Hakilo Sabit

Distributed WSN Data Stream Mining Based on Fuzzy Clustering

This paper proposes a distributed wireless sensor network (WSN) data stream clustering algorithm to minimize sensor nodes energy consumption and consequently extend the network lifetime. The paper follows the strategy of trading-off communication for computation through distributed clustering and successive transmission of local clusters. We present an energy efficient algorithm we developed, Subtractive Fuzzy Cluster Means (SUBFCM), and analyze its energy efficiency as well as clustering performance in comparison with state-of-the-art standard data clustering algorithms such as Fuzzy C-means and K-means algorithms. Simulations show that SUBFCM can achieve WSN data stream clustering with significantly less energy than that required by Fuzzy C-means and K-means algorithms.

Application of neural network and electrodynamic sensor as flow pattern identifier

Purpose – Solid particles flowing in a pipeline is a common mode of transport in industries. This is because pipeline transportation can avoid waste through spillage and minimizes the risk of handling of hazardous materials. Pharmaceutical industries, food stuff manufacturing industries, cement, and chemical industries are a few industries to exploit this transportation technique. For such industries, monitoring and controlling material flow through the pipe is an essential element to ensure efficiency and safety of the system. The purpose of this paper is to present electrical charge tomography, which is one of the most efficient, robust, cost‐effective, and non‐invasive tomographic methods of monitoring solid particles flow in a pipeline.Design/methodology/approach – Process flow data are captured by fitting an array of 16 discrete electrodynamic sensors about the circumference of the flow pipe. The captured data are processed using two tomographic algorithms to obtain tomographic images of the flow. Th...

Multivariate spatial condition mapping using subtractive fuzzy cluster means.

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining.

Wireless Sensor Network Based Wildfire Hazard Prediction System Modeling

Abstract This paper presents Wireless Sensor Network (WSN) based Wildfire Hazard Prediction (WFHP) system. A systematic description of architectural details and requirements of WSN for WFHP applications is presented. A TrueTime model of WSN architecture is built in Matlab environment taking into account the requirements for in-network processing of WFHP for spatially explicit locations in forest layer. The model performance in terms of network latency, energy consumption, and scalability is analyzed through simulation. Verification of model sanity and performance are carried out taking real weather datasets and their corresponding wildfire hazard outputs as benchmarks. Simulation results show the e_ciency and applicability of the model to real wildfire hazard prediction system.

Data stream mining for wireless sensor networks environment: energy efficient fuzzy clustering algorithm

This paper proposes a distributed wireless sensor network data stream clustering algorithm to minimise energy consumption and consequently extend the network lifetime. The efficiency in energy usage is as a result of trading-off communication for computation through distributed clustering and successive transmission of local clusters. We present the development of our algorithm, subtractive fuzzy cluster means (SUBFCM), and analyse its energy efficiency as well as clustering performance in comparison with state-of-the-art standard data clustering algorithms such as fuzzy C-means and K-means algorithms. The significance of the SUBFCM algorithm in terms of energy efficiency and clustering performance is proved through simulations as well as experiments.