Wireless sensor networks consist of sensor nodes with sensing and com- munication capabilities. We focus on data-aggregation problems in energy- constrained sensor networks. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that net- work lifetime is enhanced. In this article we present a survey of data-aggre- gation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy. We conclude with possible future research directions.

/pdf/data-aggregation-techniques-in-sensor-networks-a-survey-4hxsfuncoq.pdf

Data-aggregation techniques in sensor networks: a survey

In this article we provide a comprehensive review of the existing literature on techniques and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a cross-layer design approach, which is likely to be needed for optimal performance. Throughout the article we identify and discuss open issues, and propose directions for future research in the area

/pdf/in-network-aggregation-techniques-for-wireless-sensor-3worsvl5sb.pdf

In-network aggregation techniques for wireless sensor networks: a survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree.

/pdf/outlier-detection-techniques-for-wireless-sensor-networks-a-1skln2unra.pdf

Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) use small nodes with constrained capabilities to sense, collect, and disseminate information in many types of applications. As sensor networks become wide-spread, security issues become a central concern, especially in mission-critical tasks. In this paper, we identify the threats and vulnerabilities to WSNs and summarize the defense methods based on the networking protocol layer analysis first. Then we give a holistic overview of security issues. These issues are divided into seven categories: cryptography, key management, attack detections and preventions, secure routing, secure location security, secure data fusion, and other security issues. Along the way we analyze the advantages and disadvantages of current secure schemes in each category. In addition, we also summarize the techniques and methods used in these categories, and point out the open research issues and directions in each area.

/pdf/sensor-network-security-a-survey-2ko4ukoih7.pdf

Sensor network security: a survey

Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.

https://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Fall%202012/Literature/InformationFusionInSensorNetworks.pdf

Information fusion for wireless sensor networks: Methods, models, and classifications

This paper targets the identification of faulty sensors and detection of the reach of events in sensor networks with faulty sensors. Typical applications include the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor identification and fault-tolerant event boundary detection. These algorithms are purely localized and thus scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20% sensors become faulty. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs, a dramatic improvement over existing works that take only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as the "events" can be modelled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.

/pdf/localized-fault-tolerant-event-boundary-detection-in-sensor-37zbkwja8q.pdf

Localized fault-tolerant event boundary detection in sensor networks

3 Centralized CDS Construction 335 3.1 Guha and Khuller’s Algorithm . . . . . . . . . . . . . . . . . . . . . 336 3.2 Ruan’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 3.3 Cheng’s Greedy Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 340 3.4 Min’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 3.5 Butenko’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

/pdf/connected-dominating-set-in-sensor-networks-and-manets-n5bp2dbz8n.pdf

Connected Dominating Set in Sensor Networks and MANETs

Large-scale wireless sensor networks are expected to play an increasingly important role in future civilian and military settings. Collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem. In this paper we propose heuristics to construct and maintain an aggregation tree in sensor networks. This aggregation tree can be used to facilitate data-centric routing. The main idea is to turn off the radio of all leaf nodes to save power, and thereby extending the network lifetime. Therefore, in order to save the number of broadcasting messages, only the nonleaf nodes in the tree are in charge of data aggregation and traffic relaying. In this paper, we propose an efficient energy-aware distributed heuristic to generate the aggregation tree, which we refer to as EADAT. Our EADAT algorithm makes no assumption on local network topology, and is based on residual power. It makes use of neighboring broadcast scheduling and distributed competition among neighbors. These novel concepts make EADAT very efficient and effective, as demonstrated by our simulation experiments with NS2.

/pdf/aggregation-tree-construction-in-sensor-networks-2gw42ydo3j.pdf

Aggregation tree construction in sensor networks

This paper targets the identification of outlying sensors (that is, outlying reading sensors) and the detection of the reach of events in sensor networks. Typical applications include the detection of the transportation front line of some vegetation or animalcule's growth over a certain geographical region. We propose and analyze two novel algorithms for outlying sensor identification and event boundary detection. These algorithms are purely localized and, thus, scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify outlying sensors with a high accuracy and a low false alarm rate when as many as 20 percent sensors report outlying readings. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs?a dramatic improvement over existing work, which takes only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as "events? can be modeled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.

/pdf/localized-outlying-and-boundary-data-detection-in-sensor-46x20cy01r.pdf

Localized Outlying and Boundary Data Detection in Sensor Networks

Summary Recent research points out that the flooding mechanism for topology update or route request in existing ad hoc routing protocols greatly degrades the network capacity. If we restrict the broadcast of control packets within a small subset of hosts in the network, the protocol overhead can be substantially reduced. This motivates our research of constructing a virtual backbone by computing a connected dominating set (CDS) in unit-disk graphs. In this paper, we propose two distributed algorithms to approximate a minimum CDS. These algorithms take linear time. Their performance is verified by a complete theoretical analysis. Copyright # 2006 John Wiley & Sons, Ltd.

/pdf/virtual-backbone-construction-in-multihop-ad-hoc-wireless-4akfjq6sro.pdf

Min Ding

Papers

Localized fault-tolerant event boundary detection in sensor networks

Connected Dominating Set in Sensor Networks and MANETs

Aggregation tree construction in sensor networks

Localized Outlying and Boundary Data Detection in Sensor Networks

Virtual backbone construction in multihop ad hoc wireless networks