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Wireless Multimedia Sensor Networks: Applications and Testbeds
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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.
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... Wireless sensor networks (WSNs), comprising a large number of sensor nodes, show impressive capability in transmitting a very large number of data with high efficiency [1,2]. Their compactness, cost-effectiveness, and ease of deployment make WSNs highly effective for a wide range of real-time applications. ...
... Lastly, by installing a STAR-RIS on a UAV, the system can flexibly adjust the position between the STAR-RIS and the sensors to improve task transmission performance. (2) Since the energy consumption minimization problem is non-convex, we decomposed the problem into four subproblems: content caching decision, computation offloading decision, UAV hovering position, and STAR-RIS resource allocation. For the subproblem of content caching decisions, the network caching decisions are optimized by utilizing a new deep reinforcement learning (DRL) algorithm. ...
... The noise power is σ 2 u , i.e., n u = n ∼ N (0, σ 2 u ). According to (2) and (3), the SNR and transmission rate of the link from sensor m to UAV u are denoted by γ m,u and R m,u . ...
Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption.
... Refs. [33][34][35] further discuss the advantages of CRN in these high-throughput application scenarios. Similarly, other CRN applications, includ ing those used in hospitals, vehicular networks, tracking, and surveillance, have varyin data densities and require significant bandwidth, low delay, and high bursts of dat However, because SUs in CRNs can access multiple channels whenever necessary an available, CRNs are well suited for bandwidth-intensive applications. ...
... Refs. [33][34][35] further discuss the advantages of CRNs in these high-throughput application scenarios. Similarly, other CRN applications, including those used in hospitals, vehicular networks, tracking, and surveillance, have varying data densities and require significant bandwidth, low delay, and high bursts of data. ...
This paper provides an overview of cognitive radio technology and its applications in the field of civil aviation. Cognitive radio technology is a relatively new and emerging field that allows for dynamic spectrum access and efficient use of spectrum resources. In the context of civil aviation, cognitive radio technology has the potential to enable more efficient use of the limited radio spectrum available for communication and navigation purposes. This paper examines the current state of cognitive radio technology, including ongoing research and development efforts, regulatory issues, and potential challenges to widespread adoption. The potential applications of cognitive radio technology in civil aviation are also explored, including improved spectrum utilization, increased safety and security, and enhanced situational awareness. Finally, the paper concludes with a discussion of future research directions and the potential impact of cognitive radio technology on the future of civil aviation. It is hoped that this paper will serve as a useful resource for researchers, engineers, and policy makers interested in the emerging field of cognitive radio technology and its potential applications in the field of civil aviation.
... have been upgraded to enable both audio and video modules as technology has advanced [2]. Some of the application areas of WMSN are surveillance networks [3], traffic avoidance [4], health care [5], smart homes [6], and environmental and agriculture monitoring [7]. ...
... If the column has been updated, then it means that new content has been found that matches the Interest packet entry and CS of a node. In this case, a node will broadcast two packets in a network: one will be a Data Algorithm 1: OnIncomingInterestPacket Data: Name, Satisfied Node ID (S_NID), ContentStore (CS), Nonce (1) bool packetUpdated = FALSE (2) if (this →Name != PITEntry) then (3) if (this →Content != CS) then (4) PIT.Insert(this →InterestPacket) ...
Wireless Multimedia Sensor Network (WMSN) is a wirelessly connected sensor network featuring multimedia devices such as cameras and microphones that can retrieve video and audio streams, photographs, and scalar sensor data. Because of the low cost of cameras and microphones, in addition to significant advances in distributed signal processing and multimedia data coding approaches, WMSNs can offer multimedia content. Moreover, the most critical difficulty in the implementation of WMSN is to improve the efficiency and lifetime of the network. The Named-Data Network (NDN) technique is used in WMSNs to increase communication and transmission mechanisms efficiency. NDN is thought to be one of the most demanding Internet architectures in the future. Unlike host-centric techniques that focus on the location of the content, NDN focuses on providing the content. In this paper, we propose a novel accumulative Interest-based content-store architecture for NDN-based WMSNs to improve the efficiency of individual nodes as well as the overall network. Furthermore, the proposed solution also controls packet flooding. The proposed scheme is named "Efficient Data Retrieval and Forwarding (E-DRAFT)". Moreover, the ndnSIM simulator is used to run comprehensive simulations. Compared to existing methodologies, the simulation results reveal that the proposed approach stops additional packet flooding and effectively stores and retrieves Data packets with enhanced processing efficiency.
... Although many new technologies have been proposed, node energy saving is still a key issue in WSN design [1], [2]. A wireless image sensor network (WISN) is also a type of WSN, in which the sensor nodes can collect image data and transmit it to a base station or other nodes for processing and storage [2], [3], [4], [5], [6], [7], [8]. As an important branch of WSNs, WISNs have broad application prospects, especially in security monitoring systems [2], [9], submarine underwater wireless sensor systems [10], and other fields. ...
For related applications based on wireless image sensor networks (WISNs), the efficiency of image signal acquisition and transmission is crucial. How to effectively reduce the data volume and the sampling rate during the signal communication process is an urgent research problem to be solved. Block compressive sensing (CS) can be used to solve the above problem. In natural images, the sparsity order of different blocks tends to vary, indicating varying levels of complexity across these blocks. Therefore, it is crucial to set the appropriate sampling rate for each block. However, we cannot directly obtain the pixel domain information of the images on the sampling side. To solve the above problem, a novel adaptive rate CS scheme based on sparsity order estimation is proposed. We first estimate the variance of each block in the measurement domain according to the Johnson-Lindenstrauss lemma. Then find the complex block and the sparse block according to the variance values. An a priori probability model is used to estimate the sparsity orders of the complex block and the sparse block, respectively. In order to reduce the complexity of the sparsity order estimation process, the sparsity orders of the remaining blocks are estimated according to a simple signal cosine similarity estimation model. Finally, the sparsity order of each block can be used to calculate its corresponding sampling rate. Experiment results indicate that the proposed scheme can obtain higher reconstruction accuracy than state-of-the-art adaptive CS schemes. Moreover, the reconstructed images exhibit a significant improvement in visual quality.
... The challenge in designing heterogeneous sensor networks is to ensure that the sensors can work together efficiently despite their differences. This requires the development of protocols and algorithms that can manage the data flow, ensure data quality, and optimize energy consumption [3]. ...
Heterogeneous sensor networks refer to a type of network that is composed of different types of sensors that are capable of gathering and transmitting various types of data. These networks are used in a wide range of applications, including environmental monitoring, healthcare, and industrial automation. The sensors in heterogeneous networks can vary in terms of the type of data they collect, their power requirements, their communication protocols, and their processing capabilities. The challenge in designing heterogeneous sensor networks is to ensure that the sensors can work together efficiently despite their differences. This requires the development of protocols and algorithms that can manage the data flow, ensure data quality, and optimize energy consumption. In this way, heterogeneous sensor networks can provide valuable insights into complex systems and enable real-time decision-making.
... Since it is unable to transmit high-resolution pictures and video streams from sensor nodes to the sink node over bandwidth-constrained wireless networks, this application poses a number of difficulties for existing EWSNs. Furthermore, standard EWSNs made up of single-core embedded sensor nodes are unable to process multimedia data (audio, image, and video in this case) meaningfully in real-time [19,20], necessitating the use of more potent embedded sensor nodes to realize this application. ...
Embedded processors are specialized microprocessors designed to perform specific tasks within a larger system or device. They are commonly used in various applications such as consumer electronics, automotive systems, industrial automation, and Internet of Things (IoT) devices. This abstract focuses on the role of embedded processors in networking applications. Networking refers to the interconnection of devices and systems to enable communication and data exchange. Embedded processors play a crucial role in networking by providing the necessary computational power and functionality to handle networking protocols, data processing, and network management tasks. They are often integrated into network devices such as routers, switches, gateways, and network interface cards. The utilisation of networked embedded systems in contemporary network situations is undeniably significant, and they are receiving an increasing amount of attention. For network connectivity, data processing, and service delivery, the research community and industry are putting forth cutting-edge embedded solutions, frequently based on network processors. Despite this, it appears to be quite difficult to get quantitative performance comparisons of such systems.
... Therefore, the traditional IP-based protocols are not applicable for wireless sensor networks, because the ID maintenance overhead is very high. In the wireless sensor networks, sometimes receiving data is more important than understanding the data IDs which should be sent [16] [17]. Secondly, sensing nodes located by the ad-hoc method should be self-organizing because the nodes ad-hoc locating requires systems to communicate and manage the resultant node distribution, especially the sensor network should be operated unsupervised [13]. ...
WSNs are infiltrating the environment in its wide sense, indoors and outdoors, in the human body, in unapproachable emplacements; they have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics. Guardian angels? Watchdogs? Whatever, they are intended to work properly, faultlessly, no matter when and where. As a consequence, it is becoming increasingly difficult to forge unique requirements regarding hardware issues and software support. This is particularly important in a multidisciplinary research and practice area such as WSNs, where close collaboration between users, application domain experts, hardware designers, and software developers is needed to implement efficient systems.In this chapter, who is who in WSNs are identified, motes, building blocks, producers, techniques, applications. A categorization of WSN applications according to their intended use is presented considering deployment, mobility, resources, cost, energy, heterogeneity, modality, infrastructure, topology, coverage, connectivity, size, lifetime, and QoS. The considered application categories, though non-exclusive, are branded as military, industrial, environmental, healthcare, daily life, and multimedia. Typical applications tasks are as follows:
Performance monitoring.
Surveillance.
Environmental monitoring.
Process control.
Tracking of personnel and goods.
Emergency management.
Robotics.
When compared with conventional mobile ad hoc networks (MANETs), WSNs have different characteristics and present different engineering challenges and considerations.KeywordsMilitary applications of WSNsIndustrial applications of WSNsEnvironmental applications of WSNsHealthcare applications of WSNsDaily life applications of WSNsMultimedia applications of WSNsRobotic WSNs
The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.
We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.
The integration of telemedicine with medical micro sensor technology
(Mobile Sensor Networks for Telemedicine applications -- MSNT) provides
a promising approach to improve the quality of people's lives. This type
of network can truly implement the goal of providing health-care
services anytime and anywhere. Our research in this field generates the
following outcomes that are reported in this paper: (1) We propose a
mobile sensor network infrastructure to support the third-generation
telemedicine applications; (2) An energy-efficient query resolution
mechanism in large-scale mobile sensor networks is used for critical
medical data collections; (3) To provide the guaranteed mobile QoS for
arriving multimedia calls, a new multi-class call admission control
mechanism is proposed which is based on dynamically forming a
reservation pool for handoff requests. We used discrete-event-based
simulation model using OPNET to verify our scheme. The simulation
results show that our system can satisfy the adaptive QoS requirements
in large-scale telemedicine sensor networks.
The use of high bit-rate multimedia sensors in networked applications poses a number of scalability challenges. In this paper, we present how IRISNET, a software infrastruc- ture for authoring wide-area sensor-enriched services, sup- ports scalable data collection from such sensors by greatly reducing the bandwidth demands. The architecture makes a number of novel contributions. First, it enables the use of application-specific filtering of sensor feeds near their sources and provides interfaces that simplify the program- ming and manipulation of these widely distributed filters. Second, its sensor feed processing API, when used by mul- tiple different services running on the same machine, auto- matically and transparently detects repeated computations among the services and eliminates as much of the redun- dancy as possible within the soft real-time constraints of the services. Third, IRISNET distinguishes between trusted and untrusted services, and provides mechanisms to hide sensi- tive sensor data from untrusted services. Using implemen- tations of a number of real world sensor-enriched services on IRISNET, we present an evaluation of the benefits of our distributed filtering architecture.
We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive pro- grams that must operate with severe memory and power constraints. nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model al- low the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption). nesC has been used to implement TinyOS, a small operating sys- tem for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.
In this paper, we detail our development of Explorebots— expandable, vision-and sensor-equipped wireless robots built around MICA motes. We developed Explorebots as a dynamic outreach for an NSF-funded Girl Scouts project. We've extended the capabilities of Explorebots to comprise a mobile network experimentation testbed. The testbed will support experimental analysis of protocols for mobile multi-hop networks. The low-cost Explorebots enable repeatable experiments without complete reliance on human subjects for mobility.
This paper provides an overview of the research aspects of our DSC06 demonstration. We present a new camera sensor network for behavior recognition. Two new technologies are explored, biologically inspired address-event image sensors and sensory grammars. This paper explains how these two technologies are used together and reports of the current status of our prototyping effort. The application of the resulting system in assisted living is also described.
As a non-invasive technology, imaging plays an important role in mobile sensing devices. Multiple communicating cameras viewing the same scene from different viewpoints can together construct a high performance surveillance system. Wireless smart cameras chal- lenge the hardware for low-power consumption and high imaging performance. In this paper we introduce a wireless smart camera based on an SIMD video-analysis processor and an 8051 micro- controller as a local host. Wireless communication is through the IEEE802.15.4 standard. Multiple cameras can establish a peer-to- peer connection and analyse the scene in a distributed fashion from several views at once. The camera constructed in this paper is to enable application research into distributed smart camera systems.
This paper argues that a camera sensor network containing hetero-geneous elements provides numerous benefits over traditional ho-mogeneous sensor networks. We present the design and implemen-tation of SensEye–a multi-tier network of heterogeneous wireless nodes and cameras. To demonstrate its benefits, we implement a surveillance application using SensEye comprising three tasks: ob-ject detection, recognition and tracking. We propose novel mech-anisms for low-power low-latency detection, low-latency wakeups, efficient recognition and tracking. Our techniques show that a multi-tier sensor network can reconcile the traditionally conflicting sys-tems goals of latency and energy-efficiency. An experimental eval-uation of our prototype shows that, when compared to a single-tier prototype, our multi-tier SensEye can achieve an order of magni-tude reduction in energy usage while providing comparable surveil-lance accuracy.
This paper presents the design of a new mote for distributed image sensing applications in wireless sensor networks. The processing and memory limitations in current mote designs are analyzed and a simple but powerful new platform is developed. The mote is based on a 32-bit ARM7 micro-controller operating at clock frequencies of up to 48 MHz and accessing up to 64 KB of on-chip RAM. An expansion interface is provided to support multiple mid-and low-resolution image sensors concurrently as well as traditional sensors. Wire-less communication is provided by the Chipcon CC2420 radio which operates in the 2.4 GHz ISM band and is compliant with the IEEE 802.15.4 standard. An integrated USB and serial debug interface allows simple programming and debugging of applications. The additional requirements of an image sensor mote are discussed along with a discussion of possible applications and research areas.