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Distributed Intelligence-Assisted Autonomic Context-Information Management: A context-based approach to handling vast amounts of heterogeneous IoT data
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap. (Immersive Networking)ORCID-id: 0000-0001-8506-5839
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

As an implication of rapid growth in Internet-of-Things (IoT) data, current focus has shifted towards utilizing and analysing the data in order to make sense of the data. The aim of which is to make instantaneous, automated, and informed decisions that will drive the future IoT. This corresponds to extracting and applying knowledge from IoT data which brings both a substantial challenge and high value. Context plays an important role in reaping value from data, and is capable of countering the IoT data challenges. The management of heterogeneous contextualized data is infeasible and insufficient with the existing solutions which mandates new solutions. Research until now has mostly concentrated on providing cloud-based IoT solutions; among other issues, this promotes real-time and faster decision-making issues. In view of this, this dissertation undertakes a study of a context-based approach entitled Distributed intelligence-assisted Autonomic Context Information Management (DACIM), the purpose of which is to efficiently (i) utilize and (ii) analyse IoT data.

To address the challenges and solutions with respect to enabling DACIM, the dissertation starts with proposing a logical-clustering approach for proper IoT data utilization. The environment that the number of Things immerse changes rapidly and becomes dynamic. To this end, self-organization has been supported by proposing self-* algorithms that resulted in 10 organized Things per second and high accuracy rate for Things joining. IoT contextualized data further requires scalable dissemination which has been addressed by a Publish/Subscribe model, and it has been shown that high publication rate and faster subscription matching are realisable. The dissertation ends with the proposal of a new approach which assists distribution of intelligence with regard to analysing context information to alleviate intelligence of things. The approach allows to bring few of the application of knowledge from the cloud to the edge; where edge based solution has been facilitated with intelligence that enables faster responses and reduced dependency on the rules by leveraging artificial intelligence techniques. To infer knowledge for different IoT applications closer to the Things, a multi-modal reasoner has been proposed which demonstrates faster response. The evaluations of the designed and developed DACIM gives promising results, which are distributed over seven publications; from this, it can be concluded that it is feasible to realize a distributed intelligence-assisted context-based approach that contribute towards autonomic context information management in the ever-expanding IoT realm.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University , 2018. , 103 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 18-001
Emneord [en]
Internet of Things, Context information, Intelligence, Edge computing, Distributed computing
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-149513ISBN: 978-91-7797-087-3 (tryckt)ISBN: 978-91-7797-088-0 (digital)OAI: oai:DiVA.org:su-149513DiVA: diva2:1162562
Disputas
2018-01-24, Lilla Hörsalen. Nod building, Borgarfjordsgatan 12, Kista, 13:00 (engelsk)
Opponent
Veileder
Merknad

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 7: Submitted.

Tilgjengelig fra: 2017-12-29 Laget: 2017-12-04 Sist oppdatert: 2017-12-28bibliografisk kontrollert
Delarbeid
1. Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs
Åpne denne publikasjonen i ny fane eller vindu >>Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs
2013 (engelsk)Inngår i: RNIS: Research Notes in Information and Service Sciences, ISSN 2287-1934, Vol. 14, 721-728 s.Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In the state-of-the-art sensor networks are becoming an integral part of ubiquitous computing. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. A novel idea of clustering sensors based on context similarity is presented in this paper. The sensors are physically distributed but logically clustered based on similar context. This will enable resources (data, services) to be shared. The network is a two-tier hierarchical distributed hash tables (DHTs) system based on the HyperFlow platform. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach.

Emneord
Sensor networks, ubiquitous computing, heterogeneous contexts, logical clustering
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-95584 (URN)10.4156/rnis.vol14.131 (DOI)
Tilgjengelig fra: 2013-10-31 Laget: 2013-10-31 Sist oppdatert: 2018-01-11bibliografisk kontrollert
2. On Performance of Logical-Clustering of Flow-Sensors
Åpne denne publikasjonen i ny fane eller vindu >>On Performance of Logical-Clustering of Flow-Sensors
2013 (engelsk)Inngår i: International Journal of Computer Science Issues, ISSN 1694-0784, E-ISSN 1694-0814, Vol. 10, nr 5, 1-13 s.Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In state-of-the-art Pervasive Computing, it is envisioned that unlimited access to information will be facilitated for anyone and anything. Wireless sensor networks will play a pivotal role in the stated vision. This reflects the phenomena where any situation can be sensed and analyzed anywhere. It makes heterogeneous context ubiquitous. Clustering context is one of the techniques to manage ubiquitous context information efficiently to maximize its potential. Logical-clustering is useful to share real-time context where sensors are physically distributed but logically clustered. This paper investigates the network performance of logical-clustering based on ns-3 simulations. In particular reliability, scalability, and reachability in terms of delay, jitter, and packet loss for the logically clustered network have been investigated. The performance study shows that jitter demonstrates 40 % and 44 % fluctuation for 200 % increase in the node per cluster and 100 % increase in the cluster size respectively. Packet loss exhibits only 18 % increase for 83 % increase in the packet flow-rate.

Emneord
Pervasive Computing, Wireless sensor networks, ubiquitous, context, distributed, logical-clustering, ns-3
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-96364 (URN)
Tilgjengelig fra: 2013-11-20 Laget: 2013-11-20 Sist oppdatert: 2018-01-11bibliografisk kontrollert
3. Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense
Åpne denne publikasjonen i ny fane eller vindu >>Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense
2014 (engelsk)Inngår i: Proceedings of 2014 Science and Information Conference, IEEE Computer Society, 2014, 64-71 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Crowdsourcing was initially devised as a method for solving problems through soliciting contributions from a large online community. Crowdsourcing is facing new challenges to handle the increase of information in real-time from a vast number of sources in Internet-of-Things (IoT) scenarios. Thus we seek to leverage the power of social web, smart-devices, sensors, etc., fusing these heterogeneous sources into distributed context information in order to enable novel crowdsourcing scenarios. This mandates research in efficient management of heterogeneous and distributed context information through logical-clustering. Logical-clustering can efficiently filter out similar context information obtained from distributed sources based on context similarity. However, the efficiency of logical-clustering is challenged by the distribution of context information in crowdsourcing scenarios. Publish/Subscribe mechanism can counter this challenge. To this end, we propose a scalable publish/subscribe model, MediaSense, which is based on p2p technologies. This paper presents our approach to a scalable logical-clustering concept. The evaluation of our approach applied to MediaSense can achieve a rate of approximately 3530 messages/sec for publish/subscribe events. Moreover, this approach further achieves 99% increase for subscription matching and 163% improvement in memory requirements in comparison with other approaches.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2014
Emneord
crowdsourcing, pervasive computing, context information, logical-clustering, Publish/Subscribe, MediaSense
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-110981 (URN)10.1109/SAI.2014.6918173 (DOI)978-0-9893193-1-7 (ISBN)
Konferanse
Science and Information Conference 2014, London, UK, August 27-29, 2014
Tilgjengelig fra: 2014-12-19 Laget: 2014-12-19 Sist oppdatert: 2018-01-11bibliografisk kontrollert
4. Supporting Self-Organization with Logical-clustering Towards Autonomic Management of Internet-of-Things
Åpne denne publikasjonen i ny fane eller vindu >>Supporting Self-Organization with Logical-clustering Towards Autonomic Management of Internet-of-Things
2015 (engelsk)Inngår i: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 6, nr 2, 24-33 s.Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

One of the challenges for Autonomic Management in Future Internet is to bring about self-organization in a rapidly changing environment and enable participating nodes to be aware and respond to changes. The massive number of participating nodes in Internet-of-Things calls for a new approach in regard of Autonomic Management with dynamic self-organization and enabling awareness to context information changes in the nodes themselves. To this end, we present new algorithms to enable self-organization with logical-clustering, the goal of which is to ensure that logical-clustering evolves correctly in the dynamic environment. The focus of these algorithms is to structure logical-clustering topology in an organized way with minimal intervention from outside sources. The correctness of the proposed algorithm is demonstrated on a scalable IoT platform, MediaSense. Our algorithms sanction 10 nodes to organize themselves per second and high accuracy of nodes discovery. Finally, we outline future research challenges towards autonomic management of IoT.

Emneord
autonomic management, Future Internet, Internet-of-Things, self-organization, logical-clustering, MediaSense
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-122884 (URN)10.14569/IJACSA.2015.060204 (DOI)
Tilgjengelig fra: 2015-11-11 Laget: 2015-11-11 Sist oppdatert: 2018-01-10bibliografisk kontrollert
5. Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)
Åpne denne publikasjonen i ny fane eller vindu >>Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)
2018 (engelsk)Inngår i: Applied Computing and Informatics, ISSN 1578-4487, E-ISSN 2210-8327, Vol. 14, nr 1, 73-87 s.Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The unprecedented prevalence of ubiquitous sensing will revolutionise the Future Internet where state-of-the-art Internet-of-Things (IoT) is believed to play the pivotal role. In the fast forwarding IoT paradigm, hundreds of billions of things are estimated to be deployed which would give rise to an enormous amount of data. Cloud computing has been the prevailing choice for controlling the connected things and the data, and providing intelligence based on the data. But response time and network load are on the higher side for cloud based solutions. Recently, edge computing is gaining growing attention to overcome this by employing rule-based intelligence. However, requirements of rules do not scale well with the proliferation of things. At the same time, rules fail in uncertain events and only offer pre-assumed intelligence. To counter this, this paper proposes a novel idea of leveraging the belief-network with the edge computing to utilize as an IoT edge-controller the aim of which is to offer low-level intelligence for IoT applications. This low-level intelligence along with cloud-based intelligence form the distributed intelligence in the IoT realm. Furthermore, a learning approach similar to reinforcement learning has been proposed. The approach, i.e. enabling a Future IoT Controller (FITC) has been verified with a simulated SmartHome scenario which proves the feasibility of the low-level intelligence in terms of reducing rules domination, faster response time and prediction through learning experiences at the edge.

Emneord
Future Internet, Internet of Things, edge computing, distributed intelligence, belief-network
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-149262 (URN)10.1016/j.aci.2017.05.001 (DOI)
Tilgjengelig fra: 2017-11-24 Laget: 2017-11-24 Sist oppdatert: 2018-01-13bibliografisk kontrollert
6. Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT
Åpne denne publikasjonen i ny fane eller vindu >>Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT
2017 (engelsk)Inngår i: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 109, 335-342 s.Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Future Internet is expected to be driven by prevalence of the Internet of Things (IoT). This prevalence of IoT promises to impact every aspect of human life in the foreseeable future where computing paradigm would witness huge influx of IoT data. Context is gaining growing attention to make sense of the data and it is envisaged that context-aware computing would act as an indispensable enabler for IoT. Contextualizing the collected IoT data enables to reap value from the data and to harvest the knowledge. Reasoning the contextualized data, that is, context information is imperative to the vision of harvesting knowledge. Edge computing is also expected to play a vital role in IoT to reduce dependency on cloud based solution, to achieve faster response, and to provide intelligence closer to the IoT things. The combination of context-awareness and edge solution would be inseparable in the future IoT. Furthermore, IoT vision comprises of different IoT applications controlled by a capable controller at the edge, an edge controller necessitates to counter the challenge of providing knowledge for each of the IoT applications. Therefore, such a controller requires to offer different context-aware reasoning to alleviate the intelligence-of-things. In view of this, this paper proposes a multi-modal context-aware reasoner the aim of which is to provide knowledge at the edge for each IoT application. The context-aware reasoning has been verified with rules-based and Bayesian reasoning for three IoT applications and initial results suggest that it is promising to realize such multimodal reasoning at the edge with low latency.

Emneord
Internet of Things (IoT), context-aware, edge computing, multimodal, reasoning
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-144910 (URN)10.1016/j.procs.2017.05.360 (DOI)
Konferanse
The 8th International Conference on Ambient Systems, Networks and Technologies (ANT-2017), Madeira, Portugal, May 16-19, 2017
Tilgjengelig fra: 2017-06-29 Laget: 2017-06-29 Sist oppdatert: 2018-01-13bibliografisk kontrollert
7. Supporting IoT Data Similarity at the Edge towards Enabling Distributed Clustering
Åpne denne publikasjonen i ny fane eller vindu >>Supporting IoT Data Similarity at the Edge towards Enabling Distributed Clustering
2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Hundreds of billions of things are expected to be integrated for heterogeneous Internet-of-Things (IoT) applications which promises to drive the Future Internet. This variant IoT data mandates intelligent solutions to make sense of current data in real-time closer to the data-origin. Clustering physically distributed data would enable efficient utilization where finding similarity becomes the central issue. To counter this, Jaro-Winkler and Jaccard-like algorithm have been proposed and extended to a distributed protocol to enable distributed clustering at the IoT edge computing. Performance study, on a scalable IoT platform and an edge device, shows feasibility and effectiveness of the approach with respect to efficiency and applicability. 

Emneord
IoT, distributed data, clustering similarity, edge computing
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-149498 (URN)
Konferanse
WorldCist'18 - 6th World Conference on Information Systems and Technologies, Naples, Italy, 27 - 29 March 2018
Merknad

Submitted.

Tilgjengelig fra: 2017-12-03 Laget: 2017-12-03 Sist oppdatert: 2018-01-13

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