Hasibur Rahman

Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense

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.

Supporting Self-Organization with Logical-clustering Towards Autonomic Management of Internet-of- Things

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 afford high accuracy of nodes discovery. Finally, we outline future research challenges towards autonomic management of IoT.

Reasoning Service Enabling SmartHome Automation at the Edge of Context Networks

The popular concept of SmartHome means that the appliances such as lighting, heating and door locks are controllable remotely through for example remote controls or mobile phones. The concept is becoming more and more realizable due to recent advancements in Internet-enabled technologies. SmartHomes can become even more intelligent and automated by exploiting such intelligent and affordable Internet-enabled technologies. However, this necessitates a context-aware system that provides services to respond to the context changes to enable such SmartHome automation at the edge of today’s context-centric networks. To this end, this paper designs and develops a context-aware reasoning service for home automation which provides a novel way to connect SmartHomes through the use of a distributed context exchange network overlay. It enables mobility service application to communicate with and control SmartHomes remotely.

Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT

Abstract: 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.

Realising Dynamism in MediaSense Publish/Subscribe Model for Logical-Clustering in Crowdsourcing

The upsurge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in pervasive computing which is coined as crowdsourcing. The pervasiveness of computing devices leads to a fast varying computing where it is imperative to have a model for catering the dynamic environment. The challenge of efficiently distributing context information in logical-clustering in crowdsourcing scenarios can be countered by the scalable MediaSense PubSub model. MeidaSense is a proven scalable PubSub model for static environment. However, the scalability of MediaSense as PubSub model is further challenged by its viability to adjust to the dynamic nature of crowdsourcing. Crowdsourcing does not only involve fast varying pervasive devices but also dynamic distributed and heterogeneous context information. In light of this, the paper extends the current MediaSense PubSub model which can handle dynamic logical-clustering in crowdsourcing. The results suggest that the extended MediaSense is viable for catering the dynamism nature of crowdsourcing, moreover, it is possible to predict the near-optimal subscription matching time and predict the time it takes to update (insert or delete) context-IDs along with existing published context-IDs. Furthermore, it is possible to foretell the memory usage in MediaSense PubSub model.

Supporting IoT Data Similarity at the Edge Towards Enabling Distributed Clustering

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 edge. Performance study, on a scalable IoT platform and an edge device, shows feasibility and effectiveness of the approach with respect to efficiency and applicability.

Entity Configuration and Context-Aware reasoNer (CAN) Towards Enabling an Internet of Things Controller

The Internet of Things (IoT) paradigm has so far been investigating into designing and developing protocols and architectures to provide connectivity anytime and anywhere for anything. IoT is currently fast forwarding towards embracing a paradigm shift namely Internet of Everything (IoE) where making intelligent decisions and providing services remains a challenge. Context plays an integral role in reasoning the collected data and to provide context-aware services and is gaining growing attention in the IoT paradigm. To this end, a Context-Aware reasoNer (CAN) has been proposed and designed in this chapter. The proposed CAN is a generic enabler and is designed to provide services based on context reasoning. Discovering and filtering entities, i.e. entity configuration, become pivotal in analysing context reasoning to provide right services to right context entities at the right time. This chapter leverages the concept of entity configuration and CAN towards enabling an IoT controller. The chapter further demonstrates use cases and future research directions towards generic CAN development and facilitating context-aware services to IoE.