Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

On feedback-based rateless codes for data collection in vehicular networks

The ability to transfer data reliably and with low delay over an unreliable service is intrinsic to a number of emerging technologies, including digital video broadcasting, over-the-air software updates, public/private cloud storage, and, recently, wireless vehicular networks. In particular, modern vehicles incorporate tens of sensors to provide vital sensor information to electronic control units (ECUs). In the current architecture, vehicle sensors are connected to ECUs via physical wires, which increase the cost, weight and maintenance effort of the car, especially as the number of electronic components keeps increasing. To mitigate the issues with physical wires, wireless sensor networks (WSN) have been contemplated for replacing the current wires with wireless links, making modern cars cheaper, lighter, and more efficient. However, the ability to reliably communicate with the ECUs is complicated by the dynamic channel properties that the car experiences as it travels through areas with different radio interference patterns, such as urban versus highway driving, or even different road quality, which may physically perturb the wireless sensors. This thesis develops a suite of reliable and efficient communication schemes built upon feedback-based rateless codes, and with a target application of vehicular networks. In particular, we first investigate the feasibility of multi-hop networking for intra-car WSN, and illustrate the potential gains of using the Collection Tree Protocol (CTP), the current state of the art in multi-hop data aggregation. Our results demonstrate, for example, that the packet delivery rate of a node using a single-hop topology protocol can be below 80% in practical scenarios, whereas CTP improves reliability performance beyond 95% across all nodes while simultaneously reducing radio energy consumption. Next, in order to migrate from a wired intra-car network to a wireless system, we consider an intermediate step to deploy a hybrid communication structure, wherein wired and wireless networks coexist. Towards this goal, we design a hybrid link scheduling algorithm that guarantees reliability and robustness under harsh vehicular environments. We further enhance the hybrid link scheduler with the rateless codes such that information leakage to an eavesdropper is almost zero for finite block lengths. In addition to reliability, one key requirement for coded communication schemes is to achieve a fast decoding rate. This feature is vital in a wide spectrum of communication systems, including multimedia and streaming applications (possibly inside vehicles) with real-time playback requirements, and delay-sensitive services, where the receiver needs to recover some data symbols before the recovery of entire frame. To address this issue, we develop feedback-based rateless codes with dynamically-adjusted nonuniform symbol selection distributions. Our simulation results, backed by analysis, show that feedback information paired with a nonuniform distribution significantly improves the decoding rate compared with the state of the art algorithms. We further demonstrate that amount of feedback sent can be tuned to the specific transmission properties of a given feedback channel

Optimal quantised bits for estimation over a capacity and power limited, lossy channel

This letter considers the problem of estimating the state of a scalar dynamical system over a wireless channel that is lossy, capacity, and power limited. The limited power assumption, which is valid for most real problems, links the number of quantisation bits and packet error rate. As the number of quantisation bits increases, the quantisation noise decreases but the packet loss rate increases. It is shown that the estimation error variance (EEV) is minimised for a range of quantisation bits. Further, it is shown that operating beyond the optimal range sharply increases the EEV. Simulation results corroborating the analytical results are also presented