Channel-aware distributed detection in wireless sensor networks

This paper reviews the classical decentralized decision theory in the light of new constraints and requirements. The central theme that transcends various aspects of signal processing design is that an integrated channel-aware approach needs to be taken for optimal detection performance given the available resources.     

Cooperative OFDM for semi distributed detection in wireless sensor networks

In this paper, parallel distributed detection in wireless sensor network (WSN) is investigated. Sensors are assumed to be transmitting their local decisions to a fusion center through a wireless fading channel using cooperative transmission. To enable cooperative transmission, sensors are divided into groups where sensors in each group help each other in transmitting their decisions in a way that the fusion center receives each local decision as an orthogonal frequency division multiplexed (OFDM) block. The fusion center detects all OFDM blocks sent by all groups to process them in order to obtain a final (global) decision. Using this cooperative transmission scheme enables the fusion center to apply diversity combining methods in order to reduce the fading effects of the channel. Optimal and sub-optimal fusion rules are derived for such system. Simulation results are provided to show the performance improvement that can be obtained compared to the conventional system where each local decision is transmitted to the fusion center individually and no diversity technique is applied at the fusion center.     

A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks

Wireless Networks - Energy conservation and fault tolerance are the most two important challenging issues for the development of large scale wireless sensor networks (WSNs). Failure of cluster...     

Anomaly detection in wireless sensor networks

Anomaly detection in wireless sensor networks is an important challenge for tasks such as fault diagnosis, intrusion detection, and monitoring applications. The algorithms developed for anomaly detection have to consider the inherent limitations of sensor networks in their design so that the energy consumption in sensor nodes is minimized and the lifetime of the network is maximized. In this survey article we analyze the state of the art in anomaly detection techniques for wireless sensor networks and discuss some open issues for research.     

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.     

无线传感器网络分布式单向链路检测算法

针对在存在单向链路的网络中如何检测单向链路和如何利用单向链路的问题,提出了3种分布式算法.这3种算法的基本思想是通过Beacon数据包交换一部分链路信息帮助发现单向链路.实验结果显示,使用提出的分布式链路检测算法可以将路由路径的平均长度减少37.8%～39.24%,路由层平均数据发送成功率提高23.82%.模拟实验表明,提出的分布式单向链路检测算法具有很好的可扩展性.     

无线传感器网络中的定位异常检测

在无线传感器网络中位置信息有着重要应用,但是定位过程容易受到恶意攻击者的攻击或环境因素的干扰。为了增加节点定位的安全性。本文提出一种基于节点部署模型的检测方法用来提高定位的安全性能,该方法独立于节点定位过程,根据节点位置的邻居发现和部署知识的一致性判断节点位置是否异常。在具体的异常判断过程中使用一种由马氏距离定义差异矩阵作为比较工具。最后通过Matlab仿真实验分别从检测率,错误警报率两个方面评估验证了该方法的正确性。     

Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes

Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.     

Dynamic clustering of distributed source coding in wireless sensor networks

There are correlations of data in adjacent sensor nodes in wireless sensor networks (WSNs). Distributed source coding (DSC) is an idea to improve the energy efficiency in WSNs by compressing the sensor data with correlations to others. When utilizing the DSC, the network architecture that, deciding which nodes to transmit the side information and which nodes to compress according to the correlations, influences the compression efficiency significantly. Comparing with former schemes that have no adaptations, a dynamic clustering scheme is presented in this article, with which the network is partitioned to clusters adaptive to the topology and the degree of correlations. The simulation indicates that the proposed scheme has higher efficiency than static clustering schemes.     

Collaborative beamforming for distributed wireless ad hoc sensor networks

The performance of collaborative beamforming is analyzed using the theory of random arrays. The statistical average and distribution of the beampattern of randomly generated phased arrays is derived in the framework of wireless ad hoc sensor networks. Each sensor node is assumed to have a single isotropic antenna and nodes in the cluster collaboratively transmit the signal such that the signal in the target direction is coherently added in the far-field region. It is shown that with N sensor nodes uniformly distributed over a disk, the directivity can approach N, provided that the nodes are located sparsely enough. The distribution of the maximum sidelobe peak is also studied. With the application to ad hoc networks in mind, two scenarios (closed-loop and open-loop) are considered. Associated with these scenarios, the effects of phase jitter and location estimation errors on the average beampattern are also analyzed.     

Hierarchical distributed management clustering protocol for wireless sensor networks

In recent years, there has been a marked increase in the use of wireless sensor networks in various environments such as crisis areas, military operations, and monitoring systems. These networks do not use a fixed network infrastructure and therefore they are a popular choice for highly dynamic environments. One of the main concerns in these networks is the topology management issue, which the clustering method is a subfield for that. The main objective of clustering methods is optimizing the energy consumption. This paper proposes a new clustering protocol, which uses many parameters such as the activity history of each node, local and general state of nodes and their resources condition to determine the best cluster heads and members of each cluster that can increase the network lifetime, fair resource consumption and network coverage.     

Gravitational outlier detection for wireless sensor networks

Accuracy of sensed data and reliable delivery are the key concerns in addition to several other network‐related issues in wireless sensor networks (WSNs). Early detection of outliers reduces subsequent unwanted transmissions, thus preserving network resources. Recent techniques on outlier detection in WSNs are computationally expensive and based on message exchange. Message exchange‐based techniques incur communication overhead and are less preferred in WSNs. On the other hand, machine learning‐based outlier detection techniques are computationally expensive for resource constraint sensor nodes. The novelty of this paper is that it proposes a simple, non message exchange based, in‐network, real‐time outlier detection algorithm based on Newton's law of gravity. The mechanism is evaluated for its accuracy in detecting outliers, computational cost, and its influence on the network traffic and delay. The outlier detection mechanism resulted in almost 100% detection accuracy. Because the mechanism involves no message exchanges, there is a significant reduction in network traffic, energy consumption and end‐to‐end delay. An extension of the proposed algorithm for transient data sets is proposed, and analytic evaluation justifies that the mechanism is reactive to time series data. Copyright © 2016 John Wiley & Sons, Ltd.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

A dynamic-clustering reactive routing algorithm for wireless sensor networks

The clustering is a key routing method for large-scale wireless sensor networks, which effective extends the lifetime and the expansibility of network. In this paper, a node model is defined based on the structure and transmission principle of neuron, and a dynamic-clustering reactive routing algorithm is proposed. Once the event emergences, the cluster head is dynamic selected in the incident region according to the residual energy. The data collected by the cluster head is sent back to the Sink along the network backbone. Two kinds of accumulation ways are designed to increase the efficiency of data collection. Meanwhile through the fluctuation of action-threshold, the cluster head can trace the changing speed of incident; the nodes outside the incident region use this fluctuation to send data periodically. Finally, the simulation results verify that the DCRR algorithm extends the network’s lifetime considerably and adapts to the change of network scale. The analysis shows that DCRR has more prominent advantages under low and middle load.

Optimal probabilistic encryption for distributed detection in wireless sensor networks based on immune differential evolution algorithm

The problem of binary hypothesis testing is considered in a bandwidth-constrained low-power wireless sensor network operating over insecure links. To prevent passive eavesdropping from enemy fusion center (EFC), the sensor observations are randomly flipped according to pre-deployed flipping rates before transmission. Accordingly, a constrained optimization problem is formulated to minimize the fusion error of ally fusion center (AFC) while maintain EFC’s error at high level. We demonstrated that the fusion error is a non-convex function of the flipping rates, thus an immune based differential evolution algorithm is designed to search the optimal flipping rates, such that the EFC always gets high error probability at the cost of a small degeneration of the AFC’s fusion performance. Furthermore, the optimal thresholds of the fusion rules are calculated based on the statistics of the sensor data, which further degenerates the detection performance of the EFC, since it is not aware of the statistics of the sensor observations after data flipping, resulting in its threshold does not match the observations. Simulation results demonstrated that the AFC can appropriately acquire the original nature state, while the EFC is prevent to detect the target regardless of the signal-to-noise and sensor numbers.     

A unified approach of simultaneous state estimation and anomalous node detection in distributed wireless sensor networks

Detection of anomalous node in distributed wireless sensor networks is extremely important for powerful inference and network reliability. In this paper, we propose a powerful linear statistical model for estimating the state values of the sensor nodes longitudinally, and the estimated state values are used for detecting the anomalous nodes. Our proposed approach is powerful because it considers the effect of the nearest neighbors on the current state values and then detects the anomalous nodes based on the estimated state values. Our method can estimate the missing state values of the sensor nodes, which are kept in sleep mode for energy conservation. We also propose an alternative Bayesian model that is computationally faster for state estimation and anomaly detection. The effectiveness of the proposed model is investigated through extensive simulation studies, and the usefulness of our algorithm is numerically assessed. The performance of the proposed approach is compared to that of the traditional approaches through simulation studies. The proposed model can be effectively used in security surveillance, pattern recognition, habitat monitoring, etc.     

FP-MAC: A distributed MAC algorithm for 802.15.4-like wireless sensor networks

In this paper we focus on the problems of high latency and low throughput arising from the periodic operation of MAC protocols for wireless sensor networks. In order to meet both design criteria we propose an energy-efficient, low delay, fast-periodic MAC algorithm, namely FP-MAC, that is exclusively designed for 802.15.4-like networks utilizing in full the standard’s physical layer. Our proposal relies on the short periodic communication operation of the nodes comprising the WSN. This is achieved by decreasing the actions that a node needs to perform at the start of every communication period and by incorporating a variable radio-on operation. Moreover, the algorithm introduces differences in nodes’ scheduling to further reduce delay. Local synchronization and the crucial task of determining the proper timing for transmission and reception of data is achieved through the periodic broadcast of special synchronization frames at the beginning of each on-period. FP-MAC is evaluated and compared to S-MAC and T-MAC through extensive simulations, showing a significant improvement in terms of low energy consumption and average MAC delay.     

An indicator kriging method for distributed estimation in wireless sensor networks

The problem of distributed estimation in a wireless sensor network with unknown observation noise distribution is investigated, where each sensor only sends quantized data to a fusion center. The sensing field is modeled as a spatially random field. The objective was to accurately estimate a hidden parameter at the location where no sensor exists, while minimizing the total energy consumption. Driven by the lack of a prior knowledge of the sensing field and the existence of some outliers, an indicator kriging estimator is developed for distributed estimation under imperfect communication channels between the sensors and the fusion center. The tradeoff between estimation performance and energy consumption is formulated as an optimization problem, and a global search algorithm is proposed to approximate the solution. The results show that the proposed indicator kriging estimator achieves better performance than the inverse distance estimator and the simple averaging estimator. Moreover, the proposed search algorithm can schedule the sensors to reach the tradeoff. Copyright © 2012 John Wiley & Sons, Ltd.     

An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.