scholarly journals Using Delay Tolerant Networks as a Backbone for Low-Cost Smart Cities

2019 ◽  
Author(s):  
Oluwashina Madamori ◽  
Esther Max-Onakpoya ◽  
Christan Grant ◽  
Corey Baker
Keyword(s):  
Smart Cities ◽  
Low Cost ◽  
Delay Tolerant Networks ◽  
Delay Tolerant

Delay Tolerant Networks

2019 ◽  
pp. 448-480 ◽  
Author(s):  
Vandana Kushwaha ◽  
Ratneshwer Gupta
Keyword(s):  
Vehicular Networks ◽  
Low Cost ◽  
Delay Tolerant Networks ◽  
Opportunistic Networks ◽  
Delay Tolerant Network ◽  
Data Packets ◽  
Security Issues ◽  
Terrestrial Environments ◽  
Routing Congestion ◽  
Delay Tolerant

Opportunistic networks are one of the emerging evolutions of the network system. In opportunistic networks, nodes are able to communicate with each other even if the route between source to destination does not already exist. Opportunistic networks have to be delay tolerant in nature (i.e., able to tolerate larger delays). Delay tolerant network (DTNs) uses the concept of “store-carry-forward” of data packets. DTNs are able to transfer data or establish communication in remote area or crisis environment where there is no network established. DTNs have many applications like to provide low-cost internet provision in remote areas, in vehicular networks, noise monitoring, extreme terrestrial environments, etc. It is therefore very promising to identify aspects for integration and inculcation of opportunistic network methodologies and technologies into delay tolerant networking. In this chapter, the authors emphasize delay tolerant networks by considering its architectural, routing, congestion, and security issues.

A Fuzzy Routing Method in UAV Delay Tolerant Networks

2012 ◽  
Vol E95.B (9) ◽  
pp. 2769-2773 ◽  
Author(s):  
Xuanya LI ◽  
Linlin CI ◽  
Wenbing JIN
Keyword(s):  
Delay Tolerant Networks ◽  
Routing Method ◽  
Delay Tolerant

Energy Optimal Epidemic Routing for Delay Tolerant Networks

2009 ◽  
Vol E92-B (12) ◽  
pp. 3927-3930 ◽  
Author(s):  
Jeonggyu KIM ◽  
Jongmin SHIN ◽  
Dongmin YANG ◽  
Cheeha KIM
Keyword(s):  
Delay Tolerant Networks ◽  
Epidemic Routing ◽  
Delay Tolerant

A Topology Control Strategy with Efficient Path for Predictable Delay-Tolerant Networks

2019 ◽  
Vol E102.B (12) ◽  
pp. 2183-2198
Author(s):  
Dawei YAN ◽  
Cong LIU ◽  
Peng YOU ◽  
Shaowei YONG ◽  
Dongfang GUAN ◽  
...  
Keyword(s):  
Control Strategy ◽  
Topology Control ◽  
Delay Tolerant Networks ◽  
Delay Tolerant

Quasi Fair Forwarding Strategy for Delay Tolerant Networks

2012 ◽  
Vol E95.B (11) ◽  
pp. 3585-3589 ◽  
Author(s):  
Seok-Kap KO ◽  
Hakjeon BANG ◽  
Kyungran KANG ◽  
Chang-Soo PARK
Keyword(s):  
Delay Tolerant Networks ◽  
Forwarding Strategy ◽  
Delay Tolerant

Performance Evaluation of Data Transmission in Maritime Delay-Tolerant-Networks

2013 ◽  
Vol E96.B (6) ◽  
pp. 1435-1443 ◽  
Author(s):  
Shuang QIN ◽  
Gang FENG ◽  
Wenyi QIN ◽  
Yu GE ◽  
Jaya Shankar PATHMASUNTHARAM
Keyword(s):  
Performance Evaluation ◽  
Data Transmission ◽  
Delay Tolerant Networks ◽  
Delay Tolerant ◽  
Evaluation Of Data

Trust-Threshold Based Routing in Mobile Ad Hoc Delay Tolerant Networks

2011 ◽  
Author(s):  
MoonJeong Chang ◽  
Ing-Ray Chen ◽  
Fenye Bao ◽  
Jin-Hee Cho
Keyword(s):  
Ad Hoc ◽  
Delay Tolerant Networks ◽  
Mobile Ad Hoc ◽  
Delay Tolerant

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.

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