Mengenal pasti masalah pemahaman dan hubungannya dengan latar belakang matematik, gaya pembelajaran, motivasi dan minat pelajar terhadap bab pengawalan kos makanan di Sekolah Menengah Teknik (ert) Rembau: satu kajian kes.

Kajian ini dijalankan untuk mengkaji hubungan korelasi antara latar belakang Matematik, gaya pembelajaran, motivasi dan minat dengan pemahaman pelajar terhadap bab tersebut. Responden adalah seramai 30 orang iaitu terdiri daripada pelajar tingkatan lima kursus Katering, Sekolah Menengah Teknik (ERT) Rembau, Negeri Sembilan. Instrumen kajian adalah soal selidik dan semua data dianalisis menggunakan program SPSS versi 10.0 untuk mendapatkan nilai min dan nilai korelasi bagi memenuhi objektif yang telah ditetapkan. Hasil kajian ini menunjukkan bahawa hubungan korelasi antara gaya pembelajaran pelajar terhadap pemahaman pelajar adalah kuat. Manakala hubungan korelasi antara latar belakang Matematik, motivasi dan minat terhadap pemahaman pelajar adalah sederhana. Nilai tahap min bagi masalah pemahaman pelajar, latar belakang Matematik, gaya pembelajaran, motivasi dan minat terhadap bab Pengawalan Kos Makanan adalah sederhana. Kajian ini mencadangkan penghasilan satu Modul Pembelajaran Kendiri bagi bab Pengawalan Kos Makanan untuk membantu pelajar kursus Katering dalam proses pembelajaran mereka

IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis

We conduct the most comprehensive study of WLAN traces to date. Measurements collected from four major university campuses are analyzed with the aim of developing fundamental understanding of realistic user behavior in wireless networks. Both individual user and inter-node (group) behaviors are investigated and two classes of metrics are devised to capture the underlying structure of such behaviors. For individual user behavior we observe distinct patterns in which most users are 'on' for a small fraction of the time, the number of access points visited is very small and the overall on-line user mobility is quite low. We clearly identify categories of heavy and light users. In general, users exhibit high degree of similarity over days and weeks. For group behavior, we define metrics for encounter patterns and friendship. Surprisingly, we find that a user, on average, encounters less than 6% of the network user population within a month, and that encounter and friendship relations are highly asymmetric. We establish that number of encounters follows a biPareto distribution, while friendship indexes follow an exponential distribution. We capture the encounter graph using a small world model, the characteristics of which reach steady state after only one day. We hope for our study to have a great impact on realistic modeling of network usage and mobility patterns in wireless networks.Comment: 16 pages, 31 figure

Efficient Information Dissemination in Vehicular Networks with Privacy Protection

Vehicular ad hoc network (VANET) is a key component of intelligent transportation System (ITS). In VANETs, vehicles and roadside units exchange information for the purpose of navigation, safe driving, entertainment and so on. The high mobility of vehicles makes efficient and private communications in VANETs a big challenge. Improving the performance of information dissemination while protecting data privacy is studied in this research. Meet-Table based information dissemination method is first proposed, so as to improve the information dissemination, and to efficiently distribute information via utilizing roadside units, Cloud Computing, and Fog Computing. A clustering algorithm is proposed as well, to improve the stability for self-organized cluster-based dissemination in VANETs on highways. Then, fuzzy neural networks are used to improve the stability and security of routing protocols, AODV, and design a novel protocol, GSS-AODV. To further protect data privacy, a multi-antenna based information protection approach for vehicle-to-vehicle(V2V) communications is also proposed

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes

Reputation-aware Trajectory-based Data Mining in the Internet of Things (IoT)

Internet of Things (IoT) is a critically important technology for the acquisition of spatiotemporally dense data in diverse applications, ranging from environmental monitoring to surveillance systems. Such data helps us improve our transportation systems, monitor our air quality and the spread of diseases, respond to natural disasters, and a bevy of other applications. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. While previous research has attempted to improve the quality of the IoT data, they are limited in terms of better realization of the sensing context and resiliency against malicious attackers in real time. For instance, the data fusion techniques, which process the data in batches, cannot be applied to time-critical applications as they take a long time to respond. Furthermore, context-awareness allows us to examine the sensing environment and react to environmental changes. While previous research has considered geographical context, no related contemporary work has studied how a variety of sensor context (e.g., terrain elevation, wind speed, and user movement during sensing) can be used along with spatiotemporal relationships for online data prediction. This dissertation aims at developing online methods for data prediction by fusing spatiotemporal and contextual relationships among the participating resource-constrained mobile IoT devices (e.g. smartphones, smart watches, and fitness tracking devices). To achieve this goal, we first introduce a data prediction mechanism that considers the spatiotemporal and contextual relationship among the sensors. Second, we develop a real-time outlier detection approach stemming from a window-based sub-trajectory clustering method for finding behavioral movement similarity in terms of space, time, direction, and location semantics. We relax the prior assumption of cooperative sensors in the concluding section. Finally, we develop a reputation-aware context-based data fusion mechanism by exploiting inter sensor-category correlations. On one hand, this method is capable of defending against false data injection by differentiating malicious and honest participants based on their reported data in real time. On the other hand, this mechanism yields a lower data prediction error rate