Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of Things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep ${Q}$ -network enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop an RL-based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.

Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems

The increasing number of heterogeneous devices connected to the Internet, together with tight 5G requirements have generated new challenges for designing network infrastructures. Industrial verticals such as automotive, smart city and eHealthcare (among others) need secure, low latency and reliable communications. To meet these stringent requirements, computing resources have to be moved closer to the user, from the core to the edge of the network. In this context, ETSI standardized Multi-Access Edge Computing (MEC). However, due to the cost of resources, MEC provisioning has to be carefully designed and evaluated. This survey firstly overviews standards, with particular emphasis on 5G and virtualization of network functions, then it addresses flexibility of MEC smart resource deployment and its migration capabilities. This survey explores how the MEC is used and how it will enable industrial verticals.

Toward Enabled Industrial Verticals in 5G: A Survey on MEC-Based Approaches to Provisioning and Flexibility

Internet of things (IoT), mobile edge computing (MEC), and unmanned aerial vehicle (UAV) have attracted significant attention in both industry and academic research. By consolidating these technologies, IoT can be facilitated with improved connectivity, better data transmission, energy saving, and other advantages. However, the communication between these entities is subject to potential cyber threats. In addition, the integrity of the data must be maintained after storing into local storage. Blockchain is a data structure that supports features like pseudonymity, data integrity etc. This paper represents a blockchain based data acquisition process in which information is gathered from IoTs using UAV as a relay and is securely kept in blockchain at MEC server. In the proposed scheme, data are encrypted prior to transfer to MEC server with the assistance of a UAV. Upon receiving the data, MEC server validates the data and the identity of the sender. Successful validation is followed by stocking of the data into blockchain, subsequent to obtaining consent from the validators. Security analysis is conducted in order to show the feasibility of the proposed secure scheme. Finally, the performance of the proposed scheme is analyzed via simulation and implementation.

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BUAV: A blockchain based secure UAV-assisted data acquisition scheme in Internet of Things

This paper presents a blockchain enabled secure data acquisition scheme utilizing an unmanned aerial vehicle (UAV) swarm where data are collected from the Internet of Things (IoT) devices and subsequently, forwarded to the nearest server through the UAV swarm. Before initiating data acquisition, the UAV swarm shares a shared key with the IoT devices in order to maintain communications. However, prior to transmitting data, the IoT devices encrypt the data and forward it to the UAV swarm. Upon receiving the data, the UAV swarm implements a two-phase validation utilizing the π-hash bloom filter and the digital signature algorithm to validate the sender; in addition, prior to forwarding data to the nearest server, it performs encryption. However, before adding data in blockchain, consent from all validators is required. Finally, the data are stored in blockchain with the approval of validators. A security analysis is performed to demonstrate the feasibility of the proposed scheme. Finally, the effectiveness of the proposed scheme is manifested through the implementation and simulation. The security analysis and the performance results show that UAV assist the IoT devices both in terms of connectivity and energy consumption, and provides security against the threats mentioned in the paper.

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BUS: A Blockchain-Enabled Data Acquisition Scheme With the Assistance of UAV Swarm in Internet of Things

Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G. 

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AI Models for Green Communications Towards 6G

Considering the exponential increase in mobile traffic, requiring denser cellular access networks, the use of renewable energy (RE) to power base stations (BSs) may contribute to reduction of the huge operational cost faced by mobile network operators (MNOs) due to power supply. Furthermore, the smart grid (SG) paradigm is deeply changing the energy market, envisioning an active interaction between grid and customers. Hence, MNOs can combine renewable-powered BSs with properly designed energy management strategies to improve the interaction with the SG, with the twofold objective of reducing the energy bill and providing ancillary services. We propose a stochastic model to investigate a renewable-powered mobile network dynamically adapting its energy consumption to accomplish SG requests, and receiving rewards in return, by exploiting two techniques. First, resource on demand (RoD) can dynamically switch off unneeded BSs. Second, an energy management policy takes efficient decisions about using/harvesting locally produced RE, depending on SG requests. The proposed techniques highly increase the probability of responding to ancillary service demands, up to 90%, also depending on the RE system size, with RoD raising, by up to 20%, the probability of accomplishing SG requests. Consequently, cost saving can become equal to the total cost or even greater, providing revenues to the MNO.

Energy Management and Base Station On/Off Switching in Green Mobile Networks for Offering Ancillary Services

The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.

Greener RAN Operation Through Machine Learning

Three factors make power supply one of the most urgent and challenging issues for the future of mobile networks. First, the expected fast growth of mobile traffic raises doubts about the sustainability of mobile communications, that already account for 0.5% of the woldwide energy consumption. Second, power supply has become far the largest component of the operational costs of running a network. Third, the deployment of network infrastructures in emerging countries is strategic, but, in these countries, the power grid is not always reliable. Renewable energy sources can help to cope with these issues. However, one of their main drawbacks is the intermittent and difficult to predict energy generation profile. The feasibility of renewable power supply for base station (BS) powering depends then by the possibility to reduce the BS consumption and to adapt it to the amount of available energy.In this paper, we consider a cluster of BSs powered with photovoltaic (PV) panels and equipped with energy storage units. Resource on Demand (RoD) strategies are implemented to reduce the cluster energy consumption and to adapt it to energy availability. The results show that resource of demand can effectively be applied to make off-grid BS deployment feasible.

Radio Resource Management for Improving Energy Self-sufficiency of Green Mobile Networks

Traditionally, we rely highly upon fossil fuels for our energy provisioning, but there are drawbacks to using these fossil fuels: the risk for depletion in the future; the increased cost as already observed during the last few years, and the high impact on climate change. One virtually carbon-neutral alternative to fossil fuels are renewable energy sources, like solar energy. In this paper, we investigate the energy and network performance of a wireless access network powered by the traditional electricity grid and a photovoltaic panel system. An energy-aware management system for the future wireless access networks is proposed. This system consists of the management of the energy provisioning and storage system and the application of the proposed energy-saving strategies which aim to reduce the energy footprint through the traditional grid in case a renewable energy shortage occurs. To evaluate the network’s performance, this paper proposes a deployment tool with the above described energy-aware management system. The results show that it is promising to further investigate a more evolved and complex energy-aware management system.

Accounting for the Varying Supply of Solar Energy When Designing Wireless Access Networks

In this paper we focus on the design of the power system for off-grid cellular base stations powered by a photovoltaic solar panel and a battery. Several papers already tackled this problem, with different approaches, modeling either the day-to-day behavior, or the hourly dynamics. In addition, the meteorological characteristics were modeled using a variable number of levels. Different approaches produced different results, hardly comparable. In this paper, we discuss the choice of parameter quantization for time, weather, and energy storage, with the objective of deriving guidelines for the development of accurate and credible models that can support the power system design. Our investigation shows that quantization has an important impact on the mathematical model outputs. Hence, quantization must be carefully taken into account, to achieve a correct dimensioning of the power system.

Daniela Renga

Papers

Energy Management and Base Station On/Off Switching in Green Mobile Networks for Offering Ancillary Services

Greener RAN Operation Through Machine Learning

Radio Resource Management for Improving Energy Self-sufficiency of Green Mobile Networks

Accounting for the Varying Supply of Solar Energy When Designing Wireless Access Networks

The Impact of Quantization on the Design of Solar Power Systems for Cellular Base Stations