Opportunistic sensing for road pavement monitoring

Road surface state monitoring is of main concern for road infrastructure owners. Hence dedicated measurement campaigns using laser scanning and image analysis are performed on a regular basis. Yet, this type of monitoring comes at a high labor cost and thus it is often limited in coverage and update frequency. This paper proposes opportunistic sensing as an alternative approach. Using sound and vibration sensing in cars that are on the road for other purposes and exploiting the advent of cheap communication, big data, and machine learning, timely information on road state is obtained. Results are compared to laser scanning for spatial frequencies between 0.1 and 100 cycles/m showing the applicability of the method. Results are also used for classification and labeling of road surfaces regarding their effect on rolling noise. Mapping illustrates the coverage of highways and local roads obtained in a few months with as few as seven cars

Roads’ Traffic and Conditions Monitoring using Smart Phones: Gaza Strip as a Project Scope

In this paper we introduce briefly our joint research project "Smart Privacy Preserving Roads’ Traffic and Conditions Monitoring" (Smart P2 Road) and describe the achieved progress and the current status of the project. The motivation for our research project is the increasing need for road status and traffic monitoring services as the demand for motorized transportation is growing while having limited resources of highways, parks and roads. We use smartphones as a platform for implementing our system because they come equipped with a wide range of sensors. The most important sensors for our project are the accelerometers and gyroscopes which constitute the inertial measurement unit (IMU) and the positioning devices based on a satellite constellation such as GPS, GLONASS and Galileo. Moreover, they have detailed maps of the roads for easier navigation. Other professional road monitoring sensors such as Antilock Brake System (ABS) sensor which already exists in modern vehicles can be interfaced in a modular fashion to our system. The uniqueness of Smart P2 Road is to integrate several sensors and devices with a strong emphasis on trust, privacy, security and dependability for reliable use by public users and local authorities. The integrity is verified through experimental monitoring of roads across Gaza Strip, Palestine and Amman, Jordan. In those experiments we collect various sensory data, keeping in mind respecting the privacy of the driver as a priority. These collected data were processed and classified using k-means algorithm. The preliminary results show a success rate of 95% for the underdevelopment system

Towards Zero Touch Next Generation Network Management

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques