ISSN: 2409-0026
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
2024
Issue 1
2024
Issue 1
2022
Issue 1
2020
Issue 23Issue 22Issue 21Issue 20
2019
Issue 19Issue 18Issue 17Issue 16
2018
Issue 15
2017
Issue 14Issue 13Issue 12Issue 11
2016
Issue 10Issue 9Issue 8Issue 7
2015
Issue 6Issue 5Issue 4Issue 3
2014
Issue 2Issue 1

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/casa/index

Editor(s)-in-Chief: Phan Cong Vinh

EAI Endorsed Transactions on Context-aware Systems and Applications (CASA) is a place for highly original ideas about how context-aware systems are going to shape networked computing systems of the future. Hence, it focuses on rigorous approaches and cutting-edge solutions which break new ground in dealing with the properties of context-awareness.

INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions

 

The works that will be presented in the journal will focus on the following topics:

Context-awareness of Computation and Communication

Fundamentals:

• Context-aware models

• Context-aware control

• Context-aware algorithms

• Context-aware networks

• Context-aware computing

• Context-awareness calculi

• Context-awareness representation

• Context-awareness-based systems

• Logic in context-awareness

• Context-awareness reasoning

• Formal methods of context-awareness

• Context-awareness-based optimization and swarm Intelligence

• Theory, computational models and algorithms in context detection for computing

• Awareness of context in collaboration or crowdsourcing

• Context recognition and artificial intelligence (AI)

• Privacy issues

Context-aware Systems:

• Routing, transport, and reliability issues of context-aware systems

• Data dissemination and replication in context-aware systems

• Applications and middleware support, mobile social networking applications

• Mobility models and statistical analysis of mobility traces

• Context and social awareness mechanisms and algorithms

• Co-existence of opportunistic networks with infrastructure mobile wireless networks

• Service composition in autonomic and opportunistic networks

• Cognition-driven information processing and decision making

• Performance modeling, scaling laws, and fundamental limits for autonomic and opportunistic communications

• Game-theoretic insights into the operation of autonomic and opportunistic networks

• Participatory and urban sensing in autonomic and opportunistic networks

• Trust, security, and reputation in context-aware systems

• Autonomic and opportunistic communication testbeds and prototypes, measurement data from real experiments

• Socio-economic models for autonomic and opportunistic communications

• Mobile data measurement and collection platforms for context detection

• Automated systems to model and detect context

Context-aware Technologies and Applications

• Context-aware information retrieval

• Context-aware profiling, clustering, and collaborative filtering

• Machine learning for context-aware information retrieval and ontology learning

• Context-aware e-learning/tutoring

• Ubiquitous and context-aware computing

• Use of context-aware technologies in UI/HCI

• Context-aware advertising

• Recommendations for mobile users

• Context-awareness in portable devices

• Context-aware services

• Social Agents and Avatars

• Emotion and Personality

• Virtual Humans

• Autonomous Actors

• Awareness-based Animation

• Social and Conversational Agents

• Inter-Agent Communication

• Social Behavior

• Crowd Simulation

• Understanding Human Activity

• Memory and Long-term Interaction

• Context representations and signal characteristics that describe and identify the context

• Context detection algorithms/approaches using data collected with mobile devices, wearable devices, and pervasive sensors (e.g. RF sensors)

• User studies and evaluation techniques for context detection

• The novel use of context information in computing applications

• Integration of context into lifelogging applications.

• Applications of context information for the work-life balance, for a healthier life and behavior

• Applications of context information in situational or social awareness

• Application of context information in health care

• Applications of context information in industrial production

• Applications of context information in pervasive games

• Applications of context information in mobile multimedia devices

Nature of Computation and Communication

• autonomic computing/communicating

• Amorphous computing

• Artificial Intelligence

• Artificial immune computing

• Artificial Life

• Artificial neural computing

• Big data

• Biologically-inspired computing/communicating

• Cellular automata

• Cellular computing

• Collective intelligence in computing/communicating

• Collision-based computing

• Computation/communication based on chaos and dynamical systems

• Computation based on physical principles such as relativistic, optical, spatial, collision-based computing

• Cognitive computing

• Context-aware computing/communicating

• Data Mining

• DNA computing

• Evolutionary computing

• Fractal geometry

• Fuzzy computing

• Hypercomputation

• Image Processing

• Machine Learning

• Massive parallel computing

• Membrane computing

• Molecular computing

• Morphological computing

• Natural Language Processing

• Optical computing/communicating

• Physarum computing

• Quantum computing/communicating

• Relativistic computing

• Spatial computing

• Swarm intelligence in computing/communicating

• Wetware computing

  • Abdur Rakib (The University of Nottingham, UK, Malaysia Campus)
  • Adina Magda Florea (University Politehnica of Bucharest, Romania)
  • Ashad Kabir (Swinburne University of Technology, Australia)
  • Chien-Chih Yu (National ChengChi University, Taiwan)
  • David Sundaram (University of Auckland, NZ)
  • Emil Vassev (University of Limerick, Ireland)
  • Giacomo Cabri (Università di Modena e Reggio Emilia, Italy)
  • Giovanna Di Marzo Serugendo (University of Geneva, Switzerland)
  • Giuseppe De Pietro (ICAR-CNR, Italy)
  • Kurt Geihs (University of Kessel, Germany)
  • Mirko Viroli (University of Bologna, Italy)
  • Ondrej Krejcar (University of Hradec Kralove, Czech Republic)
  • Vangalur Alagar (Concordia University, Canada)
  • Muhammad Athar Javed Sethi (University of Engineering and Technology Peshawar Pakistan)
  • Yaser Jararweh (Jordan University of Science and Technology, Jordan)

  • Toward Modeling Linguistic Fuzzy Spanning Trees Based on Hedge Algebra

    Appears in:
    casa 24(1):

    Authors:
    Nguyen Van Han

    Published:
    27th Sep 2024

    Abstract:
    This paper presents an innovative approach to modeling Linguistic Fuzzy Maximum Spanning Trees (L-FMSTs) using Hedge Algebra (HA). HA provides a robust framework for quantifying linguistic terms, which is essential for handling the vagueness inherent in natural language. By integrating HA with L-FMSTs, we aim to enhance the interpretability and performance of fuzzy systems in applications requiring complex decision-making and optimization.

  • Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques

    Appears in:
    casa 24(1):

    Authors:
    Bhimambika Y Balannanavar, Vijayalaxmi N Rathod, Geeta Hukkeri, Anjanabhargavi Kulkarni, R.H Goudar, Dhananjaya G M, Anjali H Kugate

    Published:
    18th Jul 2024

    Abstract:
    One of the most reliable information sources is video, and in recent years, online and offline video consumption has increased to an unprecedented degree. One of the main difficulties in extracting information from videos is that unlike images, where information can be gleaned from a single frame, a viewer must watch the entire video in order to comprehend the context. In this work, we try to use various algorithmic techniques, such as deep neural networks and local features, in conjunction with a variety of clustering techniques, to find an efficient method of extracting interesting key frames from videos to summarize them. Video summarization plays a major role in video indexing, browsing, compression, analysis, and many other domains. One of the fundamental elements of video structure analysis is key frame extraction, which pulls significant frames out of the movie. An important frame from a video that may be used to summarize videos is called a key frame. We provide a technique that leverages convolutional neural networks in our suggested model, static video summarization, and key frame extraction from movies.

  • UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

    Appears in:
    casa 24(1):

    Authors:
    Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc, Tran Cao Minh, Vuong Xuan Chi, Ha Minh Tan

    Published:
    12nd Jan 2024

    Abstract:
    In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."

  • UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

    Appears in:
    casa 24(1):

    Authors:
    Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc, Tran Cao Minh, Vuong Xuan Chi, Ha Minh Tan

    Published:
    12nd Jan 2024

    Abstract:
    In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."

  • Manipulation of the Multi-Vehicle System for the Industrial Applications

    Appears in:
    casa 23(1):

    Authors:
    Lourve Vincent

    Published:
    2nd Oct 2023

    Abstract:
    This approach should indicate some challenges in routing and scheduling for the multi-vehicle system. The proposed method delivers a novel method to generate the free-collision trajectory as well as optimal route from starting point to destination. The estimated time at one node and the classification of load level support vehicle to decide which proper route is and stable movement is reached. From these results, it could be observed that the proposed approach is feasible and effective for many applications. The proposed method for routing and scheduling might be useful in the multi-vehicle system. In the large scale system, some intelligent schemes should be considered to integrate.

  • Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

    Appears in:
    casa 23(1):

    Authors:
    Nguyen Cong Nhut

    Published:
    25th Sep 2023

    Abstract:
    The COVID-19 pandemic can be considered a human disaster, it has claimed the lives of many people. We only know the number of deaths due to COVID-19 through government statistics, but on days when there are no statistics, how do we know whether people died that day or not? This study aims to predict the number of new deaths per day due to COVID 19 in Vietnam on days when observational data is not available and predict the number of deaths in the future. The study used COVID-19 data from the World Health Organization (WHO). A total of 260 days were collected and the author processed and standardized the data. Based on available data, the author uses Kriging interpolation statistical method to build a forecast model. As a result, the author has selected a prediction model suitable for a highly reliable data set, the regression coefficient and correlation coefficient are close to 1, the error between the model’s prediction results compared to data. There are days when the prediction error is almost zero. The study has built a future forecast map of the number of new deaths per day due to COVID-19. The article concludes that applying the Kriging statistical method is appropriate for COVID-19 data. This research opens up new research directions for related fields such as earthquakes, mining, groundwater, environment, etc.

  • Facial Sentiment Recognition using artificial intelligence techniques.

    Appears in:
    casa 23(1):

    Authors:
    Vuong Xuan Chi, Phan Cong Vinh

    Published:
    22nd Sep 2023

    Abstract:
    Facial emotion recognition technology is used to analyze and recognize human emotions based on facial expressions. This technology uses deep learning models to classify facial expressions, eyes, eyebrows, mouth, and other facial expressions to determine a person's emotions. The application of facial emotion recognition in the field of education is a potential way to evaluate the level of student absorption after each class period. Using cameras and emotion recognition technology, the system can record and analyze students' facial expressions during class. In this paper, we use the Convolutional Neural Network (CNN) algorithm combined with the linear regression analysis method to build a model to predict students' facial emotions over a period of time camera recorded.

  • The data preprocessing in improving the classification quality of network intrusion detection systems

    Appears in:
    casa 23(1):

    Authors:
    Hoàng Ngoc Thanh

    Published:
    6th Sep 2023

    Abstract:
    Stream-based intrusion detection is a growing problem in computer network security environments. Many previous researches have applied machine learning as a method to detect attacks in network intrusion detection systems. However, these methods still have limitations of low accuracy and high false alarm rate. To improve the quality of classification, this paper proposes two solutions in the data preprocessing stage, that is, the solution of feature selection and resampling of the training dataset before they are used for training the classifiers. This is based on the fact that there is a lot of class imbalanced data in the training dataset used for network intrusion detection systems, as well as that there are many features in the dataset that are irrelevant to the classification goal, this reduces the quality of classification and increases the computation time. The data after preprocessing by the proposed algorithms is used to train the classifiers using different machine learning algorithms including: Decision Trees, Naive Bayes, Logistic Regression, Support Vector Machines, k Nearest Neighbor and Artificial Neural Network. The training and testing results on the UNSW-NB15 dataset show that: as with the Reconnaissance attack type, the proposed feature selection solution for F-Measure achieves 96.31%, an increase of 19.64%; the proposed oversampling solution for F-Measure achieves 6.99%, an increase of 3.17% and the proposed undersampling solution for F-Measure achieves 94.65%, an increase of 11.42%.

  • Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification

    Appears in:
    casa 23(1):

    Authors:
    Dang Nhu Phu, Vuong Xuan Chi, Phan Cong Vinh, Ha Minh Tan, Nguyen Kim Quoc

    Published:
    14th Aug 2023

    Abstract:
    Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.

  • A Framework for Utilizing Permutational Multiple Analysis of Variance as a Precursor for Nonparametric Statistical Learning with Cyber Network Data

    Appears in:
    casa 23(1):

    Authors:
    John Pickard, Thomas Woolman

    Published:
    12nd Jul 2023

    Abstract:
    INTRODUCTION: Although scientific hypothesis testing methodologies are well established, their application to falsifiable hypothesis testing for assessing causal relationships potentially identified by machine learning and artificial intelligence models is rare due to the primarily nonparametric statistical nature of these systems. OBJECTIVES: The primary objective of this study is to demonstrate the potential for applying nonparametric statistical tests to a mixed qualitative and quantitative cyber network dataset as a method to pre-assess the feasibility of applying forms of statistical hypothesis testing before a machine learning algorithm models the data. METHODS: A mixture of permuted analysis of variance models augmented by the use of transformed non-Euclidean multivariate distances between curated dependent variable classes produced this research data. Quasi-experimental data   from an enclosed laboratory environment utilizing a monitored, locally unrestricted network that introduced known Internet of Things (IoT) malware software supplied network flow events. RESULTS: A PERMANOVA model was executed against 62,000 records of the network flow observations, using Euclidean distance measurements with variable-dependent relationship ordering, using terms added sequentially (first to last) in the order encountered in the raw network flow dataset, using 200 permutations. This precursor test resulted in a p-value for the PERMANOVA model that incorporated terms added sequentially of 0.02985, providing an F value of 0.00017 with which to determine the ratio of explained to unexplained variance. Utilizing an analysis of the F values for all of the residuals, we show 29,998 degrees of freedom with a residual F model score of 0.99983, indicating that there is a strong proportion of explained to unexplained variance across all of the independent variables contained in the model. The model is thus statistically significant with a p-value below the alpha test statistic of 0.05. CONCLUSION: This research has demonstrated that it is possible to apply tests of falsifiability that incorporate reproducible methods into the quasi-experiment design and apply this to the field of machine learning. Applied to AI/ML (artificial intelligence/machine learning) models, this pre-assessment methodology supports the appropriateness of cyber network flow datasets in which a final test of statistical significance would be required. The authors believe that this represents a substantially useful precursor assessment stage for the suitability and reliability of the utilization of any nonparametric statistical learning algorithms applied to cyber network data predictive analytics.

  • Review of Network on Chip Routing Algorithms

    Appears in:
    casa 20(22): e5

    Authors:
    Khurshid Ahmad, Muhammad Athar Javed Sethi

    Downloads:
    3422

    Abstract:
    System on chip (SoC) is an integrated circuit in which components are communicating through the bus interconnection system. Network on chip (NoC) is a communication network for a multiprocessor system on chip (MPSoC). In NoC architecture node/ component of MPSOC are communicating through a network. The performance of NoC architecture depends on topology, routing algorithm and switching technique. In this paper, different NoC routing algorithms are review using basic parameters of NoC architecture and also provide some information about these parameters. It is concluded that most of the researchers are interested in design of the NoC routing algorithm, which efficiently transmits data from source to destination. When the routing algorithm is congestion aware, fault-tolerant, deadlock-free and live-lock free, then the latency of algorithm decreases and throughput increases.

  • Document Level Sentiment Analysis: A survey

    Appears in:
    casa 18(13): e2

    Authors:
    F. Barigou, G. Belalem, S. Behdenna

    Downloads:
    2841

    Abstract:
    Sentiment analysis becomes a very active research area in the text mining field. It aims to extract people's opinions, sentiments, and subjectivity from the texts. Sentiment analysis can be performed at three levels: at document level, at sentence level and at aspect level. An important part of research effort focuses on document level sentiment classification, including works on opinion classification of reviews. This survey paper tackles a comprehensive overview of the last update of sentiment analysis at document level. The main target of this survey is to give nearly full image of sentiment analysis application, challenges and techniques at this level. In addition, some future research issues are also presented.

  • Implementation of NoC on FPGA with Area and Power Optimization

    Appears in:
    casa 19(16): e5

    Authors:
    Momil Ijaz, Muhammad Athar Javed Sethi, Huma Urooj

    Downloads:
    2614

    Abstract:
    On-chip bus-based communication has many shortcomings to it, including resource sharing, delay, latency and cost (power and area). Network on Chip (NoC) is an innovation that is planned to eliminate the shortcomings to buses such as compact systems, size, speed, power and area. The goal of working was to design a usable and researchable general-purpose 2x2 mesh NoC architecture, which is not application specific, and have optimized area and power. Desired NoC was coded and deployed on FPGA Spartan-3 kit in a generic mode, with the efficient area and power utilization than traditional deployments.

  • Understanding Social Interaction with Human Computer Interaction (HCI) Adaptation

    Appears in:
    casa 19(18): e1

    Authors:
    W. V. Siricharoen

    Downloads:
    2550

    Abstract:
    Human computer interaction (HCI) is an initial study that people use complex technological inventions. The secondary focus, but more importantly, is the strategy of these inventions to smooth this use. HCI is what happens when users and computers system get together to accomplish something. The paper will propose the idea of people involve with HCI in the way of making their life change. The brief of HCI research aspect is revisited in the introduction. The social interaction and social media are addressed. Also, the paper discusses the changing in daily life with technology. The paper shows the impact of the technology on the way people communicate and change in work and social habits.

  • How instability in virtual economies of mobile digital games drives and ruins profit

    Appears in:
    casa 19(18): e2

    Authors:
    P. C. Lohse

    Downloads:
    2320

    Abstract:
    The digital gaming business has changed in the last years. Digital games are no longer just products. They developed into services. People play games not only on a stationary device, but on mobile devices, too. The change of gaming devices also had an impact on game design. Competition between mobile digital games mostly takes place at a download price of zero. Today the Freemium monetization method is the dominant monetization method. Players of digital games often have to go through a core loop by repeating the same tasks inside the game. The stability of the virtual economy inside the game affects the game’s core loop. This paper discusses the role of instability in virtual economies in the context of core loops. A small instability inside a virtual economy can increase revenue of a digital game. Players tend to stay in a game because of sunk cost fallacy. If instability inside a virtual economy happens for a too long time players will quit playing and revenue will decrease.

  • Real Time Burning Image Classification Using Support Vector Machine

    Appears in:
    casa 17(12): e4

    Authors:
    T.S. Hai, N.T. Thuy, L.H. Thai, L.M. Triet

    Downloads:
    2142

    Abstract:
    Burning image classification is critical and attempted problems in medical image processing. This paper has proposed the real time image classification for burning image to automatically identify the degrees of burns in three levels: II, III, and IV. The proposed model uses the multi-colour channels extraction and binary based on adaptive threshold. The proposed model uses One-class Support Vector Machine instead of traditional Support Vector Machine (SVM) because of unbalanced degrees of burns images database. The classifying precision 77.78% shows the feasibility of our proposed model.

  • A hybrid feature selection method for credit scoring

    Appears in:
    casa 17(11): e2

    Authors:
    Nam Nguyen Ha, Hien Nguyen Thi Bao, Sang Ha Van

    Downloads:
    1916

    Abstract:
    Reliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring model based on parallel GBM (Gradient Boosted Model), filter and wrapper approaches to evaluate the applicant’s credit score from the input features. Feature scoring expression are combined by feature important (Gini index) and Information Value. Backward sequential scheme is used for selecting optimal subset of relevant features while the subset is evaluated by GBM classifier. To reduce the running time, we applied parallel GBM classifier to evaluate the proposed subset of features. The experimental results showed that the proposed method obtained a higher predictive accuracy than a baseline method for some certain datasets. It also showed faster speed and better generalization than traditional feature selection methods widely used in credit scoring.

  • Counting Brown Planthoppers in Light-Trap Images Using Morphological Operations

    Appears in:
    casa 19(17): e2

    Authors:
    Hiep Xuan Huynh, Nguyen Huu Van Long, Nghi Cong Tran, An Cong Tran

    Downloads:
    1687

    Abstract:
    As brown plant hopper (BPH) is one of the most dangerous kinds of insect for rice plant, in recent years, there has been increased concern in counting them in light trap images to control their spread in order to reduce their damage on rice plant. This paper proposes an approach to counting BPHs in light trap images based on morphological operations. By applying these operations appropriately, combined with some noise removal techniques based on color, BPHs in the light trap images can be identified. In addition, it is common that the BPHs in the light-trap images are overlapped due to the layout of the light trap. Therefore, an approach to counting the overlapped BPHs based on their size is also introduced while the sequential region labeling algorithm is used to count the number of the separate BPHs. The experimental results show that our proposed approach is suited to identifying and counting the BPHs in images which are overlapped.

  • Towards a Context-Aware Framework for Improving Collaboration of Users in Groupware Systems

    Appears in:
    casa 14(1): e4

    Authors:
    Carmen Mezura-Godoy, Edgard Benítez-Guerrero, Luis G. Montané-Jiménez

    Downloads:
    1651

    Abstract:
    A Context-Aware Groupware System (CAGS) enables the members of a team to communicate, cooperate and coordinate their activities to achieve a common goal, by providing them tools that are aware of their current execution context and adapt accordingly. CAGS can be found in several domains such as entertainment, particularly Collaborative First-Person-Shooter (FPS) video games. In CAGS, the means of collaboration traditionally provided to users (e.g. text and audio messaging) are not necessarily adequate: for instance, in a FPS, messages can distract the gamer due to the speed of the game. This paper reports a study that, for Collaborative FPS, identifies advantages/disadvantages of current means of collaboration and social behaviors that arise when players interact face-to-face or remotely. Based on this study, a context-aware conceptual model and architecture is proposed for CAGS aimed to improve user collaboration.

  • Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments

    Appears in:
    casa 15(5): e3

    Authors:
    Nirmalya Roy, H M Sajjad Hossain, Md Abdullah Al Hafiz Khan

    Downloads:
    1641

    Abstract:
    Accurate estimation of localized occupancy related informa- tion in real time enables a broad range of intelligent smart environment applications. A large number of studies using heterogeneous sensor arrays reflect the myriad requirements of various emerging pervasive, ubiquitous and participatory sensing applications. In this paper, we introduce a zero- configuration and infrastructure-less smartphone based lo- cation specific occupancy estimation model. We opportunis- tically exploit smartphone’s acoustic sensors in a conversing environment and motion sensors in absence of any conver- sational data. We demonstrate a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data and a change point detection algorithm for locomotive motion of the users to infer the occupancy. We augment our occupancy detection model with a fingerprinting based methodology using smart- phone’s magnetometer sensor to accurately assimilate loca- tion information of any gathering. We postulate a novel crowdsourcing-based approach to annotate the semantic lo- cation of the occupancy. We evaluate our algorithms in dif- ferent contexts; conversational, silence and mixed in pres- ence of 10 domestic users. Our experimental results on real-life data traces in natural settings show that using this hybrid approach, we can achieve approximately 0.76 error count distance for occupancy detection accuracy on aver- age.
Publisher
EAI
ISSN
2409-0026
Number of Volumes
10
Last Published
2024-01-12
EAI Logo

About EAI

Community

Publish with EAI