Performance Analysis of Heterogeneous Networks for Robotic Navigation
Hersch Nathan [1], Md. Saeid Anwar [2], Anuradha Ravi [2], Nirmalya Roy[2]
[1] Department of Electrical and Computer Engineering, University of Kentucky, 512 Administration Drive, KY 40506
[2] Department of Information Systems, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250
Hersch Nathan [1], Md. Saeid Anwar [2], Anuradha Ravi [2], Nirmalya Roy[2]
[1] Department of Electrical and Computer Engineering, University of Kentucky, 512 Administration Drive, KY 40506
[2] Department of Information Systems, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250
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Presented at the 26th Annual Summer Undergraduate Research Fest
Hosted by University of Maryland, Baltimore County's College of Natural and Mathematical Sciences This work was the result of a Research Experience for Undergraduates in Smart Computing and Communications hosted by Mobile, Pervasive and Sensor Computing Lab (MPSC Lab). 
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Abstract:
During disaster recovery, it is imperative to take the assistance of robots to navigate hostile terrains. Robots can autonomously make application-oriented decisions and send data (such as images) to human personnel for decision-making. Communication in a disaster-struck environment can be challenging with the destruction of communication infrastructure or lack thereof. Establishing satellite-based communication can be a costly affair. The requirement for wireless networks in far-reach areas led to the inception of LoRa (Long-Range) networks, which leverage Chirp Spread Spectrum (CSS) technology for long-range communication over low bandwidth. Thus, devices equipped with LoRa can communicate small chirps of data over a long-range, making them power efficient to sustain their battery life for a longer duration. Per regulations, in the United States LoRa exists on the 902-928Mhz band with power restrictions. LoRaWAN is a WAN protocol built on top of LoRa, which has typically been used to transmit small amounts of data from low-power sensor networks.
In this project, we first set up a LoRaWAN network to interface and interact with UAVs and UGVs. We then analyze the performance of LoRaWAN network on varying workloads and monitor the computation and communication power consumption of a bot while employing the LoRa network. We further explore the possibility of transmitting image data over the LoRaWAN network. We leverage the low bandwidth of LoRaWAN to send feature representatives of the images (rather than sending raw image data) that can be processed at an edge node for object classification applications. To lay down a path for decision-making (selecting the best possible network) in a heterogeneous network environment, we compare sending images and feature representatives of the raw images over WiFi via MQTT (as proposed by previous works) and LoRaWAN. We analyze the performance (delay and power consumption) of WiFi and LoRaWAN given varying workloads.
This research was partly supported through a Research Experience for Undergraduates (REU) funded by NSF Grant #2050999, ANL Grant #W911NF2120076, ONR Grant #N00014-23-1-2119, and NSF CNS EAGER Grant #2233879.
During disaster recovery, it is imperative to take the assistance of robots to navigate hostile terrains. Robots can autonomously make application-oriented decisions and send data (such as images) to human personnel for decision-making. Communication in a disaster-struck environment can be challenging with the destruction of communication infrastructure or lack thereof. Establishing satellite-based communication can be a costly affair. The requirement for wireless networks in far-reach areas led to the inception of LoRa (Long-Range) networks, which leverage Chirp Spread Spectrum (CSS) technology for long-range communication over low bandwidth. Thus, devices equipped with LoRa can communicate small chirps of data over a long-range, making them power efficient to sustain their battery life for a longer duration. Per regulations, in the United States LoRa exists on the 902-928Mhz band with power restrictions. LoRaWAN is a WAN protocol built on top of LoRa, which has typically been used to transmit small amounts of data from low-power sensor networks.
In this project, we first set up a LoRaWAN network to interface and interact with UAVs and UGVs. We then analyze the performance of LoRaWAN network on varying workloads and monitor the computation and communication power consumption of a bot while employing the LoRa network. We further explore the possibility of transmitting image data over the LoRaWAN network. We leverage the low bandwidth of LoRaWAN to send feature representatives of the images (rather than sending raw image data) that can be processed at an edge node for object classification applications. To lay down a path for decision-making (selecting the best possible network) in a heterogeneous network environment, we compare sending images and feature representatives of the raw images over WiFi via MQTT (as proposed by previous works) and LoRaWAN. We analyze the performance (delay and power consumption) of WiFi and LoRaWAN given varying workloads.
This research was partly supported through a Research Experience for Undergraduates (REU) funded by NSF Grant #2050999, ANL Grant #W911NF2120076, ONR Grant #N00014-23-1-2119, and NSF CNS EAGER Grant #2233879.