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

Software project management has main role in the Software industry. It includes different processes and knowledge areas. The triple constraint of the software project like time, cost and scope is directly dependent on the requirement of the project. Agile methodology is the iterative way for developing the software project for frequent changes, fast delivery and reduce risk. Software project management also plays important role in agile based software project. Agile methodology influence software project management at 10 knowledge areas. In this study we carried out survey from different software companies and it shows that almost every software company uses agile development (Scrum) and has a positive impact on the software project management.

The Influence of Agile Methodology (Scrum) on Software Project Management

Agile methodologies gained fame due to the fact of producing high-quality software systems. Maintenance effort is almost more than half of the total effort invested in any software system during its lifespan. A well-discussed issue within the community of researchers and engineers is how to use agile methodologies for maintaining the developed software because agile software development life cycle doesn’t have the specifically planned mechanism for maintenance. To bridge this gap, we used the theoretical and empirical technique to formulate factors that should be followed during the agile maintenance including planning for the maintenance; the on-site customer should be present, iterative maintenance, documentation update after each phase and maintenance should be testable.

Scrum Software Maintenance Model: Efficient Software Maintenance in Agile Methodology

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

This work is about the autonomous detection of a road traffic incident by exploiting road surveillance camera videos. Timely and autonomous detection of an incident is paramount for the reduction of traffic congestion so that countermeasures can be taken at the earliest. This paper presents a novel Fuzzy Logic based analysis framework and a video based traffic data extraction scheme to decide upon the right traffic conditions. The existing road traffic analysis approaches as reported in literature do not extract data from the road camera videos; rather they use already available data to validate their schemes. However, in the proposed approach a complete scheme is proposed which takes a raw road camera video and autonomously extracts the relevant data for the subsequent Fuzzy Logic based traffic analysis. To show the efficacy of the proposed scheme, unprocessed surveillance videos of both urban and motorway scenarios are used. The results indicate that the traffic flow and their statistics are adequately determined through the proper selection of membership functions and rule formulation. Owing to the use of fuzzy logic, our proposed framework is seen to be robust enough to reject the noisy data coming from surveillance videos.

Traffic event detection from road surveillance vide os based on fuzzy logic

Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.

Muhammad Abbas

Papers

The Influence of Agile Methodology (Scrum) on Software Project Management

Scrum Software Maintenance Model: Efficient Software Maintenance in Agile Methodology

Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

Traffic event detection from road surveillance vide os based on fuzzy logic

Trajectory based vehicle counting and anomalous event visualization in smart cities