Posts Tagged ‘network’
By 2020, more than 24 billion internet-connected devices will be installed globally — that’s more than 4 devices for every human on earth.
The Internet of Things first reached users on PCs. Then it migrated to smartphones, tablets, smartwatches, and TVs.
This growth surely brings several benefits, as it will change the way people fulfill everyday tasks and potentially change the world. Having a smart home is undoubtedly cool and will amaze your guests, but smart lighting can also reduce overall energy consumption and lower your electric bill.
New developments would allow connected cars to link up with smart city infrastructure to create an entirely different ecosystem for the driver, who is simply used to the traditional way of getting from Point A to Point B. And there are many other examples of positive changes IoT may bring to our lifes.
But with all of these benefits comes risk, as the increase in connected devices gives hackers and cyber criminals more entry points.
Late last year, a group of hackers took down a power grid in a region of western Ukraine to cause the first blackout from a cyber attack. And this is likely just the beginning, as these hackers are looking for more ways to strike critical infrastructure, such as power grids, hydroelectric dams, chemical plants, and more.
What is already being done to Secure The IoT?
The great thing about IoT security is that previously ignored, it has now become an issue of high concern, even at the federal government level. Several measures are already being taken to gap holes and prevent security breaches at the device level, and efforts are being led to tackle major disasters before they come to pass.
Now security firms and manufacturers are joining ranks to help secure the IoT world before it spins out of control. IT giant Microsoft has started taking measures and has promised to add BitLocker encryption and Secure Boot technology to the Windows 10 IoT, their operating system for IoT devices and platforms such as the Raspberry Pi.
BitLocker is an encryption technology that can code entire disk volumes, and it has been featured in Windows operating systems since the Vista edition. This can be crucial to secure on-device data. Secure Boot is a security standard developed by members of the PC industry to help make sure that your PC boots using only software that is trusted by the PC manufacturer. Its implementation can prevent device hijacking.
The IoT security issue has also given rise to new alliances. A conglomeration of leading tech firms, including Vodafone, founded the Internet of Things Security Foundation, a non-profit body that will be responsible for vetting Internet-connected devices for vulnerabilities and flaws and will offer security assistance to tech providers, system adopters and end users.
Other companies are working on setting up platforms that will enable large networks of IoT devices to identify and authenticate each other in order to provide higher security and prevent data breaches.
What should we know to protect ourselves and minimize risks of hacking attacks?
Security must be addressed throughout the device lifecycle, from the initial design to the operational environment:
1. Secure booting: When power is first introduced to the device, the authenticity and integrity of the software on the device is verified using cryptographically generated digital signatures. In much the same way that a person signs a check or a legal document, a digital signature attached to the software image and verified by the device ensures that only the software that has been authorized to run on that device, and signed by the entity that authorized it, will be loaded. The foundation of trust has been established, but the device still needs protection from various run-time threats and malicious intentions.
2. Device authentication: When the device is plugged into the network, it should authenticate itself prior to receiving or transmitting data. Deeply embedded devices often do not have users sitting behind keyboards, waiting to input the credentials required to access the network. How, then, can we ensure that those devices are identified correctly prior to authorization? Just as user authentication allows a user to access a corporate network based on user name and password, machine authentication allows a device to access a network based on a similar set of credentials stored in a secure storage area.
3. Firewalling and IPS: The device also needs a firewall or deep packet inspection capability to control traffic that is destined to terminate at the device.
4. Updates and patches: Once the device is in operation, it will start receiving hot patches and software updates. Software updates and security patches must be delivered in a way that conserves the limited bandwidth and intermittent connectivity of an embedded device and absolutely eliminates the possibility of compromising functional safety.
What is evident is that the IoT will play an important role in our lives in the near future, and its security is one of the major issues that must be addressed via active participation by the entire global tech community. Next several years will show whether all of the innovations will revolutionize the world or will bring us to a new era of digital insecurity and chaos. Time will tell.
Business Development Manager
Professional Software Development
As the Internet of Things begins to revolutionize businesses, economies and our society, IoT platforms are coming up being the core basis in the overall IoT infrastructure. IoT platforms, in simple words, are just about connecting the sensors to data networks and integrating with back-end applications to provide insight into huge volumes of data.
However developing for the Internet of Things is a complicated undertaking, and almost nobody chooses to do it from scratch. IoT data platforms provide a starting point by integrating many of the tools needed to operate a deployment from device control to data prediction and grasp into one service. Ready-built IoT platforms can meet the needs of any company and smoothly accommodate constant growth and change. In the light of the possibilities offered by IoT, many high tech companies started taking advantage of it. For the time being there are more than 300 hundred various IoT platforms on the market and the number is continuing to grow. So, let’s see what features of IoT platforms take into consideration while choosing one for your business.
Before selecting an appropriate solution which may be suitable for your organization, you must determine:
1. Three different types of IoT platforms. Here they are listed from most complex to least complex:
- Application enablement and development (AEP/ADP): This encompasses platforms that offer modules, widget-based frameworks or templates for producing (with minimal or no coding) actual end-user applications. These platforms are capable of turning data into either intelligence or action very quickly. The vivid examples of such platforms are Oracle, ThingWorx and etc.
- Network/Data, and subscriber management (NM): In the wireless carrier and mobile virtual network operator (MVNO) space, this kind of platforms try to streamline connecting cellular M2M data, so you don’t have to build much of the data infrastructure behind it. For instance Cisco and Aeris do network management as well as device management, while Jasper and Wyless do more sheer network management.
- Device management (DM): These platforms are more about monitoring device statuses, troubleshooting issues, configuring embedded device settings and administrating the provisioning and health of the endpoints. Usually in the IoT space this fairly elementary software is provided by hardware vendors. Like both Digi and Intel provide pure device cloud management.
While these platforms can be found as distinct standalone products, it is becoming increasingly common to find vendors that combine two or all three types in a single offering.
2. Implementation, integration support and device management. Device management is one of the most significant features expected from any IoT software platform. The IoT platform should maintain a number of devices connected to it and track their proper operation status; it should be able to handle configuration, firmware (or any other software) updates and provide device level error reporting and error handling. Ultimately, users of the devices should be able to get individual device level statistics.
To make implementation smooth, the provider should possess convincing manuals, blogs and feasibly lively developer-community around the IoT platform.
Support for integration is another vital feature expected from an IoT software platform. The API should provide the access to the important operations and data that needs to be disclosed from the IoT platform. It’s typical to use REST APIs to achieve this aim.
3. Comprehensive Information Security. There are four main technological building blocks of IoT: hardware, communication, software backend and applications. It’s essential that for all these blocks security is a must-have element. To prevent the vulnerabilities on all levels, the IoT infrastructure has to be holistically designed. On the whole, the network connection between the IoT devices and the IoT software platform would need to be encrypted and protected with a strong encryption mechanism to avoid potential attacks. By means of separation of IoT traffic into private networks, strong information security at the cloud application level, requiring regular password updates and supporting updateable firmware by way of authentication, signed software updates and so on can be pursued to enhance the level of security present in an IoT software platform. Nonetheless while security ought to be scalable, it is unfortunately usually a trade-off with convenience, quick workflows and project cost.
4. Flexible Database. There are four major “V” for databases in IoT space:
- Volume (the database should be able to store massive amount of generated data)
- Variety (the database should be able to handle different kind of data produced by various devices and sensors)
- Velocity (the database should be able to make instant decisions while analyzing streaming data)
- Veracity ( the database should be able to deal with ambiguous data in some cases produced by sensors)
Therefore an IoT platform usually comes with a cloud-based database solution, which is distributed across various sensor nodes.
5. Data analytics.
A lot of IoT cases go beyond just action management and require complicated analytics in order to get the most out of the IoT data-stream. There are four types of analytics which can be conducted on IoT data:
- Real-time analytics (on the fly analysis of data),
- Batch analytics (runs operations on an accumulated set of data),
- Predictive analytics (makes predictions based on different statistical and machine learning technologies)
- Interactive analytics (runs numerous exploratory analysis on either streaming or batch data)
While choosing the right IoT platform, it’s better to keep in mind that the analytics engine should comprise all dynamic calculations of sensor data, starting from basic data clustering to complex machine learning.
6. Pricing and the budget. The IoT platform market features a diversity of pricing methodologies underlying various business strategies. And sometimes providers’ costs aren’t always transparent. Thus it’s very important to check out all the nuances of your provider’s pricing pattern, so you are not plainly bought into introductory teaser rates or into the prices for the base model.
Further you should bear in mind that you licensing cost for the chosen platform is just the beginning. The major expense can turn out to be the integration itself, as well as hiring consultants (if you are not able to do it on your own) to support the system.
Therefore, it’s extremely vital to brainstorm what your entire IoT system will look like at scale and choose which features are most critical to you chiefly — and only afterwards decide what sort of platform you need.
A lot of companies do this backward. They get the IoT platform and believe they’re getting the complete necessary solution—then realize the mistake half a year into development. Thus it’s critical to be aware of this before you get started.
Also it should be mentioned that some companies don’t use IoT platforms—they’re developing their own platforms in-house. Yet, depending on how you want to go to market, it may be clever to research pre-built options. Depending on your situation, you may save a lot of time and money by partnering with one of these platforms.
Have you ever faced the difficulties of choosing the IoT platform for your business? If yes, can you please let me know what kind of difficulties? And what do you think is it better to use a ready-built IoT platform or develop your own from the scratch? Looking forward to getting your ideas and comments.
Business Development Manager
Professional Software Development
If the experts’ estimates regarding IoT are correct, it means that in 5-10 years there will be more than 50 billion interconnected devices in the world. And they all will generate zettabytes of data, which can be and should be collected, organized and used for various purposes. Hence the tight correlation between IoT and Big Data is hard to ignore, because IoT and Big Data are like Romeo and Juliet – they are created for each other. The unprecedented amount of data produced by IoT would be useless without the analytic power of Big Data. Contrariwise, without the IoT, Big Data would not have the raw materials from which to model solutions that are expected of it.
What are the impacts of IoT on Big Data?
The IoT revolution means that almost every device or facility will have its own IP address and will be interconnected. They are going to generate a huge amount of data, spewing at us from different sides – household appliances, power stations, automobiles, train tracks and shipping containers etc. That’s why the companies will have to update technologies, instruments and business processes in order to be able to cope with such great amount of data, benefit from its analysis and finally gain profit. The influence of Big Data on IoT is obvious and it is conducted by various means. Let’s take a closer look at the Big Data areas impacted by IoT.
Methods and facilities of Data Storage
IoT produces a great and stable flow of data, which hits companies’ data storage. In response to this issue, many companies are shifting from their own storage framework towards the Platform as a Service (PaaS) model. It’s a cloud-based solution, which supports scalability, flexibility, compliance, and an advanced architecture, creating a possibility to store useful IoT data.
There are few options of models in the modern cloud storage: public, private and hybrid. Depending on the specific data nature, the companies should be very accurate while choosing a particular model. For instance, a private model is suitable for the companies who work with extremely sensitive data or with the information which is controlled by the government legislation. In other cases, a public or hybrid option will be a perfect fit.
Changes in Big Data technologies
While collecting the relevant data, companies need to filter out the excessive information and further protect it from getting attacked. It presupposes using highly productive mechanism that comprises particular software and custom protocols. Message Queue Telemetry Transport (MQTT) and Data Distribution Service (DDS) are two of the most widely used protocols. Both of them are able to help thousands of devices with sensors to connect with real-time machine-to-machine networks. MQTT gathers data from numerous devices and puts the data through the IT infrastructure. Otherwise, DDS scatters data across devices.
After receiving the data, the next step is to process and store it. The majority of the companies tend to install Hadoop and Hivi for Big Data storage. However there are some companies which prefer to use NoSQL document databases, as Apache CouchDB and others. Apache CouchDB is even more suitable, because it provides high throughput and very low latency.
Filtering out redundant data
One of the main challenges with Internet of Things is data management. Not all IoT data is relevant. If you don’t identify what data should be transmitted promptly, for how long it should be stored and what should be eliminated, then you could end up with a bulky pile of data which should be analyzed. Executive director of Product Marketing Management at AT&T, Mobeen Khan, says: “Some data just needs to be read and thrown away”.
The survey carried out by ParStream (an analytical platform for IoT) shows that almost 96 % of companies are striving to filter out the excessive data from their devices. Nevertheless only few of them are able to do it efficiently. Why is it happening? Below you can see the statistics, depicting the main problems which most of the companies are facing with the data analysis procedure. The percentage figure points out the percentage of the respondents to the ParStream survey confronting the challenge.
• Data collection difficulties – 36%
• Data is not captured accurately – 25%
• Slowness of data capture – 19%
• Too much data to analyze in a right way – 44%
• Data analyzing and processing means are not developed enough – 50%
• Existing business processes are not adjustable to allow efficient collection – 24%
To perform the action of filtering out the data effectively, organizations will need to update their analysis capabilities and make their IoT data collection process more productive. Cleaning data is a procedure that will become more significant to companies than ever.
Data security challenges
The IoT has made an impact on a security field and caused challenges which can’t be resolved by traditional security systems. Protecting Big Data generated from IoT arouses complications as this data comes from various devices, producing different types of data as well as different protocols.
The equally important issue is that many security specialist lack experience in providing data security for IoT. Particularly, any attack can not only threaten the data but also harm the connected device itself. And here is the dilemma when a huge amount of sensitive information is produced without the pertinent security to protect it.
There are two things that can help to prevent attacks: a multilayered security system and a thorough segmentation of the network. The companies should use software-defined networking (SDN) technologies combined with network identity and access policies for creating a dynamic network fragmentation. SDN-based network segmentation also should be used for point-to-point and point-to-multipoint coding based on the merger of some software-defined networking and public key infrastructure (SDN/PKI). In this case data security mechanisms will be keeping pace with the growth of Big Data in IoT.
IoT requires Big Data
With the emerging of IoT step by step many questions arises: Where is the data coming from IoT going to be stored? How is it going to be sorted out? Where will the analysis be conducted? Obviously, the companies which will be able to cope with these issues the next few years are going to be in prime position for both profits and influence over the evolution of our connected world. The vehicles will become smarter, more able to maintain larger amounts of data and probably able to carry out limited analytics. However as IoT grows and companies grow with IoT, they will have many more challenges to resolve.
What do you think about the evolving of Big Data in IoT? Have you already experienced the challenges of Big Data in IoT? And do you have any ideas about the progressive solutions to these challenges? I’ll be happy to hear your opinion in the comments below. Please, feel free to share your thoughts.
Business Development Manager
Professional Software Development
In today’s business and technology world you can’t have a conversation without touching upon the issue of big data. Some would say big data is a buzzword and the topic is not new at all. Still from my point of view recently, for the last two-three years, the reality around the data has been changing considerably and so it makes sense to discuss big data so hotly. And the figures prove it.
IBM reports we create 2.5 quintillion bytes of data every day. In 2011 our global output of data was estimated at 1.8 billion terabytes. What impresses it that 90 percent of the data in the world today was created in the past two years according to Big Blue. In the information century those who own the data and can analyze it properly and then use it for decision-making purpose will definitely rule the world. But if you don’t have the tools to manage and perform analytics on that never-ending flood of data, it’s essentially garbage.
Big data is not really a new technology, but a term used for a handful of technologies: analytics, in-memory databases, NoSQL databases, Hadoop. They are sometimes used together, sometimes not. While some of these technologies have been around for a decade or more, a lot of pieces are coming together to make big data the hot thing.
Big data is so hot and is changing things for the following reasons:
– It can handle massive amounts of all sorts of information, from structured, machine-friendly information in rows and columns toward the more human-friendly, unstructured data from sensors, transaction records, images, audios and videos, social media posts, logs, wikis, e-mails and documents,
– It works fast, almost instantly,
– It is affordable because it uses ordinary low-cost hardware.
Big data is possible now because other technologies are fueling it:
-Cloud provides affordable access to a massive amount of computing power and to loads of storage: you don’t have to buy a mainframe and a data center, and pay just for what you use.
-Social media allows everyone to create and consume a lot of interesting data.
-Smartphones with GPS offer lots of new insights into what people are doing and where.
-Broadband wireless networks mean people can stay connected almost everywhere and all the time.
The majority of organizations today are making the transition to a data-driven culture that leverages data and analytics to increase revenue and improve efficiency. For this a complex approach should be taken, so called MORE approach as Avanade recommends:
-Merge: to squeeze the value out of your data, you need to merge data from multiple sources, like structured data from your CRM and unstructured data from social news feeds to gain a more holistic view on the point. The challenge here is in understanding which data to bring together to provide the actionable intelligence.
-Optimize: not all data is good data, and if you start with bad data, with data-driven approach you’ll just be making bad decisions faster. You should identify, select and capture the optimal data set to make the decisions. This involves framing the right questions and utilizing the right tools and processes.
-Respond: just having data does mean acting on it. You need to have the proper reporting tools in place to surface the right information to the people who need it, and those people then need the processes and tools to take action on their insights.
-Empower: data can’t be locked in silos, and you need to train your staff to recognize and act on big data insights.
And what is big data for your company? Why do you use it? And how do you approach a data-driven decision-making model in your organization?
Would be interesting to hear your point.