A cross-layer framework for sensor data aggregation for IoT applications in smart cities

2016 ◽  
Author(s):  
Abrar Alkhamisi ◽  
Mohamed Saleem Haja Nazmudeen ◽  
Seyed M. Buhari
Keyword(s):  
Data Aggregation ◽  
Smart Cities ◽  
Sensor Data ◽  
Cross Layer ◽  
Iot Applications

scholarly journals Real Time Sensor Data transmission to the IoT Applications using MQTT-SN and MQTT

2019 ◽  
Vol 8 (4) ◽  
pp. 6371-6378
Keyword(s):  
Data Transmission ◽  
Sensor Node ◽  
Smart Cities ◽  
Fog Computing ◽  
Transport Layer ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Design Development ◽  
Iot Applications ◽  
Application Protocols

Internet of Things (IoT) Technology rapidly growing area and active research happening in different Layers of Internet protocol stacks. IOT stack have Physical layer& Data Link layer, Network Layer, Transport layer and Application layer protocols and Applications. Now IOT has many applications like Smart cities, Smart Homes, Environmental monitoring, Agricultural Application and Medical data transmission from remote places to expert Hospital. Numbers of IOT Application Protocols (MQTT, MQTT-SN, COAP, XMPP, and HTPP) are available and implemented in rich resource Environments like good computing power and bandwidth. However all Application protocols are not suitable under lossy wireless sensor network environment. In IOT Environment consists of Sensor Nodes, End Systems and related applications. Latency and bandwidth problems exist between sensor node and End Systems in two tier IOT Environment. To resolve this issue, we proposed Three Tier IOT Architecture; it consists of Sensor Node, IOT Gateway /Fog Computing Node, End Systems and Applications. In this paper we proposed flexible design, development and integration of IOT gateway for different IOT applications using MQTT and MQTT-SN Application Messaging Protocols.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

Design and Analysis of a RFID Reader Microstrip Array antenna for IoT Applications in Smart Cities

2022 ◽  
Vol 17 (5) ◽  
pp. 0-0
Keyword(s):  
Smart Cities ◽  
Array Antenna ◽  
The Internet ◽  
Substrate Thickness ◽  
Iot Applications ◽  
Rfid Reader ◽  
Array Antennas ◽  
Microstrip Array ◽  
The Internet Of Things ◽  
Microstrip Array Antenna

This paper presents the design of 2*1 and 4*1 RFID reader microstrip array antenna at 2.4GHz for the Internet of things (IoT) networks which are Zigbee, Bluetooth and WIFI. The proposed antenna is composed of identical circular shapes radiating patches printed in FR4 substrate. The dielectric constant εr and substrate thickness h are 4.4 and 1.6mm, respectively. The 2*1 and 4*1 array antennas present a gain improvement of 27.3% and 61.9%, respectively. The single,2*1 and 4*1 array antennas were performed with CADFEKO.

Practice Report “Smart Disaster Management” — Combining Smart City Data and Citizen Participation to Increase Disaster Resilience

i-com ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 177-193
Author(s):  
Daniel Wessel ◽  
Julien Holtz ◽  
Florian König
Keyword(s):  
Citizen Participation ◽  
Smart City ◽  
Development Process ◽  
Smart Cities ◽  
Government Support ◽  
Sensor Data ◽  
Specific Information ◽  
Mutual Support ◽  
The Everyday

Abstract Smart cities have a huge potential to increase the everyday efficiency of cities, but also to increase preparation and resilience in case of natural disasters. Especially for disasters which are somewhat predicable like floods, sensor data can be used to provide citizens with up-to-date, personalized and location-specific information (street or even house level resolution). This information allows citizens to better prepare to avert water damage to their property, reduce the needed government support, and — by connecting citizens locally — improve mutual support among neighbors. But how can a smart city application be designed that is both usable and able to function during disaster conditions? Which smart city information can be used? How can the likelihood of mutual, local support be increased? In this practice report, we present the human-centered development process of an app to use Smart City data to better prepare citizens for floods and improve their mutual support during disasters as a case study to answer these questions.

A Sensor Data Aggregation System Using Mobile Agents

2017 ◽  
pp. 39-65
Author(s):  
Tomoki Yoshihisa ◽  
Yuto Hamaguchi ◽  
Yoshimasa Ishi ◽  
Yuuichi Teranishi ◽  
Takahiro Hara ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Mobile Agents ◽  
Sensor Data

scholarly journals Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

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