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  • 1.
    Palm, Emanuel
    et al.
    Luleå tekniska universitet.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Bayesian system for cloud performance diagnosis and prediction2017Ingår i: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, Piscataway, NJ: IEEE Computer Society, 2017, 371-374 s., 7830706Konferensbidrag (Refereegranskat)
    Abstract [en]

    The stochastic nature of the cloud systems makes cloud quality of service (QoS) performance diagnosis and prediction a challenging task. A plethora of factors including virtual machine types, data centre regions, CPU types, time-of-the-day, and day-of-the-week contribute to the variability of the cloud QoS. The state-of-the-art methods for cloud performance diagnosis do not capture and model complex and uncertain inter-dependencies between these factors for efficient cloud QoS diagnosis and prediction. This paper presents ALPINE, a proof-of-concept system based on Bayesian networks. Using a real-life dataset, we demonstrate that ALPINE can be utilised for efficient cloud QoS diagnosis and prediction under stochastic cloud conditions

  • 2.
    Mitra, Karan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Ranjan, Rajiv
    Newcastle University.
    ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction2017Ingår i: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, 281-288 s., 8034996Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors such as virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of modeling the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes, develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and show that it predicts cloud performance with high accuracy of 91.93%.

  • 3.
    Belyakhina, Tamara
    et al.
    Luleå tekniska universitet.
    Zaslavsky, Arkady
    CSIRO, Melbourne.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Jayaraman, Prem Prakash
    Swinburne University of Technology, Melbourne.
    DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems2017Ingår i: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, 75-86 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    The rapidly emerging Internet of Things supports many diverse applications including environmental monitoring. Air quality, both indoors and outdoors, proved to be a significant comfort and health factor for people. This paper proposes a smart context-aware system for indoor air quality monitoring and prediction called DisCPAQ. The system uses data streams from air quality measurement sensors to provide real-time personalised air quality service to users through a mobile app. The proposed system is agnostic to sensor infrastructure. The paper proposes a context model based on Context Spaces Theory, presents the architecture of the system and identifies challenges in developing large scale IoT applications. DisCPAQ implementation, evaluation and lessons learned are all discussed in the paper.

  • 4.
    Zhalgasbekova, Aigerim
    et al.
    Luleå tekniska universitet.
    Zaslavsky, Arkady
    CSIRO, Melbourne.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Jayaraman, Prem Prakash
    Swinburne University of Technology, Melbourne.
    Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring2017Ingår i: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, 53-65 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Opportunistic sensing advance methods of IoT data collection using the mobility of data mules, the proximity of transmitting sensor devices and cost efficiency to decide when, where, how and at what cost collect IoT data and deliver it to a sink. This paper proposes, develops, implements and evaluates the algorithm called CollMule which builds on and extends the 3D kNN approach to discover, negotiate, collect and deliver the sensed data in an energy- and cost-efficient manner. The developed CollMule software prototype uses Android platform to handle indoor air quality data from heterogeneous IoT devices. The CollMule evaluation is based on performing rate, power consumption and CPU usage of single algorithm cycle. The outcomes of these experiments prove the feasibility of CollMule use on mobile smart devices.

  • 5.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Applied Machine Learning: Forecasting Heat Load in District Heating System2016Ingår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, 478-488 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from ten residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 hours. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 hour.

  • 6. Nanda, Rohan
    et al.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    BayesForSG:: A Bayesian Model for Forecasting Thermal Load in Smart Grids2016Ingår i: SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York: ACM Digital Library, 2016, 2135-2141 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Forecasting the thermal load demand for residential buildings assists in optimizing energy production and developing demand response strategies in a smart grid system. However, the presence of a large number of factors such as outdoor temperature, district heating operational parameters, building characteristics and occupant behavior, make thermal load forecasting a challenging task. This paper presents an efficient model for thermal load forecast in buildings with different variations of heat load consumption across both winter and spring seasons using a Bayesian Network. The model has been validated by utilizing the realistic district heating data of three residential buildings from the district heating grid of the city of Skellefteå, Sweden over a period of four months. The results from our model shows that the current heat load consumption and outdoor temperature forecast have the most influence on the heat load forecast. Further, our model outperforms state-of-the-art methods for heat load forecasting by achieving a higher average accuracy of 77.97% by utilizing only 10% of the training data for a forecast horizon of 1 hour.

  • 7. Ngo, Khoi
    et al.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart Regions2016Ingår i: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015: Boston, United States, 13 - 17 October 2015, Piscataway, NJ: IEEE Communications Society, 2016, 563-568 s., 7454565Konferensbidrag (Refereegranskat)
    Abstract [en]

    The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.

  • 8.
    Shah, Tejal
    et al.
    The University of New South Wales, Australia.
    Ali, Yavari
    School of Computer Science, RMIT university, Australia.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Jayaraman, Prem Prakash
    School of Software and Electrical Engineering, Swinburne University of Technology, Australia.
    Rabhi, Fethi
    School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia.
    Ranjan, Rajiv
    School of Computing, Newcastle University, Newcastle, UK.
    Remote health care cyber-physical system: quality of service (QoS) challenges and opportunities2016Ingår i: IET Cyber-Physical Systems: Theory & Applications, ISSN 2398-3396, Vol. 1, nr 1, 40-48 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

  • 9.
    Shah, Tejal
    et al.
    School of Computer Science and Engineering, University of New South Wales.
    Yavari, Ali
    Royal Melbourne Institute of Technology, Melbourne.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Jayaraman, Prem Prakash
    RMIT University, Melbourne.
    Rabhi, Fethi
    University of New South Wales.
    Ranjan, Rajiv
    School of Computing Science, Newcastle University, Newcastle upon Tyne.
    Remote Healthcare Big Data Processing in Real-Time: Quality of Service Challenges and Opportunities2016Ingår i: Handbook on Big Data Technologies, Springer-Verlag GmbH , 2016Kapitel i bok, del av antologi (Refereegranskat)
  • 10. Louis, Baptiste
    et al.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    CloudSimDisk: Energy-Aware Storage Simulation in CloudSim2015Ingår i: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC): Limassol, 7-10 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, 11-15 s., 7431390Konferensbidrag (Refereegranskat)
    Abstract [en]

    The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.

  • 11.
    Mitra, Karan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Granlund, Daniel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    M2C2: A Mobility Management System for Mobile Cloud Computing2015Ingår i: IEEE Wireless Communications and Networking Conference, 2015: WCNC 2015, 9-12 Mars 2015, New Orleans, LA, Piscataway, NJ: IEEE Communications Society, 2015, 1608-1613 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mobile devices have become an integral part of our daily lives. Applications running on these devices may avail storage and compute resources from the cloud(s). Further, a mobile device may also connect to heterogeneous access networks (HANs) such as WiFi and LTE to provide ubiquitous network connectivity to mobile applications. These devices have limited resources (compute, storage and battery) that may lead to servicedisruptions. In this context, mobile cloud computing enables offloading of computing and storage to the cloud. However, applications running on mobile devices using clouds and HANs are prone to unpredictable cloud workloads, network congestion and handoffs. To run these applications efficiently the mobile device requires the best possible cloud and network resources while roaming in HANs. This paper proposes, develops and validates a novel system called M2C2 which supports mechanismsfor: i.) multihoming, ii.) cloud and network probing, and iii.) cloudand network selection. We built a prototype system and performed extensive experimentation to validate our proposed M2C2. Our results analysis shows that the proposed system supports mobility efficiently in mobile cloud computing.

  • 12.
    Jayaraman, Prem
    et al.
    RMIT University, Melbourne.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Shah, Tejal
    School of Computer Science and Engineering, University of New South Wales.
    Georgeakopoulos, Dimitros
    Royal Melbourne Institute of Technology, Melbourne.
    Ranjan, Rajiv
    School of Computing Science, Newcastle University, Newcastle upon Tyne.
    Orchestrating Quality of Service in the Cloud of Things Ecosystem2015Ingår i: 2015 IEEE International Symposium on Nanoelectronic and Information Systems: Indore, 21-23 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, 185-190 s., 7434422Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cloud of Things (CoT) is a vision inspired by Internet of Things (IoT) and cloud computing where the IoT devices are connected to the clouds via the Internet for datastorage, processing, analytics and visualization. CoT ecosystem will encompass heterogeneous clouds, networks and devices to provide seamless service delivery, for example, in smart cites. To enable efficient service delivery, there is a need to guarantee a certain level of quality of service from both cloud and networksperspective. This paper discusses the Cloud of Things, cloud computing, networks and new quality of service management research issues arising due to realisation of CoT ecosystem vision.

  • 13.
    Mitra, Karan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Mobile Cloud Computing System for Emergency Management2014Ingår i: I E E E Cloud Computing, ISSN 2325-6095, Vol. 1, nr 4, 30-38 s., 7057583Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Emergency management systems deal with the dynamic processing of data, where response teams must continuously adapt to the changing conditions at the scene of the emergency. Response teams must make critical decisions in highly demanding situations using large volumes of sensor data. Mobile devices have limited processing, storage, and battery resources; therefore, the sensed data from the scene of the emergency must be transmitted and processed quickly using the best available networks and clouds. Mobile cloud computing (MCC) is expected to play a critical role in the computation and storage offloading of sensor data to the best available clouds. However, applications running on mobile devices using clouds and heterogeneous access networks such as Wi-Fi and 3G are prone to unpredictable cloud workloads, network congestion, and handoffs. This article presents M2C2, a system for mobility management in MCC, that supports mechanisms for multihoming, cloud and network probing, and cloud and network selection. Through a prototype implementation and experiments, the authors show that M2C2 supports mobility efficiently in MCC scenarios such as emergency management.

  • 14.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach2014Ingår i: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm 2014): Venice, 3-6 Nov. 2014, Piscataway, NJ: IEEE Communications Society, 2014, 554-559 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi- family apartment buildings in a District Heating System (DHS). The forecasting model is built using six and eleven weeks of data from five building substations. The external factors and internal factors influencing the heat load in substations are parameters used as our model’s input. Short-term forecast models are generated using four supervised Machine Learning (ML) techniques: Support Vector Regression (SVR), Regression Tree, Feed Forwards Neural Network (FFNN) and Multiple Linear Regression (MLR). Performance comparison among these ML methods was carried out. The effects of combining the internal and external factors influencing heat load at substations was studied. The models are evaluated with varying horizon up to 24-hours ahead. The results show that SVR has the best accuracy of 5.6% MAPE for the best-case scenario.

  • 15.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Complex activity recognition and context validation within social interaction tools2013Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Human activity recognition using sensing technology is crucial in achieving pervasive and ubiquitous computing paradigms. It can be applied in many domains such as health-care, aged-care, personal-informatics, industry, sports and military. Activities of daily living (ADL) are those activities which users perform in their everyday life and are crucial for day-to-day living. Human users perform a large number of these complex activities at home, office and outdoors. This thesis proposes, develops and validates a mechanism which combines knowledge and data-driven approaches for the recognition of complex ADLs. This thesis focuses on the challenging task of capturing data from sensors and recognizing user’s complex activities which are concurrent, interleaved or varied-order in nature. We propose, develop and validate mechanisms and algorithms to infer complex activities which are concurrent and interleaved and build a test-bed which enables the sharing of this information on social interaction tools (SITs). In particular, we propose, develop and validate a novel context-driven activity theory (CDAT) to build atomic and complex activity definitions using domain knowledge and activity data collected from real-life experimentation. We develop a novel situation- and context-aware activity recognition system (SACAAR) which recognizes complex activities which are concurrent and interleaved and validate it by performing extensive real-life experimentation. We build a novel context-driven complex activity recognition algorithm to infer complex concurrent and interleaved activities. We build and validate an extended-SACAAR system which utilizes probabilistic and Markov chain analysis to discover complex activity signatures and associations between atomic activities, context attributes and complex activities. Our proposed algorithms achieve an average accuracy of 95.73% for complex activity recognition while maintaining Complex Activity Recognition and Context Validation within Social Interaction Tools computational efficiency. We build an ontological extension to SACAAR called semantic activity recognition system (SEMACT) which is based on CDAT and evaluate it using ontological reasoning for activity recognition. It achieves a high accuracy of 94.35% for the recognition of complex activities which are both concurrent and interleaved.We propose and develop novel mechanisms for context validation within SITs. Users might be keen on sharing their activity related information with family and friends. The wide scale use of SITs has made anytime, anywhere communication of user’s activities with family, friends and other interested parties possible. It is important that any activity-based updates based on user’s current context shared on these SITs are up-to-date, correct and not misleading. This thesis further focuses on the validation of context such as activity-based updates within social interaction tools. We validate the correctness and freshness of activity-based updates on SITs for a user by matching them against the inferred activity information by our SACAAR system. This provides the user with a mechanism to always have a correct and timely update which does not mislead their friends, family and other followers. We perform extensive experimentation and our validation algorithm is able to detect 81% of the incorrect updates made to social interaction tools. We propose, develop and implement a novel context-aware Twitter validator which checks and validates user’s tweets on Twitter. During the course of the thesis work, 9 peer-refereed international conference papers and 1 journal paper have been produced.

  • 16.
    Saguna, Saguna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Chakraborty, Dipanjan
    IBM Research Lab., New Delhi.
    Building activity definitions to recognize complex activities using an online activity toolkit2012Ingår i: IEEE 13th International Conference on Mobile Data Management, MDM 2012, Piscataway, NJ: IEEE Communications Society, 2012, 344-347 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    One of the biggest challenges in the field of activity recognition is gathering training data for building activity inference models. To address this problem, we have developed an online activity toolkit for gathering activity data from online users. We use this data to build activity definitions for use in our system which is based on Context-Driven Activity Theory. We use Markov chain analysis to assign weights to activities and context attributes of a complex activity as well as to build activity signatures based on transition and path probabilities. Our demo is intended to show how complex activities and associated atomic activities and context attributes can be described using an activity toolkit. The toolkit is used to take input from users available online and the results analysis of different complex activities can be viewed online in near real-time using the graphical user interface (GUI).

  • 17.
    Saguna, Saguna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Paris, Cécile
    CSIRO, Australia.
    Context-Aware Twitter Validator (CATVal): a system to validate credibility and authenticity of Twitter content for use in decision support systems2012Ingår i: Fusing Decision Support Systems into the Fabric of the Context / [ed] Ana Respício; Frada Burstein, Anávissos, Greece: IOS Press, 2012, 323-334 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Decision support systems (DSS) are beginning to use content sourced from social networks such as Twitter to provide decision makers with information to make timely and critical decisions. Misleading information obtained from Twitter can lead to adverse outcomes as well as cause trust issues within DSSs. In this paper, we propose and investigate a context-aware Twitter validator (CATVal) system to validate credibility and authenticity of Twitter content at run-time for use in DSS. We build, store and update a credibility index for Twitter users and verify user's context information each time a user tweets. The proposed system can benefit a DSS by providing credible and dependable information while detecting misleading and false information sourced from Twitter and possible other social media.

  • 18.
    Saguna, Saguna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Chakraborty, Dipanjan
    IBM Research, India Research Lab.
    Complex activity recognition using context driven activity theory in home environments2011Ingår i: Smart spaces and next generation wired/wireless networking: 11th international conference, NEW2AN 2011 and 4th Conference on Smart Spaces, RuSMART 2011, St. Petersburg, Russia, August 22-15, 2011 ; proceedings / [ed] Sergey Balandin; Yevgeni Koucheryavy; Honglin Hu, Heidelberg: Encyclopedia of Global Archaeology/Springer Verlag, 2011, 38-50 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a context driven activity theory (CDAT) and reasoning approach for recognition of concurrent and interleaved complex activities of daily living (ADL) which involves no training and minimal annotation during the setup phase. We develop and validate our CDAT using the novel complex activity recognition algorithm on two users for three weeks. The algorithm accuracy reaches 88.5% for concurrent and interleaved activities. The inferencing of complex activities is performed online and mapped onto situations in near real-time mode. The developed systems performance is analyzed and its behavior evaluated

  • 19.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Inferring multiple activities of mobile users with activity algebra2011Ingår i: 12th International Conference on Mobile Data Management (MDM), 2011: 6-9 June Luleå, Sweden, Piscataway, NJ: IEEE Communications Society, 2011, 23-26 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a novel multiple activity recognition and reasoning approach where we use on-body sensor information along with other context information to infer mobile user activities which are both concurrent and interleaved. We develop and validate an activity algebra and a complex activity recognition algorithm for detecting these multiple activities. Activities are mapped onto situations using spatio-temporal analysis. We validate our approach by implementing a prototype and performing experiments in different scenarios using mobile devices

  • 20.
    Saguna, Saguna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Chakraborty, Dipanjan
    Recognizing concurrent and interleaved activities in social interactions2011Ingår i: Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), Piscataway, NJ: IEEE Communications Society, 2011, 230-237 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Social networks constitute an important research area where users are involved in social interactions and inform each other of activities they perform. In this paper a complex activity recognition system (SEMACT) is proposed, built and validated. Different activities performed by users form an activity hierarchy. We perform semantic reasoning by using ontological constructs and rules to recognize both concurrent and interleaved complex activities at different levels of granularity of this activity hierarchy. Different application domains require activity recognition systems to define and recognize activities at different levels of granularity. Our system tackles this problem by recognizing complex activities which are then shared across application domains using a generic API. A test-bed and prototype are built to validate our SEMACT system. Extensive experimentation is performed which demonstrates that high accuracy of 94.35% was achieved for the recognition of complex activities both concurrent and interleaved within computationally feasible time

  • 21.
    Saguna, Saguna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Chakraborty, Dipanjan
    IBM Research, India Research Lab.
    Towards a robust concurrent and interleaved activity recognition of mobile users2011Ingår i: 12th International Conference on Mobile Data Management (MDM), 2011: Lulea, Sweden 6-9 June 2011, Piscataway, NJ: IEEE Communications Society, 2011, 297-298 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a situation and context-aware complex activity recognition system where we use on-body sensor information along with other context information to infer mobile user activities which are both concurrent and interleaved. We develop and validate our complex activity recognition algorithm for detecting these multiple complex activities. Activities are mapped onto situations using spatio-temporal analysis. We further build a test-bed in the social-networking domain to test and validate our approach in different scenarios using mobile devices

  • 22.
    Saguna, Saguna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Chakraborty, Dipanjan
    IBM Research, India Research Lab.
    CrysP: multi-faceted activity-infused presence in emerging social networks2010Ingår i: Smart spaces and next generation Wired/Wireless networking: third Conference on Smart Spaces, ruSMART 2010, and 10th international conference, NEW2AN 2010, St. Petersburg, Russia, August 23-25, 2010 ; proceedings / [ed] Sergey Balandin; Roman Dunaytsev; Yevgeni Koucheryavy, Encyclopedia of Global Archaeology/Springer Verlag, 2010, 50-61 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents Crystal Presence (CrysP) approach wher- e we fuse presence and activity in the realm of social networking. CrysP functions on the basis of the activity-infused multi-faceted presence information. This information is derived from user's involvement in multiple activities at any point in time. We propose and develop the CrysP System (CrysPSys) to determine CrysP from the user and his/her environmental context information (mainly relating to user activity, moods/emotions and cognition/thoughts) and then sharing the relevant CrysP with the appropriate devices, services and social-networks (corporate, personal and interest networks) and blogs (text, micro, video, photo and voice). We also highlight the benefits and challenges faced in developing such a system. This paper proposes the architecture and its test-bed and prototype implementation.

  • 23. Saguna, Saguna
    Detecting and sharing multiple activity-infused crystal presence information with social interaction tools2010Ingår i: The Seventh International Conference on Pervasive Services (ICPS 2010), 2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    Humans are engaging in information exchange related to their activities using a wide range of social interaction tools. There is a need to automate this process of exchanging information as manual updates are time consuming and intrusive.In our research, we aim to solve the problem of detecting and inferring a user’s multiple activity infused presence information and then share this with the correct social interaction tool. Humans also inherently multitask in varying time spans and their higher level activities can be formed by different types of lower level activities which can be shared with different social interaction tools accordingly. We propose a system which can infer these multiple activities fromwearable sensors as well as other context information, then determine a user’s multiple presence information, i.e. crystal presence based on these activities by spatio-temporal analysis and then automatically share the correct presence information with the correct social-interaction tool automatically.

  • 24.
    Banerjee, N
    et al.
    IBM Research.
    Chakraborty, D
    IBM Research.
    Dasgupta, K
    IBM Research.
    Mittal, S
    IBM Research.
    Nagar, S
    IBM Research.
    Saguna, Saguna
    R-U-In?-Exploiting Rich Presence and Converged Communications for Next-Generation Activity-Oriented Social Networking2009Ingår i: Tenth International Conference on Mobile Data Management: Systems, Services and Middleware (MDM 2009), Piscataway, NJ: IEEE Communications Society, 2009, 222- s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    With the growing popularity of social networking, traditional Internet Service Providers (ISPs) and telecom operators have both started exploring new opportunities to boost their revenue streams. The efforts have facilitated consumers to stay connected to their favorite social networks,be it from an ISP portal or a mobile device. The use of Web 2.0 technologies and converged communication tools has further led to a rise in both user-generated content as well as contextual information (i.e. rich presence) about users - including their current location, availability, interests and moods. In this evolving landscape, social networking players need to innovate for value-centric usage models that increase customer stickiness,along with business models to monetize the social media. To this end, we present R-U-In? - an activity-oriented social networking system for users to collaborate and participate in activities of mutual interest. Activities can be initiated and scheduled on-demand and be as ephemeral as the user interests themselves. R-U-In? leverages contextual modeling and reasoning techniques to enable "social search" based on real-time user interests and finds potential matches for the proposed activity. Further, it exploits next-generation presence and communication technologies to manage the entire activity lifecycle in real-time. Initial survey results, based on a prototype implementation of R-U-In?, attest to the promise of realtime activity-oriented social networking - both in terms of an effective collaboration tool for value-oriented social networking users and an enhanced end-user experience.

  • 25. Saguna, Saguna
    et al.
    Jayaraman, P
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Monash University, Melbourne, VIC.
    Determining user presence using context in a decentralized unified messaging system (IPAD-UMS)2008Ingår i: 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (Mobiquitous '08), 2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    UMS can be described as a system that allows for communication between users via a number of communication technologies over heterogeneous network infrastructures[1]. It integrates networking technologies such as Ethernet, ATM, IEEE 802.11, GSM and UMTS alongwith various devices such as laptops, tablet PCs, desktops, mobile phones, smartphones, PDAs, fax machines and landline phones. In other words, it allows the users to access their different types of messages by logging into one inbox from these different devices[2]. The user is able to create, modify, access and administer messages belonging to various formats on a single device to which he/she has immediate access. This form of 'anywhere', 'anytime' access to 'anykind' of messages enables the UMS to become ubiquitous and pervasive[3, 4].

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