Temporal and spatial outlier detection in wireless sensor networks

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph‐based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state‐of‐the‐art methods in terms of accuracy and work well with various data types.     

Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

Low quality sensor data limits WSN capabilities for providing reliable real-time situation-awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.     

Trajectory-based multi-dimensional outlier detection in wireless sensor networks using Hidden Markov Models

Wireless sensor networks (WSNs) have been increasingly available for monitoring the traffic, weather, pollution, etc. Outlier detection in WSNs is an essential step for many important applications, such as abnormal event detection, fraud analysis, etc. While existing efforts focus on identifying individual outliers from sensory data, the unsupervised high semantic outlier detection in WSNs is more challenging and has received far less attentions. In addition, the correlation between multi-dimensional sensory data has not yet been considered when detecting outliers in WSNs. In this paper, based on multi-dimensional Hidden Markov Models, we propose a trajectory-based outlier detection algorithm by model training and model-based likelihood estimation. Our data preprocessing, clustering, model training and model updating schemes are developed to reduce the computational complexity and enhance the detecting performance. We also explore the possibility and feasibility of adapting the proposed algorithm to real-time outlier detections. Experimental results show that our methods achieve good performance on detecting various kinds of abnormal trajectories composed of multi-dimensional data.     

Efficient REBTA data reporting algorithm for object tracking in wireless sensor networks

Object tracking is widely referred as one of the most interesting applications of wireless sensor networks (WSNs). This application is able to detect and track objects and report information about these objects to a central base station. One of the major drawbacks in the current research in WSNs is the quality of the data reporting where the major research focus is dedicated to localization of objects; however, few of these works were concentrated on the data reporting. An efficient data reporting algorithm for object tracking in WSNs is proposed in this paper. The main objective of this paper is to enhance the WSN lifetime by achieving both minimum energy and balancing such consumption in sensor nodes during reporting operation. Furthermore, in our model, the enhancement of network reliability is considered. Finally, it reduces the effects of congestion by sufficiently utilizing the under loaded nodes to improve the network throughput. This paper formulates the object tracking problem in large‐scale WSN into 0/1 integer linear programming problem, and then proposes a reliable energy balance traffic aware approach to solve the optimization problem. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in network lifetime, throughput, end‐to‐end delay, energy balance, and complexity for both homogeneous and heterogeneous networks.     

A scalable correlation‐based approach for outlier detection in wireless body sensor networks

Outlier detection is one of the prominent research domain in the field of data mining and big data analytics. Nowadays, most of the data in healthcare centers are remotely monitored and are generated from different wireless sensors. The core objective of outlier detection in this domain is the recognition of the true physiologically anomalous data and the anomalies because of faulty sensors. In real healthcare monitoring scenario, various sensors are related to each other. So, while detecting outliers in wireless body sensor networks (WBSNs), correlation among different sensor nodes is of major concern. Most of the existing outlier detection techniques consider the sensors to be linearly correlated, which may not always be the case in real life applications. The traditional techniques for outlier detection are also not scalable to big data. To address the above issues, in this paper, we propose an approach for outlier detection that is scalable to big data and also handles the nonlinearly correlated attributes efficiently. The proposed approach is implemented on Hadoop map reduce framework for the rapid processing of big data. The evaluation results are validated using the simulated dataset of WBSNs taken from the Physionet library. The results are compared with various existing outlier detection approaches and demonstrated that the proposed approach is more effective in spotting the physiological outliers and sensor anomalies accurately.     

ETPS-MAC: Energy Traffic Priority Scheduling-based QoS-aware MAC protocol for hierarchical WSNs

The evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Monitoring Systems (SMSs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in SMS needs an intelligent scheduling mechanism that efficiently handles the dynamic traffic load without sacrificing the energy efficiency of network. This paper presents a centralized TDMA scheduling based medium access control (MAC) protocol, called Energy Traffic Priority Scheduling MAC (ETPS-MAC) that accommodates variable traffic load while maintaining Quality-of-Service (QoS) assurance in hierarchical WSNs. The ETPS-MAC protocol employs priority scheduling algorithm which considers two factors for assigning priority, the energy factor and the traffic load factor to avoid packet buffering and maintains minimum data packet delay in case of high traffic load. Moreover, a novel rank-based clustering mechanism in FPS-QMAC protocol prolongs the network lifetime by minimizing the distance between the cluster head (CH) and the base station (BS). Both analytical and simulation models demonstrate the superiority of the ETPS-MAC protocol in terms of energy consumption, transmission delay, data throughput and message complexity when compared with the existing TDMA based MAC protocols.     

Closed-form Solution for Joint Localization and Synchronization in Wireless Sensor Networks With and Without Beacon Uncertainties

In Wireless Sensor Networks (WSNs), the use of the same set of measurement data for simultaneous localization and synchronization is potentially useful for achieving higher estimation accuracy, and lower communication overhead and power consumption. In this paper, we first analyze the impact of asynchronous sensor nodes (SNs) on the accuracy of time-based localization schemes, and the impact of inaccurate SN location information on the accuracy of synchronization based on packet delay measurement, to illustrate the necessity and significance of simultaneous localization and synchronization of SNs. We then consider the joint localization and synchronization problem for two cases. In the first case, we assume that the beacon information is perfectly known. The Maximum Likelihood (ML) estimator is first formulated, which is computationally expensive. A new closed-form Joint Localization and Synchronization I (JLS-I) estimator is then proposed to provide a computationally efficient solution. In the second case, we assume that the beacon locations and timings are known inaccurately, and develop the ML and JLS-II estimators accordingly. JLS-II is based on Weighted Least Square and Generalized Total Least Square, and is of low complexity. The Cramer-Rao Lower Bounds (CRLBs) and the analytical Mean Square Errors of the proposed estimators are derived, and we also analytically show that JLS-I can achieve the corresponding CRLB. Simulation results demonstrate the effectiveness of the proposed estimators compared to other approaches. With only a three-way message exchange, JLS-I can attain the CRLB and JLS-II can provide close to optimal performance in their respective scenarios. They are also robust against the Geometric Dilution of Precision problem, and outperform existing algorithms in NLOS scenarios. Our results demonstrate the advantages of JLS-I and JLS-II in reduced computational complexity with lower power consumption and communication cost while achieving high estimation accuracy. They are therefore attractive solutions to the simultaneous localization and synchronization problem in WSNs where energy and network resources are the most important considerations.     

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Sensor node energy conservation is the primary design parameters in wireless sensor networks (WSNs). Energy efficiency in sensor networks directly prolongs the network lifetime. In the process of route discovery, each node cooperates to forward the data to the base station using multi‐hop routing. But, the nodes nearer to the base station are loaded more than the other nodes that lead to network portioning, packet loss and delay as a result nodes may completely loss its energy during the routing process. To rectify these issues, path establishment considers optimized substance particle selection, load distribution, and an efficient slot allocation scheme for data transmission between the sensor nodes in this paper. The selection of forwarders and conscious multi‐hop path is selected based on the route cost value that is derived directly by taking energy, node degree and distance as crucial metrics. Load distribution based slot allocation method ensures the balance of data traffic and residual energy of the node in areal‐time environment. The proposed LSAPSP simulation results show that our algorithm not only can balance the real‐time environment load and increase the network lifetime but also meet the needs of packet loss and delay.     

Wireless sensor networks for intrusion detection: packet traffic modeling

Performance evaluation of wireless sensor network (WSN) protocols requires realistic data traffic models since most of the WSNs are application specific. In this letter, a sensor network packet traffic model is derived and analyzed for intrusion detection applications. Presented analytical work is also validated using simulations.     

VANET: A novel service for predicting and disseminating vehicle traffic information

The arrival of cloud computing technology promises innovative solutions to the problems inherent in existing vehicular ad hoc network (VANET) networks. Because of the highly dynamic nature of these networks in crowded conditions, some network performance improvements are needed to anticipate and disseminate reliable traffic information. Although several approaches have been proposed for the dissemination of data in the vehicular clouds, these approaches rely on the dissemination of data from conventional clouds to vehicles, or vice versa. However, anticipating and delivering data, in a proactive way, based on query message or an event driven has not been defined so far by these approaches. Therefore, in this paper, a VANET‐Cloud layer is proposed for traffic management and network performance improvements during congested conditions. For the traffic management, the proposed layer integrates the benefits of the connected sensor network (CSN) to collect traffic data and the cloud infrastructure to provide on‐demand and automatic cloud services. In this work, traffic services use a data exchange mechanism to propagate the predicted data using a fuzzy aggregation technique. In the evaluation phase, simulation results demonstrate the effectiveness of the proposed VANET‐Cloud layer to dramatically improve traffic safety and network performance as compared with recent works.     

一种新的无线传感器网络中异常节点检测定位算法

无线传感器网络中异常节点检测是确保网络数据准确性和可靠性的关键步骤。基于图信号处理理论,该文提出了一种新的无线传感器网络异常节点检测定位算法。新算法首先对网络建立图信号模型,然后基于节点域-图频域联合分析的方法,实现异常节点的检测和定位。具体而言,第1步是利用高通图滤波器提取网络信号的高频分量。第2步首先将网络划分为多个子图,然后筛选出子图输出信号的特定频率分量。第3步对筛选出的子图信号进行阈值判断从而定位疑似异常的子图中心节点。最后通过比较各子图的节点集合和疑似异常节点集合,检测并定位出网络中的异常节点。实验仿真表明,与已有的无线传感器网络中异常检测方法相比,新算法不仅有着较高的异常检测概率,而且异常节点的定位率也较高。     

HDF: Hybrid Debugging Framework for Distributed Network Environments

Debugging in distributed environments, such as wireless sensor networks (WSNs), which consist of sensor nodes with limited resources, is an iterative and occasionally laborious process for programmers. In sensor networks, it is not easy to find unintended bugs that arise during development and deployment, and that are due to a lack of visibility into the nodes and a dearth of effective debugging tools. Most sensor network debugging tools are not provided with effective facilities such as real‐time tracing, remote debugging, or a GUI environment. In this paper, we present a hybrid debugging framework (HDF) that works on WSNs. This framework supports query‐based monitoring and real‐time tracing on sensor nodes. The monitoring supports commands to manage/control the deployed nodes, and provides new debug commands. To do so, we devised a debugging device called a Docking Debug‐Box (D2‐Box), and two program agents. In addition, we provide a scalable node monitor to enable all deployed nodes for viewing. To transmit and collect their data or information reliably, all nodes are connected using a scalable node monitor applied through the Internet. Therefore, the suggested framework in theory does not increase the network traffic for debugging on WSNs, and the traffic complexity is nearly O(1).     

WSNS中基于Fusion-Bayes的离群点检测

随着无线传感器网络技术的发展,数据采集量越来越大,维数也不断提高。然而现有的离群点检测算法多是面向单维或低维度数据,对此文中提出了基于Fusion-Bayes的离群点检测算法。该检测方法首先利用数据转换技术将不同数据属性转换成统一格式,使得各属性可以进行融合运算;然后再利用贝叶斯方法对融合后的属性进行离群点检测。通过实验得出,多维数据属性融合后的检测结果相比于单维属性或低维属性的检测更加准确、效果更好。     

A Game Theory Based Multi Layered Intrusion Detection Framework for Wireless Sensor Networks

Most of the existing intrusion detection frameworks proposed for wireless sensor networks (WSNs) are computation and energy intensive, which adversely affect the overall lifetime of the WSNs. In addition, some of these frameworks generate a significant volume of IDS traffic, which can cause congestion in bandwidth constrained WSNs. In this paper, we aim to address these issues by proposing a game theory based multi layered intrusion detection framework for WSNs. The proposed framework uses a combination of specification rules and a lightweight neural network based anomaly detection module to identify the malicious sensor nodes. Additionally, the framework models the interaction between the IDS and the sensor node being monitored as a two player non-cooperative Bayesian game. This allows the IDS to adopt probabilistic monitoring strategies based on the Bayesian Nash Equilibrium of the game and thereby, reduce the volume of IDS traffic introduced into the sensor network. The framework also proposes two different reputation update and expulsion mechanisms to enforce cooperation and discourage malicious behavior among monitoring nodes. These mechanisms are based on two different methodologies namely, Shapley Value and Vickery–Clark–Grooves (VCG) mechanism. The complexity analysis of the proposed reputation update and expulsion mechanisms have been carried out and are shown to be linear in terms of the input sizes of the mechanisms. Simulation results show that the proposed framework achieves higher accuracy and detection rate across wide range of attacks, while at the same time minimizes the overall energy consumption and volume of IDS traffic in the WSN.     

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion,aggregation, and routing scheme

Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.     

Location‐aided medium access control for low data rate UWB wireless sensor networks

This paper presents a distributed medium access control (MAC) protocol for low data rate ultra‐wideband (UWB) wireless sensor networks (WSNs), named LA‐MAC. Current MAC proposal is closely coupled to the IEEE 802.15.4a physical layer and it is based on its Impulse‐Radio (IR) paradigm. LA‐MAC protocol amplifies its admission control mechanism with location‐awareness, by exploiting the ranging capability of the UWB signals. The above property leads to accurate interference predictions and blocking assessments that each node in the network can perform locally, limiting at the same time the actions needed to be performed towards the admission phase. LA‐MAC is evaluated through extensive simulations, showing a significant improvement in many critical parameters, such as throughput, admission ratio, energy consumption, and delay, under different traffic load conditions. Copyright © 2010 John Wiley & Sons, Ltd.     

Enabling novel premium service classes in DiffServ over MPLS‐enabled network

Network resources dimensioning and traffic engineering influence the quality in provisioned services required by the Expedited Forwarding (EF) traffic in production networks established through DiffServ over MPLS‐enabled network. By modeling EF traffic flows and the excess of network resources reserved for it, we derive the range of delay values which are required to support these flows at DiffServ nodes. This enables us to develop an end‐to‐end (e2e) delay budget‐partitioning mechanism and traffic‐engineering techniques within a framework for supporting new premium QoS levels, which are differentiated based on e2e delay, jitter and loss. This framework enables ingress routers to control EF traffic flow admission and select appropriate routing paths, with the goal of EF traffic balancing, avoiding traffic congestion and getting the most use out of the available network resources through traffic engineering. As a result, this framework should enable Internet service providers to provide three performance levels of EF service class to their customers provided that their network is DiffServ MPLS TE aware. Copyright © 2008 John Wiley & Sons, Ltd.     

RAPS: a precision‐adaptive protocol towards improved data fidelity in wireless sensor networks

Achieving high data quality and efficient network resource utilization is two major design objectives of wireless sensor networks (WSNs). However, these two objectives are often conflictive. By allowing sensors to report sampled data at high rates, fine‐grained data quality can be obtained. However, the limited resources of a WSN make it difficult to support very high traffic rate. Therefore, the capability of adaptively adjusting sensor nodes' traffic‐generating rates on the basis of the availability of network resources and application requirements is critical. This issue has attracted much attention recently, and some work has been carried out. To achieve high data quality and improved utilization of network resources, in this paper, we propose rate‐based adaptive precision setting (RAPS) protocol, which works in a way that each sensor can adaptively adjust its traffic‐generating rate on the basis of the current network resources availability and application requirements. RAPS introduces the following two key factors into its design: application's precision requirement and packet arrival rate. Analytical and simulation results show that RAPS can achieve improved data quality while reducing packet delivery latency. Copyright © 2012 John Wiley & Sons, Ltd.     

Throughput Evaluation of a Novel Scheme to Mitigate the Congestion over WSNs

In recent years, due to fast development of wireless sensor networks (WSNs), the numbers of nodes are increasing, and their scope of applications is continuously expanding, including environmental monitoring, military and smart home applications. The power supply, memory and computing power of wireless sensor nodes are greatly hampered in WSNs so that the WSNs are classified as a task-oriented framework. This study focused on exploring problems caused by traffic congestion on the WSNs with a large amount of flow, such as packet loss, bandwidth reduction, and waste of energy on the sensor nodes. On the other hand, a cooperative strong node mechanism is presented and named as Cooperative Strong Node Mechanism, in which a threshold is set to determine whether the node traffic is over or not. When the load exceeds, the privilege of corresponding sensor nodes is upgraded so that it can command its child nodes to change the transmission path to distribute the traffic effectively. Furthermore, when the traffic exceeds preset overall network flow, new sensor nodes are added in the network to relieve the traffic. This novel proposed mechanism can not only increase network throughput and effectively prevent the occurrence from congestion problems, but is suitable for a variety of routing protocols.     

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

The performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster‐based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end‐to‐end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster‐based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network.