Share risk and energy: Sampling and communication strategies for multi-camera wireless monitoring networks

Abstract

Abstract-In the context of environmental monitoring, outdoor wireless cameras are vulnerable to natural hazards. To benefit from the inexpensive imaging sensors, we introduce a multicamera monitoring system to share the physical risk. With multiple cameras focusing at a common scenery of interest, we propose an interleaved sampling strategy to minimize per-camera consumption by distributing sampling tasks among cameras. To overcome the uncertainties in the sensor network, we propose a robust adaptive synchronization scheme to build optimal sampling configuration by exploiting the broadcast nature of wireless communication. The theory as well as simulation results verify the fast convergence and robustness of the algorithm.Under the interleaved sampling configuration, we propose three video coding methods to compress correlated video streams from disjoint cameras, namely, distributed/independent/joint coding schemes. The energy profiling on a two-camera system shows that independent and joint coding perform substantially better. The comparison between two-camera and single-camera system shows 30%-50% per-camera consumption reduction. On top of these, we point out that MIMO technology can be potentially utilized to push the communication consumption even lower.

Event-driven video coding for outdoor wireless monitoring cameras

“…In the later case, we also need to send the position array of those event blocks so that the reconstruction is possible. In practice, a standard data compression scheme such as zlib 2 can be used to reduce the size of this position array before transmission. The detailed algorithm for the encoding procedure is shown in Algorithm 3.…”

“…To reduce the block effect, we can first create a blending mask using the position array of event blocks, and then apply blending algorithms such as 1 This is to meet the block size of JPEG standard. 2 See http://zlib.net/. pyramid blending [8], Poisson image editing [9] to achieve seamless stitching.…”

“…Thanks to the availability of low-cost image sensor chips, wireless monitoring cameras begin to emerge in various outdoor monitoring applications [1] [2]. In these tasks, one or more static monitoring cameras are deployed to detect some unknown events, such as natural hazards or unexpected intrusions.…”

Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events

“…Due to severe resource constraints, in particular, energy and bandwidth, a sound body of literature has centered on extending the lifetime of WSNs from different perspectives, e.g., aggressively reducing the spatial sampling rate, the rate assignment, utility-based sensing and communication [1], [2], [3], [4], [5], [6], [7]. Although these schemes all attempt to reduce the energy cost of sensors, they may not be feasible in practice, due to the following serious limitations:…”

Howis the weather: Automatic inference from images

“…Due to the restricted density of weather stations (e.g. only around 30 major stations across Switzerland), low-cost wireless sensor networks have emerged to collect local environmental information [1] [2]. Among all available sensing capabilities, image sensors provide unique visual information of the target field.…”