List of Articles
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Open Access Article
1 - A Survey of Two Dominant Low Power and Long Range Communication Technologies
Yas Hosseini Tehrani Seyed Mojtaba Atarodi ZIba FazelThe Internet of Things (IoT) connects various kinds of things such as physical devices, vehicles, home appliances, 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, accu MoreThe Internet of Things (IoT) connects various kinds of things such as physical devices, vehicles, home appliances, 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 is increasing rapidly. Machina Research estimates that the IoT will consist of about 2.6 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 the higher rate signal to combat the high receiver noise in a wideband receiver. Both LoRa and NB-IoT standards were developed to improve security, power efficiency, and interoperability for IoT devices. They support bidirectional communication, and both are designed to scale well, from a few devices to millions of devices. LoRa operates in low frequencies, particularly in an unlicensed spectrum, which avoids additional subscription costs in comparison to NB-IoT, but has lower Quality of Service. NB-IoT is designed to function in a 200kHz carrier re-farmed from GSM, with the additional advantage of being able to operate in a shared spectrum with an existing LTE network. But in the other hand, it has lower battery lifetime and capacity. This paper is a survey on both systems. The review includes an in-depth study of their essential 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. Manuscript profile -
Open Access Article
2 - A Novel User-Centric Method for Graph Summarization Based on Syntactical and Semantical Attributes
Nosratali Ashrafi Payaman Mohammadreza KangavariIn this paper, we proposed an interactive knowledge-based method for graph summarization. Due to the interactive nature of this method, the user can decide to stop or continue summarization process at any step based on the summary graph. The proposed method is a general MoreIn this paper, we proposed an interactive knowledge-based method for graph summarization. Due to the interactive nature of this method, the user can decide to stop or continue summarization process at any step based on the summary graph. The proposed method is a general one that covers three kinds of graph summarization called structural, attribute-based, and structural/attribute-based summarization. In summarization based on both structure and vertex attributes, the contributions of syntactical and semantical attributes, as well as the importance degrees of attributes are variable and could be specified by the user. We also proposed a new criterion based on density and entropy to assess the quality of a hybrid summary. For the purpose 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 developed application in Java. Finally, we generated summaries of different sizes and values for the structure contribution (α parameter). We calculated the values of density and entropy for each summary to assess their qualities based on the proposed criterion. The experimental results show that the proposed criterion causes to generate a summary with better quality. Manuscript profile -
Open Access Article
3 - Modeling the Inter-arrival Time of Packets in Network Traffic and Anomaly Detection Using the Zipf’s Law
Ali Naghash Asadi Mohammad Abdollahi AzgomiIn this paper, a new method 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. Some data sets may follow MoreIn this paper, a new method 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. Some data sets may follow from the Zipf’s law, but we show that each data set can be converted to the data set following from the Zipf’s law by changing the definition of categories. We use this law to model the inter-arrival time of packets in the normal network traffic and then we show that this model can be used to simulate the inter-arrival time of packets. 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 the 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, the TCP_Flood and UDP_Flood attacks are added to the inter-arrival time of packets and they are detected with high detection rate. We show that the Zipf’s law can create an accurate model of the feature to classify the feature values and obtain the rank of its categories, and this model can be used to simulate the feature values and detect anomalies. The evaluation results of the proposed method on MAWI and NUST traffic collections are presented in this paper. Manuscript profile -
Open Access Article
4 - An Improved Sentiment Analysis Algorithm Based on Appraisal Theory and Fuzzy Logic
Azadeh Roustakiani Neda Abdolvand Saeideh Rajaei HarandiMillions 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 MoreMillions 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 three 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. Manuscript profile -
Open Access Article
5 - Toward Energy-Aware Traffic Engineering in Intra-Domain IP Networks Using Heuristic and Meta-Heuristics Approaches
Muharram MansoorizadehBecause 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 i MoreBecause 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 would be deactivated and their load would be 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 fourth approach is based on genetic algorithms that randomly search 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 hence energy can be saved up to 37%. Manuscript profile -
Open Access Article
6 - Lifetime Improvement Using Cluster Head Selection and Base Station Localization in Wireless Sensor Networks
maryam najimi Sajjad NankhoshkiThe 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 eff MoreThe 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. Manuscript profile -
Open Access Article
7 - Using Discrete Hidden Markov Model for Modelling and Forecasting the Tourism Demand in Isfahan
Khatereh Ghasvarian Jahromi Vida Ghasvarian JahromiTourism 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 MoreTourism 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 Artificial Neural Network, 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 three other methods, DHMM performs better in predicting tourism demand for the next year, both for Iranian and foreign tourists. Manuscript profile