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Combining Frame Rate Adaptation and Similarity Detection for Video Sensor Nodes in Wireless Multimedia Sensor Networks
Abstract
Wireless Multimedia Sensor Networks (WMSNs) are composed of small embedded video sensors that allow continuous monitoring of a given territory. They collect and analyze frames from different video sensors deployed in the area of interest. One of the most important challenges in WMSN is the big data problem affecting the energy resources of the video sensors. Cameras and video-sensors send images and videos which costs in terms of memory storage, bandwidth and energy. In this paper, we propose a technique that adapts the frame rate at the level of each video-sensor. Our aim is to reduce the number of frames sent to the coordinator without losing any important information. Our approach is based on analyzing similarity between consecutive frames for each sensor. The proposed algorithm calculates the similarity by the aggregation of color and edge similarities between frames. The results of the proposed algorithm show a reduction in terms of energy consumption and sent data while guaranteeing the detection of critical events.
... This algorithm adapts the frame rate and reduce the number of images sent from the sensor node to the coordinator. This algorithm is compared to our most recent algorithm in Salim et al. (2016). For the security challenge, the one-round algorithm from Noura et al. (2018) is adapted to our approach and scenario. ...
... This process is energy consuming due to the huge number of frames captured and sent by the sensor nodes to the coordinator. WVSN operates periodically if the sensor nodes do not detect any intrusion in the monitored area of interest [24]. Figure 1 shows the architecture of WVSN, where the network can be divided into several areas composed of a certain number of sensor nodes connected to a coordinator, and different coordinators from different areas are connected to the sink. ...
... For the transmission process, a frame is sent to the coordinator only if it represents a difference while comparing it with the last frame sent to the coordinator and it is called a critical frame as in [24]. In this paper, the comparison is done using the norm L2 relative error function while using a predefined threshold. ...
Wireless sensor networks (WSN) have become the rising stars of technology. Every object in this world tends to be sensorly developped, monitored and controlled. Monitoring an area of interest for security reasons for mil-litary applications, catastrophic natural events, ... gives each captured frame a huge importance to be able to achieve this surveillance system. Thus, Wireless Video Sensor Networks (WVSN) represents the leading technology to implement this kind of surveillance systems. A WVSN consists of three different layers: The video-sensor node, the coordinator and the sink. A video sureveil-lance system can have hundreds or thousands of video-sensor nodes. Hence, several challenges exist in such a densely deployed system. The leading challenge is for sure the energy consumption problem for capturing, processing and transmitting several images on the network, but it is not the only challenge. Data transmitted on the network from several sensor nodes to a coordinator must be secured. Thus, the emergence of the security challenge in WVSN. In this paper, on the sensor-node level, for data reduction, a new algorithm has been proposed. This algorithm adapts the frame rate and reduce the number of images sent from the sensor node to the coordinator. This algorithm is compared to our most recent algorithm in [1]. For the security challenge, the 2 Christian Salim * et al. one-round algorithm from [2] is adapted to our approach and scenario. This approach is validated by experimentation using Cpp for OpenCV on Raspberry Pi 3 and by comparing it to other previous, existing approaches.
... Normally if there is no critical event in the area of interest, the WVSN operates periodically [1]. To reduce the energy consumption related to the sensing process on the sensor level, a simple probabilistic method has been proposed to adapt the frame rate of every sensor node, depending on the level of criticality, the position and the trajectory direction of the intrusion in the scene (the movement's vector) based on a probability based prediction [2]. ...
... In other terms, this vector also represents an alert that an intrusion is taking place in the monitored area. In the kinematic based approach (KBA) proposed in this paper, every sensor node starts the sensing process by taking as a frame rate the minimum frame rate possible [1]. When this sensor is triggered by another node (after the probability study of the trajectory and the new location of the intrusion) that an intrusion may pass by its FOV, it adapts the frame rate according to the level of probability sent in the alarm message from the sending sensor. ...
... Local processing can involve simple image processing algorithms (such as background substraction for motion/object detection, and edge detection). According to a predefined threshold of similarity, the node decides whether to send this frame to the coordinator or not [1]. ...
Recently, Wireless Video Sensor Networks (WVSNs)
have been one of the most used technologies for surveillance,
event tracking, nature catastrophe and other sudden events.
Those networks are composed of small embedded camera motes
which help to extract the needed information for the monitored
zone of interest. A WVSN is divided into 3 different layers:
the video sensor-node layer, the coordinator layer and the sink.
Every video sensor-node is in charge of capturing the raw data of
images and videos and sending it to the coordinator for further
analysis before sending the analyzed data to the sink. In a normal
scenario, the load of collected images and videos from different
sensor nodes on the same network is huge. Sending all the
images from all the sensor nodes to the coordinator consumes
a lot of energy on every sensor, and may cause a bottleneck.
In this paper, some processing and analysis are added based
on the similarity between frames on the sensor-node level to
send only the important frames to the coordinator. Kinematic
functions are defined to predict the next step of the intrusion
and to schedule the monitoring system accordingly. Compared to
a fully scheduling approach based on predictions, this approach
minimizes the transmission on the network. Thus, it reduces the
energy consumption and the possibility of any bottleneck while
guaranteeing the detection of all the critical events at the sensor-
node level as shown in the experiments.
... None of the above protocols takes into account the case when a critical frame is detected on a sensor node as mentioned in [19]. This frame must have priority over all the other frames in the coordinator's queue for quicker detection and reaction from the coordinator. ...
... If the frame is critical, the protocol considers it has priority in the process over a non critical frame on the network. For data reduction in our approach, the sensing and transmission techniques are adopted from [19]. Sensor nodes are usually scattered in a capture area. ...
... In this section, we present a brief overview about the MASRA (Multimedia Adaptive Sampling Rate Algorithm) algorithm [19] where the network and sensor-nodes operate periodically. In our approach, the MASRA algorithm is used for data reduction on the sensor node level while sensing and transmitting data to the coordinator. ...
Wireless sensor networks (WSNs) continue their
ascending developement to be among the leaders of technology.
Furthermore, images are of paramount importance in several
applications based on WSNs. Capturing, processing and
transmitting the image face several challenges, mainly because
of their highly needed power consumption. The huge number
of images sensed and transmitted in a Wireless Video Sensor
Network (WVSN) increases the dataflow on the overall network.
A WVSN consists of three different layers: the video-sensor
node, the coordinator and the sink. Sending images at the same
time from different sensor nodes to a coordinator causes several
problems. Besides energy consumption and bandwidth usage
that represent the two major challenges in WSN, the queue
of images on the coordinator can cause slower detection of
intrusions and thus slower reaction from the coordinator. These
reasons increase the need of a mac-layer protocol to control the
network. We propose a new modified communication protocol
based on the S-MAC protocol. This solution consists of adding
a priority bit to the S-MAC protocol. Our approach is validated
by experimentation using raspberry pi 3 and by simulation in
OMNET++.
... energy-efficient and low cost solution. In WVSN, the system normally operates periodically, sending all the captured frames to the sink [12]. However, the system can be an event driven system sending only the frames that show a change in the area of interest. ...
... The TDR algorithm compares consecutive captured frames on the node based on a norm simple euclidean distance similarity method [11]. According to the degree of similarity with the last frame sent to the sink, the node decides whether to send this frame or not [12]. If the decision is to send the image, the node creates a new image img di f f that only includes the difference between the two compared images. ...
Nowadays, to improve animal well being in livestock farming or beekeep-ing application, a wireless video sensor network (WVSN) can be deployed to early detect injury or Asiatic hornets attacks. WVSN represents a low-cost monitoring solution compared to other technologies such as the closed circuit television technology (CCTV). WVSNs are composed of low-power resource-constrained video sensor nodes (motes). These nodes capture frames from videos at a given frequency (frame rate) and wirelessly send them to the sink. The big amount of data transferred from the nodes to the sink consumes a lot of energy on the sensor node, which represents a major challenge for energy-limited nodes. In this paper, we introduce two complementary mechanisms to reduce the overall number of frames sent to the sink. First, the Transmission Data Reduction algorithm (TDR) run on the sensor node leverages the similarity degree of consecutive images. Second, the Inter-Nodes Similarity algorithm (INS) exploits the spatio-temporal correlation between neighbouring nodes in order reduce the number of captured frames. The results show a 95% data reduction, surpassing other techniques in the literature by 30% at least.
... One of the simplest ways to decrease the volume of multimedia data before transmission to the base station by utilizing spatialtemporal data reduction. In [46], the suggested method compares frame similarities SOS specifically focuses on the green color in the region of interest of an RGB video frame, disregarding red and blue colors because the human visual system is more sensitive to green. Video summarization reduces network consumption by extracting key frames and eliminating duplicated frames. ...
The potential of Internet of Things and wireless sensor networks technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. Wireless multimedia sensor network represents a distinct subdomain within the broader scope of wireless sensor networks. It has found diverse applications in the context of future smart cities, particularly in areas such as healthcare monitoring, home automation, transportation systems, and vehicular networks. A wireless multimedia sensor network is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by wireless multimedia sensor network across the Internet of Things network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and wireless multimedia sensor network lifespan. One of the solutions to these kinds of problems is data transmission reduction. Therefore, this systematic literature review will review various techniques used in data transmission reduction, ranging from data transmission redundancy reduction to machine learning algorithms. Additionally, this review investigates the range of applications and the challenges encountered within the domain of wireless multimedia sensor networks. This work can serve as a basic strategy and a road map for scholars interested in data reduction techniques for intelligent surveillance systems using WMSN in Internet of Things networks.
... One of the easiest ways to reduce the amount of multimedia data before it is transmitted to the base station is to use spatial-temporal data reduction. In [10], the suggested method compares frame similarities using edge and colour characteristics to choose which frame should be sent. Sending every frame would be wasteful of resources (both energy and bandwidth), so only the unique frames will be sent to the sink. ...
The potential of IoT (Internet of Things) and WSN (Wireless Sensor Networks) technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. The Wireless Multimedia Sensor Network (WMSN) has become a special field of WSN. This has led to many uses in the smart cities of the future, such as monitoring health care, home automation, transportation, and vehicle networks. A WMSN is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by WMSNs across the IoT network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and WMSN lifespan. One of the solutions to these kinds of problems is data transmission reduction. In this paper, we present a detailed roadmap for scholars exploring video transmission in WMSN, with a specific emphasis on data reduction techniques for intelligent surveillance systems in IoT networks. The primary objective is to examine the current challenges and propose an effective solution based on the authors’ research findings. Additionally, the paper provides an extensive survey of video transmission techniques in WSNs, offering valuable insights for further investigations in this field.
... Research work is continuously being carried out for developing smart and intelligent surveillance systems that cost minimum network resources. In [10], authors have presented an adaptive mechanism for controlling the frame rate of video streams being transmitted to the coordinator by calculating the similarity between consecutive frames with no loss of information. The idea presented in [11] revolves around increasing the lifetime of a smart camera network. ...
With the increased use of smart phones, Wireless Multimedia Sensor Networks (WMSNs) will have opportunities to deploy such devices in several contexts for data collection and processing. While smart phones come with richer resources and can do complex processing, their battery is still limited. Background subtraction (BS) and compression techniques are common data reduction schemes, which have been used for camera sensors to reduce energy consumption in WMSNs. In this paper, we investigate the performance of various BS algorithms and compression techniques in terms of computation and communication energy, time, and quality. We have picked five different BS algorithms and two compression techniques and implemented them in an Android platform. Considering the fact that these BS algorithms will be run within the context of WMSNs where the data is subject to packet losses and errors, we also investigated the performance in terms of packet loss ratio in the network under various packet sizes. The experiment results indicated that the most energy-efficient BS algorithm could also provide the best quality in terms of the foreground detected. The results also indicate that data reduction techniques including BS algorithms and compression techniques can provide significant energy savings in terms of transmission energy costs.
Applications for monitoring critical areas, based on Wireless Video Sensor Networks (WVSN), require simultaneously good level of coverage of the entire region of deployment, high frame capture speed and low power consumption in order to increase network lifetime. The question is how to find a tradeoff between these requirements. In this paper, we first propose two new approaches based on the cover set concept to help a node to find its redundancy level which is defined by the cardinality of its cover set. Then, thanks to a model based on behavior functions modeled by quadratic Bezier curves, we link the frame capture speed of video node to its redundancy level and to its criticality which is assigned to a node according to its position in the network. Border node has high criticality while interior one has low criticality. To identify boundary nodes, we propose an algorithm based on Greedy Perimeter Stateless Routing protocol (GPSR). Then, we propose an algorithm to schedule the activity of sensor nodes according to the redundancy level and criticality of each node. Finally, we introduce a new concept called backward border surveillance based on the cover set approach. Simulation results are presented to show performances of the two proposed approaches in terms of percentage of coverage, number of nodes with cover set, size of this latter, the impact of our scheduling algorithm on network lifetime and the incidence of backward border surveillance concept on quality of surveillance.
The design and analysis of routing algorithm is an important issue in wireless sensor networks (WSNs). Most traditional geographical routing algorithms cannot achieve good
performance in duty-cycled networks. In this paper, we propose a k-connected overlapping clustering approach with energy awareness, namely, k-OCHE, for routing in WSNs. The basic idea of this
approach is to select a cluster head by energy availability (EA)
status. The k-OCHE scheme adopts a sleep scheduling strategy of
CKN, where neighbors will remain awake to keep it k connected,
so that it can balance energy distributions well. Compared with
traditional routing algorithms, the proposed k-OCHE approach
obtains a balanced load distribution, consequently a longer
network lifetime, and a quicker routing recovery time.
Considering the high energy consumption of image acquisition, computation, and transmission in wireless multimedia sensor networks (WMSNs), two-tier network structure is usually used to lighten the energy consumption burden on camera sensors. Thus, a camera sensor can only be actuated when an event is detected by scalar sensors within its field of view (FoV). In this paper, we study the event-driven camera actuation problem and propose a distributed collaborative camera actuation scheme based on sensing-region management (DCCA-SM). The basic idea of this scheme is to divide the whole sensing field into many sensing regions which are covered by different sets of camera sensors. During the running of the network, by forming a cluster of the scalar sensors in each sensing region, the events occurring in each sensing region can be managed by the scalar cluster head. Therefore, by hearing from the scalar cluster heads, each camera sensor can know the exact coverage overlaps without changing information with the neighboring camera sensors. Meanwhile, sensing-region management avoids repeatedly event reporting from scalar sensors. In order to show the performance of the DCCA-SM, a simulation has been conducted. The comparative performance evaluations demonstrate effectiveness and energy efficiency of the proposed scheme.
Energy conservation techniques for wireless sensor networks generally assume that data acquisition and processing have energy consumption that is significantly lower than that of communication. Unfortunately, this assumption does not hold in a number of practical applications, where sensors may consume even more energy than the radio. In this context, effective energy management should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an adaptive sampling algorithm that estimates online the optimal sampling frequencies for sensors. This approach, which requires the design of adaptive measurement systems, minimizes the energy consumption of the sensors and, incidentally, that of the radio while maintaining a very high accuracy of collected data. As a case study, we considered a sensor for snow-monitoring applications. Simulation experiments have shown that the suggested adaptive algorithm can reduce the number of acquired samples up to 79% with respect to a traditional fixed-rate approach. We have also found that it can perform similar to a fixed-rate scheme where the sampling frequency is known in advance.
This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
The availability of low-cost hardware is enabling the development of wireless multimedia sensor networks (WMSNs), i.e., networks of resource-constrained wireless devices that can retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, ongoing research on prototypes of multimedia sensors and their integration into testbeds for experimental evaluation of algorithms and protocols for WMSNs are described. Furthermore, open research issues and future research directions, both at the device level and at the testbed level, are discussed. This paper is intended to be a resource for researchers interested in advancing the state-of-the-art in experimental research on wireless multimedia sensor networks.
Key frames play an important role in video abstraction. Clustering is a popular approach for key-frame extraction. In this paper, we propose a novel method for key-frame extraction based on dominant-set clustering. Compared with the existing clustering-based methods, the proposed method dynamically decides the number of key frames depending on the complexity of video shots, produces key frames in a progressive manner and requires less computation. Experimental results on different types of video shots have verified the effectiveness of the method.
Wireless multimedia sensor nodes sense areas that are uncorrelated to the areas covered by radio neighbouring sensors. Thus, node clustering for coordinating multimedia sensing and processing cannot be based on classical sensor clustering algorithms. This paper presents a clustering mechanism for Wireless Multimedia Sensor Networks (WMSNs) based on overlapped Field of View (FoV) areas. Overlapping FoVs in dense networks cause the wasting of power due to redundant area sensing. The main aim of the proposed clustering method is energy conservation and network lifetime prolongation. This objective is achieved through coordination of nodes belonging to the same cluster to perform assigned tasks in a cooperative manner avoiding redundant sensing or processing. A paradigm in this concept, a cooperative scheduling scheme for object detection, is presented based on the proposed clustering method.
Multimedia data are applicable in various domains such as education, advertising, entertainment and communication. Multimedia data can be in the form of documents, and require adequate spatial and temporal presentation models. Sometimes, due to the noisy problems such as low bandwidth, or user preferences, replacing a media-element by another one can be achieved in order to provide the better quality of presentation. This paper deals with the problem of finding adequate multimedia presentation that fulfills spatio-temporal adaptation for switching between a set of alternative and semantically equivalent media elements.
Due to its potential applications and the density of the deployed sensors, distributed wireless sensor networks are one of the highly anticipated key contributors of the big data in the future. Consequently, massive data collected by the sensors beside the limited battery power are the main limitations imposed by such networks. In this paper, we consider a periodic sensor networks (PSNs) where sensors transmit their data to the sink on a periodic basis. We propose an efficient adaptive model of data collection dedicated to PSN, in order to increase the network lifetime and to reduce the huge amount of the collected data. The main idea behind this approach is to allow each sensor node to adapt its sampling rate to the physical changing dynamics. In this way, the oversampling can be minimized and the power efficiency of the overall network system can be further improved. The proposed method is based on the dependence of measurements variance while taking into account the residual energy that varies over time. We study three well known statistical tests based on One-Way Anova model. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to minimize the amount of data retrieved by the network and conserve energy of the sensors, without loss of fidelity/accuracy.
Patient monitoring is becoming a requirement for offering a better healthcare to an increasing number of chronic patients whether they are living alone, or in nursing homes. Thus, it is necessary to constantly monitor their vital signs to effectively control their health condition and to provide urgent treatment while an emergency such as an abnormal variation of heart rate occurs. In recent years, wireless body sensor networks have emerged as a low cost solution for healthcare applications. However, processing the huge amount of raw data captured by bio medical sensors is a major challenge for this type of networks, along with energy, as in almost all the cases the only source of energy is battery with limited lifetime. In this paper we address how emergency alerts can be supported locally on each node of the network. Our approach proposes some criticality models which allow to reduce considerably the amount of sent data through the network, thus improving power efficiency by sending only critical measures when needed. Simulation results are presented to validate the performance of the proposed approach.
Lowdata delivery efficiency and high energy consumption are inherent problems in sensor networks. Finding accurate data is difficult and leads to expensive sensor readings. Adaptive data collection and aggregation techniques provide opportunities for reducing the energy consumption of continuous sensor data collection. In this paper, we propose an adaptive data collection and aggregation scheme for periodic sensor networks. Our approach is two-fold: firstly, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, over-sampling can be minimised and power efficiency can be improved. Furthermore, we propose a multiple levels activity model that uses behaviour functions modelled by modified Bezier curves to define application classes. Secondly, a new data aggregation method is applied on the set of data collected from all sensor nodes. We investigate the problem of finding all pairs of nodes generating similar datasets. Our proposed models are validated via simulation on real sensor data.
The expected lifetime of any wireless sensor network is a critical issue since sensor nodes are powered by small batteries. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is regarded as an effective technique to reduce energy consumption and prevent congestion. This paper objective is to identify near duplicate nodes that generate similar sets of collected data in periodic applications. We propose a new prefix filtering approach that avoids computing similarity values for all possible pairs of sets. We define a new filtering technique based on the quality of information. To the best of our knowledge, the proposed algorithm is a pioneer in using "sets similarity functions" for data aggregation in sensor networks. To evaluate the performance of the proposed method, experiments on real and synthetic sensor data have been conducted. The analysis and the results show the effectiveness of our method dedicated to sensor networks.
In randomly deployed visual wireless sensor networks for surveillance applications, the scheduling of sensor nodes can be seen from the risk perspective: different parts of the area of interest may have different risk levels according to the pattern of observed events such as the number of detected intrusions. In this paper, we propose a multiple-level activity model that uses behavior functions to define application classes and allows for adaptive scheduling based on the application criticality and on the availability of multiple cover sets per sensor node. The paper then describes how an adaptive scheduling model can be defined in order to dynamically schedule nodes by varying the capture speed according to nodes’ environment. Simulation results are presented to validate the performance of the proposed approach in terms of percentage of active nodes, percentage of coverage and stealth time under intrusion scenarios.
The availability of low-cost hardware such as CMOS cameras and microphones has fostered the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., networks of wirelessly interconnected devices that are able to ubiquitously retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, the state of the art in algorithms, protocols, and hardware for wireless multimedia sensor networks is surveyed, and open research issues are discussed in detail. Architectures for WMSNs are explored, along with their advantages and drawbacks. Currently off-the-shelf hardware as well as available research prototypes for WMSNs are listed and classified. Existing solutions and open research issues at the application, transport, network, link, and physical layers of the communication protocol stack are investigated, along with possible cross-layer synergies and optimizations.
Given the high cost of processing and communicating the multimedia data in wireless multimedia sensor networks (WMSNs), it is important to reduce possible data redundancy. Therefore, camera sensors should only be actuated when an event is detected within their vicinity. In the meantime, the coverage of the event should not be compromised. In this paper, we propose a low-cost distributed actuation scheme which strives to turn on the least number of cameras to avoid possible redundancy in the multimedia data while still providing the necessary event coverage. The basic idea of this scheme is the collaboration of camera sensors that have heard from scalar sensors about an occurring event to minimize the possible coverage overlaps. This is done by either counting the number of scalar sensors or determining the event boundaries with scalar sensors. Through simulation, we show how the distributed scheme performs in terms of coverage under several centralized and random deployment schemes. We also compare the performance with the case when all the cameras in the vicinity are actuated and when blockages in the region exist.
We consider the problem of minimizing the energy needed for data fusion in a sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which we develop a low-complexity inverse-log scheduling (ILS) algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by centralized scheduling protocols, we further derive a distributed inverse-log protocol that is applicable to networks with a large number of nodes. Focusing on large-scale networks with high total data rates, we analyze the energy consumption of the ILS. Our analysis reveals how its energy gain over traditional time-division multiple access depends on the channel and the data-length variations among different nodes.
In this paper, we report a CMOS image sensor for spatial-temporal multiresolution images. This image sensor simultaneously generates two outputs: one for normal images (<30 fps) of stationery background and the other for adaptable frame-rate images (over 960 fps) of moving objects. The moving objects are tracked with a reduced spatial resolution in the region-of-interest (ROI). The entire image is reconstructed from the two images with the details in stationery objects and the suppressed motion blur in moving objects. High frame rate only applies to the ROI; therefore, the proposed scheme can significantly reduce power consumption by adjusting the optimal use of bandwidth. In order to provide real-time decision of ROI, the sensor employs on-chip motion-detection circuits based on the inter-pixel switch operation by storing the previous frame signal in a floating diffusion node. This allows a small pixel implementation without adding any extra in-pixel memory components. A prototype chip has been implemented using a 0.35 mum 1P4M standard CMOS process and multiresolution images have been successfully captured. The fabricated chip operates at 3.3 V and consumes 75 mW in the multiresolution readout of a 256times256 normal image at 30 fps and a 64times64 ROI image at 240 fps.
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