Continuous data aggregation and capacity in probabilistic wireless sensor networks

Due to the existence of many probabilistic lossy links in Wireless Sensor Networks (WSNs) (Liu et al., 2010)  [25], it is not practical to study the network capacity issue under the Deterministic Network Model (DNM). A more realistic one is actually the Probabilistic Network Model (PNM). Therefore, we study the Snapshot Data Aggregation (SDA) problem, the Continuous Data Aggregation (CDA) problem, and their achievable capacities for probabilistic WSNs under both the independent and identically distributed (i.i.d.) node distribution model and the Poisson point distribution model in this paper. First, we partition a network into cells and use two vectors to further partition these cells into equivalent color classes. Subsequently, based on the partitioned cells and equivalent color classes, we propose a Cell-based Aggregation Scheduling (CAS) algorithm for the SDA problem in probabilistic WSNs. Theoretical analysis of CAS and the upper bound capacity of the SDA problem show that the achievable capacities of CAS are all order optimal in the worst case, the average case, and the best case. For the CDA problem in probabilistic WSNs, we propose a Level-based Aggregation Scheduling (LAS) algorithm. LAS gathers the aggregation values of continuous snapshots by forming a data aggregation/transmission pipeline on the segments and scheduling all the cell-levels in a cell-level class concurrently. By theoretical analysis of LAS and the upper bound capacity of the CDA problem, we prove that LAS also successfully achieves order optimal capacities in all the cases. The extensive simulation results further validate the effectiveness of CAS and LAS.     

Securing distributed data storage and retrieval in sensor networks

Sensor networks have been an attractive platform for pervasive computing and communication. However, they are vulnerable to attacks if deployed in hostile environments. The past research of sensor network security has focused on securing information in communication, but how to secure information in storage has been overlooked. Meanwhile, distributed data storage and retrieval have become popular for efficient data management in sensor networks, which renders the absence of schemes for securing stored information to be a more severe problem. Therefore, we propose three evolutionary schemes, namely, the simple hash-based (SHB) scheme, the enhanced hash-based (EHB) scheme, and the adaptive polynomial-based (APB) scheme, to deal with the problem. All the schemes have the properties that only authorized entities can access data stored in the sensor network, and the schemes are resilient to a large number of sensor node compromises. The EHB and the APB schemes do not involve any centralized entity except for a few initialization or renewal operations, and thus support secure, distributed data storage and retrieval. The APB scheme further provides high scalability and flexibility, and hence is the most suitable among the three schemes for real applications. The schemes were evaluated through extensive analysis and TOSSIM-based simulations.     

分布式WSN联合概率覆盖节点调度算法

利用概率覆盖探测模型,提出了一种分布式的基于联合概率覆盖的节点调度算法。节点在本地通过与其一跳邻节点的信息交互,获取本地节点所在区域的所有覆盖匹配集,根据邻节点的工作状态判断本地所在区域被概率覆盖情况;最后,节点将根据判断结果调度本地节点进入工作状态或休眠状态。仿真结果表明,该算法执行效率高于CCP和DPCP算法,能够在保证网络概率覆盖前提下,关闭大量冗余节点,保证网络工作节点数目稳定,延长了网络寿命。     

传感器网络中基于树的分布式数据收集算法

数据收集是传感器网络的根本任务,由于传感器网络节点能量有限、易失效等因素,如何设计一个低能耗的、可扩展性强的数据收集机制是传感器网络的一个关键问题。为此,提出一种基于树的分布式数据收集算法,其基本思想是：基站发送广播信息,根据节点到基站的最小跳数构造出网络的层次结构,由层次结构生成以基站为树根的树型传输网络,并基于该网络模型收集数据。理论分析和仿真实验表明该算法具有较低的复杂度,能有效地延长网络的生命周期,并具有良好的扩展性和容错性。     

Efficient distributed data scheduling algorithm for data aggregation in wireless sensor networks

With the rapid development of applications for wireless sensor networks, efficient data aggregation methods are becoming increasingly emphasized. Many researchers have studied the problem of reporting data with minimum energy cost when data is allowed to be aggregated many times. However, some aggregation functions used to aggregate multiple data into one packet are unrepeatable; that is, every data is aggregated only at most once. This problem motivated us to study reporting data with minimum energy cost subject to that a fixed number of data are allowed to be aggregated into one packet and every data is aggregated at most once. In this paper, we propose novel data aggregation and routing structures for reporting generated data. With the structures, we study the problem of scheduling data to nodes in the networks for data aggregation such that the energy cost of reporting data is minimized, termed MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING. In addition, we show that MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING is NP-complete. Furthermore, a distributed data scheduling algorithm is proposed accordingly. Simulations show that the proposed algorithm provides a good solution for MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING.     

基于混合计算模型的分布式传感器网络数据收集

首先分析了传感器网络中两种计算模型的特点;然后针对装甲集群状态监控的实际需求,提出了基于无线传感器网络的装备状态监控系统的基本结构.分析了用于前端数据收集和传输的两层传感器网络的不同特点,结合client/server和移动agent模型的优点,提出基于混合计算模型的分布式传感器网络数据收集机制,在相对独立的车载监测网络中基于client/server模型建立数据收集机制,在车载监测网络之间基于移动agent模型建立数据收集机制.     

Integrated probabilistic data association

This paper presents an integrated probabilistic data association algorithm which provides recursive formulas for both data association and track quality (probability of track existence), allowing track initiation and track termination to be fully integrated into the association and smoothing algorithm. Integrated probabilistic data association is of similar computational complexity to probabilistic data association and as demonstrated by simulation, achieves comparable performance to the more computationally expensive interactive multiple model probabilistic data association algorithm which also integrates initiation and tracking     

Entity resolution for distributed probabilistic data

The problem of entity resolution over probabilistic data (ERPD) arises in many applications that have to deal with probabilistic data. In many of these applications, probabilistic data is distributed among a number of nodes. The simple, centralized approach to the ERPD problem does not scale well as large amounts of data need to be sent to a central node. In this paper, we present FD (Fully Distributed), a decentralized algorithm for dealing with the ERPD problem over distributed data, with the goal of minimizing bandwidth usage and reducing processing time. FD is completely distributed and does not depend on the existence of certain nodes. We validated FD through implementation over a 75-node cluster and simulation using the PeerSim simulator. We used both synthetic and real-world data in our experiments. Our performance evaluation shows that FD can achieve major performance gains in terms of bandwidth usage and response time.     

On computing mobile agent routes for data fusion in distributed sensor networks

The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.     

一个分布式传感器网络数据访问控制方案的改进

分析了HUR J提出的一个细粒度分布式传感器网络数据访问控制方案,指出其具有抗合谋攻击性和前向保密性问题,并采用委托系统控制器生成更新属性群密钥的方法弥补这两项缺陷。新方案同样支持细粒度的访问控制,安全性更强。     

Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers

In a multitarget environment, when tracking crossing targets, a model is needed for the situation where the measurements from two targets are merged into one due to an inherent resolution threshold. A multidimensional model for the merged measurements is proposed and the resulting pdf is presented. This model is applied to augment the Joint Probabilistic Data Association (JPDA) algorithm used for tracking multiple targets in a cluttered environment, so that it can handle, in a more realistic manner, the situation of crossing targets. An extension to maneuvering targets is also presented.     

Energy-efficient distributed clustering in wireless sensor networks

The deployment of wireless sensor networks in many application areas requires self-organization of the network nodes into clusters. Clustering is a network management technique, since it creates a hierarchical structure over a flat network. Quite a lot of node clustering techniques have appeared in the literature, and roughly fall into two families: those based on the construction of a dominating set and those which are based solely on energy considerations. The former family suffers from the fact that only a small subset of the network nodes are responsible for relaying the messages, and thus cause rapid consumption of the energy of these nodes. The latter family uses the residual energy of each node in order to decide about whether it will elect itself as a leader of a cluster or not. This family’s methods ignore topological features of the nodes and are used in combination with the methods of the former family. We propose an energy-efficient distributed clustering protocol for wireless sensor networks, based on a metric for characterizing the significance of a node, w.r.t. its contribution in relaying messages. The protocol achieves small communication complexity and linear computation complexity. Experimental results attest that the protocol improves network longevity.     

传感器网络一致性分布式滤波算法

为了改善分布式传感器网络的估计性能,提出了一种基于状态预测一致的滤波算法.在对局部估计值进行一致化处理的基础上,重点研究了利用邻居节点前一时刻的估计值对当前局部状态预测值进行修正来提高估计精度.给出了一种一致性增益的选择方法,利用李雅普诺夫方法得到了算法收敛的充分条件,并讨论了影响算法收敛速度的因素.仿真结果表明了算法的有效性,并发现节点度较大的传感器在网络估计中发挥着重要作用,可通过调整这类节点的一致性系数来改善算法性能.     

Radiation detection with distributed sensor networks

In any assessment of potential terrorist attacks, the nuclear threat takes center stage. Although weapons-grade nuclear materials arc heavily guarded, a plausible scenario involves terrorists detonating a simple radiological dispersion device (ROD) capable of broadcasting nonfissile but highly radioactive particles over a densely populated area. In most cases, a motor vehicle has to transport the device and its payload commonly known as a "dirty bomb" - to the target destination. As a final defense against such a weapon, select traffic choke points in the US have large portal monitoring systems to help detect illicit isotopes. The distributed sensor network project at Los Alamos National Laboratory, in cooperation with the University of New Mexico, is developing a network of radiation detectors that, coupled with other sensors that collect supportive data, is suitable for ROD interdiction in either urban or rural environments. Compared to a portal monitor, a DSN is much less visible, uses less power per detector, is hand carried and thus more rapidly deployable, and simplifies coverage of multiple transport avenues. Also, to function effectively, portal monitoring systems typically require slow or halted traffic, whereas our DSN can be tailored for any moderate traffic speed.     

Tracking frequent items over distributed probabilistic data

Tracking frequent items (also called heavy hitters) is one of the most fundamental queries in real-time data due to its wide applications, such as logistics monitoring, association rule based analysis, etc. Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of real-time data is usually collected from multiple sources in a distributed environment. Unfortunately, data collected from each source is often uncertain due to various factors: imprecise reading, data integration from multiple sources (or versions), transmission errors, etc. In addition, due to network delay and limited by the economic budget associated with large-scale data communication over a distributed network, an essential problem is to track the global frequent items from all distributed uncertain data sites with the minimum communication cost. In this paper, we focus on the problem of tracking distributed probabilistic frequent items (TDPF). Specifically, given k distributed sites S = {S ₁, … , S _k }, each of which is associated with an uncertain database \(\mathcal {D}_{i}\) of size n _i , a centralized server (or called a coordinator) H, a minimum support ratio r, and a probabilistic threshold t, we are required to find a set of items with minimum communication cost, each item X of which satisfies P r(s u p(X) ≥ r × N) > t, where s u p(X) is a random variable to describe the support of X and \(N={\sum }_{i=1}^{k}n_{i}\). In order to reduce the communication cost, we propose a local threshold-based deterministic algorithm and a sketch-based sampling approximate algorithm, respectively. The effectiveness and efficiency of the proposed algorithms are verified with extensive experiments on both real and synthetic uncertain datasets.     

无线传感器网络中一种有效的分布式簇划分算法

提出了一种快速有效的分布式簇划分算法，为每个节点设定一个初始时间，最先到期的节点成为簇头。考虑到簇头选举的合理性，时间衰减与节点连通度相关，并辅以随机化的方法消除时间同步对算法的影响。通过仿真验证该簇划分算法的有效性，并定量分析了通信半径与平均簇头个数的关系。     

A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

A common approach to improve the reliability of query results based on error-prone sensors is to introduce redundant sensors. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are executed. Moreover, some sensors may not be working properly and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensor nodes to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensor nodes, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for several common aggregate queries. Our statistics-based sensor node selection algorithm is demonstrated in a number of simulation experiments, which shows that a small number of sensor nodes can provide accurate and robust query results.     

Secure, dependable and publicly verifiable distributed data storage in unattended wireless sensor networks

In unattended wireless sensor networks (UWSNs), sensed data are stored locally or at designated nodes and further accessed by authorized collectors on demand. This paradigm is motivated by certain scenarios where historical or digest data (e.g., average temperature in a day), instead of real-time data, are of interest. The data are not instantly forwarded to a central sink upon sensing, thereby saving communication energy for transmission. Such a paradigm can also improve data survivability by making use of...