Balancing computational and transmission power consumption in wireless image sensor networks

2006 ◽  
Author(s):  
L. Ferrigno ◽  
S. Marano ◽  
V. Paciello ◽  
A. Pietrosanto
Keyword(s):  
Sensor Networks ◽  
Power Consumption ◽  
Image Sensor ◽  
Transmission Power

scholarly journals DISTANCE AND HOPS-BASED ENERGY ESTIMATION IN WIRELESS SENSOR NETWORKS

2017 ◽  
Vol 10 (13) ◽  
pp. 85 ◽  
Author(s):  
Pradeep Kumar Ts ◽  
Sayali Chitnis
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Energy Estimation ◽  
Wide Range ◽  
Implementation Level

The world of internet of things (IoT) and automation has been catching a robust pace to impact wide range of commercial and domestic applications for some time now. The IoT holds ad-hoc or wireless sensor networks (WSNs) at its very primary implementation level, the hardware level. The increasing requirement of these networks demands a renewed and better design of the network that improves the already existing setbacks of WSNs, which is mainly the power consumption and optimization. Routing highly affects the power consumed in the nodes in WSNs, hence having a modified routing algorithm which is specific to the application and meets its needs, particularly it is a good approach instead of having a generalized existent routing approach. Currently, for WSN having adequate number of nodes, routing involves maximum number of nodes and hops so as to reduce power consumption. However, for restricted areas and limited nodes, this scenario concludes with using up more number of nodes simultaneously resulting in reducing several batteries simultaneously. A routing algorithm is proposed in this paper for such applications that have a bounded region with limited resources. The work proposed in this paper is motivated from the routing algorithm positional attribute based next-hop determination approach (PANDA-TP) which proposes the increase in number of hops to reduce the requirement of transmission power. The aim of the proposed work is to compute the distance between the sending and receiving node and to measure the transmission power that would be required for a direct(path with minimum possible hops) and a multi-hop path. If the node is within the thresh-hold distance of the source, the packet is undoubtedly transferred directly; if the node is out of the thresh-hold distance, then the extra distance is calculated. Based on this, the power boosting factor for the source node, and if necessary, then the extra number of nodes that would be required to transmit is determined. An extra decision-making step is added to this approach which makes it convenient to utilize in varied situations. This routing approach suits the current level of robustness that the WSNs demand. 

Deterministic Deployment for Wireless Image Sensor Nodes

2014 ◽  
Vol 8 (1) ◽  
pp. 668-674
Author(s):  
Junguo Zhang ◽  
Yutong Lei ◽  
Fantao Lin ◽  
Chen Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Visual Information ◽  
Image Sensor ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Effective Coverage ◽  
Area Of Interest ◽  
Key Issues

Wireless sensor networks composed of camera enabled source nodes can provide visual information of an area of interest, potentially enriching monitoring applications. The node deployment is one of the key issues in the application of wireless sensor networks. In this paper, we take the effective coverage and connectivity as the evaluation indices to analyze the effect of the perceivable angle and the ratio of communication radius and sensing radius for the deterministic circular deployment. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes. When the nodes are deployed in the same monitoring area in the premise of ensuring connectivity, rhombus deployment is optimal when √2 < rc / rs < √3 . The research results of this paper provide an important reference for the deployment of the image sensor networks with the given parameters.

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

Spatial feature-based prioritization for transmission of point cloud data in 3D-image sensor networks

2021 ◽  
pp. 1-1
Author(s):  
Masamichi Oka ◽  
Ryoichi Shinkuma ◽  
Takehiro Sato ◽  
Eiji Oki ◽  
Takanori Iwai ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Point Cloud ◽  
Image Sensor ◽  
Point Cloud Data ◽  
3D Image ◽  
Spatial Feature ◽  
Cloud Data ◽  
Feature Based
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