughput due to redundant traffics, contention, and collision. Several methods for creating connected dominant sets (CDS) have been proposed to overcome such bottleneck where only the members of the CDS participate in packet forwarding. However, the state-of-the-art CDS construction algorithms aim at minimising number of forwarding and overlook contention minimisation. In this paper, we provide a novel mathematical analysis of contention and show that contention is mostly dependent on node density and the transmission radius. Then a new centralised algorithm is devised which intellectually selects member nodes while creating a CDS. This helps to reduce contention. Since collecting global network topology information is very difficult to achieve, a distributed algorithm and a hybrid distributed algorithm have also been devised. Finally, the proposed algorithms have been implemented in the state-of-the-art NS-2 simulator where their performance has been captured under realistic settings. The results show a significant reduction in contention. Interestingly, the proposed algorithms' contention and delay performance outperform some other state-of-the-art algorithms' performance although the number of forwarding is increased marginally.

Identification of drug-target interaction by a random walk with restart method on an interactome network

BackgroundIdentification of drug-target interactions acts as a key role in drug discovery. However, identifying drug-target interactions via in-vitro, in-vivo experiments are very laborious, time-consuming. Thus, predicting drug-target interactions by using computational approaches is a good alternative. In recent studies, many feature-based and similarity-based machine learning approaches have shown promising results in drug-target interaction predictions. A previous study showed that accounting connectivity information of drug-drug and protein-protein interactions increase performances of prediction by the concept of ‘guilt-by-association’. However, the approach that only considers directly connected nodes often misses the information that could be derived from distance nodes. Therefore, in this study, we yield global network topology information by using a random walk with restart algorithm and apply the global topology information to the prediction model.ResultsAs a result, our prediction model demonstrates increased prediction performance compare to the ‘guilt-by-association’ approach (AUC 0.89 and 0.67 in the training and independent test, respectively). In addition, we show how weighted features by a random walk with restart yields better performances than original features. Also, we confirmed that drugs and proteins that have high-degree of connectivity on the interactome network yield better performance in our model.ConclusionsThe prediction models with weighted features by considering global network topology increased the prediction performances both in the training and testing compared to non-weighted models and previous a ‘guilt-by-association method’. In conclusion, global network topology information on protein-protein interaction and drug-drug interaction effects to the prediction performance of drug-target interactions.

Enabling Controlling Complex Networks with Local Topological Information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulfilling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired final state in finite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefined state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efficiently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.

An Energy Efficient Distributed Approach-Based Agent Migration Scheme for Data Aggregation in Wireless Sensor Networks

Abstract The use of mobile agents for collaborative processing in wireless sensor network has gained considerable attention. This is when mobile agents ar e used for data aggregation to exploit redundant and correlated data. The efficiency of agent-based data aggregation depends on the agent migration scheme. However, in general, most of the proposed schemes are centralized approach-based schemes where the sink node determines the migration paths for the agents before dispatching them in the sensor network. The main limitations with such schemes are that they need global network topology information for deriving the migration paths of the agents, which incurs additional communication overhead, since each node has a very limited communication range. In addition, a centralized approach does not provide fault tolerant and adaptive migration paths. In order to solve such problems, we have proposed a distributed approach-based scheme for determining the migration path of the agents where at each hop, the local information is used to decide the migration of the agents. In addition, we also propose a local repair mechanism for dealing with the faulty nodes. The simulation results show that the proposed scheme performs better than existing schemes in the presence of faulty nodes within the networks, and manages to report the aggregated data to the sink faster.

HubAlign: an accurate and efficient method for global alignment of protein-protein interaction networks.

Motivation: High-throughput experimental techniques have produced a large amount of protein–protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency.Results: This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins.Availability: HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zipContact: jinboxu@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Hierarchical Spatial Clustering in Multihop Wireless Sensor Networks

Wireless sensor networks have been widely deployed for environment monitoring. The resource-limited sensor nodes usually transmit the sensing readings to Sink node collaboratively in a multihop manner to conserve energy. In this paper, we consider the problem of spatial clustering for approximate data collection that is feasible and energy-efficient for environment monitoring applications. Spatial clustering aims to group the highly correlated sensor nodes into the same cluster for rotatively reporting representative data later. Through a thorough investigation of a real-world environmental data set, we observe strong temporal-spatial correlation and define a novel similarity measure metric to inspect the similarity between any two sensor nodes, which take both magnitude and trend of their sensing readings into consideration. With such metric, we propose a clustering algorithm named as HSC to group the most similar sensor nodes in a distributed way. HSC runs on a prebuilt data collection tree, and thus gets rid of some extra requirements such as global network topology information and rigorous time synchronization. Extensive simulations based on realworld and synthetic data sets demonstrate that HSC performs superiorly in clustering quality when compared with the alternative algorithms. Furthermore, approximate data collection scheme combined with HSC can reduce much more communication overhead while incurring modest data error than with other algorithms.

An extended fault-tolerant link-state routing protocol in the internet

Link-state routing protocols, such as OSPF and IS-IS, are widely used in the Internet today. In link-state routing protocols, global network topology information is first collected at each node. A shortest path tree (SPT) is then constructed by applying Dijkstra's shortest path algorithm at each node. Link-state protocols usually require the flooding of new information to the entire (sub)network after changes in any link state (including link faults). Narvaez et al. proposed a fault-tolerant link-state routing protocol without flooding. The idea is to construct a shortest restoration path for each unidirectional link fault. Faulty link information is distributed only to the nodes in the restoration path and only one restoration path is constructed. It is shown that this approach is loop-free. However, the Narvaez et al. approach is inefficient when a link failure is bidirectional because a restoration path is unidirectional and routing tables of nodes in the path are partially updated. In addition, two restoration paths may be generated for each bidirectional link fault. In this paper, we extend the Narvaez et al. protocol to efficiently handle a bidirectional link fault by making the restoration path bidirectional. Several desirable properties of the proposed extended routing protocol are also explored. A simulation study is conducted to compare the traditional link-state protocol, the source-tree protocol, the Narvaez et al. unidirectional restoration path protocol, and the proposed bidirectional restoration path protocol.