e 2 -mac: energy efficient medium access for massive m2m communications,

Each of these verticals comprises multiple IoT-based applications with various requirements. Since there are many use-cases of the IoT paradigm, it should be approached from a given vertical perspective, e.g., smart health, smart cities, smart manufacturing (Industry 4.0), smart transport, etc. In such a holistic approach, AI, among other machine learning tools, is employed in every stage of the solution including connectivity, storage, computing, and analytics.

In this work, we argue that IoT solutions should not be addressed through a layered perspective but, instead, a holistic optimisation approach is needed to generate the desired added value efficiently. The default application of AI is to draw actionable insights from the data in order to generate value to the given vertical. Analytics, which uses AI, is the added layer that transforms such an environment into a smart one. Moreover, local scripts result is real-time actions and do not expose the privacy of the data, whereas cloud computing incurs latency due to the transmission network and requires stringent security measures to protect the data.Īn environment, which is rich in IoT devices that are connected to a platform, qualifies as digitised, and often as intelligent.

On the other hand, running scripts locally in the device and reporting the resulting events to the cloud reduce the cloud service cost but limits the visibility to the actual data this still results in big data. Reporting raw data to the cloud is very costly as every bit gets charged and may also exhaust the battery of the device this results in massive data. Again, multiple choices are possible in this aspect with different strengths and weaknesses. Once these devices are connected, they start reporting the sensed or measured data to the platform. Wireless technology is key to connect these devices through gateways or aggregation points but, similarly, a wide range of wireless protocols and standards are available and competing.

IoT devices have become pervasive but cover a broad range of technologies and standards. However, there is a lot of ambiguity of what is meant by that and scepticism about the actual value generated by the IoT. The Internet of Things (IoT) is today’s buzzword, often coupled with Big Data and Artificial Intelligence (AI). We propose a reinforcement learning technique and demonstrate significant reduction in energy consumption while meeting the quality requirements of all related applications.

Based on a smart port application, we identify energy efficiency, security, and response time as essential quality features and consider a wireless realisation of IoT connectivity using short range and long-range technologies. In this work, we present a context-aware framework that jointly optimises the connectivity and computational speed of the IoT network to deliver the qualities required by each vertical. Moreover, each of many verticals employing IoT networks dictates distinctive and differential network qualities. Today, there are tens of wireless technologies competing for IoT and a myriad of IoT devices with disparate capabilities and constraints. The Internet of Things (IoT) is spreading much faster than the speed at which the supporting technology is maturing.

E 2 -mac: energy efficient medium access for massive m2m communications,