Neamah Hasan Neamah AL-Naffakh

Waste collection has become a major issue all over the world, especially when it is offered as a service for businesses; unlike public waste collection where the parameters are relatively homogeneous. This industry can greatly benefit from new sensing technologies and advances in artificial intelligence that have been achieved over the last few years. However, in most situations waste management systems are based on obsolete technologies, with a low level of interoperability and thus offering static processes. The most advanced solutions are generally limited to statistical, non-predictive approaches and have a limited view of reality, making them weakly effective in dealing with day-to-day business issues (overflowing containers, poor quality of service, etc.). This paper presents a case study currently being developed in Luxembourg with a company offering a business waste collection service, which has a significant amount of constraints to consider. Our main objective is to investigate the use of multiple waste data sources to derive useful indicators for improving collection processes. We start with company-owned historical data and then investigate GPS information from tracking devices positioned on collection trucks. Furthermore, we analyze data collected from ultrasonic sensors deployed on almost 50 different containers to measure fill levels. We describe the deployment steps and show that this approach, combined with anomaly detection and prediction techniques, has the potential to change the way this business operates. We also discuss the interest of the different datasets presented and multi-objective optimization issues. To the best of our knowledge, this article is the first major work dedicated to the world of professional waste collection.

The waste recycling industry has grown considerably in the recent years and many solutions have become democratized around smart waste collection. However, existing decision support systems generally rely on a limited flow of information and offer an often static or statistically based approach, focusing on specific use-cases (e.g., individuals, municipalities). This paper introduces SWAM, a new smart waste collection platform currently being elaborated in Luxembourg that targets businesses and large entities (e.g., restaurants, shopping centers). SWAM aims to consider multiple sources of combined information in its decision-making process and go further in the routing optimization process. The platform notably uses ultrasonic sensors to measure the filling level of containers, and smartphones with embedded intelligence to understand a driver's actions. This paper presents our experience on the development and deployment of this platform in Luxembourg, as well as the relevant options on the choice of sensing and communication technologies available in the market. It also presents the system architecture and two fundamental components. Firstly, a data management layer, which implements models for analyzing and predicting the filling patterns of the containers. Secondly, a multi-objective optimization layer, which compiles the collection routes that minimize the impact on the environment and maximize the service quality. This paper is intended to serve as a practical reference for the deployment of waste management systems, which have many technological components and can greatly fluctuate depending on the use cases to be covered.

Smartwatches, which contain an accelerometer and gyroscope, have recently been used to implement gait/activity-based biometrics. However, many research questions have not been addressed in the prior work such as the training and test data was collected in the same day from a limited dataset, using unrealistic activities (e.g., punch) and/or the authors did not carry out any particular study to identify the most discriminative features. This paper aims to highlight the impact of these factors on the biometric performance. The acceleration and gyroscope data of the gait and game activity was captured from 60 users over multiple days, which resulted in a totally of 24 h of the user’s movement. Segment-based approach was used to divide the time-series acceleration and gyroscope data. When the cross-day evaluation was applied, the best obtained EER was 0.69%, and 4.54% for the walking and game activities respectively. The EERs were significantly reduced into 0.05% and 2.35% for the above activities by introducing the majority voting schema. These results were obtained by utilizing a novel feature selection process in which the system minimizing the number of features and maximizing the discriminative information. The results have shown that smartwatch-based activity recognition has significant potential to recognize individuals in a continuous and user friendly approach.

Gait recognition is a technique that identifies or verifies people based upon their walking patterns. Smartwatches, which contain an accelerometer and gyroscope have recently been used to implement gait-based biometrics. However, this prior work relied upon data from single sessions for both training and testing, which is not realistic and can lead to overly optimistic performance results. This paper aims to remedy some of these problems by training and evaluating a smartwatch-based biometric system on data obtained from different days. Also, it proposes an advanced feature selection approach to identify optimal features for each user. Two experiments are presented under three different scenarios: Same-Day, Mixed-Day, and Cross-Day. Competitive results were achieved (best EERs of 0.13% and 3.12% by using the Same day data for accelerometer and gyroscope respectively and 0.69% and 7.97% for the same sensors under the Cross-Day evaluation. The results show that the technology is sufficiently capable and the signals captured sufficiently discriminative to be useful in performing gait recognition.

Activity recognition that recognises who a user is by what they are doing at a specific point of time is attracting an enormous amount of attention. Whilst previous research in activity recognition has focused on wearable dedicated sensors (body worn sensors) or using a smartphone’s sensors (e.g. accelerometer and gyroscope), little attention is given to the use of wearable devices – which tend to be sensor-rich highly personal technologies. This paper presents a thorough analysis of the current state of the art in transparent and continuous authentication using acceleration and gyroscope sensors and an advanced feature selection approach to select the optimal features for each user. Two experiments are conducted; the first experiment used all the extracted features (i.e., 143 unique features) while (for comparison) a more selective set of only 30 features are used in the second experiment. The best results of the first experiment are average Euclidean distance scores of 0.55 and 1.41 for users’ intra acceleration and gyroscope signals respectively and 3.33 and 5.85 for users’ inter acceleration and gyroscope activities accordingly- providing sufficient disparity in distance to suggest a strong classification performance. In comparison, the second experiment demonstrated stronger results when evaluated (at best the average Euclidean distance scores is 0.03 and 0.19 for users’ intra acceleration and gyroscope signals respectively and 1.65 and 1.1 for users’ inter acceleration and gyroscope activities). The findings demonstrate that the technology is sufficiently capable and the nature of the signals captured sufficiently discriminative to be useful in performing activity recognition. Moreover, the proposed feature selection approach could offer better results and reduce the computational overhead on digital devices

A secure, user-convenient approach to authenticate users on their mobile devices is required as current approaches (e.g., PIN or Password) suffer from security and usability issues. Transparent Authentication Systems (TAS) have been introduced to improve the level of security as well as offer continuous and unobtrusive authentication (i.e., user friendly) by using various behavioural biometric techniques. This paper presents the usefulness of using smartwatch motion sensors (i.e., accelerometer and gyroscope) to perform Activity Recognition for the use within a TAS. Whilst previous research in TAS has focused upon its application in computers and mobile devices, little attention is given to the use of wearable devices - which tend to be sensor-rich highly personal technologies. This paper presents a thorough analysis of the current state of the art in transparent and continuous authentication using acceleration and gyroscope sensors and a technology evaluation to determine the basis for such an approach. The best results are average Euclidean distance scores of 5.5 and 11.9 for users' intra acceleration and gyroscope signals respectively and 24.27 and 101.18 for users' inter acceleration and gyroscope activities accordingly. The findings demonstrate that the technology is sufficiently capable and the nature of the signals captured sufficiently discriminative to be useful in performing Activity Recognition.

2017 International Conference of the Biometrics Special Interest Group (BIOSIG)

2016 11th International Conference for Internet Technology and Secured Transactions (ICITST)

IFIP International Conference on Trust Management

the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Communities