Journal of Electrical and Computer Engineering 

About: Journal of Electrical and Computer Engineering is an academic journal published by Hindawi Publishing Corporation. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 2090-0147. It is also open access. Over the lifetime, 1043 publications have been published receiving 10017 citations.

Internet of Things: Architectures, Protocols, and Applications

Abstract: The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network

Abstract: The Internet of Things has broad application in military field, commerce, environmental monitoring, and many other fields. However, the open nature of the information media and the poor deployment environment have brought great risks to the security of wireless sensor networks, seriously restricting the application of wireless sensor network. Internet of Things composed of wireless sensor network faces security threats mainly from Dos attack, replay attack, integrity attack, false routing information attack, and flooding attack. In this paper, we proposed a new intrusion detection system based on -nearest neighbor (-nearest neighbor, referred to as KNN below) classification algorithm in wireless sensor network. This system can separate abnormal nodes from normal nodes by observing their abnormal behaviors, and we analyse parameter selection and error rate of the intrusion detection system. The paper elaborates on the design and implementation of the detection system. This system has achieved efficient, rapid intrusion detection by improving the wireless ad hoc on-demand distance vector routing protocol (Ad hoc On-Demand Distance the Vector Routing, AODV). Finally, the test results show that: the system has high detection accuracy and speed, in accordance with the requirement of wireless sensor network intrusion detection.

A survey of scheduling and interference mitigation in LTE

Abstract: Among the goals of 3GPP LTE networks are higher user bit rates, lower delays, increased spectrum efficiency, support for diverse QoS requirements, reduced cost, and operational simplicity. Resource scheduling and interference mitigation are two functions which are key to achieving these goals. This paper provides a survey of related techniques which have been proposed and shown to be promising. A brief discussion of the challenges for LTE-Advanced, the next step in the evolution, is also provided.

A secure image encryption algorithm based on Rubik's cube principle

Abstract: In the past few years, several encryption algorithms based on chaotic systems have been proposed as means to protect digital images against cryptographic attacks. These encryption algorithms typically use relatively small key spaces and thus offer limited security, especially if they are one-dimensional. In this paper, we proposed a novel image encryption algorithm based on Rubik's cube principle. The original image is scrambled using the principle of Rubik's cube. Then, XOR operator is applied to rows and columns of the scrambled image using two secret keys. Finally, the experimental results and security analysis show that the proposed image encryption scheme not only can achieve good encryption and perfect hiding ability but also can resist exhaustive attack, statistical attack, and differential attack.

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Abstract: Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low ( 25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.