Research of Applying Machine Learning Methods to Outlier Detection in Wireless Sensor Networks

兵庫県立大学大学院201

Application of rasch model on resilience in higher education: an examination of validity and reliability of Malaysian academician happiness index (MAHI)

This preliminary study was conducted to examine and verify the validity and reliability of the instrument on the Malaysian Academician Happiness Index (MAHI) on resilience. MAHI could be seen as a tool to measure the level of happiness and stress of academicians before determining how resilient the academicians were. Resilience can be defined as a mental ability of a person to recover quickly from illness or depression. MAHI instrument consisted of 66 items. The instrument was distributed to 40 academicians from three groups of universities which were the Focus University, Comprehensive University and Research University is using a survey technique. The instrument was developed to measure three main constructs which were the organization, individual and social that would affect the happiness and stress levels of academicians. This preliminary study employed the Rasch Measurement Model uses Winsteps software version 3.69.1.11. to examine the validity and reliability of the items. The results of the analysis of the MAHI instrument showed that the item reliability was 0.87, person reliability was 0.83 and value of Alpha Cronbach was 0.84. Meanwhile, misfit analysis showed that only there was one item with 1.46 logit that could be considered for dropping or needed improvement. Therefore, it highlighted that most of the items met the constructs’ need and can be used as a measurement indicator of MAHI. The implication of this instrument can help Malaysian academicians to be more resilient in facing challenges in the future

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known

Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Abstract: Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy

Node localization for indoor tracking using artificial neural network

Wireless sensor network (WSN) always comes up with the need of deploying either mobile or immobile sensor nodes or both. Wireless communication among these nodes is crucial and it requires identifying the location of these nodes within a specific region. Global positioning system (GPS) is widely used for location tracking. However, when it comes to WSN, GPS has its limitations, due to its high power consumption and the overhead of additional hardware cost. The research challenge here lies in the efficient location tracking of wireless sensor nodes, especially in closed indoor and outdoor environments. This paper comes up with a simple and easy-to-implement technique using artificial neural networks (ANNs) to manipulate the location of the sensor nodes. In this paper, the back-propagation network training algorithm for providing supervised learning to multilayer perceptron is generalized to synthesize the WSN and gives out 2D Cartesian coordinates of the nodes. The technique is both cost-efficient and achieves 98% accuracy