IDC predicts that 45% of all data created by devices connected to the Internet of Things (IoT) will be stored, processed, and actioned using edge computing.
IoT is a rapidly growing market – it has been forecast that the number of IoT connected devices will surpass the number of mobile phones by the end of 2018, with a projected market of $200 billion by 2023.
IoT allows connected devices to communicate data to central processors which process that information and issue relevant actions and instructions directly back to the appropriate devices. Developments in IoT connected devices have provided businesses with limitless opportunities to innovate and use cloud computing technology to maximize the uses and effectiveness of their products.
Edge computing is the next step in IoT development. While cloud computing means that data is transferred centrally to be processed, edge computing provides local and device-to-device processing. This change reduces the pressure on the cloud computing network, and it eliminates any lag caused transmission and data processing. Take, for example, a valve on an oil pipeline network. If the pipeline valve detects it’s under too much pressure, previously it would send this data to the central server which would calculate that the valve needs to close by 25%, and this information would be passed back through the cloud to the pipeline valve, which would close as instructed. With edge computing, the valve works out that it’s under too much pressure using local or onboard computing software, making the decision locally to close by 25%, on top of which it can share this information with other local pipeline valves so that they can adjust their behavior accordingly.
Two critical areas of development for edge computing technology are automotive and vision sensory technology. For autonomous vehicles to operate, they must process and act on data for every single second they are in operation. Not only is this a great deal of data to transfer via cloud computing – roughly a terabyte of data per day per vehicle – but this can put users at risk in low signal or ‘spotty’ signal locations where information such as the significance of a person who suddenly steps onto the road cannot be transferred and processed quickly enough. Vision sensory technology also includes autonomous vehicles, as well as drones and other camera equipment, which can be used to monitor and take action remotely. This technology could involve using drones to inspect remote areas or dangerous environments such as high-power electricity cables or security systems installed on residential or business properties.
Another sector keen to embrace edge computing as part of the adoption of IoT technology is healthcare, where there are continuous monitoring and support devices attributed to ensuring the well-being of patients. In hospitals, there is a high level of data being transferred at any given time with all patients being connected to monitoring devices that will alert staff to any changes in a patient’s state that require intervention. Using edge computing reduces the lag in data transfer, enabling healthcare professionals (and systems) to provide an even more immediate response in critical situations. Personal healthcare devices are also critical opportunities for cloud computing to demonstrate a high impact and improvement in all of our lives. These devices continue to monitor an individual’s state of being outside of the healthcare environment, including vital stats such as heart rate, breathing, movement and stability – all essential information that can be recorded and, if necessary, can trigger alerts with healthcare professionals or family advocates. Using edge computing to do this work reduces the strain on healthcare systems and enables individuals with health concerns to live with more autonomy and security. In general, this could also pose an improvement in health consciousness for society as a whole.