Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges

Nowadays, video cameras are deployed in large scale for spatial monitoring of physical places (e.g., surveillance systems in the context of smart cities). The massive camera deployment, however, presents new challenges for analyzing the enormous data, as the cost of high computational overhead of sophisticated deep learning techniques imposes a prohibitive overhead, in terms of energy consumption and processing throughput, on such resource-constrained edge devices. To address these limitations, this paper envisions a collaborative intelligent cross-camera video analytics paradigm at the network edge in which camera nodes adjust their pipelines (e.g., inference) to incorporate correlated observations and shared knowledge from other nodes' contents. By harassing redundant spatio-temporal to reduce the size of the inference search space in one hand, and intelligent collaboration between video nodes on the other, we discuss how such collaborative paradigm can considerably improve accuracy, reduce latency and decrease communication bandwidth compared to non-collaborative baselines. This paper also describes major opportunities and challenges in realizing such a paradigm.Comment: First International Workshop on Challenges in Artificial Intelligence and Machine Learnin

Low-Cost Traffic Sensing System Based on LoRaWAN for Urban Areas

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a largescale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lComment: 7 pages, accepted to Emerging Topics in Wireless (EmergingWireless) in CoNEXT 202

Low-cost traffic sensing system based on LoRaWAN for urban areas

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a large-scale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lessons learned from such a deployment for traffic sensing.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

LEARNING-Driven FRAMEWORKS FOR SELF-DRIVEN PROTOCOLS for NEXT-Generation NETWORKS

The emergence of machine learning approaches for wireless communication protocol design has become a key paradigm for future wireless communication and systems, particularly for the fifth and sixth generations of mobile communications. These approaches are essential to improve resource management, networking, mobility management, etc., to accommodate the growing needs for data traffic. However, there are limitations to improving algorithmic approaches due to device heterogeneity, service diversification, dynamic network environment, network scale, and the growth in the amount of data related to applications, users, and networks. This thesis proposes novel learning-driven frameworks to design communication protocols that can learn how to decide near-optimal protocols in different environmental contexts, such as device characteristics, application requirements, user objectives, and network conditions. It presents the feasibility of these approaches through simulation, emulation, implementation, and experimental evaluation in different realistic settings, not only by simulation but also by experimental evaluation in practical networks for multi-user mobile networks. Additionally, this research describes a cross-layer dual-phase learning-driven approach to show the feasibility of combining lower-layer multi-user MAC capabilities with upper-layer application requirements, such as multi-user video streaming, to exploit an unexplored cross-layer approach. Overall, this thesis builds a foundation for on-device learning-assisted communication protocol design, shifting from rule-based protocol design to the design and development of self-driven protocols for next-generation networks. The proposed frameworks have shown great potential to realize orders of magnitude increase in data rates, decrease in delay, and protocol\u27s robustness, which could provide new insights into protocol design optimization