Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices

Ant-based evidence distribution with periodic broadcast in attacked wireless network

In order to establish trust among nodes in large wireless networks, the trust certicates need to be distributed and be readily accessible. However, even so, searching for trust certicates will still become highly cost and delay especially when wireless network is suering CTS jamming attack. We believe the individual solution can lead us to solve this combination problems in the future. Therefore, in this work, we investigate the delay and cost of searching a distributed certicate and the adverse eects of fabiricated control packet attacks on channel throughput and delivery ratio respectively, and propose two techniques that can improve the eciency of searching for such certicates in the network and mitigate the CTS jamming attack's eect. Evidence Distribution based on Periodic Broadcast (EDPB) is the rst solution we presented to help node to quickly locate trust certicates in a large wireless sensor network. In this solution, we not only take advantages from swarm intelligence alogrithm, but also allow nodes that carrying certicates to periodically announce their existence. Such announcements, together with a swarm-intelligence pheromone pdate procedure, will leave traces on the nodes to lead query packets toward the certicate nodes. We then investigate the salient features of this schema and evaluate its performance in both static and mobile networks. This schema can also be used for other essential information dissemination in mobile ad hoc networks. The second technqiue, address inspection schema (AIS) xes vulnerabilities exist in distribution coordinating function (DCF) dened in IEEE 802.11 standard so that each node has the ability to beat the impact of CTS jamming attack and furthermore, benets network throughput. We then perform ns-2 simulations to evaluate the benet of AIS

SENOCLU, Energy Efficient Approach for Unsupervised Node Clustering in Sensor Networks

Acquisition and analysis of data from sensor networks, where nodes operate in unsupervised way, has become a ubiquitous issue. The biggest challenge in this process is related to limited energy, computational and memory capacity of sensor nodes. Therefore, the main goal of our work is to devise and evaluate the contribution of an energy efficient algorithm for data acquisition in sensor networks. The proposed SENOCLU algorithm considers specific requirements of sensor network application like energy efficiency, state change detection, load balancing, high-dimensions of the sensed data etc. By applying these techniques, this algorithm contributes in filling the gap between distributed clustering and high-dimensional clustering algorithms that are available in the literature. This work evaluates the contribution of this algorithm in comparison to other competing state-of-the-art techniques. The experiments show that by applying SENOCLU algorithm better life times of sensor networks are achieved and longer monitoring of different phenomena is provided