Abstract
A vast majority of the Internet of Things (IoT) devices will be connected in a topology where the edge-devices push data to a local gateway, which forwards the data to a cloud for further processing. In sizeable outdoor deployment regions, the edge-devices may experience poor connectivity due to their distant locations and limited transmission power. Repeaters or relays must be placed at a few locations to ensure reliable connectivity to either a gateway or another node in the network. A big challenge in achieving reliable connectivity and coverage is the outdoor propagation environment being heterogeneous. Engineers often deploy networks based on resource-intensive field visits, detailed surveys, measurements, initial test deployments, followed by fine-tuning. For scalability to large scale IoT deployments, automated network planning tools are essential. Such tools should predict connectivity based on the edge-device locations using available Geographical Information System (GIS) data, identify the need for relays/repeaters, and, if needed, suggest the number of relays needed with their locations. Furthermore, such tools should also be extended to suggest the minimum number and locations of base stations that maximise coverage. In this paper, we propose an automated network deployment framework using a black box received signal strength estimation oracle that provides signal strength estimates between candidate pairs of transceiver locations in a heterogeneous deployment region. Our proposed methodology uses either Ant Colony Optimisation (ACO) or Differential Evolution (DE) to identify the number and locations of relays for meeting specified quality of service constraints. We discuss adaptations of our techniques to handle scenarios with multiple gateways. Further, we show the effectiveness of these algorithms to find suitable candidate base station locations to provide coverage in a heterogeneous propagation environment that meets the specified quality of service constraints. We then demonstrate the effectiveness of our algorithms in two deployment regions.
Highlights
Enabling automation in outdoor Internet of Things (IoT) network deployment is crucial for the expansion of IoT systems at the currently projected scales
Given a collection of data sources, i.e., edge-devices with data to push into the IoT network, and a destination node, all of which are located on a Geographical Information System (GIS), the connectivity problem is to identify suitable relay locations that can help transport data from the IoT edgedevices to the gateway by meeting certain specified Quality of Service (QoS) constraints
We focus on the second subproblem, which includes network deployment for connectivity and network deployment for coverage
Summary
Enabling automation in outdoor Internet of Things (IoT) network deployment is crucial for the expansion of IoT systems at the currently projected scales. Given a collection of data sources, i.e., edge-devices with data to push into the IoT network, and a destination node (a gateway to a cloud computing and storage platform), all of which are located on a Geographical Information System (GIS), the connectivity problem is to identify suitable relay locations that can help transport data from the IoT edgedevices to the gateway by meeting certain specified Quality of Service (QoS) constraints We divide this problem into two subproblems. Rathod et al [2], [3] reported extensive measurements in example environments and proposed a ’library’ of propagation models They processed the GIS data for the deployment region and partitioned it into subregions of locally homogeneous propagation conditions. State-of-the-art results of [7], which are more applicable to problems arising from homogeneous propagation environments, are not directly applicable to our setting of the heterogeneous propagation environment
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