Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, 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 multivariate 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 decision tree 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 degree

Outlier detection 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

A Grid-Based Distributed Event Detection Scheme for Wireless Sensor Networks

This paper presents a grid-based distributed event detection scheme for wireless sensor networks. The network is divided into square-shaped grids of predefined grid size, where sensor nodes in each grid form a cluster with a cluster head. Event detection at each grid alone based on the readings of its member nodes is limited in event detection performance, especially for a small event region compared to the grid size. To improve the performance, each grid is further divided into 2 × 2 sub-grids of equal size. The decision on an event is made by finding a square region of 2 × 2 sub-grids, not necessarily in the same grid, that passed a predefined threshold. This process is conducted at each cluster head in a distributed manner by inter-cluster communications. Event detection is initiated when a cluster head receives an alarm from its member nodes. The cluster-head communicates with its neighboring cluster heads to exchange the number of nodes reporting an alarm. The threshold for event detection can be dynamically adjusted to reflect the number of sensor nodes in a grid and event size, if known. High event detection accuracy is achieved with a relatively low threshold without sacrificing false alarm rate by filtering most errors due to transient faults and isolating nodes with permanent faults. Experimental results show that the proposed scheme can achieve high detection accuracy, while maintaining low false alarm rate

Communication Security in Wireless Sensor Networks

A wireless sensor network (WSN) usually consists of a large number of small, low-cost devices that have limited energy supply, computation, memory, and communication capacities. Recently, WSNs have drawn a lot of attention due to their broad applications in both military and civilian domains. Communication security is essential to the success of WSN applications, especially for those mission-critical applications working in unattended and even hostile environments. However, providing satisfactory security protection in WSNs has ever been a challenging task due to various network & resource constraints and malicious attacks. This motivates the research on communication security for WSNs. This dissertation studies communication security in WSNs with respect to three important aspects. The first study addresses broadcast/multicast security in WSNs. We propose a multi-user broadcast authentication technique, which overcomes the security vulnerability of existing solutions. The proposed scheme guarantees immediate broadcast authentication by employing public key cryptography, and achieves the efficiency through integrating various techniques from different domains. We also address multicast encryption to solve data confidentiality concern for secure multicast. We propose an efficient multicast key management scheme supporting a wide range of multicast semantics, which utilizes the fact that sensors are both routers and end-receivers. The second study addresses data report security in WSNs. We propose a location-aware end-to-end security framework for WSNs, in which secret keys are bound to geographic locations so that the impact of sensor compromise are limited only to their vicinity. The proposed scheme effectively defeats not only bogus data injection attacks but also various DoS attacks. In this study, we also address event boundary detection as a specific case of secure data aggregation in WSNs. We propose a secure and fault-tolerant event boundary detection scheme, which securely detects the boundaries of large spatial events in a localized statistic manner. The third study addresses random key pre-distribution in WSNs. We propose a keyed-hash-chain-based key pool generation technique, which leads to a more efficient key pre-distribution scheme with better security resilience in the case of sensor compromise

The Distributed Convergence Classifier Using the Finite Difference

The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Resilient Wireless Sensor Networks Using Topology Control: A Review

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs

Fault Detection In Wireless Sensor Network Using Distributed Approach

In recent days, Wireless Sensor Networks are emerging as a promising and interesting area. Wireless Sensor Network consists of a large number of heterogeneous/homogeneous sensor nodes which communicates through wireless medium and works cooperatively to sense or monitor the environment. The number of sensor nodes in a network can vary from hundreds to thousands. The node senses data from Environment and sends these data to the gateway node. Mostly WSNs are used for applications such as military surveillance and disaster monitoring. We propose a distributed localized faulty sensor detection algorithm where each sensor identifies its own status to be either ”good” or ”faulty” which is then supported by its neighbors as they also check the node behavior. Finally, the algorithm is tested under different number of faulty sensors in the same area. Our Simulation results demonstrate that the time consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large number of faulty sensors existing in the network

Distributed Fault Detection In Wireless Sensor Network

In recent days, WSNs are emerging as a promising and interesting area. Wireless Sensor Network consists of a large number of heterogeneous/homogeneous sensor nodes which communicates through wireless medium and works cooperatively to sense or monitor the environment. The number of sensor nodes in a network can vary from hundreds to thousands. The node senses data from environment and sends these data to the gateway node. Mostly WSNs are used for applications such as military surveillance and disaster monitoring. We propose a distributed localized faulty sensor detection algorithm where each sensor identifies its own status to be either ”good” or ”faulty” which is then supported by its neighbors as they also check the node behavior. Finally, the algorithm is tested under different number of faulty sensors in the same area. Our Simulation results demonstrate that the time consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large number of faulty sensors existing in the networ