5G, IoT, Big Data, Analytics, AI & Cloud
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Because Digital Smart City Healthcare and Medical Systems

Because Digital Smart City Healthcare and Medical Systems | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
IoT-led innovations will drive a shift towards mature, value-based indicators for Smart City healthcare systems, improving efficiency and quality of life.
Al Sedghi's insight:
Smart Cities, by their very nature, produce significant amounts of data in their daily operations. IoT, Open Data are driving cities to collect and make available additional amounts of data – some static but increasingly large parts of it are real-time data. This data exhibits the classic characteristics of Big Data – high volume, often real-time (velocity) and extremely heterogeneous in its sources, formats and characteristics (variability). 

 This big data, if managed and analyzed well, can offer insights and economic value that cities and city stakeholders can use to improve efficiency and lead to innovate new services that improve the lives of citizens.

The growing technology that captures, manages and analyzes this Big Data, leverages technology trends such as cloud computing. Cities are now capable of accessing and using massive compute resources that were too expensive to own and manage a few years ago. Coupled with technologies like Hadoop/HDFS, Spark, Hive and a plethora of proprietary tools it is now possible for cities to use big data and analytical tools to improve the city. 

For example, Boston, USA is using big data to better track city performance against a range of indicators, and additionally identify potholes in city streets and to improve the efficiency of garbage collection by switching to a demand driven approach. New York has also developed a system (FireCast) that analyzes data from 6 city departments to identify buildings with a high fire risk. In Europe, London uses a wide variety of city data and advanced analytics to map individual neighborhoods to better understand resource allocation and planning which is made available through the Whereabouts service. In Asia, Singapore tracks real time transportation and runs a demand driven road pricing scheme to optimize road usage across the island.
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Ask The Thought Leaders: What’s the Future of the IoT? | Future of Everything

Ask The Thought Leaders: What’s the Future of the IoT? | Future of Everything | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
The concept of the “internet of things” was initially proposed in 1999 by Kevin Ashton. While it initially sounded like it was impossible, today, the concept is becoming very real. Predictions estimate that the growth of the IoT will reach 50 billion objects by 2020. So, what will a world with that many internet-connected devices …
Al Sedghi's insight:
Without a centralized IoT platform, businesses lack full visibility into the data that sensor-enabled assets generate. 

Enterprises need a central IoT platform to have full visibility into the data that sensor enabled devices produce. There are there development stages that IoT initiatives typically go through:

Stage 1: Process efficiency 

The first focus area of IoT in this stage involves gathering information from connected devices to improve operations. In this phase, IoT use is dedicated to a single business function, not a formal business-wide program. For instance, fleet operators can sensor-enable trucks to identify and repair mechanical failures before a malfunction. 

Stage 2: Create new revenue streams

During this stage, the strategy and focus is to leverage IoT data to create new revenue streams. Here is when you will need to have a central IoT platform. For example, a printing company can have a platform to remotely monitor customers connected printers for faults and cartridge replacement. The IoT platform can monitor and collect data from the connected assets and deliver the information to revenue tracking and generating systems (i.e. product lifecycle management). 

Stage 3: Business transformation 

During this stage, the enterprise is now able to change business model from selling products to selling services. In the case of a connected car, automobile manufacturers will be able to offer valuable services. There will be various opportunities to use real-time data from the vehicle. Complex analytical models running in the cloud or even on board the vehicle can forecast service events and notify the driver. The driver could be notified of a threatening issue, in a safe and non-distracting way – and be guided to the nearest dealership with a vacant service bay and parts in stock – offering convenience to the customer.
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Connected to converged: Can fog computing cure industrial IoT's pain points? - SiliconANGLE

Connected to converged: Can fog computing cure industrial IoT's pain points? - SiliconANGLE | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
Connected to converged: Can fog computing cure industrial IoT's pain points? - SiliconANGLE
Al Sedghi's insight:
Fog computing enables computing, policymaking and action-taking to occur via IoT devices and only pushes relevant data to the cloud. 

The fog extends the cloud to be closer to the things that generate and perform an action on IoT data. In this context, we refer to any IoT devices with computing, storage and network connectivity as fog nodes. Fog nodes can be deployed anywhere with a network connection such as on factory floor, on top of a power pole, near a railway track, in a car, or on an oil rig. Other examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras. 

Here is a summary on what occurs within fog computing: 

• Examines the most time-sensitive data at the network edge, close to where it is created instead of sending vast amounts of IoT data to the cloud. 

• Acts on IoT data in milliseconds, based on policy.

• Transmits selected data to the cloud for historical analysis and longer-term storage. 

Benefits of using Fog Computing 

• Reduce latency 

• Preserve network bandwidth

• Handle security concerns at various levels of the network 

• Operate consistently with rapid decisions • Gather and secure wide range of data 

• Move data to the most suitable place for processing 

• Reduce expenses of using high computing power only when needed and less bandwidth 

• Better analysis and insights of local data 

Note that that fog computing is not a substitute for cloud computing, as it works in combination with cloud computing, improving the use of available resources. But it addresses two challenges, real-time processing and action on incoming data, and optimizing the use of resources like bandwidth and computing power. Another positive element in fog computing is that it takes advantage of the distributed nature of today’s virtualized IT resources.  The enhancement to the Data-path hierarchy is empowered by the increased compute functionality that manufacturers are building into their edge routers and switches. 

Here is a real-life example: A traffic light system in a major city is equipped with smart sensors. An application developed by the city to regulate light patterns and timing is running on each edge device. The app automatically adjusts light patterns in real time, at the edge, dealing with traffic issues as they occur and improving traffic flow. Once the traffic slowdown is over, all the data collected from the traffic light system would be sent to the cloud and examined, supporting predictive analysis and letting the city regulate and improve its traffic application’s response to future anomalies. There is little value in sending a live stream of traffic sensor data to the cloud for storage and analysis. Instead, the information is processed and acted upon in the edge nodes, and only a summary is sent to the cloud for further analysis.
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7 Tips to Prepare Your Network for the Internet of Things

7 Tips to Prepare Your Network for the Internet of Things | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
To prepare the network to support a plethora of connected devices, consider these seven tips.
Al Sedghi's insight:
To prepare your network for IoT, one key recommendation is to move data storage and processing to the network edge. This potentially decreases the distance that data must travel over a network. Nevertheless, it does mean an increased number of edge devices, and a corresponding increase in costs. 

One alternative strategy is to redesign current network routing, and cash in on the capacity that is already there. Latency can be reduced 60% by redesigning network routing protocols to prevent congestion and traffic bottlenecks. Furthermore, smart Internet routing can optimize bandwidth freeing up more space for IP traffic.
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Analytics and the cloud: The Internet of Things

Analytics and the cloud: The Internet of Things | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
f low power sensors, and their ease of use coupled with having lifetimes of several years before replacement,
Al Sedghi's insight:
For critical and time sensitive remote sensing applications, it is key to develop sensors that can run on near-zero power and produce a wake-up signal when a specific signature or alert signal is discovered, such as a car or truck driving by, or a generator being switched on. For disaster situations such as earthquakes you can imagine having an ultimate geophone, where you're sensing for earthquakes, sensing vibrations in the earth.
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Can the Internet of things solve environmental crises?

Can the Internet of things solve environmental crises? | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
If so, it must consider local business and cultural needs, build business processes and market structures around the world.
Al Sedghi's insight:
We have to think about the likely limitations on IoT due to power consumption, the use of rare earth elements – all from the beginning of any relevant IoT project. How energy hungry the IoT will mainly depend on the types of devices chosen for deployment and what they will be doing.

Low-power, low-data transmitting devices – such as sensors that are used to monitor when it is time to refill vending machines – are not likely to increase energy bills. Many of these devices don’t use main building power, but instead leverage long-lasting batteries or solar energy.

But some devices that are used for video surveillance are going to be energy-hungry. In fact, these devices will require main power to operate and will drive data consumption tremendously. According to Cisco, internet video surveillance traffic almost doubled between 2014 and 2015, and is forecasted to increase ten times by 2020.

IoT can drive energy harvesting wireless technology. IoT networks gain from self-powered technology because it eliminates the need for battery replacement, making devices maintenance-free, which is particularly positive for remote areas and the deployment of billions of connected devices. Moreover, energy harvesting sensors provide IoT with a green angle, bridging between the automation world and the mobile world with the added benefit of being eco-friendly.  

The total volume of data being transmitted and stored is also forecasted to blast. Data storage has become more energy efficient over recent years. Instead of being stored on company premise servers and relying on data centers, data is gradually stored and processed in the cloud. 

To drive energy efficiency and climate change initiatives, not only policies are needed, but also interests and actions coming from the user community and leading industry organizations.
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Big Data and IoT - How The Future Of Analytics is Evolving | Analytics Training Blog

Big Data and IoT - How The Future Of Analytics is Evolving | Analytics Training Blog | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it
With each passing day, more objects and machines are getting connected to the internet transmitting the information for analysis. The objective is to harness this data to discover trends and results that can help any business with a positive impact. And why not, after all the future of technology lies in the hands of data … Continue reading "Big Data and IoT – How The Future Of Analytics is Evolving"
Al Sedghi's insight:
The biggest barrier facing enterprises considering IoT deployments will be knowing what to do with the massive amounts of information that will be gathered.

You will face different sources of data, anywhere from social media, sensors, embedded devices, etc. So, the challenge is to design for analytics – develop a strategy that you can see data more as a supply chain than a warehouse. There are going to be numerous unstructured data sources, so it is key to focus on collecting and organizing the data that you really need. 

For data analysis, the following are challenges to overcome. We need to worry about 5 Vs , Variety, Volume, Velocity & Veracity, and  Value 

Variety: defines the different types of data, which is growing every day. Structured data that has been regularly stored in databases is now being linked with unstructured data, including social media data, wearable data, and video streaming.

Volume: relates to the scale of data, how it’s obtained and warehoused. According to IBM, an approximate 2.5 quintillion bytes of data are created every single day. By the year 2020, there will be 300x more information in the world than there was in 2005, an approximate 43 trillion gigabytes. 

Velocity:  refers to the pace of data processing and

Veracity: is the ambiguity vs. the reliability of data. According to IBM, poor data quality costs the U.S. economy over $3 trillion dollars a year. 

Value:  relates to the ability of making data profitable by utilizing analyzed data to expand revenue and reduce cost.

It is evident that variety and volume of data that is being delivered through the networks is overwhelming; consequently, this impacts the velocity i.e. how quickly technology and enterprises can analyze the complete excess of information that’s collected. We should employ well-organized network connections as well as monitors and sensors to work out behavioral patterns and applications to structure these patterns. The key V to focus on is Value: monetizing on Big Data. 

Business Challenges 

We need to concentrate on analytics, accurate forecasting, evaluating/ implementing new tools / technologies, and getting real-time insights. We must determine how we can be profitable from something that impacts time, money and resources to keep up with. 

Business analytics: We must use analytics to grow our operational acumen and measure whether it’s working and who it’s working for. 

Accurate forecasting: Leveraging analytics we can make reliable implications about the future of the market. We understand what we did, so the next thing is how can we do it better? What’s next for our demographic? What can we conclude that our demographic will respond to? 

Real-time insights: are significant to ensuring that the analytics and forecasting are worth it. Time is crucial. Enterprises want to know what’s going on immediately so they can determine how to respond accordingly. Big Data can do this. 

Technology Challenges 

Workforce We must train the workforce that’s already in place and attract/hire top talent to fill the gaps. It’s a flourishing market for IoT / Big Data specialists. Specialists with expertise in such skills as VMWare, application development, open source technology, data warehousing and solid programming skills will be the ones to employ.

Infrastructure Agile IT is key in the business. From an architectural point of view, the fewer systems you have, the more agile you will be. Here is an ecosystem to focus on buildout:

 • To reduce cost, add modern systems to legacy systems initially. Use the existing architecture and practice an evolutionary approach to build the smart ecosystem of the future. No need to start from scratch; it will cost valuable time and resources. 

• Focus on build out of the infrastructure for business-critical applications. 

>>> Note that customer experience depends on high availability. 
>>> Ensure to plan for hardware, network and data management applications. 
>>> Design data storage to have the capacity to hold and update data at a low cost. 
>>>The network must be capable to cost-effectively transfer data to/from frameworks and architectures, while offering future growth.
 >>>Data management applications must be adaptable for processing, classifying and consuming vast amounts of data in real time.

• Analyze data for predictive analytics and directed marketing campaigns. 

Tools

Invest in established BI tools and applications. This will enable you to take advantage of the vast amounts of data that’s collected as well as gain valuable insight. Focus on applications that are easy-to-use and able to provide interactive interfaces to help you gain control and make informed decisions. 

Regulatory Challenges 

Be aware of current and new regulations that limit the use, storage and collection of certain types of data. Obtaining and analyzing data to improve business practices will help to increase revenue and decrease. How do you succeed Notwithstanding all the challenges, there are many prospects to monetize on IoT / Big Data and be ahead of the competition.  These include opportunities to: 

1) Grow revenue by tailoring advertising, products and services, and by leveraging predictive analytics to get real-time insights into customer behavior. 

2) Reduce costs through security, fraud prevention and network optimization.

3) Improve the Customer Experience by getting customer feedback, targeted and predictive marketing, and tailored products and services. 

To overcome challenges there must be collaboration across the three silos. For instance, the business and technology silos need to work together to discover synergies and enhance/automate IT and business processes for the most effective operation across the enterprise. Work together as a team and be smart.
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Navigating a Successful Journey toward IoT and Big Data in the Cloud | IT News Africa – Africa's Technology News Leader

Navigating a Successful Journey toward IoT and Big Data in the Cloud | IT News Africa – Africa's Technology News Leader | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it

"It is predicted that by 2020, 25 billion devices will be connected to IoT and 600 zettabytes of information will be sitting in

Al Sedghi's insight:
IoT is more about devices, data and connectivity. The real significance of Internet of Things is about creating smarter products, delivering intelligent insights and providing new business results. 

As we go in future, millions of devices will get connected, internet of things will produce an enormous inflow of Big Data. The key challenge is envisioning and revealing insights from various categories of data (structured, unstructured, images, contextual, dark data, real-time) and in context of your applications.   

I believe gaining intelligence from Big Data using artificial Intelligence technologies is the key enabler for smarter devices and a connected world. 

The final objective is to connect the data coming from sensors and other contextual information to discover patterns and associations in real-time to positively impact businesses. Current Big Data technologies must be expanded with the goal to effectively store, manage and gain value from continuous streams of sensor data. 

In the case of connected cars, if 25 gigabytes of data is being sent to the cloud every hour, the main goal must be to make sense of this data, detecting data that can be consumed and rapidly acted upon to develop actionable events. The evolution of AI technologies will be key to grow insights rapidly from massive streams of data. 

With IoT, Big Data analytics would also need to move at the edge for real-time decision making; Here are some examples: i) detecting crop patterns in agriculture plants using drones, ii) detecting suspicious activities at ATMs or iii) predicting driver behavior for a connected car.
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The Future Is Intelligent Apps – InFocus Blog | Dell EMC Services

The Future Is Intelligent Apps – InFocus Blog | Dell EMC Services | 5G, IoT, Big Data, Analytics, AI & Cloud | Scoop.it

Enterprises must pay attention to key application technology and architecture capabilities

Al Sedghi's insight:
Data gathering has advanced drastically. From automobiles, to smartgrids, to patient bodies, there are currently dashboards and platforms for collecting, organizing, and displaying detailed information that can offer a more improved set of data from gas consumption, to vital signs with sophisticated alert configuration. 

APIs and Open Source are essentially democratizing data in all these scenarios. Amid all this lies Machine Learning and Predictive Analysis. Two things can really cause a threat to putting machine learning to work: a) poor data quality and b) lack of data integration. The improvement of APIs and the trend of open sourcing can put an end to all these threats. There are a lot of open source projects on Github that software developers can leverage to integrate machine learning into their applications. Last year Google started releasing lower-level libraries like Tensorflow, which can be used in conjunction with others to entirely match the level of refinement a developer or data scientist is considering. For novice, there is a service like Amazon Machine Learning, which provides a simple UI for non-developers. 

Enterprises must pay attention to key application technology and architecture capabilities, such as data management services, user self-service, better sharable analytics, agile development leveraging latest PaaS and DevOps techniques.
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