The Internet of Things (IoT) is an emerging technology that aims to connect our environment to the internet in the same way that personal computers connected people. As this technology progresses, the IoT paradigm becomes more prevalent in our everyday lives. The nature of IoT applications necessitates devices that are low-cost, power-sensitive, integrated, unobtrusive, and interoperable with existing cloud platforms and services, for example, Amazon AWS IoT, IBM Watson IoT. As a result, these devices are often small in size, with just enough computing power needed for their specific tasks. These resource-constrained devices are often unable to implement traditional network security measures and represent a vulnerability to network attackers as a result. Few frameworks are positioned to handle the influx of this new technology and the security concerns associated with it. Current solutions fail to provide a comprehensive and multi-layer solution to these inherent IoT security vulnerabilities. This paper presents a layered approach to IoT testbed that aims to bridge multiple connection standards and cloud platforms. To solve challenges surrounding this multi-layer IoT testbed, we propose a mesh inside a mesh IoT network architecture. Our designed "edge router" incorporates 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 Internet and resilient sensor coverage to the cloud platform. To ensure confidentiality and authentication of IoT devices when interoperating with multiple service platforms, we propose optimized cryptographic techniques and software frameworks for IoT devices. We propose to extend and modify the existing open-source IDS platforms such as Snort to support IoT platforms and environments. We validate the efficacy of the proposed system by evaluating its performance and effect on key system resources. The work within this testbed design and implementation provides a solid foundation for further IoT system development.
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Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge
The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application's high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources.
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- NSF-PAR ID:
- 10098740
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
- Page Range / eLocation ID:
- 511 to 516
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PERCOMW.2019.8730577
