Low-power device for wireless sensor network for Smart Cities

Mahmud

2020

Preprint

Novel coronavirus disease 2019 (COVID-19) has found as a climacteric pandemic that spreads from human-to-human through contiguity. It is not possible to control the virus properly because its vaccine has not been discovered yet. As of now, various contact tracing approaches are being taken to contain the spread of the virus by breaking its transmission chain. Recently, few technologies are being exploited for contact tracing such as geological location, Bluetooth and google maps. Governments of some countries have already launched contact tracing applications to identify the close contact people of COVID-19 positive patients.However, these methods may not be effective without a huge amount of testing facility since the tracing action starts when a COVID-19 testing result is found positive. In this paper, we have proposed a novel AI-powered data management framework for smart city named IoT-to-Edge-to-Fog-to-cloudlet-to-Cloud (IEF2C) data management framework which will be exploited to identify the COVID-19 suspected patients in smart cities and also be used to inform the surrounding people about the suspected patient. The proposed framework is isolated into five layers which have different tasks such as data collection, processing, authentication and storage. The framework will bring all the citizens under a common platform as well as will ensure the user and device mobility. It will mitigate the information gap by helping concerned authorities and users to take proper steps in such a pandemic era.

Section: Literature Reviewmentioning

confidence: 99%

IEF2C: A novel AI-powered framework for suspected COVID-19 patient detection and contact tracing in smart cities

Mahmud

2020

Preprint

“…While wired networks entail high economic costs, wireless networks are positioned as a competitive alternative [16].…”

Section: Nbiot Suitability For Smart Grid Meteringmentioning

confidence: 99%

An integrated platform for smart energy management: the CC-SEM project

Luján¹,

Otero²,

Valenzuela-Valdivia³

et al. 2019

redin

This article describes the CC-SEM project, a research effort focused on building an integrated platform for smart monitoring, controlling, and planning energy consumption and generation in urban scenarios. The project integrates cutting-edge technologies (Big Data analysis, computational intelligence, Internet of Things, High Performance Computing and Cloud Computing), specific hardware for energy monitoring/controlling built within the project and efficient communication protocols. The proposed platform considers the point of view of both citizens and administrators, providing a set of tools for controlling home devices (for end users), planning/simulating scenarios of energy generation (for energy companies and administrators), and proposes advances in communication infrastructure for transmitting the generated data.

“…In recent decades, the research on wireless sensor networks (WSNs) has been a hotspot for numerous applications, such as, environment monitoring [ 1 ], smart cities [ 2 , 3 ], military monitoring [ 4 ], maritime resource gathering [ 5 ] and health-care surveillance [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks

Chen

Xiong

Chu

2020

Sensors

Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute i-th effective routing path decide which positions in the i-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches.