Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes

Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis.

A New System for Real-time Video Surveillance in Smart Cities Based on Wireless Visual Sensor Networks and Fog Computing

Nowadays, public security is becoming an increasingly serious issue in our society and its requirements have been extended from urban centers to all remote areas. Therefore, surveillance and security cameras are being deployed worldwide. Wireless Visual Sensor Networks nodes can be employed as camera nodes to monitor in the city without the need for any cables installation. However, these cameras are constrained in processing, memory, and energy resources. Also, they generate a massive amount of data that must be analyzed in real-time to ensure public safety and deal with emergency situations. As a result, data processing, information fusion, and decision making have to be executed on-site (near to the data collection location). Besides, surveillance cameras are directional sensors, which makes the coverage problem another issue to deal with. Therefore, we present a new system for real-time video surveillance in a smart city, in which transportations equipped with camera nodes are used as the mobile part of the system and an architecture based on fog computing and wireless visual sensor networks is adopted. Furthermore, we propose an approach for selecting the camera nodes that will participate in the tracking process and we simulated three different use cases to test the effectiveness of our system in terms of target detection. The simulation results show that our system is a promising solution for smart city surveillance applications.

Security System using Raspberry Pi

As the technology is advancing day by day, there are various alternatives occurring for the already present or previous technologies. This article suggests the working of an wireless camera. The article gives a brief idea of various technologies or software being used for to security purpose. The main goal of our design was to develop a network that allowed for the transmitting and receiving of images from camera nodes to a base station. The main objective is self powered wireless security camera.

Wireless Security Camera System

As the technology is advancing day by day, there are various alternatives occurring for the already present or previous technologies. This article suggests the working of an wireless CCTV camera. The article gives a brief idea of various technologies or software being used for to security purpose. The main goal of our design was to develop a network that allowed for the transmitting and receiving of images from camera nodes to a base station. The main objective is self powered wireless security camera.

Exploration of Multi-Node Collaborative Image Acquisition and Compression Techniques for Wireless Multimedia Sensor Networks

<span lang="EN-US">Aiming at saving energy and maximizing the network life cycle, the multi-node cooperative image acquisition and compression technology in Wireless Multimedia Sensor Networks</span><span lang="EN-US">(</span><span lang="EN-US">WMSNs) is studied deeply. </span><span lang="EN-US">T</span><span lang="EN-US">he Minimum Energy Image Collection (MEIC) problem for multiple target domains in a certain period of time in the monitoring area is proposed, the integer linear programming for Minimum Energy Image Collection (MEIC) problem is described and proved to be NP complete; then combined with the features of image acquisition of camera node,<a name="_Hlk527549560"></a> the Local Camera Coordinative Energy-saving Strategy (LCCES) is proposed, and the performance of the Local Camera Coordinative Energy-saving Strategy (LCCES) is evaluated through a lot of simulation experiments; finally, the LBT-based Multi-node Cooperative Image Compression Scheme (LBT-MCIC) is proposed. The results show that this strategy can effectively reduce the number of active camera nodes in the process of image acquisition, thus reducing the energy consumption of image acquisition</span><span lang="EN-US">.</span><span lang="EN-US"> At the same time, it also plays a role in balancing the energy consumption of camera nodes in the network, effectively solves the problem of high cost of common nodes in the image transmission scheme of two-hop cluster structure and has the characteristics of low computational complexity and high quality of reconstructed image.</span>

Joint Source and Channel Rate Allocation over Noisy Channels in a Vehicle Tracking Multimedia Internet of Things System

As an emerging type of Internet of Things (IoT), multimedia IoT (MIoT) has been widely used in the domains of healthcare, smart buildings/homes, transportation and surveillance. In the mobile surveillance system for vehicle tracking, multiple mobile camera nodes capture and upload videos to a cloud server to track the target. Due to the random distribution and mobility of camera nodes, wireless networks are chosen for video transmission. However, the tracking precision can be decreased because of degradation of video quality caused by limited wireless transmission resources and transmission errors. In this paper, we propose a joint source and channel rate allocation scheme to optimize the performance of vehicle tracking in cloud servers. The proposed scheme considers the video content features that impact tracking precision for optimal rate allocation. To improve the reliability of data transmission and the real-time video communication, forward error correction is adopted in the application layer. Extensive experiments are conducted on videos from the Object Tracking Benchmark using the H.264/AVC standard and a kernelized correlation filter tracking scheme. The results show that the proposed scheme can allocate rates efficiently and provide high quality tracking service under the total transmission rate constraints.

Hardware Middleware for Person Tracking on Embedded Distributed Smart Cameras

Tracking individuals is a prominent application in such domains like surveillance or smart environments. This paper provides a development of a multiple camera setup with jointed view that observes moving persons in a site. It focuses on a geometry-based approach to establish correspondence among different views. The expensive computational parts of the tracker are hardware accelerated via a novel system-on-chip (SoC) design. In conjunction with this vision application, a hardware object request broker (ORB) middleware is presented as the underlying communication system. The hardware ORB provides a hardware/software architecture to achieve real-time intercommunication among multiple smart cameras. Via a probing mechanism, a performance analysis is performed to measure network latencies, that is, time traversing the TCP/IP stack, in both software and hardware ORB approaches on the same smart camera platform. The empirical results show that using the proposed hardware ORB as client and server in separate smart camera nodes will considerably reduce the network latency up to 100 times compared to the software ORB.