AdhocInfra Toggle: Opportunistic Auto-configuration of Wireless Interface for Maintaining Data Sessions in WiFi Networks

ABSTRACTFor the last several years, we have been developing and characterizing “mobile” micro- and nano-instruments for use on-site (e.g., in the field). Although such portable, battery-operated instruments are much smaller that their laboratory-scale counterparts, sometimes they provide comparable performance and they often offer improved capabilities. As such, they are expected to cause a paradigm shift in classical chemical analysis by allowing practioners to “bring the lab (or part of it) to the sample”. Two classes of examples will be used as the means with which to illustrate the power of micro- and nano-instruments. One class involves a “patient” as the sample and an ingestible capsule-size spectrometer used for cancer diagnosis of the gastro intestinal tack as (part of) “the lab”. The other involves the “environment” as the sample and a portable, battery-operated, miniaturized instrument that utilizes a PalmPilot™ with a wireless interface for data acquisition and signal processing as (part of) “the lab”. To discuss how to electrically power such miniaturized instruments, mobile energy issues will be addressed. Particular emphasis will be paid to current or anticipated future applications and to the paradigm shifts that may prove essential in powering the next generation of miniaturized instruments.

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Resource Management in Hybrid DVB-RCS and WiFi Networks

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference ◽

10.1109/glocom.2008.ecp.564 ◽

2008 ◽

Cited By ~ 2

Author(s):

Paolo Chini ◽

Giovanni Giambene

Keyword(s):

Resource Management ◽

Wifi Networks

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Wi-fire: Device-free fire detection using WiFi networks

2017 IEEE International Conference on Communications (ICC) ◽

10.1109/icc.2017.7997406 ◽

2017 ◽

Cited By ~ 5

Author(s):

Shuxin Zhong ◽

Yongzhi Huang ◽

Rukhsana Ruby ◽

Lu Wang ◽

Yu-Xuan Qiu ◽

...

Keyword(s):

Fire Detection ◽

Wifi Networks ◽

Device Free

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On the Tradeoff between Performance and Programmability for Software Defined WiFi Networks

Wireless Communications and Mobile Computing ◽

10.1155/2018/1083575 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Tausif Zahid ◽

Xiaojun Hei ◽

Wenqing Cheng ◽

Adeel Ahmad ◽

Pasha Maruf

Keyword(s):

Performance Optimization ◽

Traffic Flows ◽

Network Access ◽

Performance Measurements ◽

Throughput Performance ◽

Wifi Networks ◽

Traffic Loads ◽

High Bandwidth ◽

Potential Benefits ◽

Sdn Controller

WiFi has become one of the major network access networks due to its simple technical implementation and high-bandwidth provisioning. In this paper, we studied software defined WiFi networks (SDWN) against traditional WiFi networks to understand the potential benefits, such as the ability of SDWN to effectively hide the handover delay between access points (AP) of the adoption of the SDWN architecture on WiFi networks and identify representative application scenarios where such SDWN approach could bring additional benefits. This study delineated the performance bottlenecks such as the throughput degradation by around 50% compared with the conventional WiFi networks. In addition, our study also shed some insights into performance optimization issues. All of the performance measurements were conducted on a network testbed consisting of a single basic service set (BSS) and an extended service set (ESS) managed by a single SDN controller deployed with various laboratory settings. Our evaluation included the throughput performance under different traffic loads with different number of nodes and packet sizes for both TCP and UDP traffic flows. Handover delays were measured during the roaming phase between different APs against the traditional WiFi networks. Our results have demonstrated the tradeoff between performance and programmability of software defined APs.

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Performance modeling of next-generation WiFi networks

Computer Networks ◽

10.1016/j.comnet.2016.05.024 ◽

2016 ◽

Vol 105 ◽

pp. 150-165 ◽

Cited By ~ 6

Author(s):

Antonios Michaloliakos ◽

Ryan Rogalin ◽

Yonglong Zhang ◽

Konstantinos Psounis ◽

Giuseppe Caire

Keyword(s):

Performance Modeling ◽

Next Generation ◽

Wifi Networks

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Exploratory approach for network behavior clustering in LoRaWAN

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03121-z ◽

2021 ◽

Author(s):

Domenico Garlisi ◽

Alessio Martino ◽

Jad Zouwayhed ◽

Reza Pourrahim ◽

Francesca Cuomo

Keyword(s):

Smart Cities ◽

Wide Area ◽

Area Network ◽

Network Behavior ◽

Wide Area Network ◽

Network Efficiency ◽

Exploratory Approach ◽

Wireless Interface ◽

Iot Devices ◽

Set Up

AbstractThe interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach.

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