IoT & Cloud

Next time when you receive a health status from your watch or a power bill from your Utilities provider – thank Cloud Computing for making these interactions with the hardware a possibility. Generating intelligent business analytics from the devices will not only make human interactions meaningful but also have a defining effect on how we go about doing things in our everyday lives.

With access to scalable Cloud Computing resources, the devices & sensors become Bots. Such Sensors / Devices generating a large quantum of data create a unique need for computation resources that can ingest this big data and perform work flow to respond in real time.

Identifying the right Cloud Components to use with your IoT hardware is a key challenge. Single -vendor architectures won't always work and Public Cloud won't always be the complete solution. Depending on whether or not the IoT device is a proximal or a remote usage device, you could consider AllJoyn or MQTT for connectivity options. Amazon provides Kinesis, Lambda, Redshift on the public Cloud, but you could also consider Mosquito, HiveMQ in the private cloud to orchestrate the workflow. Simply put, the options are abundant and plentiful.

Mobile Pundits IoT Development Team has worked since the days of M2M to make these meaningful IoT applications. Our IoT architects have thought through the problems of how to handle and store data, which types of architecture to build, and how to create scalability for enterprises so that you don't have to. A key consideration for handling the data generated by IoT devices is the rate at which it generates that data. This has a bearing on the communication protocol and computing resources, and security considerations should be kept in mind as part of a holistic 'IoT' solution.

    • Multi-tenancy & Data security supporting a real world model can be difficult to implement.
    • Traditional technologies and apps can do this only to a certain extent, but the scale and requirements of IoT applications may require scalable parallelism and processing rates that can only be accomplished with Cloud Computing.
    • E.g. RESTful based Service might not be able to handle millions of connection requests with ease vis-à-vis a MQTT broker like Mosquito or HiveMQ.
    • Choosing tools that can help real-time decision making on the in-bound data is useful for IoT Analytics. One could consider choosing between an On-Premise solutions using Kafka/MQTT based message Broker to reduce latency and an On-Cloud data stream processing system (like Amazon Kinesis).
    • Bootstrapping this IoT datastream on to a Cloud service like AWS Kinesis gives access to scalable computational power for resource hungry real time message processing and analytics.
    • Auto-Scalable Cloud Compute resources facilitate such voluminous data processing. Further, code snippets encapsulating decision making business logic from services like.
    • Amazon Lambda can trigger rules based actions on the ingested data.
    • Most of the times the IoT devices could be monitoring very personal data. Managing identity and access control of the devices and related data is of key concern. Normal username and passwords for identity management do not work for devices effectively. If they are a hassle for human beings, then they are a bigger hassle for managing security of IoT devices.
    • Authentication could be federated using OpenID Connect and OAuth could be leveraged for Authorization (with some human interventions at the time of device provisioning). OAuth with little interventions can even be used with MQTT Brokers. In addition, services like AWS Cognito can be utilized for managing user specific data for ensuring data sanctity.
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