Local Topology Information Research Articles

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.

Geographic Segmented Opportunistic Routing in Cognitive Radio Ad Hoc Networks Using Network Coding

In cognitive radio ad hoc networks (CRAHNs), the secondary users’ links are interrupted by the arrival of primary users, which leads to a significant increase of the number of transmissions per packet. Since network coding and opportunistic routing can reduce the number of transmissions over unreliable wireless links, they are more suitable for CRAHNs. Network coding-based opportunistic routing has been well studied in previous works for traditional ad hoc networks. However, existing approaches have limitations to handle CRAHNs. First, the routing decisions of these methods aim to find one optimal forwarding set for the whole path from the source to the destination. If the channel conditions change dynamically, the pre-selected forwarding set may become unavailable or non-optimal. Second, their coding schemes are not efficient enough to cope with the dynamic spectrum variation in CRAHNs. In this paper, we attempt to overcome these limitations and propose a network-coding-based geographic segmented opportunistic routing scheme for CRAHNs, by fully embracing its characteristics. In our scheme, the whole path from the source to the destination is cut down into several smaller opportunistic route segments, where the packets are transmitted through multiple segments based on a step-by-step forwarding procedure until all the packets are delivered to the destination. Since our scheme utilizes only local spectrum opportunities, topology information, and geometric conditions to compute the forwarding set for each short-term opportunistic route segment, it can better adapt to dynamic spectrum environments and changing network topologies in CRAHNs. Furthermore, we construct a coding graph to show that our coding problem is a reduction from the maximum clique problem, and we propose an efficient network coding strategy to solve it. Simulation results show that our scheme can achieve a considerable performance, compared with the latest opportunistic routing protocols designed for CRAHNs.

A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.

A Heterogeneous Networks Fusion Algorithm Based on Local Topological Information for Neurodegenerative Disease

A Heterogeneous Networks Fusion Algorithm Based on Local Topological Information for Neurodegenerative Disease

FLAPS: bandwidth and delay-efficient distributed data searching in Fog-supported P2P content delivery networks

Due to the growing interest for multimedia contents by mobile users, designing bandwidth and delay-efficient distributed algorithms for data searching over wireless (possibly, mobile) “ad hoc” Peer-to-Peer (P2P) content Delivery Networks (CDNs) is a topic of current interest. This is mainly due to the limited computing-plus-communication resources featuring state-of-the-art wireless P2P CDNs. In principle, an effective means to cope with this limitation is to empower traditional P2P CDNs by distributed Fog nodes. Motivated by this consideration, the goal of this paper is twofold. First, we propose and describe the main building blocks of a hybrid (e.g., mixed infrastructure and “ad hoc”) Fog-supported P2P architecture for wireless content delivery, namely, the Fog-Caching P2P architecture. It exploits the topological (possibly, time varying) information locally available at the serving Fog nodes, in order to speed up the data searching operations performed by the served peers. Second, we propose a bandwidth and delay-efficient, distributed and adaptive probabilistic search algorithm, that relies on the learning automata paradigm, e.g., the Fog-supported Learning Automata Adaptive Probabilistic Search (FLAPS) algorithm. The main feature of the FLAPS algorithm is the exploitation of the local topology information provided by the serving Fog nodes and the current status of the collaborating peers, in order to run a suitably distributed reinforcement algorithm for the adaptive discovery of peer-to-peer and peer-to-fog minimum-hop routes. The performance of the proposed FLAPS algorithm is numerically evaluated in terms of Success Rate, Hit-per-Query, Message-per-Query, Response Delay and Message Duplication Factor over a number of randomly generated benchmark CDN topologies. Furthermore, in order to corroborate the actual effectiveness of the FLAPS algorithm, extensive performance comparisons are carried out with some state-of-the-art searching algorithms, namely the Adaptive Probabilistic Search, Improved Adaptive Probabilistic Search and the Random Walk algorithms.

Improved clustering algorithm based on energy consumption in wireless sensor networks

Wireless sensor networks (WSNs) are widely used in military, traffic, medical and so on. The design of routing protocol for WSNs is limited by the single nature of the local topology information. Meanwhile, the power supply of sensor networks node, communication ability and storage capacity are limited, so how to improve the efficient energy of nodes and extend the networks life cycle is the focus of current research. This study proposes the improved algorithm for the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm, considering the residual energy of the nodes and the factors of the long distance node, the T (n) is readjusted and the new method is proposed. Then the data fusion rate is introduced to allow the cluster-heads to fuse data before sending the data, and send the data to the base station. Finally, the free-space model and the multi-path fading model are adopted to avoid the excessive consumption of energy caused by the node d4. The authors’ simulation results show that the improved algorithm can reduce the energy consumption of the networks and prolongs life cycle.

Distributed consensus of large-scale multi-agent systems via linear-transformation-based partial stability approach

This paper investigates the consensus problem of large-scale multi-agent systems (MASs) with a directed communication topology, especially for the MAS whose topology contains small strongly connected components. A sufficient and necessary consensus criterion is proposed through the combination of two methods. Firstly, a communication searching algorithm is utilized to make sure that each agent obtains the local topology information of strongly connected component where it is located. Secondly, a state-linear-transformation decomposes the consensus problem into a group of stability problems based on the strongly connected components. The corresponding consensus criterion reduces the computational complexity. Moreover, according to the consensus criterion, a distributed design procedure of gain matrices is proposed based on the homotopy method.

ModuleAlign: module-based global alignment of protein-protein interaction networks.

As an increasing amount of protein-protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency. In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen-Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis. http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html canzar@ttic.edu or j3xu.ttic.edu Supplementary data are available at Bioinformatics online.

Dynamically Adaptable Improved OLSR (DA-IOLSR) Protocol

Optimized Link State Routing (OLSR) protocol is a proactive type of routing protocol that uses Multipoint Relay (MPR) set as the virtual backbone structure. The existing literature has identified various issues with respect to its backbone structure and has accordingly proposed improvements. In this paper, the focus is on improving the OLSR protocol by employing a Connected Dominating Set (CDS) based virtual backbone structure that is dynamically adaptable to rapid topology changes. A new Dynamically Adaptable Improved Optimized Link State Routing (DA-IOLSR) protocol is proposed that uses the local topology information to adapt the virtual backbone to topology changes. This assumes significance especially in networks that experience very high mobility. Changes in the network topology caused by node additions, node deletions and node mobility are taken care of. Simulations are carried out to assess the performance of DA-IOLSR protocol and OLSR protocol. Packet delivery achieved by both the protocols is examined under varying mobility by using various combinations of node speed and pause time values. It is found that DA-IOLSR protocol provides better packet delivery as compared to OLSR protocol, under varying mobility conditions.

Formal specification and verification of a distributed fault localization, isolation and supply restoration algorithm

Distributed fault localization, isolation and supply restoration (FLISR) algorithm only uses local topology information to improve system reliability in electric power enterprises. It has better adaptability than traditional centralized control architecture. To verify the correctness and reliabilit y of the distributed FLISR algorithm which applied to complex distribution network is very important in the operation of distribution network. A formal method for the specification and verification of the distributed FLISR algorithm which uses linear temporal logic (LTL) language is presented in this paper. In addition, the correctness of the algorithm in interference-free situation and the security of the algorithm in some uncertain interference such as switch action failure, communication failure and other confounding factors are proved mathematically.

Full-space local topology extraction for cross-modal retrieval.

With the ever increasing availability of various kinds of multimedia data, cross-modal retrieval, which enables information retrieval from various types of data given various types of query, has become a research hotspot. Hashing-based techniques have been developed to solve this problem, however, most previous works cannot capture the shared underlying structure of real-world multimodal data, which degrades their retrieval performances. In this paper, we propose a novel hashing method based on the extraction of the common manifold structure shared among different feature spaces. To faithfully represent the common structure, two kinds of local topology information are exploited in our method. Local angles are incorporated within the extraction of local topology of each feature space, which is then used to learn a common intermediate subspace. After heterogeneous features being embedded into this subspace, local similarities are exploited to extract the local topology between different feature spaces, and learn compact Hamming embeddings to facilitate cross-modal retrieval. The proposed method is referred to as full-space local topology extraction for hashing. Extensive comparisons with other state-of-the-art methods on three benchmark multimedia data sets demonstrate the superiority of our proposed method in terms of retrieval recall and search accuracy.

Community detection based on local topological information and its application in power grid

Community structure detection algorithm is employed in a vast amount of study to partition a network into some loosely coupled sub-networks of smaller scale. It is an effective tool to analyze and control some large-scale networks such as power grids. This paper proposes a novel algorithm based on local similarity to detect the community structure in complex network. Firstly, a new similarity index between nodes is defined to model the topological closeness of local connections in networks. Then nodes sharing high similarity are gathered to form the community structure. The results suggest the emergence of the bridging nodes and kernels within the community detection process. This proposed method performs well when it is introduced to seek out the actual community structure, kernels and bridging nodes in some benchmark networks. Thirdly, the proposed algorithm lends itself to many applications, such as detecting communities in several IEEE standard power grids and investigating the roles of bridging nodes in cascading failures. Experimentally, the proposed algorithm outperforms some other similarity indices and clustering methods. Finally, a detailed comparison helps us get the conclusion that the traditional label propagation algorithm is a special case of the proposed algorithm.

A ranking method based on self-avoiding random walk in complex networks

Evaluation of node importance is helpful to improve the invulnerability and robustness of complex networked systems. At present, the classic ranking methods of quantitatively analyzing node importance are based on the centrality measurements of network topology, such as degree, betweenness, closeness, eigenvector, etc. Therefore, they often restrict the unknown topological information and are not convenient to use in large-scale real networked systems. In this paper, according to the idea of self-avoiding random walking, we propose a novel and simplified ranking method integrated with label propagation and local topological information, in which the number of labels that node collects from propagating process quantitatively denotes the ranking order. Moreover, the proposed method is able to characterize the structural influence and importance of node in complex networked system because it comprehensively considers both the direct neighbors of node and the topological relation of node to other ones. Through performing the experiments on three benchmark networks, we obtain interesting results derived from four common evaluating indices, i. e., the coefficient of giant component, the spectral distance, the links of node, and the fragility, which indicate that the proposed method is much more efficient and effective for ranking influential nodes than the acquaintance algorithm.

Recursive Algorithms for Distributed Forests of Octrees

The forest-of-octrees approach to parallel adaptive mesh refinement and coarsening has recently been demonstrated in the context of a number of large-scale PDE-based applications. Efficient reference software has been made freely available to the public both in the form of the standalone \tt p4est library and more indirectly by the general-purpose finite element library \tt deal.II, which has been equipped with a \tt p4est backend. Although linear octrees, which store only leaf octants, have an underlying tree structure by definition, it is not fully exploited in previously published mesh-related algorithms. This is because tree branches are not explicitly stored and because the topological relationships in meshes, such as the adjacency between cells, introduce dependencies that do not respect the octree hierarchy. In this work, we combine hierarchical and topological relationships between octants to design efficient recursive algorithms that operate on distributed forests of octrees. We present three important algorithms with recursive implementations. The first is a parallel search for leaves matching any of a set of multiple search criteria, such as leaves that contain points or intersect polytopes. The second is a ghost layer construction algorithm that handles arbitrarily refined octrees that are not covered by previous algorithms, which require a 2:1 condition between neighboring leaves. The third is a universal mesh topology iterator. This iterator visits every cell in a partition, as well as every interface (face, edge, and corner) between these cells. The iterator calculates the local topological information for every interface that it visits, taking into account the nonconforming interfaces that increase the complexity of describing the local topology. To demonstrate the utility of the topology iterator, we use it to compute the numbering and encoding of higher-order $C^0$ nodal basis functions used for finite elements. We analyze the complexity of the new recursive algorithms theoretically and assess their performance, both in terms of single-processor efficiency and in terms of parallel scalability, demonstrating good weak and strong scaling up to 458,000 cores of the JUQUEEN supercomputer.

Enhancing the Performance of Geographical Routing Protocol in Highly Dynamic Mobility and Mobility Patterns

In geographical routing, each node takes the data forwarding decision based on the location information of its one-hop neighbors. Dissemination of location information is the key function of the geographic routing. The location error due to the node mobility models has direct impact on the geographical routing. Node mobility is a great challenge to the beacon based location broadcasting schemes. The general mechanism to optimize the local topology information according to the node mobility is Proactive beaconing schemes support specific mobility model, but it does not perform well under various mobility models. However, it is difficult to capture the changes in the network topology and fit the beaconing approach correctly to the various mobility models. This paper proposes the Context Aware Mobility Adaptive Location Update (CAALU) scheme which contains Distance and Traffic based Location update (DTL), Band Prediction (BP), and Conditional Location Update on Overhearing (CLUO). These schemes make CAALU more flexible to various mobility models. For supporting the mobility models, the DTL dynamically adjusts the beaconing interval based on the node distance and traffic. For simplification, it divides the node transmission area into inband and out-band region, and each node continuously exchange its location information with the out-band nodes. The BP mechanism predicts the mobile node region and broadcast the beacon only if the predicted distance error is greater than the threshold range. The CLUO mechanism employs the MAC layer overhearing concept whereby a node sends the beacon packet, when it overhears the data/beacon packet transmission from a new neighbor. Simulated results reveal the adaptability of CAALU on various mobility models.

Coupling Strength Allocation for Synchronization in Complex Networks Using Spectral Graph Theory

Using spectral graph theory and especially its graph comparison techniques, we propose new methodologies to allocate coupling strengths to guarantee global complete synchronization in complex networks. The key step is that all the eigenvalues of the Laplacian matrix associated with a given network can be estimated by utilizing flexibly topological features of the network. The proposed methodologies enable the construction of different coupling-strength combinations in response to different knowledge about subnetworks. Adaptive allocation strategies can be carried out as well using only local network topological information. Besides formal analysis, we use simulation examples to demonstrate how to apply the methodologies to typical complex networks.

Distributed k-Coverage Decision Scheme for System Deployment in Mobile Sensor Networks

K-coverage sensor deployment is an important issue in wireless sensor networks (WSNs) applications. This is because an efficient topology structure significantly affects the quality of service and lifetime of WSNs. In the present work, we study the sensor deployment problem and propose novel distributed decision schemes to guide sensor movement to achieve k-coverage deployment. In our schemes, the kth order Voronoi diagram is used to discover the regions that do not meet the k-coverage requirement. Two different movement strategies are designed to determine the optimal location of each mobile sensor through local topological information. Finally, simulation results validate the effectiveness and accuracy of the proposed schemes.

Efficient Local Broadcasting in Wireless Ad Hoc Networks Using Dynamic Approach

Broadcasting is a commonly used feature in wireless ad hoc networks. It is a common operation for route establishment and for sending control and emergency messages. The primary goal of broadcasting is to successfully reduce the number of transmissions that are required to achieve full delivery with energy consumption using local broadcast algorithm. In static approach, using the local topology information, the local algorithm determines the status of each node and also the priority function. Using this approach it is not possible to achieve the good approximation factor to the optimum solution. The hybrid local broadcast algorithm based on dynamic approach is proposed for guarantee with full delivery and successful approximation to the optimum solution without considering the node position. In dynamic approach the position of the node is determined “on-the-fly” based on local topology information. So, it is possible to get the good approximation factor to the optimum solution. The position information is the solution for getting good approximation factor. But in some applications it may not be possible to get position information because of the sheer nature of ad hoc networks that allow mobility cause dynamic, unpredicted and frequent topology changes. In those situations also the proposed local broadcast algorithm can achieve both full delivery and a constant approximation to the optimum solution in which the status of each node is decided “on-the-fly” (i.e. reactive protocol).

Location-Based Crowdsourcing for Vehicular Communication in Hybrid Networks

It is a challenge to design efficient routing protocols for vehicular ad hoc networks (VANETs) because of their highly dynamic properties. We address the vehicular communication problem in urban hybrid networks and present a hybrid routing scheme for data dissemination in VANETs. Location-based crowdsourcing of nearby roadside units (RSUs) has been applied to the infrastructural support of inter-vehicle, vehicle-to-roadside, and inter-roadside communications in hybrid VANETs. The combination of RSU resources and ad hoc networks involves an online probabilistic RSU retrieval algorithm that uses coarse- and fine-grained localization to estimate the number and location of available RSUs; a network coding based multicast routing for dense VANETs using maximum distance separation (MDS) code and local topology information from the forwarding set to achieve robust communication and max-flow min-cut data dissemination; an application of opportunistic routing, using a carry-and-forward scheme to solve the forwarding disconnection problem in sparse VANETs; and a routing switch mechanism to guarantee quality of service (QoS) under various network connectivity and deployment configurations. The performance of our hybrid routing scheme is evaluated using both simulations and real testbed experiments.

MDS-Based Wormhole Detection Using Local Topology in Wireless Sensor Networks

Wormhole attack is a severe threat to wireless sensor networks (WSNs), which has received considerable attentions in the literature. However, most of the previous approaches either require special hardware devices or depend on rigorous assumptions on the network settings, which greatly limit their applicability. In this work, we attempt to relax the limitations in prior work, and propose a novel approach to detect wormhole attacks by only local topology information in WSNs. The basic idea is as follows. Each node locally collects its neighborhood information and reconstructs the neighborhood subgraph by multidimensional scaling (MDS). Potential wormhole nodes are detected by validating the legality of the reconstruction. Then, a refinement process is introduced to filter the suspect nodes and to remove false positives. Our approach solely relies on the local connectivity information and is extremely simple and lightweight, which makes it applicable in practical systems. Extensive simulations are conducted, and the results demonstrate the effectiveness and superior performance of our approach.