Finite-time weighted average consensus with respect to a monotonic function and its application

The consensus state is an important and fundamental quantity for consensus problems of multi-agent systems, which indicates where all the dynamical agents reach. In this paper, weighted average consensus with respect to a monotonic function, which means that the trajectories of the monotonic function along the state of each agent reach the weighted average of their initial values, is studied for a group of kinematic agents with time-varying topology. By constructing a continuous nonlinear distributed protocol, such a consensus problem can be solved in finite time even though the time-varying topology involves unconnected graphs. Then the distributed protocol is employed to compute the maximum-likelihood estimation of unknown parameters over sensor networks. Compared with the existing results, the estimate scheme proposed here may reduce the costs of data communication, storage memory, book-keeping and computational overheads.     

一种高效节能的无线传感器网络数据聚合协议

在无线传感器网络的应用中,常常利用大量传感器监视若干个离散目标。针对这类目标监视网络,本文提出了一种分布式高效节能的数据聚合协议EETO。EETO将覆盖目标相关 的节点组成一个簇,簇成员都是簇头的K跳覆盖邻居,相关数据在簇头能得到及时、完全的聚合,大大减少了数据传送量。详细的模拟实验结果表明,EETO有效地减少了能量消耗,显著增强了网络的覆盖寿命。     

Decision-making and opinion formation in simple networks

In many networked decision-making settings, information about the world is distributed across multiple agents and agents’ success depends on their ability to aggregate and reason about their local information over time. This paper presents a computational model of information aggregation in such settings in which agents’ utilities depend on an unknown event. Agents initially receive a noisy signal about the event and take actions repeatedly while observing the actions of their neighbors in the network at each round. Such settings characterize many distributed systems such as sensor networks for intrusion detection and routing systems for Internet traffic. Using the model, we show that (1) agents converge in action and in knowledge for a general class of decision-making rules and for all network structures; (2) all networks converge to playing the same action regardless of the network structure; and (3) for particular network configurations, agents can converge to the correct action when using a well-defined class of myopic decision rules. These theoretical results are also supported by a new simulation-based open-source empirical test-bed for facilitating the study of information aggregation in general networks.     

EADEEG:能量感知的无线传感器网络数据收集协议

提出了一种基于簇结构的无线传感器网络数据收集协议EADEEG(an energy-aware data gathering protocol for wireless sensor networks).EADEEG通过最小化网络通信开销以及良好的能量负载平衡方法,可以有效地延长网络寿命.与以前的相关研究相比,EADEEG采用了一种全新的簇头竞争参数,能够更好地解决节点能量异构问题.此外,EADEEG也采用了一种简单而有效的簇内节点调度算法,通过控制活动节点的密度,可以在不增加额外控制开销的条件下关闭冗余节点并保证覆盖要求,因此可以进一步延长网络寿命.模拟实验证明,在节点初始能量同构和异构两种情况下,EADEEG协议都能够满足用户对覆盖率的要求,并在网络寿命上大幅度优于LEACH(low energy adaptive clustering hierarchy),PEGASIS(power-efficient gathering in sensor information systems)和DEEG(distributed energy-efficient data gathering and aggregation protocol)协议.     

System design issues in time-driven sensor networks based on stochastic sampling

This paper considers the type of sensor networks devoted to continuous monitoring. Typically, these networks constitute the hardware support for many environmental applications, where some physical phenomenon must be regularly monitored over a certain region. In line with the usual random deployment of sensor networks, the distributed sense-and-transmit process can be viewed as an extension of a technique from computer graphics called stochastic sampling, which provides an alternative to the uniform deployment imposed by multidimensional sampling theory. In addition, this paper proposes extending the randomness of the sampling process to the time domain, in order to avoid the strong synchronization requirements that sensor nodes should satisfy if they had to generate and transmit their readings periodically. The focus of the paper is system-oriented. Thus, it is assumed that packet transmissions are governed by a previously proposed MAC protocol, which can efficiently support the random nature of data with hardly detriment to the quality of the reconstructed signal. In particular, the paper focuses on the case of exponential randomization, since it allows for analytical treatment. Then, under these assumptions, closed-form expressions that relate the most relevant system-design parameters (application requirements, deployment and network configuration parameters and system behavior) are derived, and the main design tradeoffs are highlighted.     

一种无线传感器网络的多优先级数据聚集协议

无线传感器网络当前已成为一种新的分布计算模式，由于大部分无线传感器网络中节点的能源是不可补充的，因此需使用数据聚集协议提高整个网络的生命周期，传统的数据聚集协议不能保证网络中的高实时性数据的延迟较低．文中提出的DAIDA协议根据节点上的流量状况及数据的不同优先级要求自适应地调整数据链路层发送的数据包的大小，模拟结果表明，高实时性数据通过网络的延迟低于低实时性数据的延迟，并且延长了无线传感器网络的生命周期。     

基于节点分布密度的传感器网络查询处理机制

为了使传感器网络在进行数据查询时降低能耗和提高网络生命期,引入了一种分布式查询处理机制。这种机制是先将查询分发到网络后再进行优化,这种方法更具有针对性,优化效果也更明显。分簇路由协议与分布式查询有着天然的结合点。每个簇头相当于传统数据库中的一个索引,负责查询的分析、优化和数据融合。簇头根据本区域的节点分布和数据特性可以自主地选择区域内结构而不受其他区域的影响,这样就可以把每个区域看成一个自治系统,而整个传感器网络就是多个自治系统的集合。结果表明：设计查询处理机制时考虑这些因素可以降低能耗和提高网络生命期。     

Survey and application of sensor fault detection and isolation schemes

Model-based sensor fault detection, isolation and accommodation (SFDIA) is a direction of development in particular with UAVs where sensor redundancy may not be an option due to weight, cost and space implications. SFDIA via neural networks (NNs) have been proposed over the years due to their nonlinear structures and online learning capabilities. The majority of papers tend to consider single sensor faults. While useful, this assumption can limit application to real systems where sensor faults can occur simultaneously or consecutively. In this paper we consider the latter scenario, where it is assumed that a 1 s time gap is present between consecutive faults. Furthermore few applications have considered fixed-wing UAVs where full autonomy is most needed. In this paper an EMRAN RBF NN is chosen for modelling purposes due to its ability to adapt well to nonlinear environments while maintaining high computational speeds. A nonlinear UAV model is used for demonstration, where decoupled longitudinal motion is considered. System and measurement noise is also included in the UAV model as wind gust disturbances on the angle of attack and sensor noise, respectively. The UAV is assumed to operate at an initial trimmed condition of speed, 32 m/s and altitude, 1000 m. After 30 separate SFDIA tests implemented on a 1.6 GHz Pentium processor, the NN-SFDIA scheme detected all but 2 faults and the NN processing time was 97% lower than the flight data sampling time.     

Distributed parametric and nonparametric regression with on-line performance bounds computation

In this paper we focus on collaborative multi-agent systems, where agents are distributed over a region of interest and collaborate to achieve a common estimation goal. In particular, we introduce two consensus-based distributed linear estimators. The first one is designed for a Bayesian scenario, where an unknown common finite-dimensional parameter vector has to be reconstructed, while the second one regards the nonparametric reconstruction of an unknown function sampled at different locations by the sensors. Both of the algorithms are characterized in terms of the trade-off between estimation performance, communication, computation and memory complexity. In the finite-dimensional setting, we derive mild sufficient conditions which ensure that a distributed estimator performs better than the local optimal ones in terms of estimation error variance. In the nonparametric setting, we introduce an on-line algorithm that allows the agents to simultaneously compute the function estimate with small computational, communication and data storage efforts, as well as to quantify its distance from the centralized estimate given by a Regularization Network, one of the most powerful regularized kernel methods. These results are obtained by deriving bounds on the estimation error that provide insights on how the uncertainty inherent in a sensor network, such as imperfect knowledge on the number of agents and the measurement models used by the sensors, can degrade the performance of the estimation process. Numerical experiments are included to support the theoretical findings.     

A distributed energy-efficient clustering protocol for wireless sensor networks

Minimizing energy dissipation and maximizing network lifetime are among the central concerns when designing applications and protocols for sensor networks. Clustering has been proven to be energy-efficient in sensor networks since data routing and relaying are only operated by cluster heads. Besides, cluster heads can process, filter and aggregate data sent by cluster members, thus reducing network load and alleviating the bandwidth. In this paper, we propose a novel distributed clustering algorithm where cluster heads are elected following a three-way message exchange between each sensor and its neighbors. Sensor’s eligibility to be elected cluster head is based on its residual energy and its degree. Our protocol has a message exchange complexity of O(1) and a worst-case convergence time complexity of O(N). Simulations show that our algorithm outperforms EESH, one of the most recently published distributed clustering algorithms, in terms of network lifetime and ratio of elected cluster heads.     

轮询通信协议下信息物理系统分布式状态估计

本文关注的是一类信息物理系统的分布式状态估计问题. 由于传感器网络通信带宽有限, 当大量节点同时发送数据时, 可能造成数据冲突. 因此, 通过引入轮询协议减轻传感器网络通信负担, 在该协议下每个节点的测量分量将依次且周期性访问网络. 考虑滤波网络拓扑切换概率矩阵是时变的, 因此采用非齐次Markov链描述随机拓扑切换行为. 证明了估计误差以指数衰减的形式收敛,确保了滤波误差系统在均方意义下最终有界. 进一步地, 通过解决特定拓扑依赖的凸优化问题, 获得期望的分布式滤波器参数. 最后, 通过两个例子证明了所设计的分布式状态估计方法的可行性.     

基于无线传感网的温室大棚智能监控系统的研究

为了实现对温室大棚环境数据的有效监控,设计了基于无线传感网络的智能监控系统.系统的无线传感网络采用层次结构的路由协议,针对LEACH分簇算法和PEGASIS链路算法进行了融合和改进,提出了一种适用于温室大棚的链簇式路由协议.无线传感节点的设计采用了无线收发芯片nRF24L01+和微控制器STM32,汇聚节点可通过GPRS模块将环境数据上传到远端上位机PC,从而实现数据的远程监控、存储和图表分析显示等功能.     

一种能量有效的WSN路由协议

无线传感器网络中节能是首要考虑的问题。有效地延长无线传感器网络的生存时间,达到传感器节点的负载均衡是无线传感器路由网络路由协议的设计目标。由于LEACH协议存在在簇头节点的选举中未考虑节点的能量因素、簇头节点在空间上分布不均及所有簇头节点直接与Sink进行远距离数据传输过程中能量消耗过多等不足,本文提出了一种改进型的节能路由协议LEACH-ZED。LEACH-ZED采用区域划分的方式,综合考虑节点能量与到Sink节点的距离,进行簇间的多跳传输,大大改善了LEACH协议的一些缺陷。仿真表明,改进后的协议有效延长了网络的生存时间,降低了整个网络的能耗,从总体性能上看优于LEACH协议。     

Consensus seeking over directed networks with limited information communication

Existing works that deal with the problem of distributed average consensus with quantized information communication assume that the update matrices are doubly stochastic, which amounts to agents evolving on balanced directed networks with digital channels. This paper is concerned with the problem of seeking consensus via quantized information communication over a general unbalanced directed digital network. It is established that, by designing a protocol with a finite-level uniform quantization scheme, merely one bit quantized information transmitted along each connected digital channel suffices for achieving weighted average consensus with an exponential convergence rate, as long as the directed unbalanced network is strongly connected. An explicit characterization of the convergence rate of consensus is also given. By avoiding the double stochasticity assumption on the update matrix, the proposed quantized protocol is particularly suitable for the scenarios where no bidirectional and/or balanced information communication among agents is available.     

Adaptive Localized QoS-Constrained Data Aggregation and Processing in Distributed Sensor Networks

In this paper, an efficient Quality of Service (QoS)-constrained data aggregation and processing approach for distributed wireless sensor networks is investigated and analyzed. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion, based on the availability of local only information. Data aggregation is performed on the fly at intermediate sensor nodes, while at the same time the end-to-end latency constraints are satisfied. Furthermore, a localized adaptive data collection algorithm performed at the source nodes is developed that balances the design tradeoffs of delay, measurement accuracy, and buffer overflow, for given QoS requirements. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as energy efficiency, network lifetime, end-to-end latency, and data loss are also evaluated and discussed.     

Iterative Consensus for a Class of Second-order Multi-agent Systems

In this paper, the problem of leader-following consensus for a class of multi-agent systems with double integrator dynamics is investigated based on an iterative learning approach. Consensus errors of individual agents are considered as the anticipation in time, based on which a distributed iterative learning protocol is proposed for the undirected networks with fixed topology to make the followers track the leader in finite time. The dynamic of the leader is assumed to be time-varying and the state information is available to only a portion of the followers. The sufficient condition for solving the consensus problem of the multi-agent system is obtained. A simulation example is provided to demonstrate the effectiveness of the proposed method.     

Stochastic modeling of facility security-systems for analytical solutions

In this paper the author presents a modeling approach which makes use of conventional systems engineering techniques; namely, system block diagrams and networks where the transfer functions are expressed in the complex domain using Laplace transforms. Transit times, and times for the intruders and the responding guard force to overcome barriers, are both static and dynamic; the times are either deterministic or probabilistic variables obeying different and mixed probability laws.The solution methods are analytical, yielding measures of effectiveness of the security system. Modeling and solutions are presented for two cases. One case is for the simplified scenario where the reliability, sensitivity and response times of the sensors are assumed to be independent of time. The other is for the generalized case where sensor response times are time dependent and where the guard force encounters multiple barriers when attempting to intercept the intruders.     

Robust and dynamic data aggregation in wireless sensor networks: A cross-layer approach

In-network data aggregation is an effective technique to reduce communication cost in wireless sensor networks. Recent studies have focused on two issues respectively: dynamic aggregation to handle event triggered irregular traffic and robust aggregation to handle packet losses. However, how to achieve both the objectives simultaneously, i.e. dynamic and robust aggregation is still not considered. In this paper, by making use of direct support from MAC layer, we propose a cross-layer approach to realize robust and dynamic data aggregation. A new MAC protocol, DA-MAC is delicately designed to serve such purpose. With channel contention information obtained from DA-MAC, a node can dynamically determine where and when to do aggregation. To cope with packet losses, a virtual overlay, Rings is adopted to forward one packet to multiple nodes. We have conducted numerical analysis to optimize the key parameters and implemented our design in TinyOS based sensor networks. Performance evaluation though simulations and experiments shows that our approach can handle both traffic dynamics and packet losses, with less cost than similar solutions.     

应用移动Agent的无线传感器网络能量管理机制

鉴于无线传感器网络能源受限的显著特征,有必要讨论其有效的能量管理机制。目前,大多数研究成果仅考虑到拓扑控制、路由、数据融合等某一方面的节能问题,往往缺乏对网络活动整体能耗的测控,因而缺乏完整性。本文应用移动Agent技术,从多个角度探讨了传感器网络的节能问题,包括拓扑和簇的生成、数据采集等。提出了若干基于移动A-gent的能量管理算法,并通过仿真实验和算法性能比较,立足于平均能耗、通信流量和负载、网络生命周期等几个方面,分别总结了其优缺点及可行性。     

Trustworthy service composition with secure data transmission in sensor networks

As the basis of the Internet of Things (IoT), sensor networks have materialized its computation and communication capability into anything in our modern lives. Service composition provides us a promising way to cooperate various sensors to build more powerful IoT applications over sensor networks. However, the limited capability of sensor node poses great challenges not only to trustworthy service composition but also to secure data aggregation. The complex composite structure, computation-intensive evaluation, and massive data transmission become burdens for service composition in sensor networks. To overcome these issues, this paper proposes a distributed approach to enable efficient trustworthy service composition with secure data transmission in sensor networks. By analyzing dependency relationships, the rules for computing service trust and data trust are proposed based a multi-level trust model. Then, each target component service can be evaluated independently through a model checker. Moreover, an identity-based aggregate signature is introduced in the composite evaluation to guarantee the secure data transmission among different components. The experimental results show that our approach not only achieves efficient trustworthy service composition with complex invocation structures, but also reduces the costs in the secure data transmission.