Self-organization of Nodes using Bio-Inspired Techniques for Achieving Small World Properties

In an autonomous wireless sensor network, self-organization of the nodes is essential to achieve network wide characteristics. We believe that connectivity in wireless autonomous networks can be increased and overall average path length can be reduced by using beamforming and bio-inspired algorithms. Recent works on the use of beamforming in wireless networks mostly assume the knowledge of the network in aggregation to either heterogeneous or hybrid deployment. We propose that without the global knowledge or the introduction of any special feature, the average path length can be reduced with the help of inspirations from the nature and simple interactions between neighboring nodes. Our algorithm also reduces the number of disconnected components within the network. Our results show that reduction in the average path length and the number of disconnected components can be achieved using very simple local rules and without the full network knowledge.Comment: Accepted to Joint workshop on complex networks and pervasive group communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201

Using local and global knowledge in wireless sensor networks

Wireless sensor networks (WSNs) have advanced rapidly in recent years and the volume of raw data received at an endpoint can be huge. We believe that the use of local knowledge, acquired from sources such as the surrounding environment, users and previously sensed data, can improve the efficiency of a WSN and automate the classification of sensed data. We define local knowledge as knowledge about an area that has been gained through experience or experimentation. With this in mind, we have developed a three-tiered architecture for WSNs that uses differing knowledge-processing capabilities at each tier, called the Knowledge-based Hierarchical Architecture for Sensing (KHAS). A novel aligning ontology has been created to support K-HAS, joining widely used, domain-specific ontologies from the sensing and observation domains. We have shown that, as knowledge-processing capabilities are pushed further out into the network, the profit - defined as the value of sensed data - is increased; where the profit is defined as the value of the sensed data received by the end user. Collaborating with Cardiff University School of Biosciences, we have deployed a variation of K-HAS in the Malaysian rainforest to capture images of endangered wildlife, as well as to automate the collection and classification of these images. Technological limitations prevented a complete implementation of K-HAS and an amalgamation of tiers was made to create the Local knowledge Ontology-based Remote-sensing Informatics System (LORIS). A two week deployment in Malaysia suggested that the architecture was viable and that, even using local knowledge at the endpoint of a WSN, improved the efficiency of the network. A simulation was implemented to model K-HAS and this indicated that the network became more efficient as knowledge was pushed further out towards the edge, by allowing nodes to prioritise sensed data based on inferences about its content

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

The Bus Goes Wireless: Routing-Free Data Collection with QoS Guarantees in Sensor Networks

Abstract—We present the low-power wireless bus (LWB), a new communication paradigm for QoS-aware data collection in lowpower sensor networks. The LWB maps all communication onto network floods by using Glossy, an efficient flooding architecture for wireless sensor networks. Therefore, unlike current solutions, the LWB requires no information of the network topology, and inherently supports networks with mobile nodes and multiple data sinks. A LWB prototype implemented in Contiki guarantees bounded end-to-end communication delay and duplicate-free, inorder packet delivery—key QoS requirements in many control and mission-critical applications. Experiments on two testbeds demonstrate that the LWB prototype outperforms state-of-theart data collection and link layer protocols, in terms of reliability and energy efficiency. For instance, we measure an average radio duty cycle of 1.69 % and an overall data yield of 99.97 % in a typical data collection scenario with 85 sensor nodes on Twist. I

Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks

This chapter discusses the need of security and privacy protection mechanisms in aggregation protocols used in wireless sensor networks (WSN). It presents a comprehensive state of the art discussion on the various privacy protection mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA protocol and proposes a mechanism to plug that vulnerability. To demonstrate the need of security in aggregation process, the chapter further presents various threats in WSN aggregation mechanisms. A large number of existing protocols for secure aggregation in WSN are discussed briefly and a protocol is proposed for secure aggregation which can detect false data injected by malicious nodes in a WSN. The performance of the protocol is also presented. The chapter concludes while highlighting some future directions of research in secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table

Dynamic key ring update mechanism for mobile wireless sensor networks

Key distribution is an important issue to provide security in Wireless Sensor Networks (WSNs). Many of the key pre-distribution schemes proposed for static WSNs perform poorly when they are applied to Mobile Wireless Sensor Networks (MWSNs). In this paper, we propose Dynamic Key Ring Update (DKRU) mechanism for MWSNs. The aim of DKRU mechanism is to enable sensor nodes to update their key rings periodically during movement, by observing the frequent keys in their neighbors. Our mechanism can be used together with different key pre-distribution schemes and it helps to increase the performance of them. For the performance evaluation basis, we used our mechanism together with a location based key pre-distribution scheme. Our results show that DKRU mechanism increases the local and global connectivity when it is applied to MWSNs. Moreover, our mechanism does not cause a significant degradation in network resiliency

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 usage,(3) only uses 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 using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200

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