The Role of Physical Security in IoT

March 14, 2019 | 71 views

Securing the Internet of Things (IoT) continues to become more important as the value of assets, and number of connected devices, continues to increase. The distributed and fragmented nature of IoT means a larger attack surface and physical access (proximity) to the devices. The combination of these two factors make physical security a viable and potent threat. There are many examples of how a lack of sufficient physical security can pose a severe security risk to the IoT ecosystem and the effects of a security breach can quickly snowball.

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Net Solutions

Net Solutions is a global digital consultancy that helps brands leverage design, analytics and engineering to create meaningful, personalized customer experiences. Established in the year 2000, we are 230+ people strong, organisation. Our clients include leading brands like Unilever, Microsoft, Harvard Business Review, IMG, Mothercare and Euro Car Parts as well as successful global startups.

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AI's Impact on Improving Customer Experience

Article | July 20, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | August 8, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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SOFTWARE

Language Models: Emerging Types and Why They Matter

Article | August 2, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

Net Solutions

Net Solutions is a global digital consultancy that helps brands leverage design, analytics and engineering to create meaningful, personalized customer experiences. Established in the year 2000, we are 230+ people strong, organisation. Our clients include leading brands like Unilever, Microsoft, Harvard Business Review, IMG, Mothercare and Euro Car Parts as well as successful global startups.

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At the Orange Business Summit, which has brought together more than 1,000 enterprise-customers of Orange Business Services, Stéphane Richard, the Orange Group’s Chairman & CEO, presented Orange’s ambition and commitment to work with businesses to develop future uses of 5G. Stéphane Richard, Chairman & CEO of the Orange Group, said: “5G will not be just another new development in mobile uses. This is a technological leap that announces a significant transformation of business models for all companies and in all sectors of activity.” “More broadly, we must make every effort to make France one of the leading countries in the development of 5G. This future network is an opportunity to unite and consolidate all our resources, our know-how, our advanced industries, our French Tech and our talents at the service of our country.” This approach based on anticipation and support was illustrated through the announcement of several joint innovation projects with companies that are market leaders in key sectors of the economy, such as the manufacturing industry, energy or transport, and that have an eye on the future.

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Pod Group Provides Secure Connectivity for Klevio’s Smart Door Entry Application

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Pod Group, a provider of platforms, software and connectivity services for the Internet of Things (IoT) and connected enterprises has announced today that it has signed an agreement to provide an IoT connectivity solution to Klevio, a provider of remote keyless access applications. The Klevio One device is a Wi-Fi / 4G enabled smart intercom and smartphone app that enables both consumers and businesses to remotely open doors to their properties from anywhere in the world. The Klevio One device operates like an ordinary intercom with a doorbell function, the ability to speak to visitors and open both private and communal doors. However, unlike an ordinary intercom, it also allows property owners to perform these functions remotely, via an app on their smartphone. It is also possible to create digital time-limited keys, perfect for short term lets or property management. The device can be easily installed on an existing door lock to make it “smart” by fitting an electric strike to the doorframe. For Klevio, the reliability and security of the connectivity is key. Since Wi-Fi cannot always be guaranteed, it is essential to have a resilient 4G connection with consistent coverage even in remote areas. Klevio has chosen to connect its smart door entry application using Pod Group’s secure, multi-network connectivity based on “Best Signal” technology. The solution includes multi-network SIM cards offering connectivity on over 600 networks in 185+ countries. The SIM automatically swaps network if the device loses signal, ensuring seamless connectivity. Pod’s connectivity management platform F2M allows customers such as Klevio to manage their SIM base globally from one centralized interface. The platform provides in-depth analysis of the connectivity of each device, as well as end-to-end network security monitoring and threat detection.

Read More

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