Security and the smart city: A systematic review

Wireless Body Area Network is an emerging technology that is used primarily in the area of healthcare applications. It is a low-cost network having the capability of transportability and adaptability. It can be used in location independent and long-term remote monitoring of people without disturbing their daily activities. In a typical WBAN system, sensing devices are either implanted or etched into the human body that continuously monitors his physiological parameters or vital signs. In such a network, trusts among the stakeholders (healthcare providers, users, and medical staff, etc.) are found of high importance and regarded as the critical success factor for the reliability of information exchange among them. In remote patient monitoring, the implementation of trust and privacy preservation is crucial, as vital parameters are being communicated to remote locations. Nonetheless, its widespread use, WBAN, has severe trust and privacy risks, limiting its adaptation in healthcare applications. To address trust and privacy-related issues, reliable communication solutions are widely used in WBANs. Given the motivation, in this paper, we have proposed a trust-based communication scheme to ensure the reliability and privacy of WBAN. To ensure reliability, a cooperative communication approach is used, while for privacy preservation, a cryptography mechanism is used. The performance of the proposed scheme is evaluated using MATLAB simulator. The output results demonstrated that the proposed scheme increases service delivery ratio, reliability, and trust with reduced average delay. Furthermore, a fuzzy-logic method used for ranking benchmark schemes, that has been concluded that the proposed scheme has on top using comparative performance ranking.

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A Trust-Based Energy-Efficient and Reliable Communication Scheme (Trust-Based ERCS) for Remote Patient Monitoring in Wireless Body Area Networks

Traffic management system is a cornerstone of a Smart city. In the current problems of the world, urban mobility is one of the major problems, especially in metropolitan cities. Previous traffic management systems are not capable enough to tackle this growth of traffic on the road networks. The purpose of this paper is to propose a smart traffic management system using the Internet of Things and a decentralized approach to optimize traffic on the roads and intelligent algorithms to manage all traffic situations more accurately. This proposed system is overcoming the flaws of previous traffic management systems. The system takes traffic density as input from cameras which is abstracted from Digital Image Processing technique and sensors data, resultantly giving output as signals management. An algorithm is used to predicts the traffic density for future to minimize the traffic congestion. Besides this, RFIDs are also used to prioritize the emergency vehicles like ambulance, fire brigade etc. by implementing RFID tags in such vehicles. In the case of emergency situations, such as fire explosion or burning of something, fire and smoke sensors are also deployed on the road to detect such situations. Moreover, a mobile application is connected to a centralized server which intimates to nearby rescue department about fire explosion with the location to take further action. In addition, the native user can ask about future traffic condition at a particular node. The proposed system is validated by constructing a prototype and deploying it in a city of Pakistan. A web application is also there to provide useful information in graphical formats to the higher authorities of the smart city which is fruitful in future road planning.

Smart traffic management system using Internet of Things

Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).

Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey

Image fusion plays a significant role in various computer vision applications. However, designing an efficient image fusion technique is still a challenging task. In this paper, a novel deep belief networks based image fusion framework is proposed to improve the performance of the image fusion process further. We have initially, evaluated the fusion dataset by applying various feature extraction techniques. Thereafter, features selection techniques are applied to select potential features. Finally, the image fusion machine learning model is built by using a deep belief network model. Extensive experiments reveal that the proposed technique outperforms existing image fusion techniques.

Fusion of medical images using deep belief networks

On the mutual information of relaying protocols

Wireless sensor network monitors various events occurring in the surrounding environment. Its major application is in border protection, disaster monitoring and in security surveillance. Sensor networks have sensitive data and work in open and unprotected environment. In such networks nodes need to communicate with each other and security is the major concern in nodes communication. For this purpose many trust management approaches are proposed so far. Trust management helps to improve the security of wireless sensor networks. In this paper, various trust approaches that are used for wireless sensor networks have been reviewed and evaluated on the basis of defined parameters and limitations of each technique has been identified Moreover best approach amongst them has also been proposed.

Evaluation of trust management approaches in wireless sensor networks

Human activity recognition is gaining increasing importance because of its implication in remote monitoring application including security, health and fitness apps. This paper provides an analysis of different machine learning techniques for recognizing human activity. Firstly, all the recent work related to human activity recognition using accelerometer data is analyzed and presented in the paper. In this study the accelerometer used in smartphones as well as those embedded in wearable devices are compared and recognition methodologies applied on both the devices are presented. The dataset used in this project is a transformed version of "Activity Recognition using Cell Phone Accelerometers," by the Wireless Sensor Data Mining WSDM. Some important features were extracted from the data and based on it different models were assessed using Matlab Classification Learner App. Four distinct machine learning techniques were applied on the dataset, namely, linear regression, logistic regression, support vector machine and neural network. For the purposed of applying classifier Weka tool is used. The results of these algorithms are compared and presented in the form of tables and graphs and Bagged Tree is identified to be the best algorithm based on accuracy results.

Evaluating Machine Learning Techniques on Human Activity Recognition Using Accelerometer Data

Agile development is conventional these days and with the passage of time software developers are rapidly moving from Waterfall to Agile development. Agile methods focus on delivering executable code quickly by increasing the responsiveness of software companies while decreasing development overhead and consider people as the strongest pillar of software development. As agile development overshadows Waterfall methodologies for software development, it comes up with some distinct challenges related to testing of such software. Our study is going to discuss the challenges this approach has stirred up. Some of the challenges are discussed in this paper with possible solutions and approaches used for resolving these challenges. Also, the tools in practice are mentioned to improve the efficiency of the process. (Abstract)

A Comparative Study of Agile Methods, Testing Challenges, Solutions & Tool Support

To enable an effective traffic management and signal control, it is important to know the road traffic density. In recent years, video surveillance based systems and monitoring tools have been widely used for obtaining road traffic density for traffic management. To address the needs of autonomous traffic data extraction and video analysis, a vast body of research exists. However, these schemes are either prone to noise or the analysis methods are based on the manually provided data. Here, a state-of-the-art algorithm is developed for measuring the traffic density from the processing of surveillance videos obtained from different sources and conditions. The developed algorithm, keeping the user input to the minimum, automatically detects the traffic data. To get rid of the noise and false alarms, salient motion based method is used for the detection of the objects of interest. To show the efficacy of the proposed scheme, several raw surveillance videos are acquired and our algorithm is tested on them without any apriori information about the videos or their pertaining field conditions. For benchmark purposes, the outcomes of the developed algorithm are compared with that of a classical baseline method. The experimental results indicate that the traffic density is adequately determined and gives better accuracy than the classical approach. This is despite the fact that no threshold tuning for the individual videos is done in this algorithm.

Muhammad Abbas

Papers

On the mutual information of relaying protocols

Evaluation of trust management approaches in wireless sensor networks

Evaluating Machine Learning Techniques on Human Activity Recognition Using Accelerometer Data

A Comparative Study of Agile Methods, Testing Challenges, Solutions & Tool Support

Automated vehicle density estimation from raw surveillance videos