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AIoT research

Research projects regarding AIoT security

Bosch Research boosts trust in and the reliability of AIoT

AIoT research

Shaping the transformation: the use of artificial intelligence within the Internet of Things

The Corona pandemic, climate change and digitalization will change people’s needs and desires. The focus will be on health, sustainability and data security. In order to cope with this rapid transformation and respond in a customer-centric manner, Bosch combines artificial intelligence (AI) and the Internet of Things (IoT) in the AIoT.

In the IoT, we connect intelligent products and services, generating added value for our customers. By adding AI, we create a closed value creation cycle and focus even more on users. The data resulting from the use of intelligent, connected products and the interaction between people and machines and machines themselves are the key factor in this context. By linking IoT with AI and machine learning, we can draw the right conclusions from huge quantities of data and react to these data during product engineering in seconds. We learn from the data and can thus improve our products and services on an ongoing basis.

AIoT makes it possible to develop new products more quickly. At the same time, we can optimize customers’ products during their lifetime for example by using over-the-air updates or we can add new functions.

By using data as a basis for optimizing and personalizing our products, the need for privacy and AIoT security grows. The greater people’s trust in the AIoT, the greater their acceptance. At Bosch Research, we are involved in numerous research projects in order to build this trust.

I have always acted according to the principle that I would rather lose money than trust.

Robert Bosch

Our research results in a secure and reliable AIoT

Bosch Research puts in place the technological basis for a more reliable and secure AIoT and thus allows applications and products which are even better geared to customer needs. The focus is always on handling user data in a trusting manner too. After all, customers often ask the following questions when using connected products: How do I keep track of things when dealing with complex processes? How are my data protected against unauthorized access or even manipulation? How is data misuse prevented? Quality of life is based on trust. Bosch Research takes this seriously, provides answers to questions with technological solutions and products and boosts people’s trust in AIoT:

With self-sovereign identities (SSI), users are given sovereignty over their passes and certificates in the digital world, self-learning sensors such as those in the embedded AI based siren detection project achieve greater safety during autonomous driving and automated security testing protects software against hacker attacks.

Develop, connect, improve:
the AIoT cycle

The so-called AIoT cycle – a value creation cycle comprising four phases – shows the benefits of linking AI and the IoT:

AIoT cycle

Value creation

Value creation

Connected products provide data. Bosch uses these data during research and development to improve applications and revise or supplement functions. At the same time, we can improve the security and reliability of our products on an ongoing basis and adapt them to meet the individual needs of customers. Making AI in AIoT products secure, robust and explainable is a key issue for us. Research projects such as machine learning testing and AI safety help to achieve this goal.

Products in interaction with customers

Products in interaction with customers

We deliver connected products and services to our customers. When these products are used, they generate data, which we use in the following phases of the cycle to improve products and applications. AIoT products ensure greater security for users. Research projects such as embedded AI based siren detection show this.

Data processing

Data processing

The data which are produced when connected products are used are the basis for this phase of the AIoT cycle. They are collected and stored in a structured manner. With technologies such as self-sovereign identities (SSI) and trustworthy computing, we ensure that users can keep control and maintain sovereignty over their data at all times and that these data are always protected.

AI algorithms

AI algorithms

In this phase, we process the data using AI algorithms and machine learning and gain new findings on this basis. The visual analytics research project shows how this process leads to greater security for users: in autonomous vehicles, AI is used for image recognition. In rarely occurring situations where several unusual conditions converge, so-called corner cases, the AI in the image recognition system points out weaknesses. For example when a red traffic light is hard to see from a certain angle in inclement weather. Visual analytics helps to detect blind spots and to automatically supplement the existing data set with the help of a second AI. As a result, shortcomings of the first AI are remedied and overall system accuracy can be increased.

Find out more about our projects

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Get to know our experts

Dr. Dakshina Dasari

Dr. Dakshina Dasari

Research Engineer, Dynamic Distributed Systems

Dr. Dakshina Dasari

I am a Research Engineer in the “Dynamic Distributed Systems” group and in this role I work on designing solutions to enable applications to meet their quality of service requirements when deployed on computing platforms. My job is particularly interesting since it lies at the conjunction of different disciplines including Cyber Physical systems, High Performance Computing architectures and Real-time scheduling theory. The focus is on designing and evaluating innovative methods and mechanisms for resource management which are correct-by-construction, formally sound and help in efficiently utilizing the underlying platforms, while also adhering to the application specific constraints.

Dr. Dakshina Dasari
Amit Kale

Dr. Amit Kale

Principal senior expert and group manager

Dr. Amit Kale

I am a principal senior expert in computer vision at the Research and Technology Center in India. My research is motivated by the desire to organize and manage large scale video and multi-sensor data (Peta Bytes) with the goals of being able to smartly curate the right data desired by algorithm development. This includes automated approaches to select the most representative sub set of a large set of images, and search and retrieve scenes of interest from the stored images. Our research has multiple goals such as reducing the cost of ground truth generation by removing redundancies, supporting function development and testing to find difficult cases where algorithms do not work well, which can then be used to collect more of such cases or synthetically generate them. In order to achieve this we explore the structure and representation power of deep convolutional neural networks. We develop human computer interfaces that go hand in hand with the deep learning approaches to ensure ease of usage by the end users.

Amit Kale
Dr. Jan Hendrik Metzen

Dr. Jan Hendrik Metzen

Senior Expert in Robust Scalable Perception at the Bosch Center for Artificial Intelligence

Dr. Jan Hendrik Metzen

I am a Senior Expert in Robust Scalable Perception at the Bosch Center for Artificial Intelligence. My research is motivated by the desire to advance the understanding, robustness, and applicability of deep learning-based perception. In particular, I focus on evaluating and increasing the robustness of neural networks. While neural networks excel in classifying data coming from the distribution they have been trained on, they often perform poorly on data with slightly different properties. Addressing this shortcoming is crucial for applying neural network-based perception in safety-critical domains such as automated driving, where they will inevitably be faced with situations not encountered or foreseen during training.

Dr. Jan Hendrik Metzen
Liu Ren, Ph.D.

Liu Ren, Ph.D.

VP and Chief Scientist of Integrated Human-Machine Intelligence

Liu Ren, Ph.D.

I am the Vice President and Chief Scientist of Integrated Human- Machine Intelligence (HMI) at Bosch Research in North America. I am responsible for shaping strategic directions and developing cutting-edge technologies in AI focusing big data visual analytics, explainable AI, mixed reality/AR, computer perception, NLP, conversational AI, audio analytics, wearable analytics and so on for AIoT application areas such as autonomous driving, car infotainment and advanced driver assistance systems (ADAS), Industry 4.0, smart home/building solutions, and robotics, etc. As the responsible global head, I oversee these research activities for teams in the Silicon Valley (U.S.), Pittsburgh (U.S.), and Renningen (Germany). I have won the Bosch North America Inventor of the Year Award for 3D maps (2016), Best Paper Award (2018, 2020), and Honorable Mention Award (2016) for big data visual analytics in IEEE Visualization.

Liu Ren, Ph.D.
Dr. Robert Xie

Dr. Robert Xie

Head of IoT@Life Program | Group Leader for IoT & I4.0 at Bosch Research and Technology Center in China

Dr. Robert Xie

I’m the head of IoT@Life program and group leader for IoT & I4.0 at the Bosch Research and Technology Center in China. Before joining Bosch, I was an Assistant Professor at Shanghai Jiao Tong University focusing on medical robotics and sensor systems. I received my PhD from King's College London and did a postdoc at the University of Southampton in the field of sensor systems and lab-on-chip. As an alumni of the Bosch Accelerator Program, I not only collaborate closely with Business Units for joint development, but also explore commercialization of IoT products and solutions with scalable, repeatable and profitable business models.

Dr. Robert Xie