Rahim Rahmani

Exploitation of OpenFlow in wireless sensor networks

A novel idea presented in this paper is to exploit the OpenFlow technology to address reliability, which is one of the most important factors in sensor networks. The proposed approach unified with flow-sensor and communication with controller. Hardware to programmable hardware, operating system to network operating system, vendor to owner specific, previous hardware defined networking (device monitoring, traffic controlling, and topology definition) turns to be software defined and where everything leads to a programmable and customized networking system. OpenFlow bears the characteristics to control the flow-sensor and monitor sensor traffic flow that also addresses another two important problems robust routing and load balancing. Flow-sensor displayed much better performances in comparison to typical sensors through generating less number of packets with lower simulation time in ideal scenario and even better results are possible for larger networks.

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.

Automatic Emotion Recognition through Facial Expression Analysis in Merged Images Based on an Artificial Neural Network

This paper focuses on a system of recognizing humans emotion from a detected humans face. The analyzed information is conveyed by the regions of the eye and the mouth into a merged new image in various facial expressions pertaining to six universal basic facial emotions. The output information obtained could be fed as an input to a machine capable to interact with social skills, in the context of building socially intelligent systems. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes humans expression and that are particularly relevant for the decoding of emotional expressions. Finally we use the merged image as an input to a feed-forward neural network trained by back-propagation. Such analysis of merged images makes it possible, obtain relevant information through the combination of proper data in the same image and reduce the training set time while preserved classification rate. It is shown by experimental results that the proposed algorithm can detect emotion with good accuracy.

Adaptive active queue management in heterogeneous networks

Queue management and congestion control are important to the robustness and fairness of the Internet. So far, the active queue management (AQM) algorithms show weaknesses to detect and control congestion under dynamically changing network topology. In this paper a new AQM algorithm, termed as an adaptive AQM (AAQM) is proposed to improve on both fairness and congestion control in heterogeneous networks. The performance of AAQM is compared to those ones of Blue, Drop-tail, RED and REM. The results of the simulations show that AAQM is very promising with respect to responsiveness and robustness in time-varying networks

Speech emotion recognition in emotional feedbackfor Human-Robot Interaction

For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are effi ...

A SDN-based architecture for horizontal Internet of Things services

The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

Constructing Context-centric Data Objects to Enhance Logical Associations for IoT Entities

Entities in Internet of Things (IoT) need intelligent associations to allow a flexible and dynamic system reaction towards varying user situations. Purely data-oriented association methods (e.g., data-mining, machine-learning, etc.) are limited in deduction from existing associations. Also such data-oriented methods are limited in accuracy for working with small-scale datasets (e.g., working with patterns retrieved from historical data), outputting associations based on statistics rather than logic. Moreover, existing semantic technologies (ontological-oriented or rule-oriented) are facing with either flexibility or dynamicity challenges to discover and maintain the associations. This paper proposes an alternative technique of semantically constructing context-centric data objects based on service logics for logical associations, which enables an event net based on association nets adapting for the changing situations (called context-centric). A proof-of-concept implementation is carried out based on a vehicle planning scenario to validate the data construction technique. Comparing to previous work, this technique possesses advantages of flexibility and dynamicity for entity associations based on service logics.

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.

Generic Distributed Sensing in Support of Context Awareness in Ambient Assisted Living

Researches in ambient assisted living have so far faced three important challenges: (1) Lack of a comprehensive approach to capture user needs that are generic; i.e., not limited to specific events, but as generic related to the user. (2) Lack of a highly flexible and scalable platform for the distributed sharing and processing of context between nodes in IoT networks. (3) Increased amount of communication and devices with sensors participating in the acquisition, processing and sharing of context further challenges both computation capability and storage capacity of the system. In this paper, we address these limitations and present novel support, applied in a system for remote assistance of elderly. The support comprehensively retrieves user needs from generic context, via a scalable overlay providing increment of processing capability and storage. Further, the support self-organizes entities into generic context from distributed sensing, using the Dependent Context Pattern (DCP) based on the Context Virtualizing Platform (CVP).

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.