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

NoCo: ILP-based worst-case contention estimation for mesh real-time manycores

Manycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared caches and memories and hence concentrates most of the contention that tasks suffer, with effects on the worst-case contention delay (WCD) of packets and tasks' WCET. While several proposals minimize the impact of individual NoC parameters on WCD, e.g. mapping and routing, there are strong dependences among these NoC parameters. Hence, finding the optimal NoC configurations requires optimizing all parameters simultaneously, which represents a multidimensional optimization problem. In this paper we propose NoCo, a novel approach that combines ILP and stochastic optimization to find NoC configurations in terms of packet routing, application mapping, and arbitration weight allocation. Our results show that NoCo improves other techniques that optimize a subset of NoC parameters.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015- 65316-P and the HiPEAC Network of Excellence. It also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (agreement No. 772773). Carles Hernández is jointly supported by the MINECO and FEDER funds through grant TIN2014-60404-JIN. Jaume Abella has been partially supported by the Spanish Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Enrico Mezzetti has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva-Incorporaci´on postdoctoral fellowship number IJCI-2016-27396.Peer ReviewedPostprint (author's final draft

Continuum Equilibria and Global Optimization for Routing in Dense Static Ad Hoc Networks

We consider massively dense ad hoc networks and study their continuum limits as the node density increases and as the graph providing the available routes becomes a continuous area with location and congestion dependent costs. We study both the global optimal solution as well as the non-cooperative routing problem among a large population of users where each user seeks a path from its origin to its destination so as to minimize its individual cost. Finally, we seek for a (continuum version of the) Wardrop equilibrium. We first show how to derive meaningful cost models as a function of the scaling properties of the capacity of the network and of the density of nodes. We present various solution methodologies for the problem: (1) the viscosity solution of the Hamilton-Jacobi-Bellman equation, for the global optimization problem, (2) a method based on Green's Theorem for the least cost problem of an individual, and (3) a solution of the Wardrop equilibrium problem using a transformation into an equivalent global optimization problem

WiLiTV: A Low-Cost Wireless Framework for Live TV Services

With the evolution of HDTV and Ultra HDTV, the bandwidth requirement for IP-based TV content is rapidly increasing. Consumers demand uninterrupted service with a high Quality of Experience (QoE). Service providers are constantly trying to differentiate themselves by innovating new ways of distributing content more efficiently with lower cost and higher penetration. In this work, we propose a cost-efficient wireless framework (WiLiTV) for delivering live TV services, consisting of a mix of wireless access technologies (e.g. Satellite, WiFi and LTE overlay links). In the proposed architecture, live TV content is injected into the network at a few residential locations using satellite dishes. The content is then further distributed to other homes using a house-to-house WiFi network or via an overlay LTE network. Our problem is to construct an optimal TV distribution network with the minimum number of satellite injection points, while preserving the highest QoE, for different neighborhood densities. We evaluate the framework using realistic time-varying demand patterns and a diverse set of home location data. Our study demonstrates that the architecture requires 75 - 90% fewer satellite injection points, compared to traditional architectures. Furthermore, we show that most cost savings can be obtained using simple and practical relay routing solutions

Energy-efficient traffic engineering

The energy consumption in telecommunication networks is expected to grow considerably, especially in core networks. In this chapter, optimization of energy consumption is approached from two directions. In a first study, multilayer traffic engineering (MLTE) is used to assign energy-efficient paths and logical topology to IP traffic. The relation with traditional capacity optimization is explained, and the MLTE strategy is applied for daily traffic variations. A second study considers the core network below the IP layer, giving a detailed power consumption model. Optical bypass is evaluated as a technique to achieve considerable power savings over per-hop opticalelectronicoptical regeneration. Document type: Part of book or chapter of boo