QoS Routing Algorithm for Wireless Multimedia Sensor Networks

Wu-shi, Dong; Ke, Zongwu; Chen, Niansheng; Sun, Qiang

doi:10.1007/978-3-642-04843-2_55

Cited by 7 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulation results demonstrate that the protocol is capable of providing lower delay and lower energy consumption. Dong [98] proposes a QoS routing with multiple QoS constraints, which may deal with the delay, delay jitter and bandwidth. This routing algorithm is based on genetic algorithm, which imitates the mechanism of choices and the genetic in the natural biological evolution process.…”

Section: Multiple Objective Genetic Algorithmmentioning

confidence: 99%

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

et al. 2015

View full text Add to dashboard Cite

Along with the development of the technologies of various kinds, wireless sensor networks are gradually entering into the practicalization phase. Therefore, whether its performance is good or not will directly affect the use of WSNs. Routing is one of the most important technologies. In addition, the routing performance is a significant part of the evaluation index system of wireless sensor networks. Energy efficiency is focused on in the traditional routing protocols, while quality of service (QoS) (i.e., delay, reliability, robustness) becomes important in practical application. Routing protocols should not only ensure the energy efficiency, but also realize QoS performances. As a consequence how to achieve the equilibrium between energy efficiency and other performances is the issue to be solved. In this paper, some performance trade-off mechanisms and methods that have existed in the routing protocols of wireless sensor networks are analyzed and summarized. At the same time, the related technical features of the routing protocols are mentioned, the purpose of which is to provide a better understanding of the current research issues within the field of performance trade-off mechanisms and to guide the researcher to develop the routing protocols with better performance in order to fulfill the actual requirements.

show abstract

Section: Multiple Objective Genetic Algorithmmentioning

confidence: 99%

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

et al. 2015

View full text Add to dashboard Cite

show abstract

“…FLS consists of four key components: fuzzier, inference engine, fuzzy rules, and defuzzifier, as shown in Figure 3 [32]. if SN receives the broadcasting message then (4) if Message is Level-Msg then (5) if routing level of node != 0 then (6) if routing level of node > routing level within received message then (7) Update routing level of SN (8) if Node is CH then (9) routing level ← routing level + 1 (10) Broadcast new Level-Msg (ID, routing level) (11) end if (12) end if (13) Else (14) Update routing level of SN with routing level within received message (15) end if (16) else if Message is Level-Request-Msg then (17) if routing level of node != 0 then (18) Send The fuzzifier gets crisp inputs and converts them to a fuzzy set, which is represented by a linguistic term, such as "near," "medium," or "far," using a membership function. This process is known as fuzzification.…”

Section: Fuzzy Logic Systemmentioning

confidence: 99%

“…In general, a single wireless sensor node (SN) consists of a readily available and inexpensive sensor, a data processor, memory, a receiver/transceiver, and power units for capturing scalar data, such as temperature, pressure, humidity, velocity, acceleration, and location [4]. Recently, information has been changing from scalar data to multimedia data, such as image, video, and audio data.…”

Section: Introductionmentioning

confidence: 99%

Distributed Image Compression Architecture over Wireless Multimedia Sensor Networks

Heng

So-In

Nguyen

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants.

show abstract

“…(v) Bandwidth. Bandwidth indicates bit rate of the available or consumed data capacity that can be used to send data over a link or a path in a given period [36,41,45,59,[64][65][66].…”

Section: Routing Metrics Associated With Link/path Costmentioning

confidence: 99%

“…(xiv) Jitter. Jitter represents packet delay variation or latency variation over time, which results in fluctuating packet interarrival times [65,66]. A network with a consistent delay will have no jitter; however, with variable delay, it will have high jitter.…”

Section: (X) Current Traffic Load or Number Of Active Pathsmentioning

confidence: 99%

Survey of QoS Routing Protocols in Wireless Multimedia Sensor Networks

Aswale¹,

Ghorpade²

2015

Journal of Computer Networks and Communications

View full text Add to dashboard Cite

The emergence of Wireless Multimedia Sensor Networks (WMSNs) has stimulated the refocusing of research from conventional scalar Wireless Sensor Networks (WSNs) to WMSNs. Currently, because of their prevalence WMSNs are used in different applications. Due to the unique features of WMSNs, fulfilling Quality of Service (QoS) requirements for a variety of applications is the challenge. QoS routing is a backbone of WMSNs and plays a vital role in satisfying QoS requirements. The performance of QoS routing depends upon the selection of an optimal path or paths. Path selection is based on evaluation of a cost function using various routing metrics. A careful blend of such metrics in a routing cost function guarantees a committed level of QoS. This survey uses number of routing metrics as criteria for categorizing state-of-the-art QoS WMSNs routing techniques. In addition, open issues and future research directions to further develop efficient routing protocols to guarantee QoS are discussed.

show abstract

QoS Routing Algorithm for Wireless Multimedia Sensor Networks

Cited by 7 publications

References 5 publications

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

Distributed Image Compression Architecture over Wireless Multimedia Sensor Networks

Survey of QoS Routing Protocols in Wireless Multimedia Sensor Networks

Contact Info

Product

Resources

About