A Joint Fusion, Power Allocation and Delay Optimization Approach for Wireless Sensor Networks

Xu, Mingdong; Leung, Henry

doi:10.1109/jsen.2010.2066557

Cited by 21 publications

(11 citation statements)

References 21 publications

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“…A cluster of a WSN was considered in [ 14 ] where sensor nodes collect and transmit data to the fusion center of the cluster directly or through relay nodes. Relay nodes amplify and forward data to the fusion center or the next relay node.…”

Section: Fusion Of Delay Sensitive Noise Perturbed Data Sensed Bymentioning

confidence: 99%

“…The papers selected for this review and tutorial are based on how well they covered the topics of interest and the degree to which they highlight the key design issues for WSN. In part III of this paper, we expose the reader to the following design problems in WSNs: routing for multi-hop WSNs with a single immobile sink [ 2 ], routing in a delay-tolerant WSN with a single mobile sink [ 3 ], joint routing, power and bandwidth allocation in FDMA WSNs [ 4 ], joint energy allocation and routing in WSNs with rechargeable batteries [ 5 ], routing in multi-hop single fixed sink with different objectives under distance uncertainties [ 6 ], joint routing and scheduling in WSNs with multiple sinks with different sink location possibilities [ 7 ], delay sensitive routing for underwater WSNs [ 8 ], using mobile radio frequency (RF) power charger to charge the batteries of sensors in a WSN [ 9 ], assignment of data processing tasks across the nodes in WSN [ 10 ], hierarchical clustering in a heterogeneous network [ 11 ], energy efficient co-operative broadcasting at the symbol level [ 12 ], dispatching of mobile sensor nodes in a WSN to sense a particular region of interest [ 13 ], fusion of delay sensitive noise perturbed data sensed by different nodes in a given cluster [ 14 ], Energy Optimization in Wireless Visual Sensor Networks While Maintaining Image Quality [ 15 ]. …”

Section: Part I Introductionmentioning

confidence: 99%

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Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

Ibrahim

Alfa

2017

Sensors

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This paper is intended to serve as an overview of, and mostly a tutorial to illustrate, the optimization techniques used in several different key design aspects that have been considered in the literature of wireless sensor networks (WSNs). It targets the researchers who are new to the mathematical optimization tool, and wish to apply it to WSN design problems. We hence divide the paper into two main parts. One part is dedicated to introduce optimization theory and an overview on some of its techniques that could be helpful in design problem in WSNs. In the second part, we present a number of design aspects that we came across in the WSN literature in which mathematical optimization methods have been used in the design. For each design aspect, a key paper is selected, and for each we explain the formulation techniques and the solution methods implemented. We also provide in-depth analyses and assessments of the problem formulations, the corresponding solution techniques and experimental procedures in some of these papers. The analyses and assessments, which are provided in the form of comments, are meant to reflect the points that we believe should be taken into account when using optimization as a tool for design purposes.

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Section: Fusion Of Delay Sensitive Noise Perturbed Data Sensed Bymentioning

confidence: 99%

Section: Part I Introductionmentioning

confidence: 99%

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

Ibrahim

Alfa

2017

Sensors

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“…The data delay can be defined as the duration of messages, which is transferred from sensor nodes to sink. And the data delay by TDMA takes place in three stages: data queuing, data transmission and radio propagation [19]. The delay in queuing depends on how long the message wait for transferring in sensor nodes, the delay of data transmission corresponds to the duration sensor nodes is used to transmit data in the scheduled slots, and radio propagation is the duration for signal disseminates to sink node.…”

Section: B Delaymentioning

confidence: 99%

“…In [18], global probability of error and energy consumption have been optimized, in their work, the optimization is investigated and solved for two types of fusion schemes which contain parallel decision and serial decision fusion. Mingdong Xu and Henry Leung proposed a cross-layer optimization of wireless sensor networks under the constraints of total energy consumption and transmission delay [19]. In [20], L.Liu et al…”

Section: Iintroductionmentioning

confidence: 99%

Cluster-based Routing Performance Optimization Constraint of Energy, Delay and Connectivity Metrics in Wireless Sensor Network

Cao

Wang

Xiong

et al. 2013

International Journal on Smart Sensing and Intelligent Systems

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Abstract-This paper aims to propose a framework for optimizing performance in cluster-based wireless sensor networks (WSNs) with constrains of energy consumption, delay and connectivity. For the limitation of energy storage being discussed a lot, many researches try to find more solutions to improve it. But, the other index of wireless sensor networks may be deteriorated by many routings which are dedicated to optimizing energy consumption. Thereby, multi-objectives optimization is one of significant issues in WSNs. The model of isolation nodes probability for connectivity in the clusterbased networks is adopted for the issue. Not only will the performance of Leach and existing multiobjectives optimization methods be compared under the proposed framework, but also will the performance of networks in different size of scenarios be analyzed. Simulation results show that the proposed framework combining NSGA can provide an effective sensing in terms of energy consumption, delay and connectivity in the cluster-based networks.

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“…Their work assumed that the sensors are communicating on the dedicated channels. Cross-layer optimization of fusion, power allocation and delay for a cognitive sensor network is given in [21], where the goal is to minimize the mean distortion of the system under constraints on the total energy consumption and the total delay. The work, however, did not consider any spectrum access and interference issues for the primary user.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

Naeem

Illanko

Karmokar

et al. 2013

Sensors

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Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

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A Joint Fusion, Power Allocation and Delay Optimization Approach for Wireless Sensor Networks

Cited by 21 publications

References 21 publications

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial

Cluster-based Routing Performance Optimization Constraint of Energy, Delay and Connectivity Metrics in Wireless Sensor Network

Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

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