Showing papers on "Node (networking) published in 2016"

node2vec: Scalable Feature Learning for Networks

Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

node2vec: Scalable Feature Learning for Networks

Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

On the Age of Information in Status Update Systems With Packet Management

Abstract: We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed, since the last received update was generated. In this paper, we model the source-destination link using the queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.

Identifying highly influential nodes in the complicated grief network.

Abstract: The network approach to psychopathology conceptualizes mental disorders as networks of mutually reinforcing nodes (i.e., symptoms). Researchers adopting this approach have suggested that network topology can be used to identify influential nodes, with nodes central to the network having the greatest influence on the development and maintenance of the disorder. However, because commonly used centrality indices do not distinguish between positive and negative edges, they may not adequately assess the nature and strength of a node's influence within the network. To address this limitation, we developed 2 indices of a node's expected influence (EI) that account for the presence of negative edges. To evaluate centrality and EI indices, we simulated single-node interventions on randomly generated networks. In networks with exclusively positive edges, centrality and EI were both strongly associated with observed node influence. In networks with negative edges, EI was more strongly associated with observed influence than was centrality. We then used data from a longitudinal study of bereavement to examine the association between (a) a node's centrality and EI in the complicated grief (CG) network and (b) the strength of association between change in that node and change in the remainder of the CG network from 6- to 18-months postloss. Centrality and EI were both correlated with the strength of the association between node change and network change. Together, these findings suggest high-EI nodes, such as emotional pain and feelings of emptiness, may be especially important to the etiology and treatment of CG. (PsycINFO Database Record

The H-index of a network node and its relation to degree and coreness

Abstract: Identifying influential nodes in dynamical processes is crucial in understanding network structure and function. Degree, H-index and coreness are widely used metrics, but previously treated as unrelated. Here we show their relation by constructing an operator , in terms of which degree, H-index and coreness are the initial, intermediate and steady states of the sequences, respectively. We obtain a family of H-indices that can be used to measure a node's importance. We also prove that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a node's coreness in large-scale evolving networks. Numerical analyses of the susceptible-infected-removed spreading dynamics on disparate real networks suggest that the H-index is a good tradeoff that in many cases can better quantify node influence than either degree or coreness.

An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the Internet of Things environment

Abstract: The concept of Internet of Things (IOT), which is already at our front doors, is that every object in the Internet infrastructure (II) is interconnected into a global dynamic expanding network. Sensors and smart objects are beside classical computing devices key parties of the IOT. We can already exploit the benefits of the IOT by using various weareables or smart phones which are full of diverse sensors and actuators and are connected to the II via GPRS or Wi-Fi. Since sensors are a key part of IOT, thus are wireless sensor networks (WSN). Researchers are already working on new techniques and efficient approaches on how to integrate WSN better into the IOT environment. One aspect of it is the security aspect of the integration. Recently, Turkanovic et?al.'s proposed a highly efficient and novel user authentication and key agreement scheme (UAKAS) for heterogeneous WSN (HWSN) which was adapted to the IOT notion. Their scheme presented a novel approach where a user from the IOT can authenticate with a specific sensor node from the HWSN without having to communicate with a gateway node. Moreover their scheme is highly efficient since it is based on a simple symmetric cryptosystem. Unfortunately we have found that Turkanovic et?al.'s scheme has some security shortcomings and is susceptible to some cryptographic attacks. This paper focuses on overcoming the security weaknesses of Turkanovic et?al.'s scheme, by proposing a new and improved UAKAS. The proposed scheme enables the same functionality but improves the security level and enables the HWSN to dynamically grow without influencing any party involved in the UAKAS. The results of security analysis by BAN-logic and AVISPA tools confirm the security properties of the proposed scheme.

A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime

Abstract: Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.

A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks

Abstract: Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use mobile anchor nodes (MANs), which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. A considerable body of research has addressed the mobile anchor node assisted localization (MANAL) problem. However, to the best of our knowledge, no updated surveys on MAAL reflecting recent advances in the field have been presented in the past few years. This survey presents a review of the most successful MANAL algorithms, focusing on the achievements made in the past decade, and aims to become a starting point for researchers who are initiating their endeavors in MANAL research field. In addition, we seek to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful advances in this research field.

Edge Weight Prediction in Weighted Signed Networks

Abstract: Weighted signed networks (WSNs) are networks in which edges are labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, and other social relationships between people. In this paper, we consider the problem of predicting the weights of edges in such networks. We propose two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a node captures how fair the node is in rating other nodes' likeability or trust level. We provide axioms that these two notions need to satisfy and show that past work does not meet these requirements for WSNs. We provide a mutually recursive definition of these two concepts and prove that they converge to a unique solution in linear time. We use the two measures to predict the edge weight in WSNs. Furthermore, we show that when compared against several individual algorithms from both the signed and unsigned social network literature, our fairness and goodness metrics almost always have the best predictive power. We then use these as features in different multiple regression models and show that we can predict edge weights on 2 Bitcoin WSNs, an Epinions WSN, 2 WSNs derived from Wikipedia, and a WSN derived from Twitter with more accurate results than past work. Moreover, fairness and goodness metrics form the most significant feature for prediction in most (but not all) cases.

Trust Management for SOA-Based IoT and Its Application to Service Composition

Abstract: A future Internet of Things (IoT) system will connect the physical world into cyberspace everywhere and everything via billions of smart objects. On the one hand, IoT devices are physically connected via communication networks. The service oriented architecture (SOA) can provide interoperability among heterogeneous IoT devices in physical networks. On the other hand, IoT devices are virtually connected via social networks. In this paper we propose adaptive and scalable trust management to support service composition applications in SOA-based IoT systems. We develop a technique based on distributed collaborative filtering to select feedback using similarity rating of friendship, social contact, and community of interest relationships as the filter. Further we develop a novel adaptive filtering technique to determine the best way to combine direct trust and indirect trust dynamically to minimize convergence time and trust estimation bias in the presence of malicious nodes performing opportunistic service and collusion attacks. For scalability, we consider a design by which a capacity-limited node only keeps trust information of a subset of nodes of interest and performs minimum computation to update trust. We demonstrate the effectiveness of our proposed trust management through service composition application scenarios with a comparative performance analysis against EigenTrust and PeerTrust.

Analysis of Capacity and Scalability of the LoRa Low Power Wide Area Network Technology

Abstract: In this paper we discuss and analyze the recently proposed LoRa low power wide area network (LPWAN) technology when used under the European frequency regulations First of all, we derive the performance metrics of a single LoRaWAN end device, namely uplink throughput and data transmission time Then we analyze for several illustrative application scenarios the maximum number of end devices which can be served by a single LoRaWAN base station and discuss the spatial distribution of these devices It is shown that subject to the channel composition and application requirements, a single cell may include several millions of devices Also, we show that the capacity of the uplink channel available to a LoRaWAN node strongly depends on the distance from the base station and does not exceed 2 kbit/s In the concluding section we summarize and discuss the obtained results, and point out few issues which need to be taken into account when making an application using LoRa or deploying a LoRa network

ART: An Attack-Resistant Trust Management Scheme for Securing Vehicular Ad Hoc Networks

Abstract: Vehicular ad hoc networks (VANETs) have the potential to transform the way people travel through the creation of a safe interoperable wireless communications network that includes cars, buses, traffic signals, cell phones, and other devices. However, VANETs are vulnerable to security threats due to increasing reliance on communication, computing, and control technologies. The unique security and privacy challenges posed by VANETs include integrity (data trust), confidentiality, nonrepudiation, access control, real-time operational constraints/demands, availability, and privacy protection. The trustworthiness of VANETs could be improved by addressing holistically both data trust, which is defined as the assessment of whether or not and to what extent the reported traffic data are trustworthy, and node trust, which is defined as how trustworthy the nodes in VANETs are. In this paper, an attack-resistant trust management scheme (ART) is proposed for VANETs that is able to detect and cope with malicious attacks and also evaluate the trustworthiness of both data and mobile nodes in VANETs. Specially, data trust is evaluated based on the data sensed and collected from multiple vehicles; node trust is assessed in two dimensions, i.e., functional trust and recommendation trust, which indicate how likely a node can fulfill its functionality and how trustworthy the recommendations from a node for other nodes will be, respectively. The effectiveness and efficiency of the proposed ART scheme is validated through extensive experiments. The proposed trust management theme is applicable to a wide range of VANET applications to improve traffic safety, mobility, and environmental protection with enhanced trustworthiness.

Analysis on Cache-Enabled Wireless Heterogeneous Networks

Abstract: Caching popular multimedia content is a promising way to unleash the ultimate potential of wireless networks. In this paper, we propose and analyze cache-based content delivery in a three-tier heterogeneous network (HetNet), where base stations (BSs), relays, and device-to-device (D2D) pairs are included. We advocate proactively caching popular content in the relays and parts of the users with caching ability when the network is off-peak. The cached content can be reused for frequent access to offload the cellular network traffic. The node locations are first modeled as mutually independent Poisson point processes (PPPs) and the corresponding content access protocol is developed. The average ergodic rate and outage probability in the downlink are then analyzed theoretically. We further derive the throughput and the delay based on the multiclass processor-sharing queue model and the continuous-time Markov process. According to the critical condition of the steady state in the HetNet, the maximum traffic load and the global throughput gain are investigated. Moreover, impacts of some key network characteristics, e.g., the heterogeneity of multimedia contents, node densities, and the limited caching capacities, on the system performance are elaborated on to provide valuable insight.

Device Fingerprinting in Wireless Networks: Challenges and Opportunities

Abstract: Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented. In particular, we aim to provide a detailed treatment on developing novel wireless security solutions using device fingerprinting techniques. The objectives are three-fold: (i) to introduce a comprehensive taxonomy of wireless features that can be used in fingerprinting, (ii) to provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches, and (iii) to identify key open research problems in the area of device fingerprinting and feature extraction, as applied to wireless security.

Host node device and methods for use therewith

Abstract: Aspects of the subject disclosure may include, for example, a host node device having a terminal interface that receives downstream channel signals from a communication network and send upstream channel signals to the communication network. An access point repeater launches the downstream channel signals as guided electromagnetic waves on a guided wave communication system and to extract a first subset of the upstream channel signals from the guided wave communication system. A radio wirelessly transmits the downstream channel signals to at least one client node device and to wirelessly receive a second subset of the upstream channel signals from the at least one client node device. Other embodiments are disclosed.

Client node device and methods for use therewith

Abstract: Aspects of the subject disclosure may include, for example, a client node device having a radio configured to wirelessly receive downstream channel signals from a communication network. An access point repeater (APR) launches the downstream channel signals on a guided wave communication system as guided electromagnetic waves that propagate along a transmission medium and to wirelessly transmit the downstream channel signals to at least one client device. Other embodiments are disclosed.

Method and communication node for broadband distribution

Abstract: Aspects of the subject disclosure may include, for example, a communication node having a modem that receives first data streams from a source communication node via a first plurality of twisted pair transmission lines. A multiplexer selects a first subset of the first data streams and a second subset of the first data streams. A wireless transceiver wirelessly transmits the first subset of the first data streams as radio frequency signals via an antenna to at least one device. A distribution point unit transmits the second subset of the first data streams on a second plurality of twisted pair transmission lines to a destination communication node of a distributed antenna system.

Mobility resilience and overhead constrained adaptation in directional 60 GHz WLANs: protocol design and system implementation

Abstract: High directivity of 60 GHz links introduces new link training and adaptation challenges due to both client and environmental mobility. In this paper, we design, implement and evaluate MOCA, a protocol for Mobility resilience and Overhead Constrained Adaptation for directional 60 GHz links. Since mobility-induced link blockage and misalignment cannot be countered with data rate adaptation alone, we introduce Beam Sounding as a mechanism invoked before each data transmission to estimate the link quality for selected beams, and identify and adapt to link impairments. We devise proactive techniques to restore broken directional links with low overhead and design a mechanism to jointly adapt beamwidth and data rate, targeting throughput maximization that incorporates data rate, overhead for beam alignment, and mobility resilience. We implement a programmable node and testbed using software defined radios with commercial 60 GHz transceivers, and conduct an extensive over-the-air measurement study to collect channel traces for various environments. Based on trace based emulations and the IEEE 802.11ad channel model, we evaluate MOCA under a variety of propagation environments and mobility scenarios. Our experiments show that MOCA achieves up to 2x throughput gains compared to a baseline WLAN scheme in a diverse set of operational conditions.

FINAL: Fast Attributed Network Alignment

Abstract: Multiple networks naturally appear in numerous high-impact applications. Network alignment (i.e., finding the node correspondence across different networks) is often the very first step for many data mining tasks. Most, if not all, of the existing alignment methods are solely based on the topology of the underlying networks. Nonetheless, many real networks often have rich attribute information on nodes and/or edges. In this paper, we propose a family of algorithms FINAL to align attributed networks. The key idea is to leverage the node/edge attribute information to guide (topology-based) alignment process. We formulate this problem from an optimization perspective based on the alignment consistency principle, and develop effective and scalable algorithms to solve it. Our experiments on real networks show that (1) by leveraging the attribute information, our algorithms can significantly improve the alignment accuracy (i.e., up to a 30% improvement over the existing methods); (2) compared with the exact solution, our proposed fast alignment algorithm leads to a more than 10 times speed-up, while preserving a 95% accuracy; and (3) our on-query alignment method scales linearly, with an around 90% ranking accuracy compared with our exact full alignment method and a near real-time response time.

System and method for virtualized functions in control and data planes

Abstract: Methods and apparatus for management of network slices in a communication network such as a 5 th generation wireless communication network are provided. Management planes may be provided which are separate from the plurality of network slices. A connection manager residing in a management plane receives an indication that a mobile device is to be associated with the communication network. The connection manager may reside at an access node or in the core network. A network slice is determined, and the connection manager transmits instructions, to one or more network nodes, to associate the mobile device with the network slice. The instructions may be provided to a local connection manager. The slice may be requested explicitly by the mobile device, or determined based on device and/or network requirements.

Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

Abstract: Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks (WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected samples to a sink node. In this paper, we propose an analytic model to estimate the entire network lifetime from network initialization until it is completely disabled, and determine the boundary of energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we investigate the temporal and spatial evolution of energy hole and apply our analytical results to WSN routing in order to balance the energy consumption and improve the network lifetime. Extensive simulation results are provided to demonstrate the validity of the proposed analytic model in estimating the network lifetime and energy hole evolution process.

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

Abstract: The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains.

Design of Smart Sensors for Real-Time Water Quality Monitoring

Abstract: This paper describes work that has been done on design and development of a water quality monitoring system, with the objective of notifying the user of the real-time water quality parameters. The system is able to measure the physiochemical parameters of water quality, such as flow, temperature, pH, conductivity, and the oxidation reduction potential. These physiochemical parameters are used to detect water contaminants. The sensors, which are designed from first principles and implemented with signal conditioning circuits, are connected to a microcontroller-based measuring node, which processes and analyzes the data. In this design, ZigBee receiver and transmitter modules are used for communication between the measuring and notification nodes. The notification node presents the reading of the sensors and outputs an audio alert when water quality parameters reach unsafe levels. Various qualification tests are run to validate each aspect of the monitoring system. The sensors are shown to work within their intended accuracy ranges. The measurement node is able to transmit data by ZigBee to the notification node for audio and visual display. The results demonstrate that the system is capable of reading physiochemical parameters, and can successfully process, transmit, and display the readings.

Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks

Abstract: This paper addresses the joint spectrum sharing and power allocation problem for device-to-device (D2D) communications underlaying a cellular network (CN). In the context of orthogonal frequency-division multiple-access systems, with the uplink resources shared with D2D links, both centralized and decentralized methods are proposed. Assuming global channel state information (CSI), the resource allocation problem is first formulated as a nonconvex optimization problem, which is solved using convex approximation techniques. We prove that the approximation method converges to a suboptimal solution and is often very close to the global optimal solution. On the other hand, by exploiting the decentralized network structure with only local CSI at each node, the Stackelberg game model is then adopted to devise a distributed resource allocation scheme. In this game-theoretic model, the base station (BS), which is modeled as the leader, coordinates the interference from the D2D transmission to the cellular users (CUs) by pricing the interference. Subsequently, the D2D pairs, as followers, compete for the spectrum in a noncooperative fashion. Sufficient conditions for the existence of the Nash equilibrium (NE) and the uniqueness of the solution are presented, and an iterative algorithm is proposed to solve the problem. In addition, the signaling overhead is compared between the centralized and decentralized schemes. Finally, numerical results are presented to verify the proposed schemes. It is shown that the distributed scheme is effective for the resource allocation and could protect the CUs with limited signaling overhead.

Distributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologies

Abstract: This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between predicted and assumed trajectories. Together with this penalization, an equality based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of proposed DMPC.

Age of information: The gamma awakening

Abstract: We consider a scenario where a monitor is interested in being up to date with respect to the status of some system which is not directly accessible to this monitor. However, we assume a source node has access to the status and can send status updates as packets to the monitor through a communication system. We also assume that the status updates are generated randomly as a Poisson process. The source node can manage the packet transmission to minimize the age of information at the destination node, which is defined as the time elapsed since the last successfully transmitted update was generated at the source. We use queuing theory to model the source-destination link and we assume that the time to successfully transmit a packet is a gamma distributed service time. We consider two packet management schemes: LCFS (Last Come First Served) with preemption and LCFS without preemption. We compute and analyze the average age and the average peak age of information under these assumptions. Moreover, we extend these results to the case where the service time is deterministic.

Fast and Accurate Mining the Community Structure: Integrating Center Locating and Membership Optimization

Abstract: Mining communities or clusters in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g., protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. In this paper, we attempt to address the above challenges by introducing a novel algorithm. First, a kernel function with a tunable influence factor is proposed to measure the leadership of each node, those nodes with highest local leadership can be viewed as the candidate central nodes. Then, we use a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node's dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed dynamical system is independent of the quality function used, so could also be applied in other community mining models. Our algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. We finally give demonstrative applications of the algorithm to a set of synthetic benchmark networks and also real-world networks to verify the algorithmic performance.

Dynamic Cluster Head Selection Method for Wireless Sensor Network

Abstract: In view of the wireless sensor network clustering algorithm at home and abroad, the dynamic cluster head selection methods for a wireless sensor network are put forward in order to solve the problem of the unreasonable cluster head selection that may lead to the overlapping coverage and unbalanced energy consumption in the cluster communication. Experimental results show that this method balances the network node energy in two phases compared with the existed algorithms. The network lifetime is increased by 50%, higher than that of low energy adaptive clustering hierarchy (LEACH), and increased by 30%, higher than that of distribute energy-efficient clustering algorithm (DEEC); also the survival time of the network is longer than that of energy-balanced deterministic clustering algorithm and adaptive energy optimized clustering algorithm, achieving the effectiveness of the network energy consumption, and it has the longest network lifetime.