When Wireless Crowd Charging Meets Online Social Networks: A Vision for Socially Motivated Energy Sharing

Abstract

Individual users of a social crowd are bound by finite resource capacity. Other users (such as online friends) may have surplus capacity or resources that, if shared, could be used to meet fluctuating demand. A particularly important resource in modern societies is the energy supplies of mobile portable devices like smartphones; quick energy depletion is an everyday problem in the lives of billions of smartphone users worldwide. Peer-to-peer wireless crowd charging has recently emerged as an alternative energy replenishment option. Although there have been some recent advances on this type of wireless energy sharing, to the best of our knowledge, none of the related works has considered using online social network information as input. In this paper, for the first time in the state of the art, we introduce the vision of socially motivated wireless energy sharing and we provide a holistic framework for socially aware wireless crowd charging. We present a taxonomy of the existing representative use cases, and we define a model which takes into account not only the wireless power transfer specifics, but also the online social information. After having selected a target use case (balanced crowd energy efficiency), we design two socially aware wireless energy sharing protocols. The design of each protocol is inspired by the social topology and structural characteristics of diverse datasets of online social relationships among real users. In fact, aligning to studies on psychology which conclude that users typically favor friends over strangers in allocating resources (especially when the sharing task is costly), our protocols favor energy exchanges among friends or friendly groups. For the purposes of the protocol evaluation, we simulate the energy sharing interactions based on the user encounters, as reported in the datasets. In order to demonstrate the effects of the social component, we compare the performance of our protocols to the performance of another state of the art protocol that does not use online social information. Interestingly enough, we demonstrate that online social network information can indeed influence the selected use case, in terms of energy losses, as well as level of and time elapsed until a balanced energy distribution in the crowd. We conclude the paper by providing some interesting open research directions.

Introduction

Digital relationships between individuals are becoming as important as their real world counterparts. For many people, online social networks and media provide a primary means of bonding and communication between friends, family, and coworkers. Like any community, individual users of a social network are bound by finite capacity and limited capabilities. In many cases however, other members (such as online friends) may have surplus capacity or resources that, if shared, could be used to meet fluctuating demand [2].

A particularly important resource in modern societies is the energy supplies of mobile portable devices, like smartphones [3]. As such devices (which often combine a phone, camera, music player and personal organizer) become more powerful, they are consuming more power and battery technology is not keeping pace. Unfortunately, the residual energy supplies of these smart devices and applications are limited and dependent on their battery power, which directly effects their usability. Google announced in May 2019, that it had activated over 2.5 billion Android devices [4]. Moreover, a recent survey of 1000 users, conducted by the research company GMI, showed that 89% rated long battery life as an “important” factor when buying a new smartphone - long battery life rated higher than all other features [5]. Finally, with the introduction of newer, sophisticated mobile applications and increased phone usage habits, users expect a high impact on their battery lifetime. For example, taking into account practical reports, depending on the mobile applications used, battery lifetime could be reduced from 24 hours (crowdsensing [6]) to 12 hours (GPS [7]), or even 2.5 hours (mobile gateway [8]). Consequently, quick energy depletion is an everyday problem in the lives of billions of smartphone users worldwide; users who are required to charge their smartphones frequently to keep them operational.

Wired charging, although efficient and robust, can many times be unavailable due to the high mobility of everyday users. Wireless charging has recently emerged as an alternative energy replenishment option in various types of networked environments [9], with the greatest mobile device manufacturers already adopting suitable wireless charging standards, such as the inductive charging standard Qi [10]. Among the various methods to implement wireless charging in crowds of mobile device users, the recent concept of peer-to-peer wireless energy sharing¹ [11], [12] has been gaining more and more attention lately. In the wireless energy sharing paradigm, users from the crowd carry mobile devices like smartphones which are equipped with a power transmitter and a power receiver, and are able to wirelessly share (transmit and receive) energy with other mobile devices in the surroundings. Due to the technological constraints of the current wireless power transfer technology, two mobile devices need to be within a sufficient proximity in order to effectively exchange energy. Wireless energy sharing can be implemented using either far- [13], [14] or near- [15], [16] field technologies. In the far-field case, using electromagnetic signals, the same antenna, designed to exploit its far-field properties for communication purposes, can be suitably configured for simultaneously realizing wireless power transfer via its near-field properties. In the near-field case, using inductive coupling with transmitting and receiving coils, we can achieve higher power transmission efficiency and avoid RF exposure.

As recent studies in the wireless energy sharing paradigm [17], [18] have theoretically and experimentally demonstrated, having even limited knowledge on the crowd properties, can be crucial for the energy conservation of the devices in the crowd. On the contrary, not using any knowledge at all about network structure properties, efficient energy distributions among the users cannot be achieved in a timely manner, which means that energy sharing might experience high energy losses during the energy redistribution process. Energy balancing can provide efficient utilization of scarce energy in mobile social networks and can prolong the network lifetime, as noted in the recent state-of-the-art survey [19]. Distributing energy such that each node in the network has access to similar level of energy or energy proportional to its weight (e.g., importance in the network) can be thought of a fair way of collaboration among mobile users in efficient utilization of the energy resources. For this reason, it is desirable to “fairly” replenish energy to the requested users such that as many users as possible are satisfied [20]. Otherwise, some users with sufficiently residual energy will be fully charged while others with barely left energy will miss the charging opportunities and run out of their energy very soon. Consequently, it is highly beneficial for the related use cases to employ smart ways of distributing energy by exploiting existing network information.

A key characteristic of such crowds of mobile device users is their involvement in online social networks and media. The widespread diffusion of such networks has given unforeseen opportunities to their users to share contents and mutually interact [21]. Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing [22]. As relationships within online social networks are at least partly based on real-world relationships, we can use them to infer a level of trust that underpins and transcends the online community in which they exist [2].

An interesting way of understanding how the resource sharing among users is established emerges from various studies in the domain of psychology. In particular, in the physical realm, studies in the field of social psychology traditionally suggest that simple reciprocal interactions are a potent trigger of altruism for human users, and that those interactions lead them to believe that their relationships are characterized by mutual care and commitment [23]. Evolutionary psychology suggests that such behavior is primarily the product of adaptations for reciprocal altruism, dependent on the degree of relatedness and exchange of benefits [24]. More specifically, within the context of resource allocation, numerous studies have explored various ages at which humans begin to demonstrate sharing and helping behavior [25]. A consistent finding among those studies is that, in the context of economic games, humans typically favor friends over strangers in allocating resources, especially when the sharing task is costly. In other words, the global organization of many socially networked groups is constituted of communities where people have denser and better interactions with their intra-community members than with the inter-community members [26].

Similar conclusions can be drawn about the reciprocity in the online social realm. There is a certain discrepancy between the concept of friendship in the physical realm (e.g., inner circle in Dunbar’s theory), and friendship in virtual life: Sometimes people share friendships on social networks even if they do not know each other. However, the analysis of [27], indicates that online communities have very similar structural characteristics to offline face-to-face networks. In particular, it confirms the existence of the layered structure of ego-centric social networks, and it identifies the existence of an additional network layer whose existence was only hypothesized in offline social networks. Furthermore, an additional positive verification of the social exchange theory in online communities is provided in [28], where the authors demonstrate strong empirical evidence that an increase in the reciprocity quantity of an online social user increases the reciprocity reactions from his or her imminent online social graph.

Those findings can help us introduce a social dimension in the energy sharing process, by taking advantage of the available online social network information.

Given those involvements, in this paper we suggest the exploitation of online social network information in order to define a socially aware wireless energy sharing framework and better tune the wireless crowd charging process. This suggestion reflects an impressive instance of the cyber-physical convergence concept, i.e., the process whereby the physical realm around us and the virtual realm of the Internet are deeply interwoven, constantly interacting with, and dependent upon, each other [29]. Although there have been some recent relevant works on wireless energy sharing, to the best of our knowledge, none of those works has considered the exploitation of online social network information in order to assist wireless energy sharing operations. Consequently this is the first work in the literature to combine concepts from the diverse fields of wireless energy sharing and online social networks and media. More specifically, our contribution in this paper includes the following:

• In section 2, we introduce the vision of socially motivated energy sharing. Then, we define the socially aware energy sharing framework and its fundamental components.

• In section 3, we define the social crowd charging model which takes into account not only the wireless power transfer specifics, but also (for the first time) online social network information. The model is reflecting the framework introduced in section 2 and is consequently abstracting all its fundamental components.

• In section 4, after having selected a target use case (balanced crowd energy efficiency), we design two socially aware wireless energy sharing protocols. The design of each protocol is inspired by the social topology and structural characteristics of two diverse datasets of online social relationships among users of real crowds. For the purposes of the evaluation, we use the real physical interaction traces of the crowds of users. In particular, we simulate energy sharing interactions based on the user encounters as they are reported in the datasets. In order to demonstrate the effects of the social component of the protocol design on the crowd charging process, we compare their performance to the performance of another state of the art protocol that does not use online social information. Finally, in order to highlight some scalability effects, we simulate the crowd charging process in a larger dataset for significantly more user interactions.

• In section 2 we present the current state of the art and we derive a taxonomy of the existing use cases for wireless crowd charging.

• In section 2, we provide some interesting open directions for this new, fascinating field of research.

• In section 7, we conclude the paper.