Mohammed G. H. al Zamil

Dynamic event classification for intrusion and false alarm detection in vehicular ad hoc networks

Several classification techniques have been proposed as a basis to build intrusion detection systems for vehicular ad-hoc networks. In this paper, we proposed a dynamic event classification technique to categorise communication messages for the purpose of detecting intrusions and false alarms. The contributions of this research are to: 1 propose an efficient binary classification technique to evaluate the plausibility of communication messages in VANETs based on a set of semantic patterns of actions; 2 apply a mechanism to construct association rules that handle the representation of ad-hoc conditions. The proposed technique relies on defining the classification task as an optimisation problem that maximises true-positives and minimises false-positives. A set of experiments have been performed in order to evaluate the proposed technique using two different datasets. The results indicated that our proposed technique outperformed state-of-the-art classification techniques and efficiently detect intrusions and false alarms.

An Efficient Activity Recognition Framework: Toward Privacy-Sensitive Health Data Sensing

Recent advances in wireless sensor networks for ubiquitous health and activity monitoring systems have triggered the possibility of addressing human needs in smart environments through recognizing human real-time activities. While the nature of streams in such networks requires efficient recognition techniques, it is also subject to suspicious inference-based privacy attacks. In this paper, we propose a framework that efficiently recognizes human activities in smart homes based on spatiotemporal mining technique. In addition, we propose a technique to enhance the privacy of the collected human sensed activities using a modified version of micro-aggregation approach. An extensive validation of our framework has been performed on benchmark data sets yielding quite promising results in terms of accuracy and privacy-utility tradeoff.

An ODT-based abstraction for mining closed sequential temporal patterns in IoT-cloud smart homes

Due to the large amount of usage data collected from smart home appliances in an IoT-cloud environment, efficient mining techniques are of great need to capture the behavioral patterns. Existing mining algorithms are time-consuming and error prone as the amount of data is increasing rapidly. In this paper, we propose an abstraction approach to model temporal data based on an ordered decision tree (ODT) and spatiotemporal characteristics of usage data for IoT-cloud paradigm. The contribution of this research is to provide an efficient representation in terms of average length of patterns, while preserving the spatiotemporal characteristics of original data. We performed extensive experiments on synthetic data to report the performance and provide a comparison with state-of-the-art algorithms to prove the correctness of the proposed technique, even at a low-level of abstraction. The results indicate that the proposed methodology outperform existing techniques due to the inherited power of the ODT temporal structure.

Transferring activity recognition models in FOG computing architecture

Abstract A major focus of research in the field of in-home activity recognition (AR) and home automation (HA) is the ability to transfer data models to other homes for the purpose of applying new services, annotating classified data, and generating datasets due to lack of training ones. The wide spread of fog computing as an architecture for organizing edge devices in Internet-of-Things (IoT) systems lends support to the sharing of different environmental characteristics between different fogs (smart homes). In this paper, we propose a framework that serves the transfer of data models between different smart homes in a bid to overcome the lack of training data, which prevents the development of high-performance models that utilize fog computing characteristics. Our technique incorporates the sharing of environmental characteristics (by Fogs) in order to analyze the data features at the source and target smart homes. The features, then, are mapped onto each other using a fusion method that guarantees to keep the variations between different homes by reducing the divergence between them. The hidden Markov model has also been applied in order to model activities at target homes. Three experiments have been conducted to measure the performance of the proposed framework: first, against the accuracy of feature-mapping techniques; second, measuring the performance of classifying data at target homes; and, third, the ability of the proposed framework to function well due to noise data. The results show promising indicators and highlight the limitations of the proposed methodology.

Energy-efficient load balancing in wireless sensor network: An application of multinomial regression analysis:

Wireless sensor networks have become integral components of modern and smart environments. The main challenge for such important data-acquisition tools is the limited amount of available energy. In integrated networks in which cloud systems act as a self-regulatory controller, distributing the computational load among available partitions with rich energy will positively influence the lifetime of the whole network. This article investigates the application of a modified version of multinomial logistic regression model that incorporates spatiotemporal aspects of data collected from smart environments. The contribution of this research is to propose an energy-efficient load balancing strategy based on the proposed prediction model for the purpose of enhancing the lifetime of wireless infrastructure. Our proposed algorithm grows linearly in terms of time complexity. Extensive experiments have been performed to measure the prediction error rate and the energy consumption. The results showed that the proposed model significantly reduces the error rate and distinctly maximizes the lifetime of wireless sensor networks.

An Annotation Technique for In-Home Smart Monitoring Environments

Advances in multimedia technologies have led to the emergence of smart home applications. In fact, mobile multimedia technologies provide the infrastructure to adopt smart solutions and track inhabitants’ activities. In-home activity recognition significantly enhances the performance of healthcare-monitoring and emergency-control applications for elderly and people with special needs. Developing and validating data models for such applications requires training sets that reflect a ground truth in the form of labeled or annotated data. With the accelerated development of Internet-of-Things applications, automated annotation processes have emerged understanding resident behavior in terms of activities. This paper presents a methodology for automatic data annotation by profiling sensing nodes. Our proposed methodology models activities based on spatially recognized actions, with every activity expected to have a direct relationship with a specific set of locations. Furthermore, the proposed technique validates the assignment of labels based on the temporal relations among consecutive actions. We performed experiments to evaluate our proposed methodology on CASAS data sets, which indicated that the proposed methodology achieved better performance, to a statistically significant extent, than the state-of-the-art methodologies presented in the literature.