FOGG: A Fog Computing Based Gateway to Integrate Sensor Networks to Internet

2017 ◽  
Author(s):  
Sripriya Srikant Adhatarao ◽  
Mayutan Arumaithurai ◽  
Xiaoming Fu
Keyword(s):  
Sensor Networks ◽  
Fog Computing ◽  
Integrate Sensor

ISI: Integrate Sensor Networks to Internet With ICN

2018 ◽  
Vol 5 (2) ◽  
pp. 491-499 ◽  
Author(s):  
Sripriya Srikant Adhatarao ◽  
Mayutan Arumaithurai ◽  
Dirk Kutscher ◽  
Xiaoming Fu
Keyword(s):  
Sensor Networks ◽  
Integrate Sensor

scholarly journals Energy-efficient hierarchical routing in wireless sensor networks based on Fog Computing

2020 ◽  
Author(s):  
Ademola Abidoye ◽  
Boniface Kabaso
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Data Transmission ◽  
Energy Efficient ◽  
Fog Computing ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Hierarchical Routing

Abstract Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the 21st century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize energy consumption of sensor nodes so as to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called Energy-Efficient Hierarchical routing protocol for wireless sensor networks based on Fog Computing (EEHFC). Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of Things applications. In addition, we propose an improved ant colony optimization (ACO) algorithm that can be used to construct optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses and extends the network lifetime

scholarly journals A New System for Real-time Video Surveillance in Smart Cities Based on Wireless Visual Sensor Networks and Fog Computing

2021 ◽  
pp. 175-184
Author(s):  
Afaf Mosaif ◽  
◽  
Said Rakrak
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Video Surveillance ◽  
Smart City ◽  
Fog Computing ◽  
Visual Sensor ◽  
Visual Sensor Networks ◽  
Wireless Visual Sensor Networks ◽  
Camera Nodes ◽  
New System

Nowadays, public security is becoming an increasingly serious issue in our society and its requirements have been extended from urban centers to all remote areas. Therefore, surveillance and security cameras are being deployed worldwide. Wireless Visual Sensor Networks nodes can be employed as camera nodes to monitor in the city without the need for any cables installation. However, these cameras are constrained in processing, memory, and energy resources. Also, they generate a massive amount of data that must be analyzed in real-time to ensure public safety and deal with emergency situations. As a result, data processing, information fusion, and decision making have to be executed on-site (near to the data collection location). Besides, surveillance cameras are directional sensors, which makes the coverage problem another issue to deal with. Therefore, we present a new system for real-time video surveillance in a smart city, in which transportations equipped with camera nodes are used as the mobile part of the system and an architecture based on fog computing and wireless visual sensor networks is adopted. Furthermore, we propose an approach for selecting the camera nodes that will participate in the tracking process and we simulated three different use cases to test the effectiveness of our system in terms of target detection. The simulation results show that our system is a promising solution for smart city surveillance applications.

scholarly journals Fault Detection based Connected Dominating Set (FDCDS) in Fog Computing

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 599-606
Author(s):  
Nagarjuna Valeti ◽  
V. Ceronmani Sharmila
Keyword(s):  
Wireless Sensor Networks ◽  
Cloud Computing ◽  
Sensor Networks ◽  
Fault Detection ◽  
Cloud Storage ◽  
Dominating Set ◽  
Fog Computing ◽  
Connected Dominating Set ◽  
Wireless Sensor ◽  
Cloud Data

The meaning of cloud computing is providing services by using the internet. From the Cloud Data Centres (CDC) the services are utilized by the cloud users. Presently (Internet of things) IOT playing the key role to improve the performance of the fog computing enabled applications. Migrating the wireless sensor networks with IOT becomes the most powerful and error free application based on the availability of the services, cloud storage, computation and these are transferred efficiently between server and cloud. Health domain is most widely affecting system in cloud computing as well as by using fog computing with IOT. The system causes various failures for providing the service continuously. Enabling the fog computing with the integration of cloud for the medical devices to transmit the patient information to the cloud storage has become the complicated for the IOT sensors continuously. This may cause the data loss and also reduce the performance of the medical device. To improve the continuous services within the cloud server. In this paper, the Fault detection based Connected Dominating Set (FDCDS) which provides the continuous services with the integration of fog computing and IOT devices with wireless sensor networks. Simulation shows the performance of the proposed system.

scholarly journals FROG: A Robust and Green Wireless Sensor Node for Fog Computing Platforms

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Manuel Castillo-Cara ◽  
Edgar Huaranga-Junco ◽  
Milner Quispe-Montesinos ◽  
Luis Orozco-Barbosa ◽  
Enrique Arias Antúnez
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Fog Computing ◽  
Building Blocks ◽  
Wireless Sensor ◽  
Network Resilience ◽  
Wireless Sensor Node ◽  
Smart Power ◽  
Computing Platforms

Over the past few years, we have witnessed the widespread deployment of wireless sensor networks and distributed data management facilities: two main building blocks of the Internet of things (IoT) technology. Due to the spectacular increase on the demand for novel information services, the IoT-based infrastructures are more and more characterized by their geographical sparsity and increasing demands giving rise to the need of moving from a cloud to a fog model: a novel deployment paradigm characterized by the provisioning of elastic resources geographically located as close as possible to the end user. Despite the large number of wireless sensor networks already available in the market, there are still many issues to be addressed on the design and deployment of robust network platforms capable of meeting the demand and quality of fog-based systems. In this paper, we undertake the design and development of a wireless sensor node for fog computing platforms addressing two of the main issues towards the development and deployment of robust communication services, namely, energy consumption and network resilience provisioning. Our design is guided by examining the relevant macroarchitecture features and operational constraints to be faced by the network platform. We based our solution on the integration of network hardware platforms already available on the market supplemented by smart power management and network resilience mechanisms.

scholarly journals A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jeng-Shyang Pan ◽  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Hui-Qi Zhao ◽  
Gao-Yuan Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Fog Computing ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
K Nearest Neighbor ◽  
Network Resources ◽  
Detection Model

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

scholarly journals Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Tayyabah Hasan ◽  
Fahad Ahmad ◽  
Muhammad Rizwan ◽  
Nasser Alshammari ◽  
Saad Awadh Alanazi ◽  
...  
Keyword(s):  
Quantum Computing ◽  
Deep Learning ◽  
Sensor Networks ◽  
Quantum Computer ◽  
Fog Computing ◽  
Blue Color ◽  
Self Organizing Maps ◽  
Spin Quantum ◽  
Cache Hit Ratio ◽  
Quantum Phenomena

Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.

A Novel Sensory Data Processing Framework to Integrate Sensor Networks With Mobile Cloud

2016 ◽  
Vol 10 (3) ◽  
pp. 1125-1136 ◽  
Author(s):  
Chunsheng Zhu ◽  
Hai Wang ◽  
Xiulong Liu ◽  
Lei Shu ◽  
Laurence T. Yang ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Data Processing ◽  
Mobile Cloud ◽  
Sensory Data ◽  
Processing Framework ◽  
Integrate Sensor
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