5 Essential Techniques for Improving IoT Deployment Efficiency
IoT use cases involve both stationary and mobile IoT devices, each having its own set of deployment challenges. A smart building equipped with building automation technologies is a typical example of a stationary application where IoT sensors and equipment are deployed across the building in static locations. If those locations lack good signal quality, the devices fail to perform as expected. On the other hand, if we consider smart city public transportation systems, the deployed IoT sensors in the vehicles, etc. are mobile and have to constantly adapt to variabilities in cellular signal quality in various locations.
Suboptimal signal quality is one of the top reasons for performance inefficiencies that are commonly encountered in both stationary and mobile IoT deployments. These inefficiencies ultimately impact the entire IoT application use case. To address these challenges head-on you need to adopt some key techniques that add network intelligence and insights to your existing IoT deployment workflows.
Vital Considerations for Efficient IoT Deployment
A successful IoT solution depends on a few vital functions:
To successfully deploy an IoT use case, planning is the foremost and the most vital step. Definite goals to achieve success must be clearly defined. In addition, you need to identify key metrics and define the KPIs. Technical inventory and staging etc. are all fundamental decisions that ultimately impact the outcome of the solution.
During the deployment, it is important to manage the IoT endpoints and sensors for which you need remote visibility into deployment and connectivity status. You need to identify agile tools for efficient management which ultimately accelerates deployment velocity and increases overall value over time.
Avoid the Chaos
The mass deployment of inefficient, insecure or defective IoT devices gives rise to multiple performance headaches:
- Issues like cell congestion which are local within the mobile network.
- Wide-area disruption due to signaling storms which result from capacity and performance problems within the mobile provider’s core network.
- Degradation of IoT service performance, potentially resulting in delayed communications, degradation of the service quality and even complete service outages. This increases the number of truck rolls and technicians’ billable hours to resolve the problem which again jacks up deployment cost.
- Reduction in IoT device lifetime as a result of increased power consumption due to unintelligent communication error handing.
Mobile network resources are dimensioned assuming a ‘generalized’ device usage profile and it is important that IoT devices are deployed with good network coverage to optimally utilize the mobile networks.
Benefit from the ‘N-View Advantage’
Nivid’s N-view supports both stationary and mobile IoT sensor-based applications. It works across a wide variety of sensor technologies from NB-IoT, LTE-M, and Cat-M1/M2 to LoRa and 5G. By integrating N-View in your IoT deployment process you can take advantage of its five key capabilities:
- Intelligence: The intelligence to determine reliable network coverage for identifying optimal locations to deploy IoT sensors.
- Insights: Real-time monitoring of RF and network KPIs by using a cloud-based analytics dashboard. N-View gives you the visibility to ensure optimal sensor performance to measure network performance.
- Cost Efficiency: N-View offers a low-cost solution to improve sensor deployment efficiencies by minimizing truck rolls and the associated cost of deployment.
- Flexibility: N-View works with a wide variety of IoT sensor chipsets to support the plethora of low-cost IoT device types and cellular technology that enable them. N-View’s user-friendly UI/UX simplifies field usage and the technician’s tasks.
- Edge-Cloud Advantage: To use N-View there’s no internet dependency. Measure signal offline, upload data to the cloud when online connectivity is available.
N-View helps you validate your IoT network design using real measurement data for predictable runtime performance.