Chiara Pielli

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

The radio transceiver of an Internet of Things (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; and 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.

Joint Optimization of Energy Efficiency and Data Compression in TDMA-Based Medium Access Control for the IoT

Energy efficiency is a key requirement for the Internet of Things, as many sensors are expected to be completely stand-alone and able to run for years without battery replacement. Data compression aims at saving some energy by reducing the volume of data sent over the network, but also affects the quality of the received information. In this work, we formulate an optimization problem to jointly design the source coding and transmission strategies for time-varying channels and sources, with the twofold goal of extending the network lifetime and granting low distortion levels. We propose a scalable offline optimal policy that allocates both energy and transmission parameters (i.e., times and powers) in a network with a dynamic Time Division Multiple Access (TDMA)-based access scheme.

A Dynamic Approach to Rebalancing Bike-Sharing Systems

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations’ occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City’s bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule.

QoE-aware Video Rate Adaptation algorithms in multi-user IEEE 802.11 wireless networks

The spreading of video streaming services in the last few years is presenting new challenges in wireless networking; Video Rate Adaptation (VRA) is a technique that optimizes the bandwidth usage by adapting video quality as network conditions change. We propose two Quality of Experience (QoE) aware algorithms that perform VRA while guaranteeing user satisfaction.

A game-theoretic analysis of energy-depleting jamming attacks

Jamming may be a serious issue in Internet of Things networks with battery-powered nodes, as an attacker can not only disrupt packet delivery, but also reduce the lifetime of energy-constrained nodes. In this work, we consider a malicious attacker with the dual objective of preventing communication and depleting the battery of a targeted node. We model this scenario as a multistage game, derive optimal strategies for both sides, and evaluate their consequences on network performance. Our work highlights the trade-off between node lifetime and communication reliability and how the presence of a jammer affects both these aspects.