Secure event logging in sensor networks

Health care applications based on sensors are gaining popularity. In wireless body area networks (WBANs), sensing data are gathered from a set of nodes deployed on the patient’s body and sent to a central server. In such environments, security and privacy must be top priorities. Moreover, since decision-making is performed on the basis of the collected data, it can be important to maintain the chronological order of events in a secure way. In view of this, this paper proposes a system for secure logging of events in sensor networks by gathering in a secure and reliable way all information at one central point. The system guarantees the chronological order of logged events sent by the different sensors. It also allows one to detect the modification, deletion, and addition of logged data. As a proof of concept, we have designed a prototype of the gateway sensor on an FPGA platform that is responsible for the secure logging and the secure transmission of this information. Our prototype is based on the low-cost Spartan-6 FPGA which is equipped with several hard IP-cores such as Ethernet and CompactFlash, which makes it suitable for event storage. As it turns out, the FPGA board (SP-605) is able to store 20,830 logged events, computes digests in 2.51 μs and only uses 56% of the LUTs of the FPGA. Thus, the reconfigurable nature of FPGAs makes them suitable for use in extending the capabilities of commercial gateways, in order to provide secure logging in a WSN.     

Perimeter detection in wireless sensor networks

For a mobile robotic agent to bridge the gaps between disconnected networks, it is beneficial for the robot to first determine the network coverage boundary. Several techniques have been introduced to determine the boundary nodes of a network, but the correctness of these techniques is often ill-defined. We present a technique for obtaining boundary node ground truth from region adjacency analysis of a model-based image created from a network graph. The resulting ground truth baseline is then used for quantitative comparison of several boundary detection methods including a local application of the image region adjacency analysis and the computation of the local convex hull with the addition of a perturbation value to overcome small boundary concavities in the node location point set. Given our proposed metrics of the techniques evaluated, the perturbed convex hull technique demonstrates a high success rate for boundary node identification, particularly when the convex hull is formed using two-hop neighborhoods. This technique was successfully implemented on a physical 25-node network, and the performance of this network implementation is evaluated.     

低概率事件场景的无线传感器网络构建

低概率事件场景,静态部署的小规模监测网络是较为常见的一类无线传感器应用网络.针对这一类无线传感器网络的特点,从拓扑控制角度出发,基于分层分簇思想,提出了一种固定分簇层次型网络构建方法.该方法采用固定分簇有效节省了一般周期性动态分簇聚类大量能耗,簇内成员节点基于事件概率预测的休眠调度更进一步降低了网络能耗;簇头在簇内的轮换实现了网络能耗均衡;功率控制建立了可靠的多跳路由.仿真性能分析表明:该方法节能效果明显,实施简单,易于应用推广.     

Bayesian networks based rare event prediction with sensor data

A Bayesian network is a powerful graphical model. It is advantageous for real-world data analysis and finding relations among variables. Knowledge presentation and rule generation, based on a Bayesian approach, have been studied and reported in many research papers across various fields. Since a Bayesian network has both causal and probabilistic semantics, it is regarded as an ideal representation to combine background knowledge and real data. Rare event predictions have been performed using several methods, but remain a challenge. We design and implement a Bayesian network model to forecast daily ozone states. We evaluate the proposed Bayesian network model, comparing it to traditional decision tree models, to examine its utility.     

Automatic real-time event detection for seismic networks

A low-power microprocessor-based seismic signal detection system has been developed for monitoring and recording earthquakes detected from multichannel seismic network data. The system operates on-line to the network and may be battery powered for field applications or as a precaution against power failures. The algorithm is designed to cope with varying noise conditions at the network outstations and detects local, regional, and teleseismic events. The sampling rate for each channel is 100 samples/sec. with 10-bit accuracy. The system is operating with the LOWNET network of BGS and is suitable for large or small aperture networks because 20 sec of data for each channel are stored in memory. The system is designed to detect seismic events for recording on either analogue or digital recorders depending on the requirements and preferences of the network management.     

Similarity based image selection with frame rate adaptation and local event detection in wireless video sensor networks

Wireless Video Sensor Networks (WVSNs7unding environmental information. Those sensor nodes can locally process the information and then wirelessly transmit it to the coordinator and to the sink to be further processed. As a consequence, more abundant video and image data are collected. In such densely deployed networks, the problem of data redundancy arises when information are gathered from neighboring nodes. To overcome this problem, one important enabling technology for WVSN is data aggregation, which is essential to be cost-efficient. In this paper, we propose a new approach for data aggregation in WVSN based on images and shot similarity functions. It is deployed on two levels: the video-sensor node level and the coordinator level. At the sensor node level the proposed algorithms aim at reducing the number of frames sensed by the sensor nodes and sent to the coordinator. At the coordinator level, after receiving shots from different neighbouring sensor nodes, the similarity between these shots is computed to eliminate redundancies and to only send the frames which meet a certain condition to the sink. The similarity between shots is evaluated based on their color, edge and motion information. We evaluate our approach on a live scenario and compare the results with another approach from the literature in terms of data reduction and energy consumption. The results show that the two approaches have a significant data reduction to reduce the energy consumption, thus our approach tends to overcome the other one in terms of reducing the energy consumption related to the sensing process, and to the transmitting process while guaranteeing the detection of all the critical events at the node and the coordinator levels.

     

In-network outlier detection in wireless sensor networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands.     

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.     

Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

Obstacle detection and estimation in wireless sensor networks

In wireless sensor networks (WSNs), it is likely that a deployed area contains obstacles of some form. These obstacles may potentially degrade the functionality of the WSN. If the size and location of the obstacles can be detected, their influence can be reduced. Accordingly, this paper describes a scheme for detecting obstacles in WSNs. The scheme identifies the obstacles by marking the sensor nodes around the obstacle boundaries. The scheme does not require the absolute position of individual nodes in the sensing field nor any additional hardware, and thus can significantly reduce the deployment costs. The efficiency of the scheme is demonstrated via simulations performed using the network simulator ns-2. The results show that the detection scheme needs much less overhead compared to previous research while still marking the nodes close to the obstacles precisely.     

Secure localization with attack detection in wireless sensor networks

Rapid technological advances have enabled the development of low-cost sensor networks for various monitoring tasks, where it is important to estimate the positions of a number of regular sensor nodes whose locations cannot be known apriori. We address the problem of localizing the regular nodes with range-based location references obtained from certain anchor nodes referred to as beacons, particularly in an adverse environment where some of the beacons may be compromised. We propose an innovative modular solution featuring two lightweight modules that are for dedicated functionalities, respectively, but can also be closely integrated. First, we harness simple geometric triangular rules and an efficient voting technique to enable the attack detection module, which identifies and filters out malicious location references. We then develop a secure localization module that computes and clusters certain reference points, and the position of the concerned regular node is estimated with the centroid of the most valuable reference points identified. Extensive simulations show that our attack detection module can detect compromised beacons effectively, and the secure localization module can subsequently provide a dependable localization service in terms of bounded estimation error. The integrated system turns out to be tolerant of malicious attacks even in highly challenging scenarios.     

Distributed routing for signal detection in wireless sensor networks

We study the problem of energy-efficient routing for signal detection in wireless sensor networks. Generic routing protocols use networking-centric measures such as minimum hop or minimum energy to establish routes. These schemes do not take into account the performance of application-specific algorithms that is achievable from the data collected by the nodes along the routes. Routing protocols for signal detection have recently been proposed to facilitate joint optimization of detection performance and energy efficiency by developing metrics that connect detection performance with energy consumption of each link along the routes. In existing routing for signal detection (RSD) schemes, however, the routes are computed centrally requiring complex optimization algorithms and global information such as locations and observation coefficients of all nodes in the network. Clearly, for large-scale networks, or networks with dynamically changing topologies, distributed routing schemes are more practical due to their better flexibility and scalability. We present a distributed RSD protocol where each node, based on locally available information, selects its next-hop with the goal of maximizing the detection performance associated with unit energy expenditure. We show that the proposed protocol is readily implementable in ZigBee networks, and present simulation results that reveal its significant improvements in detection performance and energy efficiency over generic routing protocols.     

Scalable continuous object detection and tracking in sensor networks

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

无线传感器网络入侵检测系统模型

为了提高无线传感器网络的安全性,针对无线传感器网络的自身特性,设计了一种入侵检测系统模型.该模型按照聚类的方法,将区域划分成簇;在每个簇中选举簇头,簇头需定期轮换;采用基于相关向量机(RVM)的入侵检测方案.实验表明:所提出的模型与其它检测模型相比具有更高的平均检测率和更低的平均误检率,且具有低能耗的特点.     

高效的无线传感器网络边缘检测算法

为了解决无线传感器网络中边缘与空洞的识别问题,分析了典型的周界边缘检测[1]和泰森多边形边缘检测方法(LVP)[2]的特点,针对周界检测方法和泰森多边形算法的不足,并综合考虑了网络能耗、检测精度和算法效率等方面的因素,提出一种高效的分布式无线传感器网络边缘检测方法。该算法充分利用分布式节点拓扑结构,根据邻居节点信息建立快速排序搜索的规则和边缘定位算法。仿真实例结果表明,该算法比周界边缘检测模型具有更高的求解质量和效率。     

LTRES: A loss-tolerant reliable event sensing protocol for wireless sensor networks

Many Wireless Sensor Network (WSN) transport protocols proposed in recent studies focus on providing end-to-end reliability as in TCP. However, traditional end-to-end reliability enforcement is energy and time consuming for common loss-tolerant applications in WSNs. In this paper, a Loss-Tolerant Reliable Event Sensing protocol (LTRES) is proposed based on the particular reliability requirements for dynamic event observation in WSNs. According to the application-specific requirements, a reliable event sensing threshold at the transport layer is determined by the sink. A distributed source rate adaptation mechanism is designed, incorporating a loss rate based lightweight congestion control mechanism, to regulate the data traffic injected into the network so that the reliability requirements can be satisfied. An equation based fair rate control algorithm is designed to improve the fairness among the traffic flows sharing the congestion path. The performance evaluations show that LTRES can provide event-based loss-tolerant reliable data transport service for multiple events with short convergence time, low loss rate and high overall bandwidth utilization.     

Empower Chinese event detection with improved atrous convolution neural networks

Neural Computing and Applications - Event Detection (ED) is an essential and challenging task in Information Extraction (IE). Recent advances in neural networks make it possible to build reliable...     

Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos

We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos. We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification tasks where many correlated observed variables are necessary to make a decision. We then compare several structure learning objective functions, which aim at finding out the structure that yields the best classification results, extending existing solutions in the literature. Experimental results on a comprehensive data set of 7 videos show that a discriminative objective function based on conditional likelihood yields the best results, while augmented approaches offer a good compromise between learning speed and classification accuracy.     

A destination-based approach for cut detection in wireless sensor networks

Wireless sensor networks often suffer from disrupted connectivity caused by its numerous aspects such as limited battery power of a node and unattended operation vulnerable to hostile tampering. The disruption of connectivity, often referred to as network cut, leads to ill-informed routing decisions, data loss and waste of energy. A number of protocols have been proposed to efficiently detect network cuts; they focus solely on a cut that disconnects nodes from the base station. However, a cut detection scheme is truly useful when a cut is defined with respect to multiple destinations (i.e. target nodes), rather than a single base station. Thus, we extend the existing notion of cut detection, and propose an algorithm that enables sensor nodes to autonomously monitor the connectivity to multiple target nodes. We introduce a novel reactive cut detection solution, the point-to-point cut detection, where given any pair of source and destination, a source is able to locally determine whether the destination is reachable or not. Furthermore, we propose a lightweight proactive cut detection algorithm specifically designed for a network scenario with a small set of target destinations. We prove the effectiveness of the proposed algorithms through extensive simulations; specifically, in our network configurations, proposed cut detection algorithms achieve more than an order of magnitude improvement in energy consumption, when coupled with an underlying routing protocol.