An evaluation of two distributed deployment algorithms for Mobile Wireless Sensor Networks

Deployment is important in large wireless sensor networks (WSN), specially because nodes may fall due to failure or battery issues. Mobile WSN cope with deployment and reconfiguration at the same time: nodes may move autonomously: i) to achieve a good area coverage; and ii) to distribute as homogeneously as possible. Optimal distribution is computationally expensive and implies high tra c load, so local, distributed approaches may be preferable. This paper presents an experimental evaluation of role-based and behavior based ones. Results show that the later are better, specially for a large number of nodes in areas with obstacles.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks

COMPARISON BETWEEN A* BASED SPLINES AND STATE LATTICE PATH PLANNING FOR AUTONOMOUS VEHICLES

Planning a path from source to destination avoiding collisions with obstacles is a basic requirement for navigation for any autonomous vehicle. Path generated using the algorithms should satisfy the constraints posed by the vehicle for which the path is being generated. Along with this, the path should also be smooth enough to avoid any jerky movements by the vehicle. Many algorithms have been designed to solve this problem. Among these algorithms, most of these come under graph search, sampling, interpolating and numerical optimization techniques. In this thesis, we have chosen two algorithms for comparison on various metrics. The first implementation is a graph based technique, A* algorithm, to find a collision free path from source to destination and using b-splines, an interpolating technique to smooth this obtained path. The second implementation is state lattice planner, which discretizes the whole search space and generates feasible trajectories which in-turn are used by A* algorithm to find a smooth path. The results obtained using these two techniques are compared on various performance metrics such as execution time, optimality, arc length, path cost, ability to find path in narrow spaces and feasibility of the generated path. Based on the observations, the execution time of the state lattice planner is less than A* based splines planner. However, the drawback of this approach is that it does not create a shortest path and that the path cost and arc length are greater than that of A* based splines approach

An assessment of employee commitment to work among UAE nationals

This research paper investigates the employee commitment to work as demonstrated by UAE national men and women across three sectors: the public, semi-public and private, specifically in the Emirates of Abu Dhabi and Dubai. The research demonstrated that the private sector has a high level of work satisfaction. In contrast, the greatest dissatisfaction was in the public and semi-public sectors which respondents opined that there was generally a lack of recognition for good work and that they did not have opportunities to engage in discussion about progress of the organisation they worked for, and weak support for the performance evaluation process, citing reservations about its fairness. Emiratis looking for career advancement should consider the private sector with a greater degree of confidence than they have thus far - a view endorsed by their own compatriots according to the outcomes of this research

Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model

Urdu language is being spoken by over 64 million people and its Roman script is very popular, especially on social networking sites. Most users prefer Roman Urdu over English grammar for communication on social networking platforms such as Facebook, Twitter, Instagram and WhatsApp. For research, Urdu is a poor resource language as there are a few research papers and projects that have been carried out for the language and vocabulary enhancement in comparison to other languages especially English. A lot of research has been made in the domain of sentiment analysis in English but only a limited work has been performed on the Roman Urdu language. Sentiment analysis is the method of understanding human emotions or points of view, expressed in a textual form about a particular thing. This article proposes a deep learning model to perform data mining on emotions and attitudes of people using Roman Urdu. The main objective of the research is to evaluate sentiment analysis on Roman Urdu corpus containing RUSA-19 using faster recurrent convolutional neural network (FRCNN), RCNN, rule-based and N-gram model. For assessment, two series of experiments were performed on each model, binary classification (positive and negative) and tertiary classification (positive, negative, and neutral). Finally, the evaluation of the faster RCNN model is analyzed and a comparative analysis is performed for the outcomes of four models. The faster RCNN model outperformed others as the model achieves an accuracy of 91.73% for binary classification and 89.94% for tertiary classification