با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
A jamming attack detection technique for opportunistic networks
یک تکنیک تشخیص حمله پارازیت برای شبکه های فرصت طلب-2022 Opportunistic networks (OppNets) are dispersed in nature, with nodes acting as resource
restrictions, with intermittent connectivity. These nodes are subject to various types of attacks,
posing a security risk in data transmission. One of the most common attacks that cause jamming
among the message forwarding nodes in infrastructure-less networks is Denial of Service (DoS)
attack. Most of the methods addressing this type of attack rely on cryptographic algorithms,
which are too difficult to implement. In this paper, a novel jamming attack detection technique
(JADT) for OppNets, is proposed, which relies on the use of some statistical measures collected
from the relay nodes and a prescribed threshold on the packet delivery ratio (PDR) to discover
a jamming attack while decrypting the acknowledgement, stopping the message transmission
and rebroadcasting the message through a different channel. The proposed JADT is evaluated
using the ONE simulator, showing its superiority against the Fuzzy Geocasting mechanism in
Opportunistic Networks (F-GSAF) scheme in terms of packet delivery ratio and overhead ratio,
under varying TTL and buffer size.
Keywords: Jamming detection | Opportunistic networks | Routing | Statistical information | Energy |
مقاله انگلیسی |
2 |
Multiple contents offloading mechanism in AI-enabled opportunistic networks
مکانیسم تخلیه محتوای چندگانه در شبکه های فرصت طلب مجهز به هوش مصنوعی-2020 With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular
and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity
communication is regarded as a potential solution. It can use unlicensed bands to forward content to users
under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is
easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the
location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition,
deep learning-based methods almost need a lot of training time. Based on real-time requirements of the
network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing
(MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents
offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation. Keywords: Opportunistic networks | MEC | Offloading | Content caching |
مقاله انگلیسی |
3 |
Immunization-based redundancy elimination in Mobile Opportunistic Networks-Generated big data
حذف افزونگی مبتنی بر ایمن سازی در شبکه های اپورتونیستی سیار - داده های بزرگ تولید شده-2018 Diverse sensors and smart devices are promising in facilitating to perform specific tasks which generate
massive data whilst such data transmission is challenged in the big data era. Under some circumstances,
these devices may form Mobile Opportunistic Networks (MONs) which are characterized by intermittent
connectivity. In such scenarios, there is a critical issue that nodes with the already delivered message
copies may continue carrying and transmitting the copies if they are not informed that the message has
been delivered. This may result in redundant data, and thus large consumption of network resources and
network performance degradation. To avoid generating, transmitting and storing unwanted data due to
redundant message copies, we propose an Immunization-Based Redundancy Elimination scheme (IBRE)
in MONs to stop useless data transmission and flush redundancy. In IBRE, each destination independently
selects the right number of ACKs distributed to respond to the variation of the amount of redundant data
in a dynamic fashion. Simulation results demonstrate that IBRE suppresses redundant data transmission
and eliminates useless data generated from redundant message copies in cost-efficient manner.
Keywords: Big data ، Redundancy elimination ، Immunization ، Multi-copy |
مقاله انگلیسی |
4 |
A personalized recommender system for pervasive social networks
یک سیستم پیشنهاد دهنده شخصی برای شبکه های اجتماعی فراگیر-2017 The current availability of interconnected portable devices, and the advent of the Web 2.0,
raise the problem of supporting anywhere and anytime access to a huge amount of con
tent, generated and shared by mobile users. On the one hand, users tend to be always con
nected for sharing experiences and conducting their social interactions with friends and
acquaintances, through so-called Mobile Social Networks, further improving their social in
clusion. On the other hand, the pervasiveness of communication infrastructures spreading
data (cellular networks, direct device-to-device contacts, interactions with ambient devices
as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter
off undesired information and to select what content should be addressed to which users,
for both (i) better user experience, and (ii) resource saving of both devices and network.
In this work, we propose a novel framework for pervasive social networks, called Perva
sive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of
interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based
recommender system (Arnaboldi et al., 2016) as a context reasoning tool able to adapt rec
ommendations to heterogeneous interest profiles of different users. p-PLIERS effectively
operates also when limited knowledge about the network is maintained. It is implemented
in a completely decentralized environment, in which new contents are continuously gen
erated and diffused through the network, and it relies only on the exchange of single nodes’
knowledge during proximity contacts and through device-to-device communications. We
evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo
2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each
scenario, we used real or synthetic mobility traces and we extracted real datasets from
Twitter interactions to characterize the generation and sharing of user contents.
Keywords: Pervasive content sharing | Mobile social networks | Opportunistic networks | Personalized recommender systems |
مقاله انگلیسی |
5 |
A social cognitive heuristic for adaptive data dissemination in mobile Opportunistic Networks
اکتشافی شناختی اجتماعی برای انتشار اطلاعات سازگار در شبکه های اپورتونیستی سیار-2017 It is commonly agreed that data (and data-centric services) will be one of the cornerstones of Future
Internet systems. In this context, mobile Opportunistic Networks (OppNets) are one of the key paradigms to
efficiently support, in a self-organising and decentralised manner, the growth of data generated by localized
interactions between users mobile devices, and between them and nearby smart devices such as IoT nodes.
In OppNets scenarios, the spontaneous collaboration among mobile devices is exploited to disseminate data
toward interested users. However, the limited resources and knowledge available at each node, and the vast
amount of data available in the network, make it difficult to devise efficient schemes to accomplish this task.
Recent solutions propose to equip each device with data filtering methods derived from human information
processing schemes, known as Cognitive Heuristics. They are very effective methods used by human brains
to quickly drop useless information and keep only the most relevant information. Althought cognitive-based
OppNet solutions proved to be efficient (with limited overheads), they can become less effective when facing
dynamic scenarios or situations where nodes cannot fully collaborate with each other, as we show in this
paper. One of the reasons is that the solutions proposed so far do not take take into account the social
structure of the environment where the nodes are moving in. In order to be more effective, the selection
of information performed by each node should take into consideration not only the relevance of content for
the local device, but also for other devices will encounter in the future due to mobility. To this end, in
this paper we propose a social-based data dissemination scheme, based on a cognitive heuristic, known as
the Social Circle Heuristic. This heuristic is an evaluation method that exploits the structure of the social
environment to make inferences about the relevance of discovered information. We show how the Social
Circle Heuristic, coupled with a cognitive-based community detection scheme, can be exploited to design an
effective data dissemination algorithm for OppNets. We provide a detailed analysis of the performance of
the proposed solution via simulation.
Keywords: Opportunistic Networks | Cognitive Heuristics | Data Dissemination | Social | Self-Organising |
مقاله انگلیسی |
6 |
On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors
در اثر تحرک انسان به طراحی شبکه های فرصت طلب شهری از سنسورها-2017 We live in a world where demand for monitoring natural and artificial phenomena is
growing. The practical importance of Sensor Networks is continuously increasing in our
society due to their broad applicability to tasks such as traffic and air-pollution monitoring,
forest-fire detection, agriculture, and battlefield communication. Furthermore, we have
seen the emergence of sensor technology being integrated in everyday objects such as cars,
traffic lights, bicycles, phones, and even being attached to living beings such as dolphins,
trees, and humans. The consequence of this widespread use of sensors is that new sensor
network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes
(e.g., mobile phones, cars). The use of smart devices carried by people in sensor network
infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS).
This kind of opportunistic network may be fruitful and economically advantageous where
the connectivity, the performance, of the scalability provided by cellular networks fail to
provide an adequate quality of service. This paper delves into the issue of understanding the
impact of human mobility patterns to the performance of sensor network infrastructures
with respect to four different metrics, namely: detection time, report time, data delivery
rate, and network coverage area ratio. Moreover, we evaluate the impact of several other
mobility patterns (in addition to human mobility) to the performance of these sensor
networks on the four metrics above. Finally, we propose possible improvements to the
design of sensor network infrastructures.
Keywords: Wireless Sensor Networks (WSNs) | Human mobility | Opportunistic networks | Social Networks of Sensors (SNoS) | Mobile Ad-Hoc Networks (MANETs) |
مقاله انگلیسی |