Combining distributed queuing with energy harvesting to enable perpetual distributed data collection applications

This is the peer reviewed version of the following article: Vazquez-Gallego F, Tuset-Peiró P, Alonso L, Alonso-Zarate J. Combining distributed queuing with energy harvesting to enable perpetual distributed data collection applications. Trans Emerging Tel Tech. 2017;e3195 , which has been published in final form at https://doi.org/10.1002/ett.3195. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.This paper presents, models, and evaluates energy harvesting–aware distributed queuing (EH-DQ), a novel medium access control protocol that combines distributed queuing with energy harvesting (EH) to address data collection applications in industrial scenarios using long-range and low-power wireless communication technologies. We model the medium access control protocol operation using a Markov chain and evaluate its ability to successfully transmit data without depleting the energy stored at the end devices. In particular, we compare the performance and energy consumption of EH-DQ with that of time-division multiple access (TDMA), which provides an upper limit in data delivery, and EH-aware reservation dynamic frame slotted ALOHA, which is an improved variation of frame slotted ALOHA. To evaluate the performance of these protocols, we use 2 performance metrics: delivery ratio and time efficiency. Delivery ratio measures the ability to successfully transmit data without depleting the energy reserves, whereas time efficiency measures the amount of data that can be transmitted in a certain amount of time. Results show that EH-DQ and TDMA perform close to the optimum in data delivery and outperform EH-aware reservation dynamic frame slotted ALOHA in data delivery and time efficiency. Compared to TDMA, the time efficiency of EH-DQ is insensitive to the amount of harvested energy, making it more suitable for energy-constrained applications. Moreover, compared to TDMA, EH-DQ does not require updated network information to maintain a collision-free schedule, making it suitable for very dynamic networks.Peer ReviewedPostprint (author's final draft

Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Channel Access in Wireless Networks: Protocol Design of Energy-Aware Schemes for the IoT and Analysis of Existing Technologies

The design of channel access policies has been an object of study since the deployment of the first wireless networks, as the Medium Access Control (MAC) layer is responsible for coordinating transmissions to a shared channel and plays a key role in the network performance. While the original target was the system throughput, over the years the focus switched to communication latency, Quality of Service (QoS) guarantees, energy consumption, spectrum efficiency, and any combination of such goals. The basic mechanisms to use a shared channel, such as ALOHA, TDMA- and FDMA-based policies, have been introduced decades ago. Nonetheless, the continuous evolution of wireless networks and the emergence of new communication paradigms demand the development of new strategies to adapt and optimize the standard approaches so as to satisfy the requirements of applications and devices. This thesis proposes several channel access schemes for novel wireless technologies, in particular Internet of Things (IoT) networks, the Long-Term Evolution (LTE) cellular standard, and mmWave communication with the IEEE802.11ad standard. The first part of the thesis concerns energy-aware channel access policies for IoT networks, which typically include several battery-powered sensors. In scenarios with energy restrictions, traditional protocols that do not consider the energy consumption may lead to the premature death of the network and unreliable performance expectations. The proposed schemes show the importance of accurately characterizing all the sources of energy consumption (and inflow, in the case of energy harvesting), which need to be included in the protocol design. In particular, the schemes presented in this thesis exploit data processing and compression techniques to trade off QoS for lifetime. We investigate contention-free and contention-based chanel access policies for different scenarios and application requirements. While the energy-aware schemes proposed for IoT networks are based on a clean-slate approach that is agnostic of the communication technology used, the second part of the thesis is focused on the LTE and IEEE802.11ad standards. As regards LTE, the study proposed in this thesis shows how to use machine-learning techniques to infer the collision multiplicity in the channel access phase, information that can be used to understand when the network is congested and improve the contention resolution mechanism. This is especially useful for massive access scenarios; in the last years, in fact, the research community has been investigating on the use of LTE for Machine-Type Communication (MTC). As regards the standard IEEE802.11ad, instead, it provides a hybrid MAC layer with contention-based and contention-free scheduled allocations, and a dynamic channel time allocation mechanism built on top of such schedule. Although this hybrid scheme is expected to meet heterogeneous requirements, it is still not clear how to develop a schedule based on the various traffic flows and their demands. A mathematical model is necessary to understand the performance and limits of the possible types of allocations and guide the scheduling process. In this thesis, we propose a model for the contention-based access periods which is aware of the interleaving of the available channel time with contention-free allocations

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ