传感器网络中的分布式滚动时域状态估计

利用分布式滚动时域方法对无线传感器网络的状态估计问题进行研究,给出了基于量化测量值的滚动时域估计算法。在无线传感器网络的环境下处理分布式状态估计问题时,减少通信的成本是非常重要的一个环节,需要将观测值量化后再传送。以往的滚动时域估计方法无法处理量化观测值的状态估计问题,而本文的方法考虑了最严格的观测值量化情况即传感器只发送一个比特至融合中心的状态估计问题。与其它传感器网络中的状态估计方法相比,该方法减少了每一步的计算量。仿真结果验证了该算法的有效性。     

多智能传感器的有限分散自治体系研究

智能系统多传感器信息融合的复杂性迫切需要开发一套合适的结构体系，目前大多数结构体系都通过融合中心对分散在不同点的多个传感器进行信息处理，而底层传感器之间缺乏必要的联系．这样导致融合中心计算和通信的负担过重而造成瓶颈，且不能使传感器之间互相启发以提高任务环境认知的效率．针对这些问题本文首先提出智能传感器的新概念，指出智能传感器须具备的５个基本能力即预测、规划、刷新、通信和同化，并在此基础上讨论了多智能传感器组成系统时的算法及信息流程．最后以主动视觉和主动触觉共同感知运动物体的位姿为例剖析了这种新思想的具体运用     

Deployment of an unreliable robotic sensor network for spatial estimation

This paper studies an optimal deployment problem for a network of robotic sensors moving in the real line. Given a spatial process of interest, each individual sensor sends a packet that contains a measurement of the process to a data fusion center. We assume that, due to communication limitations or hardware unreliability, only a fraction of the packets arrive at the center. Using convex analysis, nonsmooth analysis, and combinatorics, we show that, for various fractional rates of packet arrival, the optimal deployment configuration has the following features: agents group into clusters, clusters deploy optimally as if at least one packet from each cluster was guaranteed to reach the center, and there is an optimal cluster size for each fractional arrival rate.     

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation. Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network. These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner. We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption. Measured data is modeled as a Gaussian random variable with a changeable variance. A gird model is used to approximate the problem. We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search. Our experiments demonstrate that the algorithm is correct in a certain tolerance, and it is also efficient and scalable.     

无线传感网络的非分簇拓扑控制方法研究

无线传感网络通常由能量受限、通信半径较小的传感器节点构成,其中拓扑控制是重要的工程问题。提出了一种基于元胞自动机的非分簇的拓扑控制算法,与传统分簇方法的区别在于本方法试图通过牺牲小部分拓扑连通度和覆盖度来换取更长的系统生存时间。基于元胞自动机模型的研究表明,节点的状态转移规则对系统整体性能起决定作用,在一些规则下系统拓扑呈现稳定变化,符合对无线传感网络拓扑控制的要求。进一步探讨了该机制在工程上的具体实现问题,并与LEACH算法进行了对比,验证了以拓扑性能换取生存时间的设想。     

Mobile-agent-based collaborative sensor fusion

Information fusion can assist in the development of sensor network applications by merging capabilities, raw data and decisions from multiple sensors through distributed and collaborative integration algorithms. In this paper, we introduce a multi-layered, middleware-driven, multi-agent, interoperable architecture for distributed sensor networks that bridges the gap between the programmable application layer consisting of software agents and the physical layer consisting of sensor nodes. We adopt an energy-efficient, fault-tolerant approach for collaborative information processing among multiple sensor nodes using a mobile-agent-based computing model. In this model the sink/base-station deploys mobile agents that migrate from node to node following a certain itinerary, either pre-determined or determined on-the-fly, and fuse the information/data locally at each node. This way, the intelligence is distributed throughout the network edge and communication cost is reduced to make the sensor network energy-efficient. We evaluate the performance of our mobile-agent-based approach as well as that of the traditional client/server-based computing model, vis-à-vis energy consumption and execution time, through both analytical study and simulation. We draw important conclusions based on our findings. Finally, we consider a collaborative target classification application, supported by our architectural framework, to illustrate the efficacy of the mobile-agent-based computing model.     

Sleep–wake up scheduling with probabilistic coverage model in sensor networks1

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.     

Decentralized estimation of regression coefficients in sensor networks

Consider a wireless sensor network with a fusion center deployed to estimate a common non-random parameter vector. Each sensor obtains a noisy observation vector of the non-random parameter vector according to a linear regression model. The observation noise is correlated across the sensors. Due to power, bandwidth and complexity limitations, each sensor linearly compresses its data. The compressed data from the sensors are transmitted to the fusion center, which linearly estimates the non-random parameter vector. The goal is to design the compression matrices at the sensors and the linear unbiased estimator at the fusion center such that the total variance of the estimation error is minimized. In this paper, we provide necessary and sufficient conditions for achieving the performance of the centralized best linear unbiased estimator. We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied. When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.     

Model-based approximate querying in sensor networks

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network datasets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. This article includes and extends results that were previously published in VLDB 2004 [Desphande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In {VLDB} (2004)], and combines these techniques with the conditional planning approach published in ICDE 2005 [Deshpande, A., Guestrin, C., Madden, S., Hong, W.: Exploiting correlated attributes in acquisitional query processing. In {ICDE} (2005)].     

Convex optimization approach to identify fusion for multisensortarget tracking

We consider the problem of identity fusion for a multisensor target tracking system whereby the sensors generate reports on the target identities. Since sensor reports are typically fuzzy, incomplete, or inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identity fusion method based on the minimization of inconsistencies among the sensor reports by using a convex quadratic programming formulation. In contrast to Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach can fuse sensor reports of the form more general than that allowed by the evidential reasoning theory. Simulation results show that our method generates reasonable fusion results which are similar to that obtained via the evidential reasoning theory     

On infinite-horizon sensor scheduling

In this paper we consider the problem of infinite-horizon sensor scheduling for estimation in linear Gaussian systems. Due to possible channel capacity, energy budget or topological constraints, it is assumed that at each time step only a subset of the available sensors can be selected to send their observations to the fusion center, where the state of the system is estimated by means of a Kalman filter. Several important properties of the infinite-horizon schedules will be presented in this paper. In particular, we prove that the infinite-horizon average estimation error and the boundedness of a schedule are independent of the initial covariance matrix. We further provide a constructive proof that any feasible schedule with finite average estimation error can be arbitrarily approximated by a bounded periodic schedule. We later generalized our result to lossy networks. These theoretical results provide valuable insights and guidelines for the design of computationally efficient sensor scheduling policies.     

Decentralized robust set-valued state estimation in networked multiple sensor systems

This paper addresses a decentralized robust set-valued state estimation problem for a class of uncertain systems via a data-rate constrained sensor network. The uncertainties of the systems satisfy an energy-type constraint known as an integral quadratic constraint. The sensor network consists of spatially distributed sensors and a fusion center where set-valued state estimation is carried out. The communications from the sensors to the fusion center are through data-rate constrained communication channels. We propose a state estimation scheme which involves coders that are implemented in the sensors, and a decoder–estimator that is located at the fusion center. Their construction is based on the robust Kalman filtering techniques. The robust set-valued state estimation results of this paper involve the solution of a jump Riccati differential equation and the solution of a set of jump state equations.     

Optimal energy allocation in heterogeneous wireless sensor networks for lifetime maximization

We consider the problem of optimal energy allocation and lifetime maximization in heterogeneous wireless sensor networks. We construct a probabilistic model for heterogeneous wireless sensor networks where sensors can have different sensing range, different transmission range, different energy consumption for data sensing, and different energy consumption for data transmission, and the stream of data sensed and transmitted from a sensor and the stream of data relayed by a sensor to a base station are all treated as Poisson streams. We derive the probability distribution and the expectation of the number of data transmissions during the lifetime of each sensor and the probability distribution and the expectation of the lifetime of each sensor. In all these analysis, energy consumption of data sensing and data transmission and data relay are all taken into consideration. We develop an algorithm to find an optimal initial energy allocation to the sensors such that the network lifetime in the sense of the identical expected sensor lifetime is maximized. We show how to deal with a large amount of energy budget that may cause excessive computational time by developing accurate closed form approximate expressions of sensor lifetime and network lifetime and optimal initial energy allocation. We derive the expected number of working sensors at any time. Based on such results, we can find the latest time such that the expected number of sensors that are still functioning up to that time is above certain threshold.     

FUZZY REASONING IN A MULTIAGENT SYSTEM OF SURVEILLANCE SENSORS TO MANAGE COOPERATIVELY THE SENSOR-TO-TASK ASSIGNMENT PROBLEM

In this work, a surveillance network composed of a set of sensors and a fusion center is designed as a multiagent system. Negotiation among sensors (agents) is proposed to solve the task-to-sensor assignment problem (the allocation of tasks to sensors), addressing several aspects. First, the fusion center determines the tasks (system tasks) to be performed by the network at each management cycle. To do that, a fuzzy reasoning system determines the priorities of these system tasks by means of a symbolic inference process using the fused data received from all sensors. In addition, a fuzzy reasoning process, similar to that performed in the fusion center, is proposed to evaluate the priority of local tasks (sensor tasks) now executed by each sensor. The network coordination procedure will be based on the system-task priorities, computed in the fusion center, and on the local priorities evaluated in each sensor. Priority values for system and sensor tasks will be the basis to guide a negotiation process among sensors in the multiagent system. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the negotiation scheme, based on task priority and measured through their time-variant priority, allows the adaption of sensor operation to changing situations.     

Application-driven,energy-efficient communication in wireless sensor networks

Several sensor network applications based on data diffusion and data management can determine the communication transfer rate between two sensors beforehand. In this framework, we consider the problem of energy efficient communication among nodes of a wireless sensor network and propose an application-driven approach that minimizes radio activity intervals and prolongs network lifetime. On the basis of possible communication delays we estimate packet arrival intervals at any intermediate hop of a fixed-rate data path. We study a generic strategy of radio activity minimization wherein each node maintains the radio switched on just in the expected packet arrival intervals and guarantees low communication latency. We define a probabilistic model that allows the evaluation of the packet loss probability that results from the reduced radio activity. The model can be used to optimally choose the radio activity intervals that achieve a certain probability of successful packet delivery for a specific radio activity strategy. Relying on the probabilistic model we also define a cost model that estimates the energy consumption of the proposed strategies, under specific settings. We propose three specific strategies and numerically evaluate the associated costs. We finally validate our work with a simulation made with TOSSIM (the Berkeley motes’ simulator). The simulation results confirm the validity of the approach and the accuracy of the analytic models.     

Coverage boundary of unknown environment using mobile sensor networks

In this article, we consider the problem of optimal coverage of unknown environmental boundary using sensor networks. Since the boundary is unknown to all sensors, it is necessary for the sensors to find it first. We give a new distributed estimate policy by tracking a virtual agent using the sensor networks. Then we consider the problem of optimal coverage of the estimate boundary instead of the actual one. Moreover, an algorithm is given to deploy the sensors to the optimal configuration corresponding to the coverage problem considered in this article.     

Efficient solution techniques for the integrated coverage, sink location and routing problem in wireless sensor networks

Sensors are tiny electronic devices having limited battery energy and capability for sensing, data processing and communicating. They can collectively behave to provide an effective wireless network that monitors a region and transmits the collected information to gateway nodes called sinks. Most of the applications require the operation of the network for long periods of times, which makes the efficient management of the available energy resources an important concern. There are three major issues in the design of sensor networks: sensor deployment or the coverage of the sensing area, sink location, and data routing. In this work, we consider these three design problems within a unified framework and develop two mixed-integer linear programming formulations. They are difficult to solve exactly. However, it is possible to compute good feasible solutions of the sink location and routing problems easily, when the sensors are deployed and their locations in the sensor field become known. Therefore, we propose a tabu search heuristic that tries to identify the best sensor locations satisfying the coverage requirements. The objective value corresponding to each set of sensor locations is calculated by solving the sink location and routing problem. Computational tests carried out on randomly generated test instances indicate that the proposed hybrid approach is both accurate and efficient.     

An efficient heuristic for selecting active nodes in wireless sensor networks

Energy saving is a paramount concern in wireless sensor networks (WSNs). A strategy for energy saving is to cleverly manage the duty cycle of sensors, by dynamically activating different sets of sensors while non-active nodes are kept in a power save mode. We propose a simple and efficient approach for selecting active nodes in WSNs. Our primary goal is to maximize residual energy and application relevance of selected nodes to extend the network lifetime while meeting application-specific QoS requirements. We formalize the problem of node selection as a knapsack problem and adopt a greedy heuristic for solving it. An environmental monitoring application is chosen to derive some specific requirements. Analyses and simulations were performed and the impact of various parameters on the process of node selection was investigated. Results show that our approach outperforms a naı¨ve scheme for node selection, achieving large energy savings while preserving QoS requirements.     

Distributed Kalman filtering via node selection in heterogeneous sensor networks

In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on node selection, rather than on sensor fusion. The presented approach is particularly suitable when sensors with limited sensing capability are considered. In this case, strategies based on sensor fusion may exhibit poor results, as several unreliable measurements may be included in the fusion process. On the other hand, our approach implements a distributed strategy able to select only the node with the most accurate estimate and to propagate it through the whole network in finite time. The algorithm is based on the definition of a metric of the estimate accuracy, and on the application of an agreement protocol based on max-consensus. We prove the convergence, in finite time, of all the local estimates to the most accurate one at each discrete iteration, as well as the equivalence with a centralised Kalman filter with multiple measurements, evolving according to a state-dependent switching dynamics. An application of the algorithm to the problem of distributed target tracking over a network of heterogeneous range-bearing sensors is shown. Simulation results and a comparison with two distributed Kalman filtering strategies based on sensor fusion confirm the suitability of the approach.     

Connected K-target coverage problem in wireless sensor networks with different observation scenarios

In this paper, we consider the problem of scheduling sensor activities to maximize network lifetime while maintaining both discrete K-target coverage and network connectivity. In K-target coverage, it is required that each target should be simultaneously observed by at least K sensors. The data generated by the sensors will be transmitted to the sink node via single or multiple hop communications. As maintaining discrete target coverage cannot guarantee the network connectivity, we consider both target coverage and connectivity issues. Further, by adopting a more realistic energy consumption model, we consider the sensor activity scheduling problem and routing problem jointly. We study the problem with two observation scenarios depending on whether a sensor can distinguish the targets in its sensing area or not. For the first scenario, a more general scenario where each sensor can simultaneously observe multiple targets is considered and we develop a polynomial-time algorithm which can achieve optimal solution based on linear programming and integer theorem. For the second scenario, we show that the problem is NP-complete and develop an approximation algorithm for solving it. As the protocol cost of the optimal solution and the approximation algorithm may be high in practice, we develop a low-cost heuristic algorithm which can be implemented in a distributed fashion for both scenarios. We demonstrate the effectiveness of the heuristic algorithm through extensive simulations.