Efficient Transport Protocol for Networked Haptics Applications

The performance of haptic application is highly sensitive to communication delays and losses of data. It implies several constraints in developing networked haptic applications. This paper describes a new internet protocol called Efficient Transport Protocol (ETP), which aims at developing distributed interactive applications. TCP and UDP are transport protocols commonly used in any kind of networked communication, but they are not focused on real time application. This new protocol is focused on reducing roundtrip time (RTT) and inter packet gap (IPG). ETP is, therefore, optimized for interactive applications which are based on processes that are continuously exchanging data.ETP protocol is based on a state machine that decides the best strategies for optimizing RTT and IPG. Experiments have been carried out in order to compare this new protocol and UDP

Efficient Transport Protocol for Networked

The performance of haptic application is highly sensitive to communication delays and losses of data. It implies several constraints in developing networked haptic applications. This paper describes a new internet protocol called Efficient Transport Protocol (ETP), which aims at developing distributed interactive applications. TCP and UDP are transport protocols commonly used in any kind of networked communication, but they are not focused on real time application. This new protocol is focused on reducing roundtrip time (RTT) and interpacket gap (IPG). ETP is, therefore, optimized for interactive applications which are based on processes that are continuously exchanging data. ETP protocol is based on a state machine that decides the best strategies for optimizing RTT and IPG. Experiments have been carried out in order to compare this new protocol and UD

Wireless Resource Management in Industrial Internet of Things

Wireless communications are highly demanded in Industrial Internet of Things (IIoT) to realize the vision of future flexible, scalable and customized manufacturing. Despite the academia research and on-going standardization efforts, there are still many challenges for IIoT, including the ultra-high reliability and low latency requirements, spectral shortage, and limited energy supply. To tackle the above challenges, we will focus on wireless resource management in IIoT in this thesis by designing novel framework, analyzing performance and optimizing wireless resources. We first propose a bandwidth reservation scheme for Tactile Internet in the local area network of IIoT. Specifically, we minimize the reserved bandwidth taking into account the classification errors while ensuring the latency and reliability requirements. We then extend to the more challenging long distance communications for IIoT, which can support the global skill-set delivery network. We propose to predict the future system state and send to the receiver in advance, and thus the delay experienced by the user is reduced. The bandwidth usage is analysed and minimized to ensure delay and reliability requirements. Finally, we address the issue of energy supply in IIoT, where Radio frequency energy harvesting (RFEH) is used to charge unattended IIoT low-power devices remotely and continuously. To motivate the third-party chargers, a contract theory-based framework is proposed, where the optimal contract is derived to maximize the social welfare

Towards Tactile Internet in Beyond 5G Era: Recent Advances, Current Issues and Future Directions

Tactile Internet (TI) is envisioned to create a paradigm shift from the content-oriented communications to steer/control-based communications by enabling real-time transmission of haptic information (i.e., touch, actuation, motion, vibration, surface texture) over Internet in addition to the conventional audiovisual and data traffics. This emerging TI technology, also considered as the next evolution phase of Internet of Things (IoT), is expected to create numerous opportunities for technology markets in a wide variety of applications ranging from teleoperation systems and Augmented/Virtual Reality (AR/VR) to automotive safety and eHealthcare towards addressing the complex problems of human society. However, the realization of TI over wireless media in the upcoming Fifth Generation (5G) and beyond networks creates various non-conventional communication challenges and stringent requirements in terms of ultra-low latency, ultra-high reliability, high data-rate connectivity, resource allocation, multiple access and quality-latency-rate tradeoff. To this end, this paper aims to provide a holistic view on wireless TI along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions. First, starting with the vision of TI and recent advances and a review of related survey/overview articles, we present a generalized framework for wireless TI in the Beyond 5G Era including a TI architecture, the main technical requirements, the key application areas and potential enabling technologies. Subsequently, we provide a comprehensive review of the existing TI works by broadly categorizing them into three main paradigms; namely, haptic communications, wireless AR/VR, and autonomous, intelligent and cooperative mobility systems. Next, potential enabling technologies across physical/Medium Access Control (MAC) and network layers are identified and discussed in detail. Also, security and privacy issues of TI applications are discussed along with some promising enablers. Finally, we present some open research challenges and recommend promising future research directions

Accurate and energy-efficient classification with spiking random neural network: corrected and expanded version

Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running more bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network (RNN), a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of a spiking neural network. This is demonstrated on a number of real-world classification datasets

Network Latency in Teleoperation of Connected and Autonomous Vehicles:A Review of Trends, Challenges, and Mitigation Strategies

With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs

Neural compass or epiphenomenon? Experimental and theoretical investigations into the rodent head direction cell system

Institute for Adaptive and Neural ComputationHow does the brain convert sensory information into abstract representations that can support complex behaviours? The rodent head-direction (HD) system, whose cell ensembles represent head direction in the horizontal plane, is a striking example of a “cognitive” representation without a direct sensory correlate. It can be updated by sensory inputs fromdifferentmodalities, yet persists in the absence of external input. Together with cells tuned for place, the HD system is thought to be fundamental for navigation and spatial information processing. However, relatively few studies have sought to characterise the connection between the HD system and spatial behaviour directly, and their overall outcome has been inconclusive. In the experiments that make up the first part of this thesis, we approach this issue by isolating the self-motion component of the HDsystem. We developed an (angular) path integration task in which we show that rats rely on their internal sense of direction to return to a trial-unique starting location, allowing us to investigate the contribution of the HD system to this behaviour without influences from uncontrolled external cues. Using this path integration task, we show that rats with bilateral lesions of the lateral mammillary nuclei (LMN) are significantly impaired compared to sham-operated controls. Lesions of the LMN, which contains HDcells, are known to abolish directional firing in downstream HD areas, suggesting that impairment on the task is due to loss of HD activity. We also recorded HD cell activity as rats are performing the path integration task, and found the HD representation to correlate with the rats’ choice of return journey. Thus, we provide both causal and correlational experimental evidence for a critical role of the HD system in path integration. For the second part of this thesis, we implemented a computational model of how the HD system is updated by head movements during path integration, providing a novel explanation for HD cells’ ability to anticipate the animal’s head direction. The model predicts that such anticipatory time intervals (ATIs) should depend on the frequency spectrum of the rats’ head movements. In direct comparison with experimental recording data, we show that the model can explain up to 80% of the experimentally observed variance, where none was explained by previous models. We also consider the effects of propagating the HD signal through multiple layers, identifying several potential sources of anticipation and lag. In summary, this thesis provides behavioural, lesion, and unit-recording evidence that during path integration, rats use a directional signal provided by the head direction system. The neural mechanisms responsible for the generation and maintenance of this signal are explored computationally. The finding that ATIs depend on the statistics of head movements has methodological implications and constrains models of the HD system