Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.

Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals. In this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons

In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs). In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength). Second, quality of service (QoS) which is used to measure the network's performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-Q-Networks (DQN). Additionally, we compare the performance of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions. We show that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs.

Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1\%. Meanwhile, the RL solution is better than the baseline with improvements up to 23\%, when using the trade-off objective.

Upcoming sensing applications (acoustic or video) will have high processing requirements not satisfiable by a single node or need input from multiple sources (e.g., speaker localization). Offloading these applications to cloud or mobile edge is an option, but when running in a wireless senor network (WSN), it might entail needlessly high data rate and latency. An alternative is to spread processing inside the WSN, which is particularly attractive if the application comprises individual components. This scenario is typical for applications like acoustic signal processing. Mapping components to nodes can be formulated as wireless version of the NP-hard Virtual Network Embedding (VNE) problem, for which various heuristics exist. We propose a Reinforcement Learning (RL) framework, which relies on Q-Learning and uses either Greedy Epsilon or Epsilon Decay for exploration. We compare both exploration methods to the result of an optimization approach and show empirically that the RL framework achieves good results in terms of network delay within few number of steps.

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.

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.

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.

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.

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.