More Like this

1. A Latency-Defined Edge Node Placement Scheme for Opportunistic Smart Cities

   https://doi.org/10.1109/PerComWorkshops51409.2021.9430977  

   Madamori, Oluwashina ; Max-Onakpoya, Esther ; Erhardt, Gregory D. ; Baker, Corey E. ( January 2021 , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops))

   null (Ed.)

   Smart city projects have the potential to improve the management of environmental and public infrastructure. However, the operational and capital expenditures of smart cities can prevent cities from becoming smarter. A notable factor that influences the cost is providing cellular Internet connectivity to IoT devices. 5G has been proposed as a possible solution, but projections show that 5G will not be able to support the load of billions of IoT devices coming online. To mitigate this, people, vehicles, and other nodes in transportation networks can be exploited to transmit non-urgent data by leveraging device-to-device communication in order to reduce cellular connectivity costs associated with smart city sensors. Hence, this paper addresses cost-effective edge node placement in smart cities that opportunistically leverage public transit networks. We introduce an algorithm that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithm is evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky and results show that our algorithm outperforms betweeness and in-degree centrality metrics with a reduction in latency of over 20 minutes. 

   more » « less
2. A Decentralized Approach For Real Time Anomaly Detection In Transportation Networks

   https://doi.org/10.1109/SMARTCOMP.2019.00063  

   Wilbur, Michael ; Dubey, Abhishek ; Bruno, Leao ; Bhattacharjee, Shameek. ( June 2019 , 2019 IEEE International Conference on Smart Computing (SMARTCOMP))

   Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in smart connected cities. Given the increasing rate of urbanization, the advancement of these technologies is a critical component of mitigating demand on already constrained transportation resources. Smart transportation systems are most effectively implemented as a decentralized network, in which traffic sensors send data to small low-powered devices called Roadside Units (RSUs). These RSUs host various computation and networking services. Data driven applications such as optimal routing require precise real-time data, however, data-driven approaches are susceptible to data integrity attacks. Therefore we propose a multi-tiered anomaly detection framework which utilizes spare processing capabilities of the distributed RSU network in combination with the cloud for fast, real-time detection. In this paper we present a novel real time anomaly detection framework. Additionally, we focus on implementation of our framework in smart-city transportation systems by providing a constrained clustering algorithm for RSU placement throughout the network. Extensive experimental validation using traffic data from Nashville, TN demonstrates that the proposed methods significantly reduce computation requirements while maintaining similar performance to current state of the art anomaly detection methods. 

   more » « less
3. Towards a Heterogeneous Internet-of-Things Testbed via Mesh inside a Mesh: Poster Abstract

   https://doi.org/10.1145/2994551.2996710  

   Miao, Luwen ; Liu, Kaikai ( January 2016 , SenSys '16 Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems)

   Connectivity is at the heart of the future Internet-of-Things (IoT) infrastructure, which can control and communicate with remote sensors and actuators for the beacons, data collections, and forwarding nodes. Existing sensor network solutions cannot solve the bottleneck problems near the sink node; the tree-based Internet architecture has the single point of failure. To solve current deficiencies in multi-hop mesh network and cross-domain network design, we propose a mesh inside a mesh IoT network architecture. Our designed "edge router" incorporates these two mesh networks together and performs seamlessly transmission of multi-standard packets. The proposed IoT testbed interoperates with existing multi-standards (Wi-Fi, 6LoWPAN) and segments of networks, and provides both high-throughput Internet and resilient sensor coverage throughout the community. 

   more » « less
4. An REU/RET Project: IoT Platform and Network Data Visualization

   Toutsop, O. ( November 2021 , 2021 Fall ASEE Middle Atlantic Section Meeting)

   Internet of Things (IoT) is a connected network of devices that exchange data using different protocols. The application of IoT ranges from intelligent TVs and intelligent Refrigerators to smart Transportation. This research aims to provide students with hands-on training on how to develop an IoT platform that supports device management, connectivity, and data management. People tend to build interconnected devices without having a basic understanding of how the IoT platform backend function. Studying the Arm Pelion will help to understand how IoT devices operate under the hood. This past summer, Morgan State University has hosted undergraduate engineering students and high school STEM teachers to conduct IoT security research in the Cybersecurity Assurance & Policy (CAP) Center. The research project involved integrating various hardware sensor devices and real-time data monitoring using the Arm Pelion IoT development platform. Some of the student/teacher outcomes from the project include: 1) Learning about IoT Technology and security; 2) Programming an embedded system using Arm Mbed development board and IDE; 3 3) Developing a network of connected IoT devices using different protocols such as LWM2M, MQTT, CoAP; 4) Investigating the cybersecurity risks associated with the platform; and 5) Using data analysis and visualization to understand the network data and packet flow. First, the student/teacher must consider the IoT framework to understand how to address the security. The IoT framework describes the essential functions of an IoT network, breaking it down into separate layers. These layers include an application layer, middleware layer, and connectivity layer. The application layer allows the users to access the platform via a smartphone or any other dashboard. The Middleware layer represents the backend system that provides edge devices with data management, messaging, application services, and authentication. Finally, the connectivity layer includes devices that connect the user to the network, including Bluetooth or WiFi. The platform consists of several commercial IoT devices such as a smart camera, baby monitor, smart light, and other devices. We then create algorithms to classify the network data flow; to visualize the packets flow in the network and the structure of the packets data frame over time. 

   more » « less
5. Sparta: Heat-Budget-Based Scheduling Framework on IoT Edge Systems

   https://doi.org/10.1007/978-3-030-96504-4_2  

   Zhang, M. ; Krintz, C. ; Wolski, R. ( December 2021 , Edge Computing)

   Co-location of processing infrastructure and IoT devices at the edge is used to reduce response latency and long-haul network use for IoT applications. As a result, edge clouds for many applications (e.g. agriculture, ecology, and smart city deployments) must operate in remote, unattended, and environmentally harsh settings, introducing new challenges. One key challenge is heat exposure, which can degrade the performance, reliability, and longevity of electronics. For edge clouds, these problems are exacerbated because they increasingly perform complex workloads, such as machine learning, to affect data-driven actuation and control of devices and systems in the environment. The goal of our work is to protect edge clouds from overheating. To enable this, we develop a heat-budget-based scheduling system, called Sparta, which leverages dynamic voltage and frequency scaling (DVFS) to adaptively control CPU temperature. Sparta takes machine learning applications, datasets, and a temperature threshold as input. It sets the initial frequency of the CPU based on historical data and then dynamically updates it, according to the applications’ execution profile and ambient temperature, to safeguard edge devices. We find that for a suite of machine learning applications and deployment temperatures, Sparta is able to maintain CPU temperature below the threshold 94% of the time while facilitating improvements in execution time by 1.04x − 1.32x over competitive approaches. 

   more » « less