Energy scarcity poses a significant challenge in the lifespan of mobile wireless networks, primarily due to the prevalent use of battery-powered devices. The lifespan of a network is the duration from the initial deployment of the network until the point when the network can no longer fulfill its intended functions due to energy depletion in a significant number of nodes. To address this issue, substantial research endeavours1 have been dedicated to optimizing energy consumption among mobile agents, thereby extending the overall network longevity. However, these efforts merely defer the inevitable requirement for energy replenishment. To ensure seamless node operation, the concept of energy harvesting2,3,4 has gained attention, drawing from diverse sources such as vibrations, radio waves, and solar power. Nonetheless, practical implementation remains restricted due to the unreliability of predictions and the typically modest energy yield from harvesting. In a similar vein, while immediate relief can be sought through battery replacement5, its viability is undermined by its high cost and often impractical nature.
Recently, an alternative approach has emerged in the form of mobile chargers, specialized devices boasting significantly greater energy reserves compared to the mobile agents within the network. These innovative chargers autonomously accumulate energy from external sources, subsequently navigating to the locations of mobile agents to periodically transfer energy. While this strategy effectively addresses the perpetual energy requirements of mobile devices, it introduces a host of challenges, including the need for efficient scheduling, and is contingent upon the availability of these specialised devices.
Motivation
To optimize energy management in mobile networks, various studies have been conducted using different approaches such as sleep schedules, harvesting, and cross-layer design5,6,7. Recently, advancements in wireless power transfer (WPT) technology have made it possible to use wireless charging as a means of supplying energy to mobile nodes to ensure the continuous operation of the networks. Previous research, particularly in the field of wireless sensor networks8,9,10, has explored the use of mobile chargers (such as robots or unmanned aerial vehicles (UAVs)) with large energy reserves to periodically charge sensors in the field. There have also been studies on the wireless charging of various mobile devices, such as smartphones11,12,13, EVs13,14,15,16, and Internet-of-Things (IoT) devices17,18,19,20. Most of these studies focus on charging mobile nodes wirelessly or with direct energy from a mobile charger or source. However, there may be situations where it is not possible to use an external charger due to environmental constraints or operational costs. In these cases, peer-to-peer energy sharing, using the resources available between network nodes, may be a useful solution, particularly in wildlife networks, disasters and emergency situations when electricity is scarce. For instance, in wildlife communication networks, animals (mobile nodes) come within each other’s communication range and can exchange data they have collected, such as environmental conditions, wildlife monitoring, and animal behavior analysis in their natural habitats. Hence, per-to-peer energy sharing can ensure continuous operation and data collection in wildlife networks, despite the energy limitations of batteries attached to the individual animals. Such applications are crucial for wildlife monitoring, conservation efforts, and studying animal behavior in their natural habitats. The network formed by animals exhibits the characteristics of an opportunistic network where there is no fixed infrastructure and node may be disconnected at times. This implies that the animals as mobile nodes are not connected together all the time as they may have different mobility patterns. A Note here for clarification; any two nodes are wirelessly connected if they are within radio range of each other but we note that this range for data communication and the range for wireless charging are different (Refer to sections “System description” and “Encounter-based energy sharing (EBES)”).
Related work
To extend the lifespan of such opportunistic network networks, it can be helpful to utilize the resources available among the nodes. One example of this is in the context of mobile social networks, where individuals can use the bidirectional energy-sharing capability of their smartphones (such as the Samsung Galaxy S10 or Huawei Mate 20 Pro) to help charge each other’s devices, a practice known as crowd charging13,21,22. This can be motivated by reciprocal altruism among friends23 or through incentives. Current wireless charging methods, such as the Qi standard22, allow compatible devices to charge their batteries when placed on a Qi charging pad. Although they are effective for short charging distances i.e., up to 4 cm, they are still more convenient than sharing energy through cables or charging devices through outlets. Peer-to-peer energy sharing can also be vital in disaster or emergency situations when energy is scarce. For instance, a group of friends or family members can equally share the available energy in their phone batteries to stay connected longer while searching for a missing member. Additionally, the charging of Internet of Things (IoT) devices can be accomplished through the crowdsourcing of energy from people’s smartphones19,24.
There are several technologies for WPT, including inductive charging, magnetic resonant coupling, and RF charging, each with its own set of benefits and drawbacks25. Many studies have evaluated the potential of WPT for sensor networks by considering the charging schedules of mobile charger nodes that recharge the sensors on a periodic basis. These studies typically involve one-way energy transfer from chargers to sensors. Mobile opportunistic networks have also been examined for bidirectional wireless energy transfer, such as peer-to-peer energy sharing systems for various purposes21. For example, mobile chargers have been used to create energy-efficient and collaborative charging schedules for devices to be crowd charged by other devices. There have also been studies on peer-to-peer wireless energy exchange as a means of balancing energy in mobile opportunistic networks, proposing various energy-sharing protocols26. This process has also been modelled in some studies taking into account the structure and formation of the network and the relationships within online social networks among users23. However, these approaches may result in significant energy loss due to nodes constantly changing their energy levels relative to the average energy in the network, leading to unnecessary energy loss. In13, authors have attempted to address this issue, but they often assume the availability of a central network or path between the source and destination, which may not be applicable in opportunistic networks.
There are also routing protocols that contributes to reduce the energy consumption of the network by choosing the right nodes to relay the traffic such that network lifetime increases and the probability of data delivery also increases. For example, Energy-Aware Epidemic (EA-Epidemic) routing protocol27 was introduced in opportunistic networks. The goal is to increase the probability of message delivery while extending the network lifetime as energy consumption reduces. In this approach, only a node with a higher remaining energy than the sending node’s energy will receive a copy of the message and store it for later transmission to other nodes or the destination node. In another work28, the authors proposed Optimized Energy Optimized Link-State Routing (OE-OLSR), aiming to improve energy management in mobile ad hoc networks. In this approach, nodes with most residual energy will be selected as next hop. The results show that the number of dead nodes can noticeably decrease.
The authors in29 also proposed a new method called Energy Efficient Routing Protocol for opportunistic networks based on Fuzzy Logic and Ant Colony (EERPFAnt), which draws inspiration from ant colony intelligence and is enhanced by the fuzzy logic technique. This method combines the nodes’ energy levels with knowledge about relays that have already received a copy of the message in order to estimate the nodes’ energy levels when they come into contact with the desired destination. However, EERPFAnt uses the Ant and Fuzzy algorithm in each choice for the node, which increases network overhead.
Contribution
As discussed in the previous section, most of the work13,21,22,23,24 focuses on the direct sharing of energy with the demanding node. The other work25,26 also considers a centralised network to make an efficient decision on choosing the right node for relaying energy based on the collected information from nodes. This implies that the network is not really opportunistic as the paths can be discovered and the relay nodes can be determined. Therefore, we propose a novel encounter-based energy sharing scheme, called EBES, for peer-to-peer wireless power transfer in wildlife networks30 that aims to prolong the lifespan of the network and minimize power transfer loss. In this network the contacts are opportunistic and the network topology is highly dynamic. Our approach takes advantage of the observation that if there is a demand for power, nodes across the network can contribute to the transfer of power directly or through multi-hops to that node. For multi-hops, we suggest selecting the best hop based on encounter rate, as a node with a high encounter rate is more likely to meet the destination and balance energy in a populated area with minimal energy loss due to the smaller number of relays. In addition, we consider the minimum efficiency required to be met before transferring power in each contact. Indeed, the duration of contact will play a very important role in the amount of energy that can be transferred, and our solution will consider different contact durations in the opportunistic networks.
The main contributions of our work are:
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Unlike existing multi-hop approaches, our solution does not rely on predetermined paths that may not be applicable in opportunistic networks. Instead, it proposes a strategy for selecting the best hop to maximize the network lifespan, increase the probability of meeting the nodes suffering from low energy, and minimize energy loss by reducing the number of hops.
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In opportunistic networks, relying on a single path discovered through a graph or probability theory may reduce the probability of power being transferred to the demanding node (e.g., animal) in sparse areas or where the dis-connectivity is frequent. Our approach takes advantage of multiple paths to contribute to power relay by sending a portion of power to eligible encountered animals.
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While existing solutions mainly focus on either maximizing the network lifespan or minimizing energy loss, our solution aims to optimize both metrics.
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We demonstrate through simulation results with different parameters and comparisons with other approaches that our energy-sharing solution, which can quickly adapt to different scenarios, significantly improves the wildlife communication network’s lifetime.
The rest of the paper is organized as follows. In section “System description”, a system description is given, and problems are formulated. Consequently, a peer-to-peer wireless power transfer solution for communication wildlife networks is proposed in section “Encounter-based energy sharing (EBES)”. The simulation studies and analysis are presented in section “Simulation studies”. Lastly, section “Conclusion” concludes the paper.
