2024-03-29T19:07:38+04:30
http://ijict.itrc.ac.ir/browse.php?mag_id=42&slc_lang=en&sid=1
42-405
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
A Proactive Context-Aware Self-Healing Scheme for 5G Using Machine Learning
Shirin
Nikmanesh
shi.nikmanesh.eng@iauctb.ac.ir
Mohammad
Akbari
m.akbari@itrc.ac.ir
Roghayeh
Joda
r.joda@itrc.ac.ir
Future mobile communication networks particularly 5G networks require to be efficient, reliable and agile to fulfill the targeted performance requirements. All layers of the network management need to be more intelligent due to the density and complexity anticipated for 5G networks. In this regard, one of the enabling technologies to manage the future mobile communication networks is Self-Organizing Network (SON). Three common types of SON are self-configuration, Self-Healing (SH) and self-optimization. In this paper, a framework is developed to analyze proactive SH by investigating the effect of recovery actions executed in sub-health states. Our proposed framework considers both detection and compensation processes. Learning method is employed to classify the system into several sub-health (faulty) states in detection process. The system is modeled by Markov Decision Process (MDP) in compensation process in which the equivalent Linear Programing (LP) approach is utilized to find the action or policy that maximizes a given performance metric. Numerical results obtained in several scenarios with different goals demonstrate that the optimized proposed algorithm in compensation process outperforms the algorithm with randomly selected actions.
component
fifth generation cellular network (5G)
self-organizing networks (SON)
self-healing
fault detection and compensation
markov decision problem (MDP)
linear programming
machine learning
K-means clustering
2018
12
01
1
10
http://ijict.itrc.ac.ir/article-1-405-en.pdf
42-406
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
On the Reselection of Seed Nodes in Independent Cascade Based Influence Maximization
Ali
Vardasbi
a.vardasbi@ut.ac.ir
Heshaam
Faili
hfaili@ut.ac.ir
Masoud
Asadpour
asadpour@ut.ac.ir
Influence maximization serves as the main goal of a variety of social network activities such as viral marketing. The independent cascade model for the influence spread assumes a one-time chance for each activated node to influence its neighbors. On the other hand, the manually activated seed set nodes can be reselected without violating the model parameters or assumptions. This view divides the influence maximization process into two cases: the simple case where the reselection of the nodes is not considered and the reselection case. In this study we will analyze real world networks in the reselection case. First we will show that the difference between the simple and the reselection cases constitutes a wide spectrum of networks ranging from the reselection-free to the reselection-friendly ones. Then we will experimentally show a significant entanglement between this and influence spread dynamics as well as other structural parameters of the network. Specifically, we show that under a realistic condition, the reselection gain of a network has a correlation of 0.73 to a newly introduced influence spread dynamic. Furthermore, we propose a measure for detecting star-like networks and experimentally show a significant correlation between our proposed measure and the reselection gain in real world networks with different edge weight models.
Influence Maximization
Network Structure
Independent Cascade
Maximization over Integer Lattice
Core Decomposition
2018
12
01
11
21
http://ijict.itrc.ac.ir/article-1-406-en.pdf
42-407
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
Network Planning Policies for Joint Switching in Spectrally-Spatially Flexible Optical Networks
Mohsen
Yaghubi-Namaad
m_yaghubi@sut.ac.ir
Akbar
Ghaffarpour Rahbar
ghaffarpour@sut.ac.ir
Behrooz
Alizadeh
alizadeh@sut.ac.ir
The spectrally and spatially flexible optical networks (SS-FON) are the promising solution for future optical transport networks. The joint switching (J-Sw) paradigm is one of the possible switching schemes for SS-FON that brings optical component integration alongside with acceptable networking performance. The network planning of J-Sw is investigated in this paper. The formulation of resource allocation for J-Sw is introduced as in integer linear programming to find the optimal solution. To find the near-optimal solution, the heuristic algorithms are initiated with sorted connection demands. The way connection demands are sorted to initiate the heuristic algorithms affects the accuracy of algorithms. Therefore, six different sorting policies are introduced for J-Sw. Moreover, the heuristic algorithm called joint switching resource allocation (JSRA) algorithm is introduced, especially for J-Sw. The heuristic algorithm performance initiated with different sorting policies is investigated through simulation for a small-size network. The optimality gap is the most important indicator that shows the effect of each sorting policy on the near-optimal solution. The new sorting policy of connection demands called descending frequency width (DFW) policy achieved the least optimality gap. Also, the JSRA performance initiated with these sorting policies is investigated for a real network topology. The obtained results indicate that DFW shows better performance than other sorting policies in realistic networks, too.
Optical transport networks
SS-FON
Space division multiplexing
Joint switching
Network planning
Static traffic
Resource allocation
RMLSSA
Sorting policies.
2018
12
01
22
31
http://ijict.itrc.ac.ir/article-1-407-en.pdf
42-408
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
OSDTM: an Offline-Structural Distributed Test Mechanism for Data Links in NoC
Babak
Aghaei
B.aghaei@iaut.ac.ir
Ahmad
Khademzadeh
Zadeh@itrc.ac.ir
Kambiz
Badie
k_badie@itrc.ac.ir
Midia
Reshadi
Reshadi@srbiau.ac.ir
Saeid
Sarhangi
saeed.sarhangi131@gmail.com
The Micro Packet Switched based Network on Chip (NoC) is emerged to address traditional non-scalable buses-based Systems on Chip (SoC) challenges such as out of order transactions, flow control and higher latencies. The NoC is disposable to a different of defects in its life which cause of such drawbacks as data missing, efficiency reduction, and eventually, entire system overwhelm. This paper is amid to propose a new Offline-Structural Distributed Test Mechanism (OSDTM) to discovering and emplacing shorts on the data links in NoC. The projected test approach encompasses three main component namely Test Pattern Producer (TPP), Test Response Compiler (TRC) that are implanted in the Network Adapter (N) as well as a Flit Comparator Block (FCB) located in the Routers(R). The FCB concern is to detect dissimilar Flits through comparison the entrance Flits. The OSDTM leads to 100% Test Coverage (TC), 82.3% Discovering Capability (DC), and 100% fault emplacement (FE) of faulty links in NoC. The experimental results illustrate that the FCB hardware cost is very insignificant in related to the hardware of Vici router.
component
Network on Chip
Biult in self test
links test mechanism
fault discovering and emplacement
2018
12
01
32
41
http://ijict.itrc.ac.ir/article-1-408-en.pdf
42-410
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
Deep Learning Based on Parallel CNNs for Pedestrian Detection
Mahmoud
Saeidi
msaeidi40@itrc.ac.ir
Ali
Ahmadi
ahmadi@eetd.kntu.ac.ir
Recently, deep learning methods, mostly algorithms based on Deep Convolutional Neural Networks (DCNNs) have yielded great results on pedestrian detection. Algorithms based on DCNNs spontaneously learn features in a supervised manner and are able to learn qualified high level feature representations to detect pedestrian. In this paper, we first review a number of popular DCNN-based training approaches along with their recent extensions. We then briefly describe recent algorithms based on these approaches. Also, we accentuate recent contributions and main challenges of DCNNs in detecting pedestrian. We analyze deep pedestrian detection algorithms from training approach, categorization, and DCNN model points of view, and ultimately propose a new deep architecture and training approach for deep pedestrian detection. The experimental results show that the proposed DCNN and training approach, achieve more accurate rate detection than the previously reported architectures and training approaches.
Parallel DCNN
Pedestrian Detection
Region-based Convolutional Neural Network (RCNN)
Single Shot Detector (SSD)
Training Approach.
2018
12
01
42
52
http://ijict.itrc.ac.ir/article-1-410-en.pdf
42-411
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
The Emotional Promulgation of Social Norms in Social Networks Based on Structural Properties
Seyed Hadi
Sajadi
sajadi@ce.sharif.edu
Jafar
Habibi
jhabibi@sharif.edu
Mohammad Amin
Fazli
fazli@sharif.edu
Social norms play an important role in regulating the behavior of societies. They are behavioral standards that are considered acceptable in a group or society and violating them will result in sanction to violator. Both governments and various cultural communities use this social component to solve various problems in society. The use of norms leads to a large reduction in community spending to control harmful behaviors. Social norms have two important aspects of promulgating and sanctioning. They are promulgated by activists in the community and, after creation, are endorsed with a sanction. Norms can be used to promote a variety of different behaviors. Online social networks have established a new and influential platform for promulgating social norms. We first redefined the Rescorla-Wagner conditional learning model in the context of social norms with the help of a norm’s intrinsic properties, and extract the main coefficients in the Rescorla-Wagner model related to it. Based on this model, we extract a network structure related parameter (i.e. clustering coefficient) for any individual in the social network to promulgate the norm with the conditional learning method. In this paper, by using the intrinsic properties of norms, we use and tune the Rescorla-Wagner conditioning model in order to obtain a new model for social norm promulgation. Based on this, we define criteria for the amount of effort required to promulgate norms in social networks. We show that there is negative correlation between the amount of effort required by each node to promulgate a norm and the clustering coefficient of that node. This result can be used to devise effective algorithms for social norms evolution.
social network
social norm
classical conditioning
clustering coefficient
Rescorla-Wagner
2018
12
01
53
60
http://ijict.itrc.ac.ir/article-1-411-en.pdf
42-412
2024-03-29
10.1002
International Journal of Information and Communication Technology Research
2251-6107
2783-4425
doi
2018
10
4
A Framework for ICT-Oriented Sustainable Development through Mapping from ICT Concerns onto Sustainability Indicators
Mohammad
Azadnia
azadnia@itrc.ac.ir
Shamsossadat
Zahedi
szahedi44@hotmail.com
Sustainable development establishes a framework in which environmental policies and development strategies interact each other, and in the process of economic development, the long-term environmental value and human society is taken into account. In addition, ICT is rapidly transforming all aspects of human life and there is less of a sense of our lives that have not been directly or indirectly affected by the ICT. Our studies of the previous frameworks on the effects of ICT on the goals of sustainable development show that most of them fall into two categories. There are a number of frameworks that have introduced general and high-level ICT impacts on sustainability development in the first category, and the second category refers to some specific technologies or specific SDGs. In this paper, we have proposed a framework that addresses all technologies and concerns related to ICT and all SDGs. The main objective of this framework is to show the impact and mapping between SDGs, ICT concepts and technologies, ICT concerns, and ICT development success factors that can be used directly to evaluate the growth and degradation of each of them. The ICT concerns classified into four categories and mapped by ICT technologies and concepts to sustainable development and its goals and ICT development and its concerns.
component
Sustainable Development
ICT Concerns
Framework.
2018
12
01
61
72
http://ijict.itrc.ac.ir/article-1-412-en.pdf