Joint channel assignment and routing in multiradio multichannel wireless mesh networks with directional antennas

The aggregate capacity of a wireless mesh network (WMN) is severely affected by interflow interference. In this paper, we propose a network architecture that incorporates directional antennas with multiple orthogonal channels to effectively enhance the performance of WMNs. First, a sectored connectivity graph is introduced to model multiradio multichannel WMNs with directional antennas. Next we formulate the topology design, directional interface assignment, channel allocation, and routing mathematically as a mixed integer linear programming problem. This problem is solved using an iterated local search algorithm to obtain optimized network resource allocation. Simulation results indicate that the proposed architecture can achieve higher packet delivery ratio while providing better network fairness. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimizing Consistency-Based Design of Context-Sensitive Gene Regulatory Networks

When designing a gene regulatory network, except in rare circumstances there will be inconsistencies in the data. Modeling data inconsistencies fits naturally into the framework of probabilistic Boolean networks (PBNs). This model consists of a family of deterministic models and the overall model is based on random switching between constituent networks, each of which determines a context. A previous paper has proposed an inference procedure for PBNs to achieve data consistency within constituent networks. This paper proposes optimization methods targeted at two data-consistent design issues having to do with network structure: (1) generalization (namely, model selection) arising from the one-to-many mapping between the data set and PBN model; (2) model reduction under constraint on network connectivity, which is typically made for computational, statistical, or biological reasons. Regarding generalization, we combine connectivity and minimal logical realization to formulate the optimality criterion and propose two algorithms to solve it, the second algorithm guaranteeing a minimally connected PBN. Regarding constrained connectivity, we rephrase it as a lossy coding problem and develop an algorithm to find a best subset of predictors from the full set of predictors with the objective of minimizing probability of prediction error     

Connectivity of Ad Hoc Networks with Link Asymmetries Induced by Shadowing

The aim of this letter is to determine the minimum node density to achieve a connected large-scale ad hoc network, where every node has the same transmitting and receiving capabilities. Due to the log-normal shadowing, links are unidirectional in general. Contrary to the prevailing opinion, we argue that such asymmetries result into a "reduced" connectivity graph, which, from the point of view of MAC and routing protocols, is to be considered the true or effective connectivity graph. Accordingly, we derive a new formula for the connection probability between two nodes in order to compute global connectivity. Finally, theoretical findings, borrowed from random graphs theory, are compared to numerical simulation results in synthetic wireless network scenarios.     

Human object interaction detection based on feature optimization and key human-object enhancement

Aiming at the problem of unclear or missing human object interaction behavior objects in complex background, we propose a human object interaction detection algorithm based on feature optimization and key human-object enhancement. In order to solve the problem of missing human behavior objects, we propose Feature Optimized Faster Region Convolutional Neural Network (FOFR-CNN). FOFR-CNN is an object detection network optimized by multi-scale feature optimization algorithm, taking into account both image semantics and image structure. In order to reduce the interference of complex background, we propose a Key Human-Object Enhancement Network. The network uses an instance-based method to enhance the features of interactive objects. In order to enrich the interaction information, we use the graph convolutional network. Experimental results on HICO-DET, V-COCO and HOI-A datasets show that the proposed algorithm has significantly improved accuracy and multi-scale object detection ability compared with other human object interaction algorithms.     

TDMA scheduling algorithms for wireless sensor networks

Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or level-based scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10–70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.     

Transactions Papers - Device Placement for Heterogeneous Wireless Sensor Networks: Minimum Cost with Lifetime Constraints

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.     

Energy efficiency of dense wireless sensor networks: to cooperate or not to cooperate

Decentralized detection in a network of wireless sensor nodes involves the fusion of information about a phenomenon of interest (PoI) from geographically dispersed nodes. In this paper, we investigate the problem of binary decentralized detection in a dense and randomly deployed wireless sensor network (WSN), whereby the communication channels between the nodes and the fusion center are bandwidth-constrained. We consider a scenario in which sensor observations, conditioned on the alternate hypothesis, are independent but not identically distributed across the sensor nodes. We compare two different fusion architectures, namely, the parallel fusion architecture (PFA) and the cooperative fusion architecture (CFA), for such bandwidth-constrained WSNs, where each sensor node is restricted to send a I-bit information to the fusion center. For each architecture, we derive expression for the probability of decision error at the fusion center. We propose a consensus flooding protocol for CFA and analyze its average energy consumption. We analyze the effects of PoI intensity, realistic link models, consensus flooding protocol, and network connectivity on the system reliability and average energy consumption for both fusion architectures. We demonstrate that a trade-off exists among spatial diversity gain, average energy consumption, delivery ratio of the consensus flooding protocol, network connectivity, node density, and Poll intensity in CFA. We then provide insight into the design of cooperative WSNs     

基于FCM的复杂网络重叠社团结构发现算法

复杂网络中的社团结构发现是对网络数据集进行数据挖掘的普遍性问题.针对网络中大量存在的重叠社团现象,提出了基于FCM的发现重叠社团结构算法,并进一步在NG模块度的基础上,给出了评价重叠社团结构的模块度函数.算法首先将网络的节点映射成欧氏空间的节点,再以此做模糊聚类得到各重叠社团结构,根据模块度函数选择最佳重叠社团结构.最后,在经典网络上的实验结果表明,算法能够得到满意度高的重叠社团结构,而且时间复杂度较低.     

Distributed Topology Construction in ZigBee Wireless Networks

Topology control is one of the important techniques in wireless multi-hop networks to preserve connectivity and extend the network lifetime. This is more significant in ZigBee, since the address assignment scheme is tightly coupled with topology construction. For example, there can be orphan nodes that cannot receive the network address and isolated from the network due to predefined network configurations. In this paper, we propose a distributed topology construction algorithm that controls the association time of each node in order to solve the orphan node problem in ZigBee as well as construct an efficient routing tree topology. The main idea of the distributed topology construction algorithm is to construct primary backbone nodes by propagating the invitation packets and controlling the association time based on the link quality. Since the dynamically selected primary nodes are spread throughout the network, they can provide backbone to accept the association requests from the remaining secondary nodes which are majority in a network. In the performance evaluation, we show that the proposed topology construction algorithm effectively solves the orphan node problem regardless of network density as well as provides efficient tree routing cost comparable to the approximation algorithm for degree constrained minimum routing cost tree (DC-MRCT) problem.     

A Dual Ring Connectivity Model for VANET Under Heterogeneous Traffic Flow

Vehicular ad-hoc network (VANET) is characterized as a highly dynamic wireless network due to the dynamic connectivity of the network nodes. To achieve better connectivity under such dynamic conditions, an optimal transmission strategy is required to direct the information flow between the nodes. Earlier studies on VANET’s overlook the characteristics of heterogeneity in vehicle types, traffic structure, flow for density estimation, and connectivity observation. In this paper, we have proposed a heterogeneous traffic flow based dual ring connectivity model to enhance both the message disseminations and network connectivity. In our proposed model the availability of different types of vehicles on the road, such as, cars, buses, etc., are introduced in an attempt to propose a new communication structure for moving vehicles in VANETl under cooperative transmission in heterogeneous traffic flow. The model is based on the dual-ring structure that forms the primary and secondary rings of vehicular communication. During message disseminations, Slow speed vehicles (buses) on the secondary ring provide a backup path of communication for high speed vehicles (cars) moving on the primary ring. The Slow speed vehicles act as the intermediate nodes in the aforementioned connectivity model that helps improve the network coverage and end-to-end data delivery. For the evaluation and the implementation of dual-ring model a clustering routing scheme warning energy aware cluster-head is adopted that also caters for the energy optimization. The implemented dual-ring message delivery scheme under the cluster-head based routing technique does show improved network coverage and connectivity dynamics even under the multi-hop communication system.     

Novel approach for replacement of a failure node in wireless sensor network

In the recent years, there has been a growing interest in wireless sensor networks (WSN). Network’s lifetime depends on energy efficiency and load balancing where connectivity is a very important factor. However, such connectivity can be lost due to the failure of some sensor nodes which creates disruptions to the network operations, lead to a reconfiguration of the network by generating energy losses, or in another case, the network mission fails. Energy conservation is a very important problem in WSN. In this paper, we propose a new solution for the connectivity problem when failure nodes are considered. The replacement of failed nodes is done in two phases: the first one is the search of redundant nodes using the clusterheads; the second phase is a restoration of connectivity. Performance evaluation of the proposed replacement approach shows that the results are globally satisfactory.     

Movement control algorithms for realization of fault-tolerant ad hoc robot networks

Autonomous and semi-autonomous mobile multirobot systems require a wireless communication network in order to communicate with each other and collaboratively accomplish a given task. A multihop communications network that is self-forming, self-healing, and self-organizing is ideally suited for such mobile robot systems that exist in unpredictable and constantly changing environments. However, since every node in a multihop (or ad hoc) network is responsible for forwarding packets to other nodes, the failure of a critical node can result in a network partition. Hence, it is ideal to have an ad hoc network configuration that can tolerate temporary failures while allowing recovery. Since movement of the robot nodes is controllable, it is possible to achieve such fault-tolerant configurations by moving a subset of robots to new locations. In this article we propose a few simple algorithms for achieving the baseline graph theoretic metric of tolerance to node failures, namely, biconnectivity. We formulate an optimization problem for the creation of a movement plan while minimizing the total distance moved by the robots. For one-dimensional networks, we show that the problem of achieving a biconnected network topology can be formulated as a linear program; the latter lends itself to an optimal polynomial time solution. For two-dimensional networks the problem is much harder, and we propose efficient heuristic approaches for achieving biconnectivity. We compare the performance of the proposed algorithms with each other with respect to the total distance moved metric using simulations.     

博弈模型在传感器网络安全中的应用

入侵检测是传感器网络安全的重要研究内容,论文基于博弈论中的非合作模型提出了一种新型传感器网络入侵检测方案。该方案用一种只有两个参与者(攻击者和传感器网络)的非零非合作博弈模型来描述传感器网络中入侵检测问题,并证明了这个博弈模型可以达到纳什均衡,据此可以制定一个防御策略有效地提高入侵被检测到的概率。模拟试验证明这一模型是有效可行的。     

A new architecture and a new metric for lightwave networks

The notion of a logically routed network was developed to overcome the bottlenecks encountered during the design of a large purely optical network. In the last few years, researchers have proposed the use of torus. Perfect shuffle, hypercube, de Bruijn graph, Kautz graph, and Cayley graph as an overlay structure on top of a purely optical network. All these networks have regular structures. Although regular structures have many virtues, it is often difficult in a realistic setting to meet these stringent structural requirements. In this paper, we propose generalized multimesh (GM), a semiregular structure, as an alternate to the proposed architectures. In terms of simplicity of interconnection and routing, this architecture is comparable to the torus network. However, the new architecture exhibits significantly superior topological properties to the torus. For example, whereas a two-dimensional (2-D) torus with N nodes has a diameter of Θ(N^0.5), a generalized multimesh network with the same number of nodes and links has a diameter of Θ(N^0.25). In this paper, we also introduce a new metric, flow number, that can be used to evaluate topologies for optical networks. For optical networks, a topology with a smaller flow number is preferable, as it is an indicator of the number of wavelengths necessary for full connectivity. We show that the flow numbers of a 2-D torus, a multimesh, and a de Bruijn network, are Θ(N^1.5), Θ(N^1.25), and Θ(N log N), respectively, where N is the number of nodes in the network. The advantage of the generalized multimesh over the de Bruijn network lies in the bet that, unlike the de Bruijn network, this network can be constructed for any number of nodes and is incrementally expandable     

D3: distributed approach for the detection of dumb nodes in wireless sensor networks

In this work, we propose D3—a distributed approach for the detection of ‘dumb’ nodes in a wireless sensor network (WSN). A dumb node can sense its surroundings, but is unable to transmit these sensed data to any other node, due to the sudden onset of adverse environmental effects. However, such a node resumes its normal operations with the resumption of favorable environmental conditions. Due to the presence of dumb nodes, the network is unable to provide the expected services. Therefore, it is prudent to re‐establish connectivity between dumb and other nodes, so that sensed data can be reliably transmitted to the sink. Before the re‐establishment of connectivity, a node needs to confirm its actual state of being dumb. Dumb behavior is dynamic in nature, and is, thus, distinct from the traditional node isolation problem considered in stationary WSNs. Therefore, the existing schemes for the detection of other misbehaviors are not applicable for detecting a dumb node in a WSN. Considering this temporal behavior of a dumb node, we propose an approach, D3, for the detection of dumb nodes. In the propose scheme, we uses cumulative sum test, which helps in detecting the dumb behavior. The simulation results show that there is 56% degradation in detection percentage with the increment in the detection threshold, whereas energy consumption and the message overhead increase by 40% with the increment in detection threshold.     

Topological design optimization of a yottabit-per-second lattice network

This paper deals with the topological design of a yotta-bit-per-second (1yotta=10/sup 24/) multidimensional network. The YottaWeb is a recently proposed architecture based upon agile optical cores that provides fully meshed connectivity with direct optical paths between edge nodes that are electronically controlled. In order to arrange the edge nodes around the agile cores (ACs) into a suitable and efficient YottaWeb, one proposal is to create a multidimensional lattice structure of ACs. The problem of designing such a structure is highly combinatorial. In this paper, we present the problem, that we call nodal arrangement problem, and we propose a meta-search procedure based on Tabu and VNS to solve it. The performance of the algorithm is gauged using a set of randomly generated networks with different distribution of traffic.     

Connectivity Analysis for Wireless Sensor Networks with Antenna Array Integrated Central Node

In a wireless sensor network (WSN), after gathering information, tiny sensor nodes need to transmit data to a sink. It is important to guarantee that each node can communicate with a sink. Due to the multi-hop communication of WSNs, an essential condition for reliable transmission is completely connectivity of a network. Adaptive or smart antenna (SA) techniques in WSNs have been a topic of active research in recent years. These techniques have been shown to be effective with respect to decreasing energy consuming via specified regions which are formed by the SA beams. In this paper, we propose a probabilistic technique to determine the network connectivity probability of the SA integrated WSN. We employ the geometric shape model to evaluate the network connectivity probability of the WSN using the SA beam specifications. The sensor node density to satisfy the desired network connectivity is determined in terms of the beam-width of the antenna array and node transmission range. The analytical results agree with the simulation results by less than 4.7 % error in the average.     

Dynamic allocation of resources to virtual path agents

One of the major problems faced in operating large networks is the enormous amount of processing and communications overhead required for setting up and tearing down the large number of connections maintained by the network. ATM and MPLS aim at solving these problems via the Virtual Path (VP) mechanism which is used to group together the connections. When a need for setting up a connection rises, the request and its resource allocation are processed by the VP agent and not by the network, thus reducing the processing cost significantly. An important question in the design of these networks is the amount of network resources to be dynamically allocated to and held by the VP agents; too high allocation will result with bandwidth resource waste, while too low allocation will result with heavy connection set-up and tear-down processing load. In this paper we deal with this problem, and at deriving simple operational rules to determine the amount of bandwidth resources to be held by the various VP agents, while balancing between bandwidth waste and connection processing overhead. We formulate the resource allocation problem by accounting both for bandwidth utilization and for connection processing constraints. Recognizing the complexity of the problem, we use a decomposition approach in which we first analyze the single link problem and then propose to use this solution as a building block in constructing algorithms for the whole network. For the single link problem we realize that the pure problem is too complex and thus formulate an approximate model and derive the optimal allocation for it. The optimal rule is expressed as a closed-form square-root allocation. Extensive numerical examination shows that the rule proposed yields very efficient allocations. For the full network problem, we propose to capitalize on the closed form structure of the single link problem solution and use it in devising algorithms for the whole network.     

Graph cut based clustering for cognitive radio ad hoc networks without common control channels

Clustering is an efficient tool to improve the routing and data transmission performance in large scale networks. However, in cognitive radio ad hoc networks (CRAHNs), clustering design is challenging due to the dynamic spectrum access and the blind information environment. In this paper, we propose a novel distributed clustering algorithm for CRAHNs, where neither a dedicated common control channel (CCC) nor prior topology information is required. First, a neighbor discovery protocol without relying on CCC is proposed to construct the local topology. Then, we model the network as a undirected graph and formulate the clustering process as a graph cut problem. We design a mincut based heuristic algorithm to approximate the optimal clustering solution. After this, we also present a synchronize protocol to achieve the global consistency of cluster memberships. Finally, we propose a proactive cluster maintenance mechanism to reduce the interferences caused by PU activities. We validate our work through comparisons with other clustering methods. The simulation results show that, by adjusting the cluster structure according to the changing spectrum, the proposed method reduces the interference and improves the network efficiency.     

Single image shadow detection via uncertainty analysis and GCN-based refinement strategy

Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e., SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.