Artificial Intelligence and Internet of Things

Artificial Intelligence and Internet of Things

The possibilities that IoT brings to the table are endless. IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT.

IoT will produce a tsunami of big data, with the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to an astronomical level. This data will hold extremely valuable insights into what’s working well or what’s not.

Also, IoT will point out conflicts that arise and provide high-value insight into new business risks and opportunities as correlations and associations are made.

Examples of such IoT data:

  • Data that helps cities predict accidents and crimes
  • Data that gives doctors real-time insight into information from pacemakers or biochips
  • Data that optimize productivity across industries through predictive maintenance on equipment and machinery
  • Data that creates truly smart homes with connected appliances
  • Data that provides critical communication between self-driving cars

That’s the good news, but it’s simply impossible for humans to review and understand all of this data with traditional methods, even if they cut down the sample size, simply takes too much time. The big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. Finding insights in terabytes of machine data is a real challenge, just ask a data scientist.

But in order for us to harvest the full benefits of IoT last mile (data), we need to improve:

  • Speed of big data analysis
  • Accuracy of big data analysis

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI (Artificial Intelligence) as the last mile of IoT.

Artificial intelligence (AI) and IoT

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is an academic field of study which generally studies the goal of emulating human-like intelligence. John McCarthy, who coined the term in 1955, defines it as “the science and engineering of making intelligent machines”

In an IoT situation, AI can help companies take the billions of data points they have and boil them down to what’s really meaningful. The general premise is the same as in the retail applications – review and analyze the data you’ve collected to find patterns or similarities that can be learned from, so that better decisions can be made. To be able to call out potential problems, the data has to be analyzed in terms of what’s normal and what’s not. Similarities, correlations and abnormalities need to be quickly identified based on the real-time streams of data. The data collected, combined with AI, makes life easier with intelligent automation, predictive analytics and proactive intervention.

AI in IoT applications:

  • Visual big data, for example – will allow computers to gain a deeper understanding of images on the screen, with new AI applications that understand the context of images.
  • Cognitive systems will create new recipes that appeal to the user’s sense of taste, creating optimized menus for each individual, and automatically adapting to local ingredients.
  • Newer sensors will allow computers to “hear,” gathering sonic information about the user’s environment.

These are just a few promising applications of Artificial Intelligence in IoT. The potential for highly individualized services are endless and will dramatically change the way people live, for example helping Pandora to determine what other songs you may like, Amazon.com to suggest other books and movies to you and your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level.

Challenges facing AI in IoT

  1. Compatibility
  2. Complexity
  3. Privacy/Security
  4. Safety
  5. Ethical and legal Issues
  6. Artificial Stupidity

What is next …?

Gartner predict that by 2018, 6 billion connected objects will be requesting support – meaning that strategies, technologies and processes will have to be in place to respond to them. It will become necessary to think of connected devices less as ‘things’, but more as customers and consumers of services in themselves – and as such in need of constant support. The need for AI will be more prominent at that stage under the pressure of the huge number of devices and sensors.

Advertisements

IoT implementation and Challenges

IoT implementation and Challenges

The Internet of Things (IoT) is the network of physical objects—devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. Implementing this concept is not an easy task by any measure for many reasons including the complex nature of the different components of the ecosystem of IoT. To understand the gravity of this task, we will explain all the five components of IoT Implementation.

Components of IoT implementation

  • Sensors
  • Networks
  • Standards
  • Intelligent Analysis
  • Intelligent Actions

Sensors

According to (IEEE) sensors can be defined as: An electronic device that produces electrical, optical, or digital data derived from a physical condition or event. Data produced from sensors is then electronically transformed, by another device, into information (output) that is useful in decision making done by “intelligent” devices or individuals (people).

Types of Sensors: Active Sensors & Passive Sensors.

The selection of sensors greatly impacted by many factors, including:

  • Purpose (Temperature, Motion, Bio…etc.)
  • Accuracy
  • Reliability
  • Range
  • Resolution
  • Level of Intelligence (dealing with noise and interference)

The driving forces for using sensors in IoT today are new trends in technology that made sensors cheaper, smarter and smaller.

Challenges facing IoT sensors:

  • Power consumption
  • Security
  • Interoperability

Networks

The second step of this implantation is to transmit the signals collected by sensors over networks with all the different components of a typical network including routers, bridges in different topologies, including LAN, MAN and WAN. Connecting the different parts of networks to the sensors can be done by different technologies including Wi-Fi, Bluetooth, Low Power Wi-Fi , Wi-Max, regular Ethernet , Long Term Evolution (LTE) and the recent promising technology of Li-Fi (using light as a medium of communication between the different parts of a typical network including sensors)

The driving forces for wide spread network adoption in IoT can be summarized as follows:

  • High Data rate
  • Low Prices of data usage
  • Virtualization (X – Defined Network trends )
  • XaaS concept (SaaS, PaaS, and IaaS)
  • IPv6 deployment

Challenges facing network implementation in IoT

  • The enormous growth in number of connected devices
  • Availability of networks coverage
  • Security
  • Power consumption

Standards

The third stage in the implementation process includes the sum of all activities of handling, processing and storing the data collected from the sensors. This aggregation increases the value of data by increasing, the scale, scope, and frequency of data available for analysis but aggregation only achieved through the use of various standards depending on the IoT application in used.

Types of Standards

Two types of standards relevant for the aggregation process; technology standards (including network protocols, communication protocols, and data-aggregation standards) and regulatory standards (related to security and privacy of data, among other issues).

Technology Standards

  • Network Protocols (e.g.: Wi-Fi)
  • Communications Protocols (e.g.: HTTP)
  • Data aggregation standards (e.g.: Extraction, Transformation, Loading (ETL))

Regulatory Standards

Set and administrated by government agencies like FTC, for example Fair Information Practice Principles (FIPP) and US Health Insurance Portability and Accountability Act (HIPAA) just to mention few.

Challenges facing the adoptions of standards within IoT

  • Standard for handling unstructured data: Structured data are stored in relational databases and queried through SQL. Unstructured data are stored in different types of noSQL databases without a standard querying approach.
  • Security and privacy issues: There is a need for clear guidelines on the retention, use, and security of the data as well as metadata (the data that describe other data).
  • Regulatory standards for data markets: Data brokers are companies that sell data collected from various sources. Even though data appear to be the currency of the IoT, there is lack of transparency about, who gets access to data and how those data are used to develop products or services and sold to advertisers and third parties.
  • Technical skills to leverage newer aggregation tools: Companies that are keen on leveraging big-data tools often face a shortage of talent to plan, execute, and maintain systems.

Intelligent Analysis

The fourth stage in IoT implementation is extracting insight from data for analysis, Analysis is driven by cognitive technologies and the accompanying models that facilitate the use of cognitive technologies.

With advances in cognitive technologies’ ability to process varied forms of information, vision and voice have also become usable. Below is a list of selected cognitive technologies that are experiencing increasing adoption and can be deployed for predictive and prescriptive analytics:

  • Computer vision refers to computers’ ability to identify objects, scenes, and activities in images
  • Natural-language processing refers to computers’ ability to work with text the way humans do, extracting meaning from text or even generating text that is
  • Speech recognition focuses on accurately transcribing human speech

Factors driving adoption intelligent analytics within the IoT

  • Artificial intelligence models can be improved with large data sets that are more readily avail- able than ever before, thanks to the lower storage
  • Growth in crowdsourcing and open- source analytics software: Cloud-based crowdsourcing services are leading to new algorithms and improvements in existing ones at an unprecedented
  • Real-time data processing and analysis: Analytics tools such as complex event processing (CEP) enable processing and analysis of data on a real-time or a near-real-time basis, driving timely decision making and action

 Challenges facing the adoptions of intelligent analytics within IoT

  • Inaccurate analysis due to flaws in the data and/or model: A lack of data or presence of outliers may lead to false positives or false negatives, thus exposing various algorithmic limitations
  • Legacy systems’ ability to analyze unstructured data: Legacy systems are well suited to handle structured data; unfortunately, most IoT/business interactions generate unstructured data
  • Legacy systems’ ability to manage real- time data: Traditional analytics software generally works on batch-oriented processing, wherein all the data are loaded in a batch and then analyzed

Intelligent Actions

Intelligent actions can be expressed as M2M and M2H interfaces for example with all the advancement in UI and UX technologies.

Factors driving adoption of intelligent actions within the IoT

  • Lower machine prices
  • Improved machine functionality
  • Machines “influencing” human actions through behavioral-science rationale
  • Deep Learning tools

 Challenges facing the adoption of intelligent actions within IoT

  • Machines’ actions in unpredictable situations
  • Information security and privacy
  • Machine interoperability
  • Mean-reverting human behaviors
  • Slow adoption of new technologies

The Internet of Things (IoT) is an ecosystem of ever-increasing complexity, it’s the next weave of innovation that will humanize every object in our life , which is the next level to automating every object in our life . Convergence of technologies will make IoT implementation much easier and faster, which in turn will improve many aspects of our life at home and at work and in between.

Securing the Internet of Things (IoT)

Securing the Internet of Things (IoT)

The Internet of Things (IoT) as a concept is fascinating and exciting, but the key to gaining real business value from it, is effective communication between all elements of the architecture so you can deploy applications faster, process and analyze data at lightning speeds, and make decisions as soon as you can.

IoT architecture can be represented by four systems:

  1. Things: These are defined as uniquely identifiable nodes, primarily sensors that communicate without human interaction using IP connectivity.
  2. Gateways: These act as intermediaries between things and the cloud to provide the needed Internet connectivity, security and manageability.
  3. Network infrastructure: This is comprised of routers, aggregators, gateways, repeaters and other devices that control data flow.
  4. Cloud infrastructure: Cloud infrastructure contains large pools of virtualized servers and storage that are networked together.

000

Next-generation trends namely, Social Networks, Big Data, Cloud Computing, and Mobility, have made many things possible that weren’t just a few years ago. Add to that, the convergence of global trends and events that are fueling today’s technological advances and enabling innovation including:

  • Efficiencies and cost-reduction initiatives in key vertical market
  • Government incentives encouraging investment in these new technology
  • Lower manufacturing costs for smart devices
  • Reduced connectivity costs
  • More-efficient wired and wireless communications
  • Expanded and affordable mobile networks

Internet of Things (IoT) is one big winner in this entire ecosystem. IoT is creating new opportunities and providing a competitive advantage for businesses in current and new markets. It touches everything—not just the data, but how, when, where and why you collect it. The technologies that have created the Internet of Things aren’t changing the internet only, but rather change the things connected to the internet—the devices and gateways on the edge of the network that are now able to request a service or start an action without human intervention at many levels.

Because the generation and analysis of data is so essential to the IoT, consideration must be given to protecting data throughout its life cycle. Managing information at this level is complex because data will flow across many administrative boundaries with different policies and intents. Generally, data is processed or stored on edge devices that have highly limited capabilities and are vulnerable to sophisticated attacks.

Given the various technological and physical components that truly make up an IoT ecosystem, it is good to consider the IoT as a system-of-systems. The architecting of these systems that provide business value to organizations will often be a complex undertaking, as enterprise architects work to design integrated solutions that include edge devices, applications, transports, protocols, and analytics capabilities that make up a fully functioning IoT system. This complexity introduces challenges to keeping the IoT secure, and ensuring that a particular instance of the IoT cannot be used as a jumping off point to attack other enterprise information technology (IT) systems.

International Data Corporation (IDC) estimates that 90% of organizations that implement the IoT will suffer an IoT-based breach of backend IT systems by the year 2017.

 Challenges to Secure IoT Deployments

Regardless of the role your business has within the Internet of Things ecosystem— device manufacturer, solution provider, cloud provider, systems integrator, or service provider—you need to know how to get the greatest benefit from this new technology that offers such highly diverse and rapidly changing opportunities.

Handling the enormous volume of existing and projected data is daunting. Managing the inevitable complexities of connecting to a seemingly unlimited list of devices is complicated. And the goal of turning the deluge of data into valuable actions seems impossible because of the many challenges. The existing security technologies will play a role in mitigating IoT risks but they are not enough. The goal is to get data securely to the right place, at the right time, in the right format, it’s easier said than done for many reasons, Cloud Security Alliance (CSA) in a recent report listed some of the challenges:

  • Many IoT Systems are poorly designed and implemented, using diverse protocols and technologies that create complex configurations.
  • Lack of mature IoT technologies and business processes
  • Limited guidance for lifecycle maintenance and management of IoT devices
  • The IoT introduces unique physical security concerns
  • IoT privacy concerns are complex and not always readily evident.
  • Limited best practices available for IoT developers
  • There is a lack of standards for authentication and authorization of IoT edge devices
  • There are no best practices for IoT-based incident response activities.
  • Audit and Logging standards are not defined for IoT components
  • Restricted interfaces available IoT devices to interact with security devices and applications.
  • No focus yet on identifying methods for achieving situational awareness of the security posture of an organization’s IoT assets.
  • Security standards, for platform configurations, involving virtualized IoT platforms supporting multi-tenancy is immature.
  • Customer demands and requirements change constantly.
  • New uses for devices—as well as new devices—sprout and grow at breakneck speeds.
  • Inventing and reintegrating must-have features and capabilities are expensive and take time and resources.
  • The uses for Internet of Things technology are expanding and changing—often in uncharted waters.
  • Developing the embedded software that provides Internet of Things value can be difficult and expensive.

Security Risks of IoT

Some real examples of threats and attack vectors that malicious actors could take advantage of are:

  • Control systems, vehicles, and even the human body can be accessed and manipulated causing injury or worse.
  • Health care providers can improperly diagnose and treat patients.
  • Intruders can gain physical access to homes or commercial businesses
  • Loss of vehicle control.
  • Safety-critical information such as warnings of a broken gas line can go unnoticed
  • Critical infrastructure damage.
  • Malicious parties can steal identities and money.
  • Unanticipated leakage of personal or sensitive information.
  • Unauthorized tracking of people’s locations, behaviors and activities..
  • Manipulation of financial transactions.
  • Vandalism, theft or destruction of IoT assets.
  • Ability to gain unauthorized access to IoT devices.
  • Ability to impersonate IoT devices.

Dealing with the challenges and threats

Gartner predicted at its security and risk management summit in Mumbai, India this year, that more than 20% of businesses will have deployed security solutions for protecting their IoT devices and services by 2017, IoT devices and services will expand the surface area for cyber-attacks on businesses, by turning physical objects that used to be offline into online assets communicating with enterprise networks. Businesses will have to respond by broadening the scope of their security strategy to include these new online devices.

Businesses will have to tailor security to each IoT deployment according to the unique capabilities of the devices involved and the risks associated with the networks connected to those devices. BI Intelligence expects spending on solutions to secure IoT devices and systems to increase five fold over the next four years.

iotsecurity
The Optimum Platform

 

Developing solutions for the Internet of Things requires unprecedented collaboration, coordination, and connectivity for each piece in the system, and throughout the system as a whole. All devices must work together and be integrated with all other devices, and all devices must communicate and interact seamlessly with connected systems and infrastructures. It’s possible, but it can be expensive, time consuming, and difficult.

The optimum platform for IoT can:

  • Acquire and manage data to create a standards-based, scalable, and secure platform.
  • Integrate and secure data to reduce cost and complexity while protecting your investment.
  • Analyze data and act by extracting business value from data, and then acting on it.

Last word…

Security needs to be built in as the foundation of IoT systems, with rigorous validity checks, authentication, data verification, and all the data needs to be encrypted. At the application level, software development organizations need to be better at writing code that is stable, resilient and trustworthy, with better code development standards, training, threat analysis and testing. As systems interact with each other, it’s essential to have an agreed interoperability standard, which safe and valid. Without a solid bottom-top structure we will create more threats with every device added to the IoT. What we need is a secure and safe IoT with privacy protected, tough trade off but not impossible.