Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective. <s> BIB002 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> Wireless power has been a topic of interest from the early 20th century until today. This paper traces the history of wireless power transmission starting with Nikola Tesla, continuing on to experiments with beaming power using microwaves. Examining the difference between near-field and far-field techniques, this paper continues into modern times explaining why near-field technique is more suitable for consumer electronic devices and exploring the near-field transmission of power via the magnetic field. Examples of short-range and midrange wireless power systems are explored. <s> BIB003 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> In this paper, a rigorous procedure for the circuit-level analysis and design of entire systems, developed to provide power wirelessly, is presented. A unified theoretical approach is first introduced, based on a two-port-equivalent circuit representation, to describe the wireless power transfer link when the transmitter and the receiver are either in the near-field or in the far-field region reciprocally. This approach allows one to compute in a straightforward manner the system figure of merit, namely the power transfer efficiency. Specific guidelines for the two configurations are then intensively discussed together with the adopted software tools based on the combination of full-wave analysis and nonlinear harmonic balance techniques. Several practical examples based on this design procedure are presented, demonstrating predicted and experimental behavior of unconventional devices for both near-field and far-field power transfer usage. <s> BIB004 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes. <s> BIB005 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control. <s> BIB006 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes. <s> BIB007 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme. <s> BIB008 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Energy Harvesting. <s> Wireless Power Transfer (WPT) technology offers unprecedented opportunities to future cellular systems, making it possible to wirelessly recharge the mobile terminals as they get sufficiently close to the Base Stations (BSs). Here, we investigate the tradeoffs involved in the recharging process as multiple mobile users move across the cellular network, by systematically measuring the charging efficiency (i.e., amount of energy transferred as opposed to that transmitted) accounting for different mobility models, speeds, frequency range and inter-BS distance. We consider dense cellular deployments, where power is transferred to the mobile users through beamforming and scheduling techniques. At first, a genie is utilized to devise optimal charging schedules, where user locations and the residual energy in their batteries are exactly known by the controller. Hence, several heuristic policies are proposed and their performance is compared against that of the genie-based approach in terms of transfer efficiency and fraction of dead nodes (whose battery is completely depleted). Our numerical results reveal that: i) an even allocation of resources among users is inefficient, whereas even a rough estimate of their location allows heuristic policies to perform close to the genie-based approach, ii) mobility matters: group mobility leads to higher efficiencies and an increasing speed is also beneficial and iii) WPT can substantially reduce the number of dead nodes in the network, although this comes at the expense of constantly transmitting power and transfer efficiencies are very low under any scenario. <s> BIB009
EH is orthogonal to what we have discussed so far. In fact, up to now we have elaborated on increasing the efficiency in the network management, whereas the aim of energy harvesting is to supply network apparatuses, reducing their carbon footprint. Current mobile systems are powered using grid energy, which inevitably emits large amounts of carbon into the atmosphere. Recently, off-grid renewable energy sources such as solar radiation and wind energy have emerged as viable and promising sources for various IT systems due to the advancement of energy harvesting techniques BIB001 BIB005 ; see Figure 4 . where the BS is powered by either solar or wind energy. The authors in BIB002 observed that solar energy is more suitable for workloads with high peak-to-mean ratio (PMR), while wind energy fits better for workloads with small PMR. This avails the development of proper strategies for renewable energy provisioning for edge servers with the objective of eliminating any chance of energy shortage. This can be achieved by selecting the appropriate renewable energy source at each time instance taking into account current and forecast traffic loads. Since MEC servers are small-scale data centers, each of which consumes less energy than conventional cloud data centers, it is expected that powering the MEC infrastructure with renewable energy sources will reduce the overall network energy consumption. This is important, especially in light of the dense deployment pattern that is foreseen in 5G systems. A challenge to be addressed for renewable energy powered MEC systems is the green-aware resource allocation and computation offloading, which should take the renewable energy constraints into account (e.g., energy availability, battery charge/discharge cycles). Also, with renewable energy sources, the energy side information (ESI), which indicates the amount of available renewable energy, will play a key role in decision making for storage and computing applications. MEC devices may also be energized through wireless power transfer (WPT) BIB004 BIB003 , when the renewable energy is insufficient. WPT may be exploited for computational offloading in mobile devices BIB006 or data offloading for MEC in future networks BIB007 BIB008 . We stress, however, that the energy transfer efficiency of current WPT techniques is still very low, and that new methods are required to increase it and make WPT appealing in practice; see BIB009 .