Optimal fusion rule for distributed detection in clustered wireless sensor networks

Aldalahmeh, Sami; Ghogho, Mounir; McLernon, Des; Nurellari, Edmond

doi:10.1186/s13634-016-0303-9

Cited by 19 publications

(17 citation statements)

References 27 publications

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“…Since then, their application to the modeling and analysis of wireless networks has been relentless [113]- [115]. Also, spatial processes have found many applications beyond communications, e.g., localization [116]- [118], caching (local data storage) [119]- [121], distributed sensing and data fusion [122]- [124]. Fundamental issues, however, remain open to use them for modeling, analyzing, optimizing wireless networks [125].…”

Section: B Tractable Analytical Framework For Modeling Analyzing mentioning

confidence: 99%

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Renzo

Debbah

Phan-Huy

et al. 2019

J Wireless Com Network

1,498

631

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Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: 1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and 2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual "more data needs more power and emission of radio waves" status quo, and motivate that future wireless networks necessitate a smart radio environment: A transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as intelligent reconfigurable meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. We will discuss, in particular, two major types of intelligent reconfigurable meta-surfaces applied to wireless networks. The first type of meta-surfaces will be embedded into, e.g., walls, and will be directly controlled by the wireless network operators via a software controller in order to shape the radio waves for, e.g., improving the network coverage. The second type of meta-surfaces will be embedded into objects, e.g., smart t-shirts with sensors for health monitoring, and will backscatter the radio waves generated by cellular base stations in order to report their sensed data to mobile phones. These functionalities will enable wireless network operators to offer new services without the emission of additional radio waves, but by recycling those already existing for other purposes. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of intelligent reconfigurable meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.

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Section: B Tractable Analytical Framework For Modeling Analyzing mentioning

confidence: 99%

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Renzo

Debbah

Phan-Huy

et al. 2019

J Wireless Com Network

1,498

631

View full text Add to dashboard Cite

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“…On the other hand, in our previous work [9] we have derived an optimal-cluster-based fusion rule (OCR) for clustered sensor networks. In this context, CHs collect the local SNs decisions and send this data to the FC over ideal channels.…”

Section: Related Workmentioning

confidence: 99%

“…Thus an exact implementation of (13) requires knowledge of θ. This problem has been circumvented in [9] by using a complexity-reduced GLRT, in which θ is coarsely estimated.…”

Section: A Decision Fusion In Ideal Channel Clustered Wsnmentioning

confidence: 99%

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Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks With Imperfect Channels

Aldalahmeh

Al-Jazzar

McLernon

et al. 2019

IEEE Trans. on Signal and Inf. Process. over Networks

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In this paper we investigate fusion rules for distributed detection in large random clustered-wireless sensor networks (WSNs) with a three-tier hierarchy; the sensor nodes (SNs), the cluster heads (CHs) and the fusion center (FC). The CHs collect the SNs' local decisions and relay them to the FC that then fuses them to reach the ultimate decision. The SN-CH and the CH-FC channels suffer from additive white Gaussian noise (AWGN). In this context, we derive the optimal log-likelihood ratio (LLR) fusion rule, which turns out to be intractable. So, we develop a sub-optimal linear fusion rule (LFR) that weighs the cluster's data according to both its local detection performance and the quality of the communication channels. In order to implement it, we propose an approximate maximum likelihood based LFR (LFR-aML), which estimates the required parameters for the LFR. We also derive Gaussian-tail upper bounds for the detection and false alarms probabilities for the LFR. Furthermore, an optimal CH transmission power allocation strategy is developed by solving the Karush-Kuhn-Tucker (KKT) conditions for the related optimization problem. Extensive simulations show that the LFR attains a detection performance near to that of the optimal LLR and confirms the validity of the proposed upper bounds. Moreover, when compared to equal power allocation, simulations show that our proposed power allocation strategy achieves a significant power saving at the expense of a small reduction in the detection performance.

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“…While the authors in [7] claim to reduce the FC control overhead, [8] derives the optimum power allocation scheme for a given maximum total network power budget in order to improve the estimation quality. Now, clustered WSNs [10] has been extensively studied in various contexts such as energy management [11], [12] and fusion rules design [13]. In the context of clustering algorithms, the authors in [14] propose a d − hop cluster partitioning to deal with the load imbalance among CHs.…”

Section: Introductionmentioning

confidence: 99%

Energy Balancing for Robotic Aided Clustered Wireless Sensor Networks Using Mobility Diversity Algorithms

Licea

Nurellari

Ghogho

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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We consider the problem of energy balancing in a clustered wireless sensor network (WSN) deployed randomly in a large field and aided by a mobile robot (MR). The sensor nodes (SNs) are tasked with monitoring a region of interest (ROI) and reporting their test statistics to the cluster heads (CHs), which they subsequently report to the fusion center (FC) over a wireless fading channel. To maximize the lifetime of the WSN, the MR is deployed to act as an adaptive relay between a subset of the CHs and the FC. To achieve this we develop a multiple−link mobility diversity algorithm (MDA) executed by the MR that will allow to compensate simultaneously for the small-scale fading at the established wireless links (i.e., the MR-to-FC as well as various CH-to-MR communication links). Simulation results show that the proposed MR aided technique is able to significantly reduce the transmission power required and thus extend the operational lifetime of the WSN. We also show how the effect of small-scale fading at various wireless links is mitigated by using the proposed multiple − link MDA executed by a MR equipped with a single antenna.

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Optimal fusion rule for distributed detection in clustered wireless sensor networks

Cited by 19 publications

References 27 publications

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks With Imperfect Channels

Energy Balancing for Robotic Aided Clustered Wireless Sensor Networks Using Mobility Diversity Algorithms

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