On the Estimation of Randomly Sampled 2D Spatial Fields under Bandwidth Constraints

In this paper, we address the problem of the estimation of a spatial field defined over a two-dimensional space with wireless sensor networks. We assume that the field is (spatially) bandlimited and that it is sampled by a set of sensors which are randomly deployed in a given geographical area. Further, we impose a total bandwidth constraint which forces the quantization error in the sensor-to-FC (Fusion Center) channels to depend on the actual number of sensors in the network. With these assumptions, we derive an analytical expression of the mean-square error (MSE) in the reconstructed random field and, on that basis, an approximate closed-form expression of the optimal sensor density which attains the best trade-off in terms of observation, sampling and quantization noises. The analysis is carried out both in Gaussian and Rayleigh-fading scenarios without transmit Channel State Information (CSI). For the latter scenario, we also derive an expression of the common and constant rate at which the observations must be quantized. Computer simulation results illustrate the dependency of the optimal operating point on the variance of the observation noise or the signal-to-noise ratio in the sensor-to-FC channels, as well as the scaling law of the reconstruction MSE (which is also derived analytically) for both scenarios

Distributed ADMM for In-Network Reconstruction of Sparse Signals With Innovations

In this paper, we tackle the in-network recovery of sparse signals with innovations. We assume that the nodes of the network measure a signal composed by a common component and an innovation, both sparse and unknown, according to the joint sparsity model 1 (JSM-1). Acquisition is performed as in compressed sensing, hence the number of measurements is reduced. Our goal is to show that distributed algorithms based on the alternating direction method of multipliers (ADMM) can be efficient in this framework to recover both the common and the individual components. Specifically, we define a suitable functional and we show that ADMM can be implemented to minimize it in a distributed way, leveraging local communication between nodes. Moreover, we develop a second version of the algorithm, which requires only binary messaging, significantly reducing the transmission load

A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design

End-to-end design of communication systems using deep autoencoders (AEs) is gaining attention due to its flexibility and excellent performance. Besides single-user transmission, AE-based design is recently explored in multi-user setup, e.g., for designing constellations for non-orthogonal multiple access (NOMA). In this paper, we further advance the design of AE-based downlink NOMA by introducing weighted loss function in the AE training. By changing the weight coefficients, one can flexibly tune the constellation design to balance error probability of different users, without relying on explicit information about their channel quality. Combined with the SICNet decoder, we demonstrate a significant improvement in achievable levels and flexible control of error probability of different users using the proposed weighted AE-based framework.Comment: 5 pages, 5 figures, to appear at SPAWC 202

Distributed algorithms for in-network recovery of jointly sparse signals

We propose a new class of distributed algorithms for the in-network reconstruction of jointly sparse signals. We consider a network in which each node has to reconstruct a different signal, but all the signals share the same support. The problem is formulated as follows: each node iteratively solves a lasso, in which the weight of the l1-norm is tuned based on information on the support gathered from the other nodes. This promotes consensus on the support, and allows the single nodes to recover their signals, even when the number of measurements is not sufficient for individual reconstruction. Numerical simulations prove that our method outperforms the state-of-the-art greedy algorithms

IoT protocols, architectures, and applications

The proliferation of embedded systems, wireless technologies, and Internet protocols have made it possible for the Internet-of-things (IoT) to bridge the gap between the physical and the virtual world and thereby enabling monitoring and control of the physical environment by data processing systems. IoT refers to the inter-networking of everyday objects that are equipped with sensing, computing, and communication capabilities. These networks can collaborate to autonomously solve a variety of tasks. Due to the very diverse set of applications and application requirements, there is no single communication technology that is able to provide cost-effective and close to optimal performance in all scenarios. In this chapter, we report on research carried out on a selected number of IoT topics: low-power wide-area networks, in particular, LoRa and narrow-band IoT (NB-IoT); IP version 6 over IEEE 802.15.4 time-slotted channel hopping (6TiSCH); vehicular antenna design, integration, and processing; security aspects for vehicular networks; energy efficiency and harvesting for IoT systems; and software-defined networking/network functions virtualization for (SDN/NFV) IoT

Traffic Aggregation Techniques for Environmental Monitoring in M2M Capillary Networks

In this paper, we address the problem of traffic aggregation in capillary M2M networks. In particular, we focus on monitoring application where sensor nodes gather information from the environment (e.g. temperature, humidity, concentration of pollutants, etc) and send it to the gateway (GW). The GW then processes and retransmits the data through a cellular network to a distant application server. In an attempt to reduce the traffic congestion in the cellular network, we propose a data compression strategy at the GW that effectively exploits the temporal and spatial correlation in the sensors observations. The data compression strategy is composed of two building blocks: an estimation block and a compression block. In the estimation block, the data streams are jointly processed by means of a multidimensional linear filter. Next, the output streams of the estimation block are compressed by resorting to a truncated version its Karhunen-Loeve expansion