Multi-rate control over AWGN channels via analog joint source-channel coding

We consider the problem of controlling an unstable plant over an additive white Gaussian noise (AWGN) channel with a transmit power constraint, where the signaling rate of communication is larger than the sampling rate (for generating observations and applying control inputs) of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and which is often much lower than the rate that can be communicated. This setting offers the opportunity of improving the system performance by employing multiple channel uses to convey a single message (output plant observation or control input). Common ways of doing so are through either repeating the message, or by quantizing it to a number of bits and then transmitting a channel coded version of the bits whose length is commensurate with the number of channel uses per sampled message. We argue that such “separated source and channel coding” can be suboptimal and propose to perform joint source-channel coding. Since the block length is short we obviate the need to go to the digital domain altogether and instead consider analog joint source-channel coding. For the case where the communication signaling rate is twice the sampling rate, we employ the Archimedean bi-spiral-based Shannon-Kotel'nikov analog maps to show significant improvement in stability margins and linear-quadratic Gaussian (LQG) costs over simple schemes that employ repetition

Multi-rate control over AWGN channels via analog joint source-channel coding

We consider the problem of controlling an unstable plant over an additive white Gaussian noise (AWGN) channel with a transmit power constraint, where the signaling rate of communication is larger than the sampling rate (for generating observations and applying control inputs) of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and which is often much lower than the rate that can be communicated. This setting offers the opportunity of improving the system performance by employing multiple channel uses to convey a single message (output plant observation or control input). Common ways of doing so are through either repeating the message, or by quantizing it to a number of bits and then transmitting a channel coded version of the bits whose length is commensurate with the number of channel uses per sampled message. We argue that such “separated source and channel coding” can be suboptimal and propose to perform joint source-channel coding. Since the block length is short we obviate the need to go to the digital domain altogether and instead consider analog joint source-channel coding. For the case where the communication signaling rate is twice the sampling rate, we employ the Archimedean bi-spiral-based Shannon-Kotel'nikov analog maps to show significant improvement in stability margins and linear-quadratic Gaussian (LQG) costs over simple schemes that employ repetition

An Adaptive Broadcasting Strategy for Efficient Dynamic Mapping in Vehicular Networks

In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios

Predictive Duty Cycling of Radios and Cameras using Augmented Sensing in Wireless Camera Networks

Energy efficiency dominates practically every aspect of the design of wireless camera networks (WCNs), and duty cycling of radios and cameras is an important tool for achieving high energy efficiencies. However, duty cycling in WCNs is made complex by the camera nodes having to anticipate the arrival of the objects in their field-of-view. What adds to this complexity is the fact that radio duty cycling and camera duty cycling are tightly coupled notions in WCNs. Abstract In this dissertation, we present a predictive framework to provide camera nodes with an ability to anticipate the arrival of an object in the field-of-view of their cameras. This allows a predictive adaption of network parameters simultaneously in multiple layers. Such anticipatory approach is made possible by enabling each camera node in the network to track an object beyond its direct sensing range and to adapt network parameters in multiple layers before the arrival of the object in its sensing range. The proposed framework exploits a single spare bit in the MAC header of the 802.15.4 protocol for creating this beyond-the-sensing-rage capability for the camera nodes. In this manner, our proposed approach for notifying the nodes about the current state of the object location entails no additional communication overhead. Our experimental evaluations based on large-scale simulations as well as an Imote2-based wireless camera network demonstrate that the proposed predictive adaptation approach, while providing comparable application-level performance, significantly reduces energy consumption compared to the approaches addressing only a single layer adaptation or those with reactive adaptation

Multi-rate control over AWGN channels via analog joint source-channel coding

We consider the problem of controlling an unstable plant over an additive white Gaussian noise (AWGN) channel with a transmit power constraint, where the signaling rate of communication is larger than the sampling rate (for generating observations and applying control inputs) of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and which is often much lower than the rate that can be communicated. This setting offers the opportunity of improving the system performance by employing multiple channel uses to convey a single message (output plant observation or control input). Common ways of doing so are through either repeating the message, or by quantizing it to a number of bits and then transmitting a channel coded version of the bits whose length is commensurate with the number of channel uses per sampled message. We argue that such “separated source and channel coding” can be suboptimal and propose to perform joint source-channel coding. Since the block length is short we obviate the need to go to the digital domain altogether and instead consider analog joint source-channel coding. For the case where the communication signaling rate is twice the sampling rate, we employ the Archimedean bi-spiral-based Shannon-Kotel'nikov analog maps to show significant improvement in stability margins and linear-quadratic Gaussian (LQG) costs over simple schemes that employ repetition

A novel network architecture for train-to-wayside communication with quality of service over heterogeneous wireless networks

In the railway industry, there are nowadays different actors who would like to send or receive data from the wayside to an onboard device or vice versa. These actors are e.g., the Train Operation Company, the Train Constructing Company, a Content Provider, etc. This requires a communication module on each train and at the wayside. These modules interact with each other over heterogeneous wireless links. This system is referred to as the Train-to-Wayside Communication System (TWCS). While there are already a lot of deployments using a TWCS, the implementation of quality of service, performance enhancing proxies (PEP) and the network mobility functions have not yet been fully integrated in TWCS systems. Therefore, we propose a novel and modular IPv6-enabled TWCS architecture in this article. It jointly tackles these functions and considers their mutual dependencies and relationships. DiffServ is used to differentiate between service classes and priorities. Virtual local area networks are used to differentiate between different service level agreements. In the PEP, we propose to use a distributed TCP accelerator to optimize bandwidth usage. Concerning network mobility, we propose to use the SCTP protocol (with Dynamic Address Reconfiguration and PR-SCTP extensions) to create a tunnel per wireless link, in order to support the reliable transmission of data between the accelerators. We have analyzed different design choices, pinpointed the main implementation challenges and identified candidate solutions for the different modules in the TWCS system. As such, we present an elaborated framework that can be used for prototyping a fully featured TWCS

Spike-based coupling between single neurons and populations across rat sensory cortices, perirhinal cortex, and hippocampus

Cortical computations require coordination of neuronal activity within and across multiple areas. We characterized spiking relationships within and between areas by quantifying coupling of single neurons to population firing patterns. Single-neuron population coupling (SNPC) was investigated using ensemble recordings from hippocampal CA1 region and somatosensory, visual, and perirhinal cortices. Within-area coupling was heterogeneous across structures, with area CA1 showing higher levels than neocortical regions. In contrast to known anatomical connectivity, between-area coupling showed strong firing coherence of sensory neocortices with CA1, but less with perirhinal cortex. Cells in sensory neocortices and CA1 showed positive correlations between within- and between-area coupling; these were weaker for perirhinal cortex. All four areas harbored broadcasting cells, connecting to multiple external areas, which was uncorrelated to within-area coupling strength. When examining correlations between SNPC and spatial coding, we found that, if such correlations were significant, they were negative. This result was consistent with an overall preservation of SNPC across different brain states, suggesting a strong dependence on intrinsic network connectivity. Overall, SNPC offers an important window on cell-to-population synchronization in multi-area networks. Instead of pointing to specific information-coding functions, our results indicate a primary function of SNPC in dynamically organizing communication in systems composed of multiple, interconnected areas

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field