Sarder Fakhrul Abedin

A Fog based system model for cooperative IoT node pairing using matching theory

The revolutionized vision of IoT has united heterogeneous devices to foster the systems of cohesive intelligent things. In addition, Fog computing has also envisioned a new form of cloud computing paradigm. Therefore, Fog provides edge computing to such IoT devices with varied capabilities and resources. However, a balanced and efficient pairing or matching strategy for edge IoT nodes is crucial to achieve the user requisite. Hence, this paper addresses the utility based matching or pairing problem within the same domain of IoT nodes by using Irvings matching algorithm under the node specified preferences to endure a stable IoT node pairing. We studied the performance of the proposed matching algorithm through simulation. The simulation results show the higher utility gain of the node pairs through refined matching algorithm over greedy approach.

A system model for energy efficient green-IoT network

The notion of IoT has ignited umpteen possibilities of different heterogeneous devices to blend within a network. In IoT, especially sensor devices are mostly deployed in an extremely resource constrained environment and thus pose the necessity to extend the capability and life expectancy of these kind of devices in terms of energy consumption. Since, the devices in IoT have limited energy sources they often run on battery with a certain energy capability, the deployment of green communication and system model in IoT has been a core challenging issue. This paper addresses the energy efficiency issues across diverse IoT driven networks by proposing a system model for G-IoT and energy efficient scheme for the IoT devices to extend the life expectancy of the whole IoT network.

EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency

The proliferation of the market in patient care services is attracting attention in the healthcare industry; however, a remote mental healthcare system is still unattainable. In this paper, an ambient intelligent system of in-home psychiatric care service for emergency psychiatry (EM-psychiatry) is proposed for the remote monitoring of psychiatric emergency patients. The emergency psychiatric states of patients are modeled as the states of the maximum-entropy Markov model (MEMM), in which sensor observations, psychiatric screening scores, and patients’ histories are considered as the observations of MEMM. A modified Viterbi, a machine-learning algorithm, is used to generate the most probable psychiatric state sequence based on such observations; then, from the most likely psychiatric state sequence, the emergency psychiatric state is predicted through the proposed algorithm. The ambient EM-psychiatry model is implemented and the performance of the proposed prediction model is analyzed using the receiver operator characteristics curves, which demonstrates that the use of the EM-psychiatric screening questionnaire with biosensor observations enhances the prediction accuracy.

Smart Agent based Dynamic Data Aggregation for Delay Sensitive Smart City Services

Smart city is the vision of modern intelligent technology toward the sustainable development of green technology and social development. Smart services e.g. smart transportation, smart health, smart home, smart grid, smart security, and IoT based applications are the key enablers of smart city, that ensure the quality life and well-being. In a bid to ensure the functionalities of those services, the IoT applications gather data from numerous IoT nodes. In such a case, it becomes more 396 정보과학회논문지 제45권 제4호(2018. 4) challenging to managing huge network traffic in the centralized network of smart city. Therefore, in this research, we have focused on the resolution of this problem through the introduction of of smart agent-based dynamic data aggregation (DDA) from distributed dense smart city network for city service fulfillment. In this research study, we purposed to model a peer to peer fully distributed system using distributed hash table chord protocol. We also proposed an algorithm for the IoT network and designed smart agent based IoT node searching algorithm for crowd sourcing. Finally, we simulated the result of the proposed smart agent based dynamic data aggregation model in an effort to achieve a higher performance gain for the proposed approach in respect to service fulfillment time and convergence.

An approach of cost optimized influence maximization in social networks

Social networks have gained huge research interest, especially in viral marketing due to their rapid boom in the past years. It is very crucial to identify the influential users in the social networks for viral and target marketing. Influence maximization (IM) problem estimates such influential users in the social networks. With an initial seed set, the IM finds a maximum number of nodes that can be activated in the network under some diffusion models e.g. Linear Threshold model or Independent Cascade model. But previous works in this field have not studied about the minimum cost, termed as opportunity cost (OC), to motivate those seed nodes. In this work, we define a novel Reverse Influence Maximization (RIM) problem to determine the opportunity cost of influence maximization. Employing the influence propagation in opposite order, the RIM determines the minimum number of nodes that must be activated in order to motivate a set of target nodes. We propose Random RIM (R-RIM) and Randomized Linear Threshold RIM (RLT-RIM) models to tackle the RIM problem. We also perform a simulation to evaluate the performance of the algorithms using two real world datasets. The result shows that the proposed models determine the optimized opportunity cost with faster running time margin.