Fuzzy-logic based routing for dense wireless sensor networks

Ortiz, Antonio M.; Royo, Fernando; Olivares, Teresa; Castillo, José Carlos; Orozco-Barbosa, Luis; Marrón, Pedro José

doi:10.1007/s11235-011-9597-y

Cited by 53 publications

(29 citation statements)

References 8 publications

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“…To illustrate, Wang 38 claim that a fuzzy-based routing scheme based on reputation, bandwidth and distance can improve the throughput of network. The task of routing data is also investigated by Ortiz et al 39 , in which fuzzy logic is utilized to perform role assignment during route establishment and maintenance. Number of hops to the base station, and the remaining node energy are considered as input variables and modeled as fuzzy number.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

Turan¹,

Kasap²,

et al. 2016

IJCIS

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In this paper, bandwidth acquisition and allocation problem of a telecommunications Bandwidth Broker (BB) is analyzed under uncertain end-user capacity requests and pay-per-byte (volume) based pricing policy. Furthermore, related objective function coefficients such as revenue and costs are modeled as fuzzy numbers in order to cope with vague market conditions. By integrating fuzzy mathematical programming and two-stage stochastic programming techniques, deterministic equivalent of single objective profit maximization problem of BB is obtained solved to optimality. In addition, infrastructure related performance measures such as delay and jitter amounts in the network are modelled via stochastic parameters that obey some known probability distributions. Two performance statistics namely fuzzy Expected Value of Perfect Information (EVPI) and fuzzy Value of Stochastic Solution (VSS) are defined to demonstrate the efficiency of proposed methodology compared to deterministic approach. In addition, several secondary performance measures such as expected capacity utilization, expected demand fulfilment ratio and capacity loss are calculated under different problem settings. In conclusion, numerical experiments showed that fuzzy stochastic method provides more profit depending upon problem size in compression with deterministic strategy.

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Section: Related Workmentioning

confidence: 99%

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

Turan¹,

Kasap²,

et al. 2016

IJCIS

View full text Add to dashboard Cite

show abstract

“…Fuzzy-logic imitates the logic of human reasoning, which is much less rigid than the calculations computers generally perform [65]. It is a useful technique since it uses human language to describe inputs and outputs, and provides a simple method for reaching a conclusion from imprecise, vague or ambiguous input information [66].…”

Section: Fuzzy Logic Algorithmmentioning

confidence: 99%

“…Network role-based routing intelligent algorithm (NORIA) [65] integrates fuzzy logic into the route decision process. The input variables include: number of hops to the base station, and the remaining node energy.…”

Section: Fuzzy Logic Algorithmmentioning

confidence: 99%

“…Artificial intelligence is an adaptive system that perceives its environment and takes actions that maximize its chances of success. So the idea of using artificial intelligence techniques to support the decisionmaking process in order to get more efficient algorithms is widely used in the recent literature [65]. There are many methods in AI, most of which can be used to implement multi-objective optimization after improvement.…”

Section: Artificial Intelligence Algorithmmentioning

confidence: 99%

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

et al. 2015

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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.

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“…Many routing algorithms and protocols have been proposed for different types of WSNs (i.e., [4,5], [8][9][10][11][12][13], [15][16][17][18], [20], [22,23]) among which we have identified a category known as query-based routing. For this category, a station S sends queries to find specific events among the WSNs.…”

Section: Introductionmentioning

confidence: 99%

RER: a real time energy efficient routing protocol for query-based applications in wireless sensor networks

et al. 2015

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Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saver protocols are used to minimize the overall energy consumed by a wireless sensor network (WSN), while energy balancer protocols efficiently distribute the consumption of energy throughout the network. Energy saver protocols are not necessarily good at balancing energy consumption and energy balancer protocols are not always efficient at reducing energy consumption. On the other hand, although delay is another important factor for WSNs energy-aware protocols do not take care of it. This paper proposes a Real time Energy efficient Routing(RER) protocol for query-based applications in WSNs, which offers an efficient trade-off between traditional energy balancing and energy saving objectives while supporting a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption.

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Fuzzy-logic based routing for dense wireless sensor networks

Cited by 53 publications

References 8 publications

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

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

RER: a real time energy efficient routing protocol for query-based applications in wireless sensor networks

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