Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Real-time image streaming over a low-bandwidth wireless camera network

In this paper we describe the recent development of a low-bandwidth wireless camera sensor network. We propose a simple, yet effective, network architecture which allows multiple cameras to be connected to the network and synchronize their communication schedules. Image compression of greater than 90% is performed at each node running on a local DSP coprocessor, resulting in nodes using 1/8th the energy compared to streaming uncompressed images. We briefly introduce the Fleck wireless node and the DSP/camera sensor, and then outline the network architecture and compression algorithm. The system is able to stream color QVGA images over the network to a base station at up to 2 frames per second. © 2007 IEEE

Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PAN) under recently-established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by the number of camera sensors deployed (spatial coverage), as well as the number of captured video frames out of which each node processes and transmits data (temporal coverage). In this paper, we explore this aspect for uniformly-formed VSNs, i.e., networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically-distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatio-temporal coverage parameters of such VSNs under a-priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results reveal that our analytic results are always within 7% of the energy consumption measurements for a wide range of settings. In addition, results obtained via a multimedia subsystem show that the optimal spatio-temporal settings derived by the proposed framework allow for substantial reduction of energy consumption in comparison to ad-hoc settings. As such, our analytic modeling is useful for early-stage studies of possible VSN deployments under collision-free MAC protocols prior to costly and time-consuming experiments in the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video Technology, 201

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

Network correlated data gathering with explicit communication: NP-completeness and algorithms

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal