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Why Edge Computing is so Important for Internet of Things

The inherent latency of cloud is no longer cutting it when it comes to fully appreciating the benefits of digital transformation. Edge computing is here to solve that problem, and by mitigating the latency associated with the cloud, it ensures that the latest IoT developments are available to businesses across every industry. Alan Conboy, CTO of Scale Computing writes this is the year of edge computing and it’s no surprise why the industry is turning to the trending technology. With only a small hardware footprint, edge computing acts as a high-performance bridge to the cloud, which more organizations are relying on.

IoT Infrastructure

2019 is the year to look seriously at edge computing, as the Internet of Things (IoT) and the global network of sensors steadily increase the amount of data that the average cloud has had to handle in the past. According to a study from the International Data Corporation (IDC), 45 percent of all data created by IoT devices will be stored, processed, analyzed and acted upon close to or at the edge of a network by 2020.

We are living in a world that is increasingly data-driven, and that data is being generated outside of the traditional data center. Edge computing places the physical computing infrastructure at the edges of the network where the data is being generated, and in many cases, those sites are where the data is needed most.

With only a small hardware footprint, infrastructure at the edge collects, processes and reduces vast quantities of data that can be uploaded to a centralized data center or the cloud. Edge computing acts as a high-performance bridge from local computer to private and public clouds.

IoT Needs Edge Computing

There’s a strong argument to say that, by definition, IoT will need edge computing to work effectively and realize its long-term potential. The inherent latency of cloud is no longer cutting it when it comes to deploying machine intelligence and getting real-time results. Edge computing is here to solve that problem, and by mitigating the latency associated with the cloud, it ensures that the latest IoT developments are available to businesses across every industry.

It is especially useful for any industry that has remote sites, such as retail, finance, industrial, remote office branch office (ROBO) and IoT. In retail, for example, retailers need reliable computing that can provide maximum uptime for point of sale, inventory management and security applications for the numerous store locations on the edges of their networks. Banks and other financial institutions with multiple branch offices also require reliable computing to support rapid, business-critical transactions.

Edge computing plays a prominent role in the continuing deployment of IoT devices as the most effective means to process the vast amount of data they produce quickly and effectively. This requirement is only likely to become more pronounced when communication of that data to the cloud may not be reliable or fast enough to be effective.

In the case of ROBO deployments, small branch locations are now increasingly running core, mission-critical applications and the infrastructure they reside on needs to evolve to match the critical nature of the workloads they are running.

Many edge computing sites have very specific computing needs and require much smaller deployments than the primary data center site. Many organizations may have dozens or hundreds of smaller edge computing sites and they cannot afford to roll out complex, expensive IT infrastructure to each site.

Meeting The Challenge at the Edge

But with many applications running on the edge becoming as critical as those in the data center, how can organizations match the resiliency, scalability, security, high-availability and human IT resources found in the data center? How can they address the growing mismatch between the importance of the applications and the infrastructure and IT that supports them at the edge?

To support critical applications with little or no on-site IT staff, edge computing systems have to be more reliable, easy to deploy and use, highly available, efficient, high performance, self-healing and affordable. In many instances, to keep applications running without dedicated IT staff onsite, systems require automation that eliminates mundane manual IT tasks where human error can cause problems.

Important Factors to Consider

Automation also keeps the systems running by monitoring for complex system failure conditions and by taking automatic actions to correct those conditions. This eliminates the downtime that would take a system offline and require an IT staffer to come onsite to bring it back online. Even when hardware components fail, automation can shift application workloads to redundant hardware components to continue operating.

Edge computing infrastructure systems need to be easy to deploy and manage because businesses with hundreds of sites cannot afford to spend weeks deploying complex hardware to each site. They need to be able to plug in the infrastructure, bring systems online and remotely manage the sites going forward. The more complex the infrastructure, the more time they will spend deploying and managing it.

Edge computing systems should also run with as little management as possible. They need to be self-healing to provide high availability for applications without requiring IT staff resources, with automated error detection, mitigation, and correction. Management tasks should be able to be performed remotely and with ease. In addition, these systems should be scalable up and down, dependent on the requirement of the edge location, to ensure organizations are not saddled with excessive overhead for resources they don’t need.

This is the year of edge computing and it’s no surprise why the industry is turning to the trending technology. With only a small hardware footprint, edge computing acts as a high-performance bridge to the cloud, which more organizations are relying on.

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Why Is IoT So Important?

The inter-networking of things or internet of things, brings clarity to the true functional purpose of IoT. The same could be said for the word, credits, in bringing clarity to the true functional purpose of digital money or digital currency. What’s so interesting and important about the evolving IoT technology in particular? Why the rapidly increasing push to adopt IoT by human beings and civil society? In particular, in the era of implementing 5G? This detailed post from TechTarget’s IoT Agenda’s Margaret Rouse and contributors Alexander Gillis, Linda Rosencrance, Sharon Shea and Ivy Wigmore explains.

What is Internet of Things or IoT?

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

thing in the internet of things can be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low or any other natural or man-made object that can be assigned an Internet Protocol (IP) address and is able to transfer data over a network.

Increasingly, organizations in a variety of industries are using IoT to operate more efficiently, better understand customers to deliver enhanced customer service, improve decision-making and increase the value of the business.

How IoT works

An IoT ecosystem consists of web-enabled smart devices that use embedded systems, such as processors, sensors and communication hardware, to collect, send and act on data they acquire from their environments. IoT devices share the sensor data they collect by connecting to an IoT gateway or other edge device where data is either sent to the cloud to be analyzed or analyzed locally. Sometimes, these devices communicate with other related devices and act on the information they get from one another. The devices do most of the work without human intervention, although people can interact with the devices — for instance, to set them up, give them instructions or access the data.

The connectivity, networking and communication protocols used with these web-enabled devices largely depend on the specific IoT applications deployed.

IoT can also make use of artificial intelligence (AI) and machine learning to aid in making data collecting processes easier and more dynamic.

An example of how an IoT system works from collecting data to taking action

Why IoT is important

The internet of things helps people live and work smarter, as well as gain complete control over their lives. In addition to offering smart devices to automate homes, IoT is essential to business. IoT provides businesses with a real-time look into how their systems really work, delivering insights into everything from the performance of machines to supply chain and logistics operations.

IoT enables companies to automate processes and reduce labor costs. It also cuts down on waste and improves service delivery, making it less expensive to manufacture and deliver goods, as well as offering transparency into customer transactions.

As such, IoT is one of the most important technologies of everyday life, and it will continue to pick up steam as more businesses realize the potential of connected devices to keep them competitive.

What is IoT (Internet of Things)

IoT benefits to organizations

The internet of things offers several benefits to organizations. Some benefits are industry-specific, and some are applicable across multiple industries. Some of the common benefits of IoT enable businesses to:

  • monitor their overall business processes;
  • improve the customer experience (CX);
  • save time and money;
  • enhance employee productivity;
  • integrate and adapt business models;
  • make better business decisions; and
  • generate more revenue.

IoT encourages companies to rethink the ways they approach their businesses and gives them the tools to improve their business strategies.

Generally, IoT is most abundant in manufacturing, transportation and utility organizations, making use of sensors and other IoT devices; however, it has also found use cases for organizations within the agriculture, infrastructure and home automation industries, leading some organizations toward digital transformation.

IoT can benefit farmers in agriculture by making their job easier. Sensors can collect data on rainfall, humidity, temperature and soil content, as well as other factors, that would help automate farming techniques.

The ability to monitor operations surrounding infrastructure is also a factor that IoT can help with. Sensors, for example, could be used to monitor events or changes within structural buildings, bridges and other infrastructure. This brings benefits with it, such as cost saving, saved time, quality-of-life workflow changes and paperless workflow.

A home automation business can utilize IoT to monitor and manipulate mechanical and electrical systems in a building. On a broader scale, smart cities can help citizens reduce waste and energy consumption.

IoT touches every industry, including businesses within healthcare, finance, retail and manufacturing.

Pros and cons of IoT

Some of the advantages of IoT include the following:

  • ability to access information from anywhere at any time on any device;
  • improved communication between connected electronic devices;
  • transferring data packets over a connected network saving time and money; and
  • automating tasks helping to improve the quality of a business’s services and reducing the need for human intervention.

Some disadvantages of IoT include the following:

  • As the number of connected devices increases and more information is shared between devices, the potential that a hacker could steal confidential information also increases.
  • Enterprises may eventually have to deal with massive numbers — maybe even millions — of IoT devices, and collecting and managing the data from all those devices will be challenging.
  • If there’s a bug in the system, it’s likely that every connected device will become corrupted.
  • Since there’s no international standard of compatibility for IoT, it’s difficult for devices from different manufacturers to communicate with each other.

IoT standards and frameworks

There are several emerging IoT standards, including the following:

  • IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) is an open standard defined by the Internet Engineering Task Force (IETF). The 6LoWPAN standard enables any low-power radio to communicate to the internet, including 804.15.4, Bluetooth Low Energy (BLE) and Z-Wave (for home automation).
  • ZigBee is a low-power, low-data rate wireless network used mainly in industrial settings. ZigBee is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard. The ZigBee Alliance created Dotdot, the universal language for IoT that enables smart objects to work securely on any network and understand each other.
  • LiteOS is a Unix-like operating system (OS) for wireless sensor networks. LiteOS supports smartphones, wearables, intelligent manufacturing applications, smart homes and the internet of vehicles (IoV). The OS also serves as a smart device development platform.
  • OneM2M is a machine-to-machine service layer that can be embedded in software and hardware to connect devices. The global standardization body, OneM2M, was created to develop reusable standards to enable IoT applications across different verticals to communicate.
  • Data Distribution Service (DDS) was developed by the Object Management Group (OMG) and is an IoT standard for real-time, scalable and high-performance M2M communication.
  • Advanced Message Queuing Protocol (AMQP) is an open source published standard for asynchronous messaging by wire. AMQP enables encrypted and interoperable messaging between organizations and applications. The protocol is used in client-server messaging and in IoT device management.
  • Constrained Application Protocol (CoAP) is a protocol designed by the IETF that specifies how low-power, compute-constrained devices can operate in the internet of things.
  • Long Range Wide Area Network (LoRaWAN) is a protocol for WANs designed to support huge networks, such as smart cities, with millions of low-power devices.

IoT frameworks include the following:

  • Amazon Web Services (AWS) IoT is a cloud computing platform for IoT released by Amazon. This framework is designed to enable smart devices to easily connect and securely interact with the AWS cloud and other connected devices.
  • Arm Mbed IoT is a platform to develop apps for IoT based on Arm microcontrollers. The goal of the Arm Mbed IoT platform is to provide a scalable, connected and secure environment for IoT devices by integrating Mbed tools and services.
  • Microsoft’s Azure IoT Suite is a platform that consists of a set of services that enables users to interact with and receive data from their IoT devices, as well as perform various operations over data, such as multidimensional analysis, transformation and aggregation, and visualize those operations in a way that’s suitable for business.
  • Google’s Brillo/Weave is a platform for the rapid implementation of IoT applications. The platform consists of two main backbones: Brillo, an Android-based OS for the development of embedded low-power devices, and Weave, an IoT-oriented communication protocol that serves as the communication language between the device and the cloud.
  • Calvin is an open source IoT platform released by Ericsson designed for building and managing distributed applications that enable devices to talk to each other. Calvin includes a development framework for application developers, as well as a runtime environment for handling the running application.

Consumer and enterprise IoT applications

There are numerous real-world applications of the internet of things, ranging from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). IoT applications span numerous verticals, including automotive, telecom and energy.

In the consumer segment, for example, smart homes that are equipped with smart thermostats, smart appliances and connected heating, lighting and electronic devices can be controlled remotely via computers and smartphones.

Wearable devices with sensors and software can collect and analyze user data, sending messages to other technologies about the users with the aim of making users’ lives easier and more comfortable. Wearable devices are also used for public safety — for example, improving first responders’ response times during emergencies by providing optimized routes to a location or by tracking construction workers’ or firefighters’ vital signs at life-threatening sites.

In healthcare, IoT offers many benefits, including the ability to monitor patients more closely using an analysis of the data that’s generated. Hospitals often use IoT systems to complete tasks such as inventory management for both pharmaceuticals and medical instruments.

Smart buildings can, for instance, reduce energy costs using sensors that detect how many occupants are in a room. The temperature can adjust automatically — for example, turning the air conditioner on if sensors detect a conference room is full or turning the heat down if everyone in the office has gone home.

In agriculture, IoT-based smart farming systems can help monitor, for instance, light, temperature, humidity and soil moisture of crop fields using connected sensors. IoT is also instrumental in automating irrigation systems.

In a smart city, IoT sensors and deployments, such as smart streetlights and smart meters, can help alleviate traffic, conserve energy, monitor and address environmental concerns, and improve sanitation.

IoT security and privacy issues

The internet of things connects billions of devices to the internet and involves the use of billions of data points, all of which need to be secured. Due to its expanded attack surface, IoT security and IoT privacy are cited as major concerns.

In 2016, one of the most notorious recent IoT attacks was Mirai, a botnet that infiltrated domain name server provider Dyn and took down many websites for an extended period of time in one of the biggest distributed denial-of-service (DDoS) attacks ever seen. Attackers gained access to the network by exploiting poorly secured IoT devices.

Because IoT devices are closely connected, all a hacker has to do is exploit one vulnerability to manipulate all the data, rendering it unusable. Manufacturers that don’t update their devices regularly — or at all — leave them vulnerable to cybercriminals.

Additionally, connected devices often ask users to input their personal information, including names, ages, addresses, phone numbers and even social media accounts — information that’s invaluable to hackers.

Hackers aren’t the only threat to the internet of things; privacy is another major concern for IoT users. For instance, companies that make and distribute consumer IoT devices could use those devices to obtain and sell users’ personal data.

Beyond leaking personal data, IoT poses a risk to critical infrastructure, including electricity, transportation and financial services.

The Future of IoT Security

History of IoT

Kevin Ashton, co-founder of the Auto-ID Center at the Massachusetts Institute of Technology (MIT), first mentioned the internet of things in a presentation he made to Procter & Gamble (P&G) in 1999. Wanting to bring radio frequency ID (RFID) to the attention of P&G’s senior management, Ashton called his presentation “Internet of Things” to incorporate the cool new trend of 1999: the internet. MIT professor Neil Gershenfeld’s book, When Things Start to Think, also appeared in 1999. It didn’t use the exact term but provided a clear vision of where IoT was headed.

IoT has evolved from the convergence of wireless technologies, microelectromechanical systems (MEMSes), microservices and the internet. The convergence has helped tear down the silos between operational technology (OT) and information technology (IT), enabling unstructured machine-generated data to be analyzed for insights to drive improvements.

Although Ashton’s was the first mention of the internet of things, the idea of connected devices has been around since the 1970s, under the monikers embedded internet and pervasive computing.

The first internet appliance, for example, was a Coke machine at Carnegie Mellon University in the early 1980s. Using the web, programmers could check the status of the machine and determine whether there would be a cold drink awaiting them, should they decide to make the trip to the machine.

IoT evolved from M2M communication, i.e., machines connecting to each other via a network without human interaction. M2M refers to connecting a device to the cloud, managing it and collecting data.

Taking M2M to the next level, IoT is a sensor network of billions of smart devices that connect people, systems and other applications to collect and share data. As its foundation, M2M offers the connectivity that enables IoT.

The internet of things is also a natural extension of supervisory control and data acquisition (SCADA), a category of software application programs for process control, the gathering of data in real time from remote locations to control equipment and conditions. SCADA systems include hardware and software components. The hardware gathers and feeds data into a computer that has SCADA software installed, where it is then processed and presented in a timely manner. The evolution of SCADA is such that late-generation SCADA systems developed into first-generation IoT systems.

The concept of the IoT ecosystem, however, didn’t really come into its own until the middle of 2010 when, in part, the government of China said it would make IoT a strategic priority in its five-year plan.

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Why Is BIG Data So Valuable?

Why is BIG Data So Valuable and are we using it the right way? Rebekah Carter of UC Today writes Big data is an ever-evolving and dynamic term that describes large volumes of data with the potential to deliver useful insights and information. Big data can inform machine learning strategies, form the basis of artificial intelligence applications, and transform business operations.

For years, Big Data was defined by the 3 V’s. Companies looked at the extreme volume of the data we collected, the variety of data available, and the velocity required for data processing. These concepts were identified in 2001 by Gartner analyst Doug Laney.

Since then, various companies have implemented their own “V’s” into the big data discussion too, such as “value” and “veracity.” In other words – how valuable is your data, and how much can you rely on it?

As new technologies make data more accessible, how is the big data environment changing, and what does it mean to the future of communications?

What is Big Data

Research shows that 80% of the world’s data is dark. This means the information has never been used to drive business decisions. For years, the world struggled to access endless forms of information, all the way from the analytics stored in customer voice conversations, to the data in images.

Today, we’re discovering new ways to collect and analyse data from almost every business touchpoint. The result is that companies can dive deeper into a range of experiences. For instance, data obtained from a workforce optimisation tool shows you where your employees are their most productive, and where they need help to boost efficiency. Data about your CCaaS strategy can show you where you have gaps in your contact centre environment, and where it may be worth building extra channels into your omnichannel environment.

Big data analytics can even help organisations to get a better sense of their customers, and the journeys they take when making a purchase. With data, you can track down all of the touchpoints where your clients interact with your business and look for ways to improve their experiences. For instance, if you find that your audience prefer SMS contact to phone conversations, you can implement an SMS strategy to update them on their order progress or shipping status.

Big data analysis can also tell you more about individual customers so that you might provide more personalised up-selling suggestions or guide them towards products that are relevant to them.

The challenge today is in accessing data, without crossing privacy and compliance boundaries. As consumers become more concerned about how their private information is used, national regulations like GDPR have come into play. These issues force companies to think more carefully about the data that they can collect, and the kind of consent they must get from clients. Businesses can’t just collect data mindlessly. Information must be gathered with a specific strategy, purpose, and a high level of consent.

Big Data Trends

MarketWatch suggests that the global big data market will reach a value of $118.52 billion by 2022. Developments in the way that we can collect and store data, along with the ever-more flexible support of the cloud has helped the big data environment to evolve. All the while, we’re seeing a number of impressive new trends appear in the market, such as:

1.      The Rise of Open Source Processing

Open Source applications like Spark and Hadoop continue to be crucial components in the big data space. Surveys suggest that 60% of enterprises expect to have open source clusters running by the end of 2019. Many companies are looking to expand their use of such technologies for data processing purposes.

As organisations continue to experiment with data mining tools, the focus will be on finding solutions that allow for the quick collection of useful information. However, enterprises will also have to be careful that their tools come with compliance in mind. Business leaders need to be able to find, access and manage the data in their stores when necessary too.

IT Consulting

2.      Edge computing and analytics

The demand for Edge computing is on the rise. Edge computing brings companies as close to endpoints and sensors as possible, to reduce traffic and latency in a range of networks. Gartner suggests that edge computing and cloud computing models will continue to evolve and complement each other in this year, and the years ahead. Cloud services may expand to live both in centralized servers and distributed on-premise servers and edge devices too.

Some people believe that edge computing and analytics will help to increase security and compliance in the business environment, due to their decentralised structure too.

3.      Predictive Analytics

One of the biggest benefits of big data comes from its ability to inform machine learning strategies. Predictive analytics is a solution born from machine learning. By gathering huge amounts of historical data, companies can predict everything from when a machine on an industrial floor needs replacing, to when customers may begin to churn.

Predictive analysis can offer companies of all shapes and sizes insights into what they can do to transform their business environment. Through predictive analysis, contact centres can even ensure that they’re prepared for changes in consumer trends, and influxes in calls.

4.      IoT

The rise of the Internet of Things is set to have a significant impact on the big data landscape. Gartner predicts that there will be 20.4 billion connected IoT devices by 2020. As such, the volume of data companies will be able to collect will grow dramatically. Organisations will need to implement new systems and technologies to handle the flood of information coming into their business.

Business leaders that can respond well to the IoT environment could discover incredible insights about how their products and services are used, or even how the industry overall is evolving.

Big Data Statistics

The market for big data is growing every second. We’re continually creating new information. For instance, the volume of data produced by companies in the US alone is enough to fill more than 10,000 libraries. Here are some of the facts you need to know about big data:

Big Data Hashtags Used on Social Media

  • #BigData
  • #Analytics
  • #Data
  • #SmartCities
  • #OpenData
  • #MachineLearning
  • #IoT
  • #DataScience
  • #BusinessIntelligence