AAIoT: Accelerating Artificial Intelligence in IoT Systems

Existing deep learning systems in the Internet of Things (IoT) environments lack the ability of assigning compute tasks reasonably which leads to resources wasting. In this letter, we propose an AAIoT, a method to allocate the inference computation of each network layer to each device in multi-layer IoT system. To our best knowledge, this is the first attempt to solve this problem. We design a dynamic programming algorithm to minimize the response time when weighing the cost of computation and transmission. Simulation results show that our approach makes significant improvements in system response time

Environment and task modeling of long-term-autonomous service robots

Utilizing service robots in real-world tasks can significantly improve efficiency, productivity, and safety in various fields such as healthcare, hospitality, and transportation. However, integrating these robots into complex, human-populated environments for continuous use is a significant challenge. A key potential for addressing this challenge lies in long-term modeling capabilities to navigate, understand, and proactively exploit these environments for increased safety and better task performance. For example, robots may use this long-term knowledge of human activity to avoid crowded spaces when navigating or improve their human-centric services. This thesis proposes comprehensive approaches to improve the mapping, localization, and task fulfillment capabilities of service robots by leveraging multi-modal sensor information and (long- term) environment modeling. Learned environmental dynamics are actively exploited to improve the task performance of service robots. As a first contribution, a new long-term-autonomous service robot is presented, designed for both inside and outside buildings. The multi-modal sensor information provided by the robot forms the basis for subsequent methods to model human-centric environments and human activity. It is shown that utilizing multi-modal data for localization and mapping improves long-term robustness and map quality. This especially applies to environments of varying types, i.e., mixed indoor and outdoor or small-scale and large-scale areas. Another essential contribution is a regression model for spatio-temporal prediction of human activity. The model is based on long-term observations of humans by a mobile robot. It is demonstrated that the proposed model can effectively represent the distribution of detected people resulting from moving robots and enables proactive navigation planning. Such model predictions are then used to adapt the robot’s behavior by synthesizing a modular task control model. A reactive executive system based on behavior trees is introduced, which actively triggers recovery behaviors in the event of faults to improve the long-term autonomy. By explicitly addressing failures of robot software components and more advanced problems, it is shown that errors can be solved and potential human helpers can be found efficiently

Denotational Semantics of Mobility in Unifying Theories of Programming (UTP)

UTP promotes the unification of programming theories and has been used successfully for giving denotational semantics to Imperative Programming, CSP process algebra, and the Circus family of programming languages, amongst others. In this thesis, we present an extension of UTP-CSP (the UTP semantics for CSP) with the concept of mobility. Mobility is concerned with the movement of an entity from one location (the source) to another (the target). We deal with two forms of mobility: • Channel mobility, concerned with the movement of links between processes, models networks with a dynamic topology; and • Strong process mobility, which requires to suspend a running process first, and then move both its code and its state upon suspension, and finally resume the process on the target upon reception. Concerning channel mobility: • We model channels as concrete entities in CSP, and show that it does not affect the underlying CSP semantics. • A requirement is that a process may not own a channel prior to receiving it. In CSP, the set of channels owned by a process (called its interface) is static by definition. We argue that making the interface variable introduces a paradox. We resolve this by introducing a new concept: the capability of a process, and show how it relates to the interface. We then define channel mobility as the operation that changes the interface of a process, but not its capability. We also provide a functional link between static CSP and its mobile version. Concerning strong mobility, we provide: • The first extension of CSP with jump features, using the concept of continuations. • A novel semantics for the generic interrupt (a parallel-based interrupt operator), using the concept of Bulk Synchronous Parallelism. We then define strong mobility as a specific interrupt operator in which the interrupt routine migrates the suspended program

Smart robotic pimobile vehicle prototye, having accident alert monitoring system

Vehicle accidents or fatality are very common owning a larger percentage of death count.The smart robotic pimobile vehicle prototype with an accident alert monitoring system helps create a shock absorber for car accidents by sending location and screen shot of accident site to emergency contact so they can alert the ambulance and also to the police station alerting them of a possible accident occurrence. This embedded robotic project is a prototype to help explain the stimulation of the software and how it works. This is achievable using the raspberry pi 3 and very important sensors like the GPS, GSM module, Accelerometer, the flame sensor. The main objective of this project is to reduced time taking to track identify and locate victim and to give an alert to relative, family and friends etc. In this research, this system is made to alert with the sensors embedded in the car, so families and love ones can have a perfect picture or result of how ugly the accident is