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No 22
Vol. 6 No. 2
Spring 2018

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The Internet of Things (IoT) connects various kinds of things such as: physical devices, vehicles, home appliances and etc. to each other enabling them to exchange data. The IoT also allows objects to be sensed or controlled remotely and results in improved efficiency, accuracy and economic benefits. Therefore, the number of connected devices through IoT are increasing rapidly. Machina Research estimates that the IoT will consist of about 25 billion objects by 2020. Different network technologies have been developed to provide connectivity of this large number of devices, like WiFi for cellular based connections, ZigBee and Bluetooth for indoor connections and Low Power Wide Area Network's (LPWAN) for low power long distance connections. LPWAN may be used as a private network, or may also be a service offered by a third party, allowing companies to deploy it without investing in gateway technology. Two available leading technologies for LPWAN are narrow-band systems and wide-band plus coding gain systems. In the first one, receiver bandwidth is scaled down to reduce noise seen by the receiver, while in the second one, coding gain is added to higher rate signal to combat the high receiver noise in a wideband receiver. This paper is a survey on both systems. The survey includes an in-depth study of their important parameters such as battery lifetime, capacity, cost, QoS, latency, reliability, and range and presents a comprehensive comparison between them. This paper reviews created testbeds of recent researches over both systems to compare and verify their performance
Yas Hosseini Tehrani - Seyed Mojtaba Atarodi - Ziba Fazel
DOI : 0
Keywords : LPWAN ، Internet of Things ، Narrowband ، Wideband ، NB-IoT ، LoRaWAN
In this paper, we proposed a user-interactive and knowledge-based method for summarizing graphs based on both structure and vertex attributes. Because of being interactive method, a user can decide to stop or continue summarization process at any step based on the resulted summary. The proposed method is a general method that covers three kinds of graph summarization named structural, attribute-based and structural/attribute-based summarization. In summarization based on both the structure and attributes, the contributions of syntactical and semantical attributes and also the importance degrees of attributes are variable and can be specified by the user. We also proposed a new criterion based on density and entropy to evaluate the quality of a hybrid summary. In the aims of evaluation, we generated a synthetic graph with 1000 nodes and 2500 edges and extracted the overall features of the graph using the Gephi tool and a new developed application in Java. Finally, we generated summaries with different sizes and different values for the structure contribution (α parameter). We calculated the values of density and entropy for each summary in order to measure their qualities based on the proposed criterion. The experimental results show that the proposed criterion causes to generate a summary with a better quality.
Nosratali Ashrafi Payaman - Mohammad Reza Kangavari
DOI : 0
Keywords : Graph summarization, summary graph, super-node, semantical summarization
In this paper, a new approach based on the Zipf’s law for modeling the features of the network traffic is proposed. The Zipf's law is an empirical law that provides the relationship between the frequency and rank of each category in the data set. We use the Zipf’s law to model the features of the network traffic and simulate them and detect anomalies. For this purpose, one of the important features of the network traffic, the inter-arrival time of TCP or UDP packets, is examined. The advantage of this law is that it can provide high similarity using less information. Furthermore, the Zipf’s law can model different features of the network traffic that may not follow from mathematical distributions. The simple approach of this law can provide accuracy and lower limitations in comparison to existing methods. The Zipf's law can be also used as a criterion for anomaly detection. For this purpose, TCP_Flood and UDP_Flood attacks are examined based on inter-arrival time of packets. We show that an accurate model of features can be created by classifying the feature values and obtaining their ranks, and this model can be used to simulate the features and detect anomalies. The results of the evaluation of the proposed method on MAWI and NUST traffic collections are presented in this paper.
Ali Naghash-Asadi - Mohammad Abdollahi Azgomi
DOI : 0
Keywords : Network traffic modeling ، Inter-arrival time ، Anomaly detection ، DoS attack ، The Zipf’s law
Millions of comments and opinions are posted daily on websites such as Twitter or Facebook. Users share their opinions on various topics. People need to know the opinions of other people in order to purchase consciously. Businesses also need customers’ opinions and big data analysis to continue serving customer-friendly services, manage customer complaints and suggestions, increase financial benefits, evaluate products, as well as for marketing and business development. With the development of social media, the importance of sentiment analysis has increased, and sentiment analysis has become a very popular topic among computer scientists and researchers, because it has many usages in market and customer feedback analysis. Most sentiment analysis methods suffice to split comments into two negative, positive and neutral categories. But Appraisal Theory considers other characteristics of opinion such as attitude, graduation and orientation which results in more precise analysis. Therefore, this research has proposed an algorithm that increases the accuracy of the sentiment analysis algorithms by combining appraisal theory and fuzzy logic. This algorithm was tested on Stanford data (25,000 comments on the film) and compared with a reliable dictionary. Finally, the algorithm reached the accuracy of 95%. The results of this research can help to manage customer complaints and suggestions, marketing and business development, and product testing.
Azadeh Roustakiani - Neda Abdolvand - Saeedeh Rajaee Harandi
DOI : 0
Keywords : Appraisal Theory, Fuzzy Logic, Sentiment Analysis, Opinion Mining
Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network all the routers and their ports are always active and consume energy. Average link utilization in internet service providers is about 30-40%. Energy aware traffic engineering aims to change routing algorithms so that low utilized links are deactivated and their load is distributed over other routes. As a consequence, by turning off these links and their respective devices and ports, network energy consumption is significantly decreased. In this paper we propose four algorithms for energy aware traffic engineering in intra-domain networks. Sequential Link Elimination (SLE) removes links based on their role in maximum network utilization. As a heuristic method, Extended Minimum Spanning Tree (EMST) uses minimum spanning trees to eliminate redundant links and nodes. Energy Aware DAMOTE (EAD) is another heuristic method that turns off links with low utilization. The forth approach is based on genetic algorithms that randomly searches for feasible network architectures in a potentially huge solution space. Evaluation results on Abilene network with real traffic matrix indicate that about 35% saving can be obtained by turning off underutilized links and routers on off-peak hours with respect to QOS. Furthermore, experiments with GA confirm that a subset of links and core nodes with respect to QOS can be switched off when traffic is in its off-peak periods and energy can be saved about 40%.
Muharram Mansoorizadeh
DOI : 0
Keywords : Energy-aware traffic engineering ، Green Networking ، Greedy Algorithms ، Genetic Algorithms
The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper selection of the cluster heads plays important role to save the energy of sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper cluster heads in wireless sensor networks. We also obtain the optimal location of the base station according to the cluster heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a sensor network optimization problem. In the proposed energy- efficient algorithm, cluster heads distance from the base station and their residual energy of the sensors nodes are important parameters for cluster head selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive sensors are remained in the wireless network compared to the baseline algorithms in different situations.
Maryam Najimi - Sajjad Nankhoshki
DOI : 0
Keywords : Wireless Sensor Nodes ، Network Lifetime ، Particle Swarm Algorithm (PSO) ، Base Station ، Cluster Head
Tourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the economic outlook. The perishable nature of most sections dependent on the tourism has turned the prediction of tourism demand an important issue for future success. The present study, for the first time, uses the Discrete Hidden Markov Model (DHMM) to predict the tourism demand. DHMM is the discrete form of the well-known HMM approach with the capability of parametric modeling the random processes. MATLAB Software is applied to simulate and implement the proposed method. The statistic reports of Iranian and foreign tourists visiting Isfahan gained by Iran Cultural Heritage, Handicrafts, and Tourism Organization (ICHHTO)-Isfahan Tourism used for simulation of the model. To evaluate the proposed method, the prediction results are compared to the results from the Grey model and Persistence method on the same data. Three errors indexes, MAPE (%), RMSE, and MAE, are also applied to have a better comparison between them. The results reveal that compared to two other methods, DHMM performs better in predicting tourism demand for the next year, both for Iranian and foreign tourists.
Khatereh Ghasvarian Jahromi - Vida Ghasvarian Jahromi
DOI : 0
Keywords : Modeling ، Tourism Demand Function ، Demand Prediction ، Discrete Hidden Markov Model ، Iran ، Isfahan

About Journal

Affiliated to :ICT Research Institute of ACECR
Manager in Charge :Habibollah Asghari
Editor in Chief :Masood Shafiei
Editorial Board :
Abdolali Abdipour
Mahmoud Naghibzadeh
Zabih Ghasemlooy
Mahmoud Moghavemi
Aliakbar Jalali
Ramazan Ali Sadeghzadeh
Hamidreza Sadegh Mohammadi
Saeed Ghazimaghrebi
Shaban Elahi
Alireza Montazemi
Ali Mohammad Djafari
Rahim Saeidi
Shohreh Kasaei
Mehrnoush Shamsfard
ISSN :2322-1437
eISSN :2345-2773

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