Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development

2018 ◽  
Vol 5 (4) ◽  
pp. 2557-2571 ◽  
Author(s):  
Franco Cicirelli ◽  
Antonio Guerrieri ◽  
Giandomenico Spezzano ◽  
Andrea Vinci ◽  
Orazio Briante ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Edge Computing ◽  
Smart Environments ◽  
Social Internet Of Things

scholarly journals Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

2019 ◽  
Vol 11 (4) ◽  
pp. 100 ◽  
Author(s):  
Maurizio Capra ◽  
Riccardo Peloso ◽  
Guido Masera ◽  
Massimo Ruo Roch ◽  
Maurizio Martina
Keyword(s):  
Internet Of Things ◽  
Mobile Devices ◽  
Information Exchange ◽  
Large Scale ◽  
Operating Time ◽  
Edge Computing ◽  
The Internet ◽  
Power Load ◽  
Battery Capacity ◽  
Iot Devices

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

scholarly journals A Blockchain-based Edge Computing Architecture for the Internet of Things

2021 ◽  
Author(s):  
Aleksandar Tošić ◽  
Jernej Vičič ◽  
Michael David Burnard ◽  
Michael Mrissa
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Smart Cities ◽  
Single Point ◽  
Economic Incentives ◽  
Edge Computing ◽  
Consensus Algorithm ◽  
The Internet ◽  
Decentralized Architecture ◽  
The Internet Of Things

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.

scholarly journals Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3026 ◽  
Author(s):  
Damián Fernández-Cerero ◽  
Jorge Yago Fernández-Rodríguez ◽  
Juan A. Álvarez-García ◽  
Luis M. Soria-Morillo ◽  
Alejandro Fernández-Montes
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
Large Scale ◽  
Low Cost ◽  
Fog Computing ◽  
Edge Computing ◽  
Computer Clusters ◽  
Single Board Computer ◽  
Data Centres

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

scholarly journals Attribute based Weighted Mechanism for Community Detection in Social Internet of Things

2021 ◽  
Author(s):  
Subash Rajendran ◽  
Jebakumar R
Keyword(s):  
Internet Of Things ◽  
Community Detection ◽  
Large Scale ◽  
Local Community ◽  
Rate Function ◽  
User Preferences ◽  
Computational Time ◽  
New Paradigm ◽  
Social Internet Of Things ◽  
Real World Datasets

Abstract A new paradigm of Internet of Things (IoT) is emerging rapidly by socializing the smarter physical devices called as Social Internet of Things (SIoT). Social relationships established between these objects make them autonomously connected for services, without any human intervention. Since SIoT is a large-scale network with huge data involved, the content spreading behaviour need to be exploited. In order to ensure the growth of the content spread, the large-scale SIoT network is divided into several communities based on the social attributes in this work. We first divided the SIoT network into high quality Sociality based Weighted Communities (SWC). Social attributes like user preferences, social similarities, and mutual friends’ degrees are main metrics for achieving the best rate function. The weighted method based on these social attributes determine the nodes to be present in their respective communities. Also, the controlling of the local community augmentation using cluster concepts is done in our approach. Finally, a Credential Acclaimed Information Spreading (CAIS) mechanism is proposed which selects the best node with the maximum credential to surge the content spreading behaviour in the detected communities of SIoT network. The proposed social-driven attribute based weighted mechanism for community detection is validated using three diverse real-world datasets such as CASAS, MIT and ARAS dataset containing 427 sensors. Investigational outcomes validate that the overall performance of the proposed method overwhelms the conventional community detection algorithms like Louvain, Girvan Newman, Bron Kerbosch, Infomax and the recent state-of-art-approaches interms of spreading outcomes, NMI, modularity, F-measure, precision, recall and computational time.

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