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Acoustic Sensor Networks


Wireless Sensor Networks (WSN) have been well researched as a generic concept. In this project, part of a DFG Forschergruppe (DFG Research Group), we are concerned with the specifics of wireless acoustic sensor networks.

On one hand, fulfilling a data acquisition task does not only depend on the acoustic environment but also on the available sensor nodes capacities (microphones, energy). On the other hand, data processing depends primarily on available resources for computing and communication which corresponds to the required quality of the data acquisition task. Another important aspect is the algorithm selection which depends mainly on the scene at hand (e.g. single or multiple sources).

Knowing that acoustic applications require a high level of resolution and given an acoustic signal processing task and a sensor network, key questions are which sensors should record, process or store acoustic data and which algorithm should be run on which node?


Given that there is a strong interdependence between data acquisition, processing and algorithm selection, we approach this interdependence as a joint question of:

  1. Role assignment and parameter selection where the algorithm choice is perceived as a parameter as well.
  2. Different roles (e.g., sensing, storing, transmitting, processing) have to be assigned to various nodes
  3. Algorithm parameters (e.g., sampling rates, FFT parameters) have to be selected for mechanisms, algorithms, and protocols on all system layers.

Distributed versions of algorithms will be developed that are tuned to the limitations of the wireless network. We investigate how data streams have to be organized for an optimal trade-off between acoustic signal processing performance and resource efficiency of the communication network.

Work Packages

The Computer Network Research Group of Paderborn University is mainly involved in the following work packages of the overall Research Group.

Work package 2: Quality-resource tradeoff of distributed algorithms

Using different distributed algorithm versions, we shall characterize their tradeoffs between obtained result quality and invested resources. For example, we will check how different packet error rates, limited data rate, limited processing power for iterative algorithms or – in particular – different choices of acoustic sensors (microphones) impact the obtained quality

Work package 3: Role assignment and parameter control

Decisions have to be taken on the basis of the current network topology, the source locations in the network, and the available distributed algorithms’ tradeoff profiles. Decisions shall select the components of the distributed algorithms which have to be deployed onto concrete nodes, apply route configuration, and select the concrete acoustic sources.

Work package 5: Cross-layer interoperability and interfacing

This work package addresses the need for a formal description by specifying which data and parameters of one layer have to be made available to which processing task of the other layer using typical network configuration formalisms. We will design ways to organize feedback loops between lower-layer mechanisms (like the role assignment) and higher-layer acoustic algorithms to exchange information to explore an option space at runtime

Work package 7: Experimental Setup

This work package will port and integrate the developed algorithms along with proposed schemes on a concrete hardware platform.


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MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops

H. Afifi, S. Auroux, H. Karl, Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2018

MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks

H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, H. Karl, in: Speech Communication; 13th ITG-Symposium, 2018, pp. 1-5

Signal processing in WASNs is based on a software framework for hosting the algorithms as well as on a set of wireless connected devices representing the hardware. Each of the nodes contributes memory, processing power, communication bandwidth and some sensor information for the tasks to be solved on the network. In this paper we present our MARVELO framework for distributed signal processing. It is intended for transforming existing centralized implementations into distributed versions. To this end, the software only needs a block-oriented implementation, which MARVELO picks-up and distributes on the network. Additionally, our sensor node hardware and the audio interfaces responsible for multi-channel recordings are presented.

Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding

H. Afifi, H. Karl, in: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019), IEEE, 2019

Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks

M. Guenther, H. Afifi, A. Brendel, H. Karl, W. Kellermann, in: 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019), 2019

By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations.

An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding

H. Afifi, H. Karl, in: 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19), 2019

Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes' computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate.

A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO

H. Afifi, H. Karl, S. Eikenberg, A. Mueller, L. Gansel, A. Makejkin, K. Hannemann, R. Schellenberg, in: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo), 2019

One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework.

A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding

H. Afifi, K. Horbach, H. Karl, in: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (WiMob 2019), 2019

Given the recent development in embedded devices, wireless senor nodes are no longer limited to data collection but they can also do processing (e.g., smartphones). Accordingly, new types of applications take an advantage of the processing and flexibility provided by the wireless network. A common property between these applications is that the processing is not running on only one single node, but it is broken-down into smaller tasks that can run over multiple nodes, i.e., exploiting the in-network processing. We study a special variant of in-network processing, where the application is given by a graph; the processing tasks have predefined connections to be executed in a predefined sequence. The problem of embedding an application graph into a network is commonly known as Virtual Network Embedding (VNE). In this paper, we present a Genetic Algorithm (GA) solution to solve this wireless VNE problem, where we take into account the interference and multi-cast properties. We show that the GA has a good performance and fast execution compared to the optimization problem.

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Information about the project:     
Project members:Holger Karl
Haitham Afifi
Type:DFG project
Started:January 2017
Contact:Holger Karl

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