Complexity in IOT
To the list of greatest inventions of the world such as the wheel, compass, steam engine, concrete, automobile, railways, airplane add 21st century’s offering the Internet of Things or IoT. Gartner estimates the total economic value-add from IoT will reach US$1.9 trillion worldwide in 2020. Other reports claim close to half the companies in sectors like oil, gas and manufacturing industries are already using Instrumented devices capable of providing valuable data. In the consumer space, it is well publicized how nearly every device is becoming smarter and more connected. What receives less attention in mainstream media is the considerable impact the Internet of Things (IoT) is having in the industrial sector. The Industrial Internet of Things (IIoT) is already helping enterprises operate more safely and productively while improving efficiency and reducing costs. The primary challenge is handling the massive amounts of data. This includes security of the data and the network as well as the analytics required to derive usable business intelligence from it. IoT technologies have to support this entire process from sensing, transforming, network, analysis and action. If it were to be a homogenous environment where, we are dealing with assets of one class or type, it would still be a scalability challenge. With number of asset types and assets of each class multiplying in the process, the complexity is getting multiplied across the layers from edge to action. There is no interoperability among these asset types and the whole technology landscape has gotten crowded with several siloed solutions and products.
In order to understand Software Complexity we need know other Complex System that nature has organized. Such as a child learning to read, structure of plant and animal or even walking process and many more. When we look these systems they appears to be very simple but there is organised complexity hidden behind it. So Complexity is an essential part these.“Software is product that Software professionals build and provides support for over a long term of time. It encompasses programs that executes in a computer of any memory size and architecture“. Computer software is single most important thing in a world stage. In these modern world ,Software application are so blended with normal life of individual, one can rarely think of living without it. There is a mutual dependency between us and software applications. Applications that exhibits rich set of behaviors, as, in reactive systems which drive or driven by events in physical world such as social media applications, applications that maintains integrity of thousands of records of information while allowing concurrent updates and queries such as Banking applications, Employees managements systems and many more. And Systems those commands and controls real world entities such as Air traffic or Railway traffic control system. Software Of these kind tend have long life span and many user depends on their proper functioning. Again Complexity is an essential part these. Software complexity is an essential part and we cannot remove a Complexity from software but we can master it via Decomposition i.e. Divide and Rule.
Challenges With Managing IoT Technologies
Integrating New Technologies Into Existing Environments
In the industrial world, it gets even more complicated because of the nature of the investments. Capital equipment that has been in the field for 20 years or more is not always a viable target for replacement, as a stove or refrigerator may be in the consumer world. Retrofitting is often the only realistic solution to bring IoT capabilities to existing equipment. However, retrofitting is neither simple nor assured. While connecting legacy equipment and systems offers big benefits and is an important step in the IoT initiatives at many industrial companies, the hurdles to implementation can be formidable. That said, companies are making important strides in this area. They’re adding stand-alone sensors and cameras to existing environments and devices to monitor and collect data about machine performance and health. These sensors attach directly to existing devices and connect to gateways to securely collect and transmit data, which can then be analyzed and used to help prevent failures and downtime.
Managing Complexity: Protocol Proliferation
Another big challenge in the deployment of the IIoT is the vast number of protocols. Some of the more common standards include:
- BLE (Bluetooth low energy)
In some ways, BLE, ZigBee, Z-Wave and Thread are similar. They’re all wireless technologies that use mesh networks to wirelessly connect and network IoT devices without involving a cellular or Wi-Fi signal. But they differ in the radio frequency they use, their operating range and the number of devices they can support at a given time.
Navigating the Internet of Things
Sensors and data-collecting devices are becoming more common every day. Use cases range from measuring the strain on a racing boat’s mast to delivering offers based on customer preference, current location and activity. The challenges loom almost as large as the opportunities. Oracle’s Internet of Things platform brings its most powerful assets to bear on this new wave of innovation across numerous industries – including telecom, healthcare, utilities, industrial automation and more. In this session, we’ll explore innovative ways to drive smarter decisions, reduce costs, and deliver real value for customers and partners.
Securing the Identity of Everything
Along with tremendous economic change, the Internet of Things (IoT) will transform the way IT organizations think about security. Instead of focusing on securing the network perimeter, IT departments will have to secure the new perimeter: people, data and devices. The new point of control will be user access to devices, data and applications. Each device will have an identity on the network, and companies will face the challenge of device tracking, registration and fraud detection.
Big Data Analytics in the Internet of Things
A world of sensors and smart devices will help us decide and do things better—but only if companies can make good use of the mountains of data they produce. To deal with the scale, scope and speed of the Internet of Things, Oracle’s big data platform brings together relational, non-relational and streaming technologies. This session will help you understand how the different approaches work seamlessly together. We’ll also share some examples from customers who are using these solutions today to uncover valuable business insights.
Risk managers do not have to wait for these new insurance endorsements or security improvements. Rather, they should investigate where and how their organizations are currently using IoT-enabled objects. “It’s up to risk managers to be proactive about this, asking questions of operations, factory personnel and supply chain managers,” said Randy Nornes, executive vice president at Aon Risk Solutions.Many risk managers are doing just that. “This is a complex quagmire we’re in,” said Leslie Lamb, director of global risk and resilience management at Cisco Systems. “It’s our job to understand the risks of the IoT the best we can, and then relay this information to the underwriting community—not through a broker, but directly. I don’t want to disintermediate the brokers, but I want to be in a position to tell our story as accurately and as transparently as possible. I want the underwriters to know exactly what our potential IoT risks are so there are no surprises.”
Network control and management of manufacturing equipment, asset and situation management, or manufacturing process control bring the IoT within the realm of industrial applications and smart manufacturing as well. The IoT intelligent systems enable rapid manufacturing of new products, dynamic response to product demands, and real-time optimization of manufacturing production and supply chain networks, by networking machinery, sensors and control systems together.[While connectivity and data acquisition are imperative for IIoT, they should not be the purpose, rather the foundation and path to something bigger. Among all the technologies, predictive maintenance is probably a relatively “easier win” since it is applicable to existing assets and management systems. The objective of intelligent maintenance systems is to reduce unexpected downtime and increase productivity. And to realize that alone would generate around up to 30% over the total maintenance costs. Industrial big data analytics will play a vital role in manufacturing asset predictive maintenance, although that is not the only capability of industrial big data. Cyber-physical systems (CPS) is the core technology of industrial big data and it will be an interface between human and the cyber world. Cyber-physical systems can be designed by following the 5C (connection, conversion, cyber, cognition, configuration) architecture, and it will transform the collected data into actionable information, and eventually interfere with the physical assets to optimize processes. An IoT-enabled intelligent system of such cases was proposed in 2001 and later demonstrated in 2014 by the National Science Foundation Industry/University Collaborative Research Center for Intelligent Maintenance Systems (IMS) at the University of Cincinnati on a band saw machine in IMTS 2014 in Chicago. Band saw machines are not necessarily expensive, but the band saw belt expenses are enormous since they degrade much faster. However, without sensing and intelligent analytics, it can be only determined by experience when the band saw belt will actually break. The developed prognostics system will be able to recognize and monitor the degradation of band saw belts even if the condition is changing, advising users when is the best time to replace band saw. This will significantly improve user experience and operator safety and ultimately save on costs.