Multiobjective Routing for Simultaneously Optimizing System Lifetime and Source-to-Sink Delay in Wireless Sensor Networks

Minhas, M.R.; Gopalakrishnan, Sathish; Leung, Victor C. M.

doi:10.1109/icdcsw.2009.83

Cited by 21 publications

(10 citation statements)

References 25 publications

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“…Note, as a particular case, that when a primary assignment does not increase lifetime, it can be used to optimize flows, as it can be assigned to any node. This result is consistent with [25], where it is stated that there exists a tradeoff between lifetime and the average number of packet hops. Our scenario is slightly different, as we employ primary nodes and do not perform routing, but the idea behind it still holds.…”

Section: Numerical Resultssupporting

confidence: 92%

On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies

Asorey-Cacheda

García-Sánchez

García‐Sánchez

et al. 2013

Sensors

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The extension of the network lifetime of Wireless Sensor Networks (WSN) is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes) carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes) are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment) to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.

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Section: Numerical Resultssupporting

confidence: 92%

On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies

Asorey-Cacheda

García-Sánchez

García‐Sánchez

et al. 2013

Sensors

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show abstract

“…The first one was to minimize the energy consumption, while the other was to shorten the total delay. Minhas et al [145] proposed a routing algorithm based on FL for finding a path that offers a desirable balance between the maximum lifetime (associated with energy consumption) and the minimum source-to-sink delay.…”

Section: B Energy-versus-latency Trade-offsmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

384

202

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Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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“…Fuzzy multi-objective routing for maximum lifetime and minimum delay (FMOLD) [68] is an extension of fuzzy maximum lifetime routing algorithm (FML), the path search process of which is based on fuzzy logic. The fuzzy membership function (edge-weight function), which formulates a multi-objective cost aggregation function that may reflect the effects of all the objectives collectively as a scalar value, offers a good balance between maximizing the network lifetime and minimizing the source-to-sink delay.…”

Section: Fuzzy Logic Algorithmmentioning

confidence: 99%

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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Multiobjective Routing for Simultaneously Optimizing System Lifetime and Source-to-Sink Delay in Wireless Sensor Networks

Cited by 21 publications

References 25 publications

On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies

On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

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

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