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Dr. Hossein Fotouhi
Mälardalen University

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Research Keywords & Expertise

0 Communication Systems
0 Mobile Communications
0 Software Defined Networking
0 Wireless Sensor Networks (WSN)
0 sensor networks

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Journal article
Published: 06 April 2021 in Journal of Sensor and Actuator Networks
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Internet of Things (IoT) networks dependent on cloud services usually fail in supporting real-time applications as there is no response time guarantees. The fog computing paradigm has been used to alleviate this problem by executing tasks at the edge of the network, where it is possible to provide time bounds. One of the challenging topics in a fog-assisted architecture is to task placement on edge devices in order to obtain a good performance. The process of task mapping into computational devices is known as Service Placement Problem (SPP). In this paper, we present a heuristic algorithm to solve SPP, dubbed as clustering of fog devices and requirement-sensitive service first (SCATTER). We provide simulations using iFogSim toolkit and experimental evaluations using real hardware to verify the feasibility of the SCATTER algorithm by considering a smart home application. We compared the SCATTER with two existing works: edge-ward and cloud-only approaches, in terms of Quality of Service (QoS) metrics. Our experimental results have demonstrated that SCATTER approach has better performance compared with the edge-ward and cloud-only, 42.1% and 60.2% less application response times, 22% and 27.8% less network usage, 45% and 65.7% less average application loop delays, and 2.33% and 3.2% less energy consumption.

ACS Style

Fariba Khosroabadi; Faranak Fotouhi-Ghazvini; Hossein Fotouhi. SCATTER: Service Placement in Real-Time Fog-Assisted IoT Networks. Journal of Sensor and Actuator Networks 2021, 10, 26 .

AMA Style

Fariba Khosroabadi, Faranak Fotouhi-Ghazvini, Hossein Fotouhi. SCATTER: Service Placement in Real-Time Fog-Assisted IoT Networks. Journal of Sensor and Actuator Networks. 2021; 10 (2):26.

Chicago/Turabian Style

Fariba Khosroabadi; Faranak Fotouhi-Ghazvini; Hossein Fotouhi. 2021. "SCATTER: Service Placement in Real-Time Fog-Assisted IoT Networks." Journal of Sensor and Actuator Networks 10, no. 2: 26.

Letter
Published: 07 October 2020 in Sensors
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Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.

ACS Style

Saedeh Abbaspour; Faranak Fotouhi; Ali Sedaghatbaf; Hossein Fotouhi; Maryam Vahabi; Maria Linden. A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition. Sensors 2020, 20, 5707 .

AMA Style

Saedeh Abbaspour, Faranak Fotouhi, Ali Sedaghatbaf, Hossein Fotouhi, Maryam Vahabi, Maria Linden. A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition. Sensors. 2020; 20 (19):5707.

Chicago/Turabian Style

Saedeh Abbaspour; Faranak Fotouhi; Ali Sedaghatbaf; Hossein Fotouhi; Maryam Vahabi; Maria Linden. 2020. "A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition." Sensors 20, no. 19: 5707.

Journal article
Published: 06 June 2020 in Sensors
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Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

ACS Style

Shirin Tahmasebi; Mohadeseh Safi; Somayeh Zolfi; Mohammad Reza Maghsoudi; Hamid Reza Faragardi; Hossein Fotouhi. Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs. Sensors 2020, 20, 3231 .

AMA Style

Shirin Tahmasebi, Mohadeseh Safi, Somayeh Zolfi, Mohammad Reza Maghsoudi, Hamid Reza Faragardi, Hossein Fotouhi. Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs. Sensors. 2020; 20 (11):3231.

Chicago/Turabian Style

Shirin Tahmasebi; Mohadeseh Safi; Somayeh Zolfi; Mohammad Reza Maghsoudi; Hamid Reza Faragardi; Hossein Fotouhi. 2020. "Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs." Sensors 20, no. 11: 3231.

Journal article
Published: 06 May 2020 in Sensors
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Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure system availability by increasing the lifetime of WSNs. Clustering is one of the routing techniques that benefits energy efficiency in WSNs. This paper provides an evolutionary clustering and routing method which is capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. The proposed method consists of two phases. First, we present a model with improved hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for cluster head (CH) selection. Second, we design a PSO-based multi-hop routing system with enhanced tree encoding and a modified data packet format. The simulation results for disaster scenarios prove the efficiency of the proposed method in comparison with the state-of-the-art approaches in terms of the overall residual energy, number of live nodes, network coverage, and the packet delivery ratio.

ACS Style

Morteza Biabani; Hossein Fotouhi; Nasser Yazdani. An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks. Sensors 2020, 20, 2647 .

AMA Style

Morteza Biabani, Hossein Fotouhi, Nasser Yazdani. An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks. Sensors. 2020; 20 (9):2647.

Chicago/Turabian Style

Morteza Biabani; Hossein Fotouhi; Nasser Yazdani. 2020. "An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks." Sensors 20, no. 9: 2647.

Conference paper
Published: 01 January 2016 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Remote health monitoring is one of the emerging IoT applications that has attracted the attention of communication and health sectors in recent years. We enable software defined networking in a wireless sensor network to provide easy reconfiguration and at run-time network management. In this way, we devise a multi-objective decision making approach that is implemented at the network intelligence to find the set of optimal paths that routes physiological data over a wireless medium. In this work, the main considered parameters for reliable data communication are path traffic, path consumed energy, and path length. Using multi-objective optimization technique within a case study, we find the best routes that provide reliable data communication.

ACS Style

Hossein Fotouhi; Maryam Vahabi; Apala Ray; Mats Björkman. Reliable Communication in Health Monitoring Applications. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016, 64 -70.

AMA Style

Hossein Fotouhi, Maryam Vahabi, Apala Ray, Mats Björkman. Reliable Communication in Health Monitoring Applications. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2016; ():64-70.

Chicago/Turabian Style

Hossein Fotouhi; Maryam Vahabi; Apala Ray; Mats Björkman. 2016. "Reliable Communication in Health Monitoring Applications." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 64-70.