Efficient network clustering for traffic reduction in embedded smart camera networks

Abstract

In this work, a clustering approach for bandwidth reduction in distributed smart camera networks is presented. Properties of the environment such as camera positions and environment pathways, as well as dynamics and features of targets are used to limit the flood of messages in the network. To better understand the correlation between camera positioning and pathways in the scene on one hand and temporal and spatial properties of targets on the other hand, and to devise a sound messaging infrastructure, a unifying probabilistic modeling for object association across multiple cameras with disjointed view is used. Communication is efficiently handled using a taskoriented node clustering that partition the network in different groups according to the pathway among cameras, and the appearance and temporal behavior of targets. We propose a novel asynchronous event exchange strategy to handle sporadic messages generated by non-frequent tasks in a distributed tracking application. Using a Xilinx-FPGA with embedded Microblaze processor, we could show that, with limited resource and speed, the embedded processor was able to sustain a high communication load, while performing complex image processing computations.

Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

“…An alternative model is a distributed network which reduces the data transfer requirements to a dedicated node, while it does not exploit the advantages of multi-camera networks [24]. A cluster-based model overcomes the limitations of centralized and distributed networks and provides scalability without the risk of single-point failures [25,26]. In a cluster-based network the smart cameras are grouped in distinct clusters, each with a local cluster head.…”

“…Once an event has been detected, the camera triggers a description of the event and identifies a region of interest. We group the co-located smart cameras into distinct clusters [25] with a predefined cluster head, which works as a gateway [49] and connects the smart cameras with the rest of the system. The cluster heads and their corresponding smart cameras are labeled as CHi and Cj where i and j represent the identifiers for the cluster head and camera, respectively.…”

Efficient zero-copy mechanism for intelligent video surveillance networks

“…VSN can be regarded as a form of sensor network but with its own characteristics: 1) higher computing, communication, and power resources per node to support more local tasks; 2) largely stationary nodes with an established backbone network [2]. In order to reduce CPU load and decrease data transmission delay, some researchers work on improving image processing algorithm [3] and video compression technology [4], whereas some researchers focus on optimizing network transmission performance, which is more straightforward and effective. This paper focuses on new light-weighting and efficient networking mechanism to save precious CPU resource and reduce network delays in video surveillance system.…”