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Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions.
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1
A Distributed Framework for Energy Trading
Between UAVs and Charging Stations for Critical
Applications
Vikas Hassija, Vinay Chamola, Dara Nanda Gopala Krishna and Mohsen Guizani, Fellow IEEE
Abstract—Use of Unmanned Aerial Vehicles (UAVs) is rapidly
increasing in various domains such as disaster management,
delivery of goods, surveillance, military, etc. Significant issues
in the expansion of UAV-based applications are the security of
(IoT to UAV) communication, and the limited flight time of the
UAVs and IoT devices considering the limited battery power.
Standalone UAVs are not capable of accomplishing several tasks,
and therefore swarm of UAVs is being explored. Security issues
in the swarm of UAVs do not allow the applications to leverage
the full benefits that one can offer. Several recent studies have
proposed the use of a distributed network of UAVs to upgrade the
level of security in the swarm of UAVs. In this paper, a framework
for secure and reliable energy trading among UAVs and charging
stations is presented. Advanced blockchain, based on the tangle
data structure is used to create a distributed network of UAVs
and charging stations. The proposed model allows the UAVs to
buy energy from the charging station in exchange for tokens.
If the UAV does not have sufficient tokens to buy the energy,
then the model allows the UAV to borrow tokens from the
charging station. The borrowed tokens can be repaid back to
the charging station with interest or late fees. A game-theoretic
model is used for deciding the buying strategy of energy for
UAVs. Numerical analysis shows that the proposed model helps in
providing increased utility for the swarm of UAVs and charging
stations in a secure and cost-optimal way as compared to the
conventional schemes. The results can eventually be applied to
IoT devices that constantly need energy to perform under ideal
conditions.
Index Terms—Blockchain, Decentralized ledger, peer-to-peer,
Energy Trading, Stackelberg game, IOTA, Tangle.
I. INTRODUCTION
There has been a paradigm shift from manual work to
automation in almost all domains of engineering in recent
years [1], [2]. The use of UAVs has been instrumental in
bringing this shift. For example, UAV’s are being used in
varied applications such as healthcare, military, surveillance,
disaster management, etc. [3], [4], [5]. However, there are
very few applications that are actually using UAVs to perform
real-time and scalable tasks [6]. This is on account of the
Manuscript received November 24, 2019; revised Jan 17, 2020; accepted
February 14, 2020.
Vikas Hassija, and Dara Nanda Gopala Krishna are with the Depart-
ment of Computer Science and IT, JIIT, Noida, India 201304 (e-mail:
vikas.hassija@jiit.ac.in, nandudara3105@gmail.com).
Vinay Chamola is with the Department of Electrical and Electron-
ics Engineering, BITS-Pilani, Pilani Campus, India 333031 (e-mail:
vinay.chamola@pilani.bits-pilani.ac.in).
Mohsen Guizani is with Computer Science and Engineering Department,
Qatar University, Qatar (e-mail: mguizani@uidaho.edu).
Digital Object Identifier: XXXXXXXXXXXX
various fundamental issues related to using UAVs in real-
life applications. The first and foremost issue is the limited
flight time of the UAV, which is due to limited battery storage
capacity [7]. The battery size in UAVs cannot be increased
due to the weight limitations. If the weight of the UAV is
large, it becomes difficult to fly the UAV at high altitude
and for a long duration [8]. Various works have proposed
the use of a swarm of UAVs instead of a standalone UAV
for various applications [9]. Although the swarm of UAVs
shows various benefits over standalone UAVs, the security
issues in the swarm of UAVs are much higher than in the case
of a single UAV [10], [11]. For longer flights or missions,
UAVs require time to time charging. In such situations, UAVs
can avail the service of intermediate charging stations [12],
[13]. The traditional way of energy trading between UAVs
and charging station is highly inefficient if the UAVs are used
in a large number. In the traditional system, all the charging
stations act in a standalone mode, and the UAVs are also
not aware of the current energy availability at a particular
charging station [14]. UAVs need energy at the minimum
possible cost and in minimum possible time. This requires a
strong peer to peer communication between the UAVs and the
charging stations [15]. Therefore, few recent works propose
the use of a distributed network of charging stations and
UAVs. Some of the works in recent years have focused on
the use of blockchain for UAV to UAV communication [16],
[17]. Blockchain is a DLT (Distributed Ledger Technology)
that allows secure peer to peer transactions among multiple
entities that are in different geographical locations [18], [19].
Blockchain technology creates an immutable distributed ledger
of all the transactions between the different nodes of the
network [20], [21]. Every node can view all the transactions
that are committed in the chain, but no node can tamper or
change the data that is committed in the chain [22], [23].
Although blockchain proves to be highly efficient in creating
a distributed network for energy trading among UAVs and
charging stations, there are few fundamental limitations of
blockchain that limit the use of this technology in such appli-
cations. Blockchain algorithm suffers from some fundamental
drawbacks such as the latency of transaction confirmation,
the scalability limitations, and the probabilistic nature of
consensus algorithms [32], [33]. The consensus algorithm used
in the blockchain is also very power-hungry [34]. Micro-
transactions cannot be added in generic blockchain as the
incentive given to the miners for such transactions ends up
to be higher than the actual transaction value. Processing fees
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TABLE I: Related Work on UAV Charging
Year Authors Unique Features
2016 Zhuzhong Qian et al. [24] Efficient Charging Association algorithm for charging UAVs based on energy demand of UAVs.
2017 David Dominique et al. [25] Contract Based Scheduling of UAVs to the Charging Stations.
2018 Jinyong Kim et al. [26] Scheduling UAVs to the Charging Stations through a centralized cloud server.
2018 Maxim Lu et al. [27] Detailed Review of different wireless charging techniques used for charging UAVs.
2018 Chiuk Song et al. [28] Wireless Charging of UAVs by strong electromagnetic fields by reducing EMI.
2019 MyungJae Shin et al. [29] Machine Learning to understand the UAV network and Auction Model for optimising cost.
2019 Sungwoo Kim et al. [30] Application of Travelling Salesman Problem to reduce the distance traveled by the UAVs.
2019 Haider Mahmood Jawad et al. [28] A Magnetic Resonant Coupling Technique for wireless power transfer to Charge UAVs.
2019 Roberto G. Ribeiro et al. [31] Application of UAVs in Mining and Mixed-Integer Linear Programming Model for charging UAV.
of transactions are increasingly high, and the size of the block
is constrained, thereby limiting the use of generic blockchain
for a large number of small transactions [35]. Various works
propose the use of other consensus algorithms such as Proof of
Stake (POS), Proof of Burn (POB), or Proof of Elapsed Time
(POET) to overcome the limitations of the generic blockchain.
However, all these consensus algorithms follow the Proof of
Work (POW) algorithm. A new distributed application cannot
be created using the POS consensus process as none of the
nodes in the network has any stake or cryptocurrency to put
on stake. In this paper, we propose a novel application of a
distributed network of charging stations and UAVs based on
advanced blockchain or IOTA. IOTA is a type of DLT that uses
a tangle data structure to store transactions. IOTA based DLT is
equally secure and distributed as traditional blockchain, but at
the same time, it provides low latency and consumes very less
power as compared to generic blockchain [36]. Unlike normal
blockchain, IOTA-based blockchain ledger does not have any
miners to process transactions [37]. There is no transaction fee
involved in IOTA, and micro-transactions are also possible.
Following are the major contributions presented in this paper:
A distributed network of charging stations and UAVs is
proposed where they can interact and negotiate for charge
price over the network. An IOTA based consensus is used
to reach an agreement among the nodes in the network.
UAVs are allowed to trade for energy with the charging
station in exchange for tokens based on their immediate
needs. If the UAV does not have sufficient tokens to buy
the energy, then the model allows the UAV to borrow
tokens from the charging station.
The borrowed tokens can be repaid back to the charging
station with interest or late fees. The charging stations
are also allowed to vary the late fees based on the time
of repayment to enhance their revenue.
A game-theoretic model is proposed to parallely enhance
the utility and revenue of both the UAV and the charging
stations.
Simulation of the proposed model is implemented, and
the numerical analysis is presented to prove that the
proposed model is better than the traditional scheme.
The rest of this paper is organized as follows. Section
II presents the recent related work in the area of UAV
charging. Section III presents the overall procedure involved
in a distributed network for energy trading between UAVs
and charging stations. Section IV presents some prelims and
background details related to IOTA technology and distributed
networks. The proposed system model is discussed in Section
V. Section VI presents the proposed game-theoretic model for
energy trading among charging stations and UAVs in exchange
for IOTA tokens. Section VI presents the strategy for optimal
price formulation for the energy trading, which maximizes
utilities of both UAVs and charging stations. The simulation
setting and numerical analysis are presented in section VII.
The final conclusions are presented in section VIII.
II. RE LATE D WORK
In this section, a survey of all the existing literature related
to UAV charging are presented along with their advantages
and limitations. Myung Jae Shin et al. [29] present a frame-
work based on machine learning and auction mechanism for
scheduling energy requirements for a network of drones.
Nowadays, machine learning-based models are also used in
UAVs in different ways. Based on the predictions made by the
machine learning algorithms, an auction model is designed
to allow the drones to bid for the energy. Authors in [26],
present a collaborative scheme for choosing the station for
drone charging. All the flying drones send their energy request
to the cloud server, and the cloud decides about the allocation
of UAVs to charging stations. The limitation of the model is
that it is completely centralized, and all the activities of the
UAVs are completely dependent on the decisions coming from
the central cloud server. This would result in lot of latency that
could be an issue in various critical applications of UAVs such
as use of drones for healthcare and medicine delivery.
David Dominique et al. [25], present a contract based UAV
charging system. The UAV is allowed to land on the charging
pad in the designated position of orientation. The charging
cost for the UAV and the revenue of the charging stations are
not considered. Authors of [30] focus on reducing the route
traveled by the UAVs for delivery, rather than increasing the
flight time. The authors use the Travelling Salesman Problem
(TSP) to calculate the minimum possible route for delivery.
Fundamental features of the TSP are analyzed, and route
distortion is defined.
Haider Mahmood Jawad et al. [38] focuses on the use of
UAVs in agricultural applications. Electromagnetic induction
(EM) is used to charge the drones on the fly to increase their
flight time and communication distance. A magnetic resonant
coupling technique is used as it allows high transfer power
and helps in minimizing the energy loss in transit. Authors of
[27] provide a detailed review of all the different wireless
charging techniques being used for charging the UAVs on
3
the fly. A detailed comparison of the working, advantages,
and limitations of each technique is presented by the authors.
Chiuk Song et al. [28] propose the ways of reducing the
electromagnetic interference in wireless charging of UAVs.
Strong electromagnetic fields are generated while transferring
the energy from source to battery. Such fields might also
deliver strong electric currents to the end-users.
Sheng Zhang et al. [24] propose a model for flexible wire-
less charging. The authors consider that the energy demand
for UAVs is not always consistent and might change based
on various factors. The fluctuations in energy consumption
are considered, and an itinerary selection and charging as-
sociation algorithm is proposed. Roberto G. Ribeiro et al.
[31] proposes the use of UAVs in the mining industry. The
periodic inspection of disasters in mines is a very important
and difficult task. Drones can be used to inspect the leakage
or other issues in mines. Solar-powered drones cannot be used
in mines due to the unavailability of solar energy. High flight
time is also required to perform an effective inspection. The
authors propose a concise mixed-integer linear programming
(MILP) model for charging station planning and drone routing.
There are several works that focus on enhancing the capa-
bilities of UAVs in multiple domains. These works also focus
on using the latest technologies to increase the overall battery
life and flight time for the drones. However, most of these
works use a centralized server or third-party cloud services to
manage UAV communication. Moreover, there are very few
works that focus on reducing the cost of UAV charging and
increasing the revenue of the charging stations simultaneously.
Centralized approaches are highly susceptible to data thefts
and also act as a single point of failure. Existing literature
lacks the concept of a distributed network of charging stations
and UAVs where the nodes can securely request for energy and
can negotiate or decide the price for charging. Furthermore, in
existing literature, the price charged by the charging stations
is considered as fixed. This restricts the charging stations from
varying the price to increase their revenue. Therefore, we
propose a distributed framework for UAV charging that is fair,
cost-optimal, accurate, and secure. Additionally, we consider
factors like dynamic pricing offered by the charging stations
according to different use cases, which is discussed in the later
section of the paper.
III. SYS TE M OVE RVIEW
Fig. 1 shows the steps involved in a distributed network for
energy trading between charging stations and UAVs. Initially,
after the charging stations and UAVs join the network, they
start with an information exchange process. These messages
are recorded on the IOTA tangle.
1. The first box shows the first step where the message
passing or information exchange takes place between the
charging stations and the UAVs. These messages include
the information about charge requirement, criticality, a
cost that drone is willing to pay, etc.
2. The second box shows the tangle creation to securely
store the information that is being exchanged between
the charging stations and the UAVs. All the messages
encrypted and digitally signed to prevent the issues
related to data integrity and non-repudiation.
3. The third box shows the possibility of energy trading
in the peer-to-peer network between UAVs and charg-
ing stations in exchange for IOTA tokens without any
intermediary or centralized controlling authority.
4. The fourth box shows that all the UAVs in the IOTA
network acquire a minimum number of IOTA tokens to
buy energy from the charging stations. If the UAVs do
not have enough tokens, then they borrow IOTA tokens
form the charging stations based on game theory.
5. The fifth box shows the occurrence of negotiations
between UAVs and charging stations based on optimal
price formulation strategy discussed in section VII.
6. The sixth box finally shows the actual allocation of a
UAV to a charging station for energy trading.
IV. BACK GRO UN D AN D IOTA PRELIMS
In the distributed network of charging stations and UAVs,
the IOTA tangle is used to record and to process the large
number of frequent micro-transactions. Tangle is a Directed
Acyclic Graph (DAG) based distributed ledger [36]. Fee-
less micro-transactions, asset transfer, and trusted identities
are some of the features provided by the IOTA tangle. All
the UAVs and charging stations act as nodes when they are
connected to the IOTA network. All the UAVs can borrow
energy from the charging station based on their energy require-
ments to increase their flight time. This process could be done
without involving any centralized third party. The UAVs can
buy energy from nearby charging stations when required [39],
[40]. Next, we discuss a few prelims required to understand
the proposed model and the solution framework.
A. Digital Identity
Digital identity is one of the important building blocks for
any distributed ledger technology [41], [42]. The level of trust
among the parties involved in energy transactions is ensured
by their digital identity [43]. The security of a large amount
of user data that is growing at a tremendous rate is often
compromised when centralized and traditional identification
methods are used [44]. A better alternative is the use of a
verified digital identity stored on a distributed ledger. Also, the
fees charged by the third parties that provide authentication
and verification services are saved by using digital identity
verification methods [45].
B. Tip Selection Algorithm
In the traditional blockchain, computing power is a major
factor for verifying whether the user is making an authentic
transaction or not [46], [47]. Miners are used for validating and
adding new transactions in the next block. The task of mining
is done by the new transactions in the IOTA tangle. All the
nodes present in the network directly or indirectly approve
the new transactions. This makes the participating nodes and
the miner indistinct. This also prevents distributed denial of
service (DDOS) attacks to the IOTA network as all nodes are
4
Fig. 1: Energy Trading between Charging Stations and UAVs.
equivalent, and no node has some special privilege. Any new
transaction that enters in the tangle is required to select and
approve two previous transactions. An edge is created between
the selected transactions and the newly added transaction. The
new transaction requires to solve a cryptographic puzzle to be
approved as a transaction. Then the new transaction waits for
its approval by the other upcoming transaction. A tip is used to
refer to an unapproved transaction in a directed acyclic graph.
The process of tip getting validated by the new transactions
is decided by the tip-selection algorithm [36]. Therefore, tip-
selection algorithms and the rate at which new transactions are
added in the tangle decide transaction confirmation latency.
A rating is given to each transaction initiated by any node
in the network using the tip selection algorithm. The rating
is equal to the number of transactions that reference it. The
transaction is considered important if its weight is larger than
other transactions in the network. To select two non-conflicting
tips for the verification of the newly arrived transaction is the
aim of the tip selection algorithm used in IOTA. For any trans-
action X, the cumulative weight CWXis defined as the own
weight plus weight of transaction that approves it. For example
if X2, X3, X4, ...XNare the transactions that approve X1. The
weight of X1is WX1and weight of X2, X3, X4, ...., XNis
WX2, WX3, WX4, ...., WXNrespectively, then
CWXα=
α
X
a=1
Xa+
N
X
b=α+c
Xb(1)
Where, Xabe any event that approves event Xαdirectly
and Xbbe any event that approves event Xαindirectly.
C. Consensus Mechanism
For every distributed ledger technology, it is imperative to
generate a level of trust among the nodes for the authentic-
ity of the transactions [48]. The cumulative weight of the
transactions calculated above is used to reach consensus in
IOTA as compared to the proof-of-work (PoW) algorithm
in a traditional blockchain [49], [50]. Consensus finality is
referred to as a final agreement among all the network nodes.
Blockchain never reaches consensus finality due to the issues
of forking and pruning. In the generic blockchain, a block that
is mined today or a transaction that is added to the main chain
today, might get pruned and removed from the main chain after
some time. There is no surety that the current transactions will
remain in the main chain forever or not. Therefore, generic
blockchain never reaches consensus finality. This is not the
case in the consensus process followed in the IOTA network.
In IOTA, when almost every participant present in the IOTA
network declares that a particular transaction is more valid
than other transactions, then consensus is achieved. In IOTA,
the consensus is distributed in the tangle, and the participant
is required to validate two past transactions for placing one’s
own new transaction in the network, as discussed above.
Apart from securing IOTA from the tip selection algorithm
and cumulative weight-based consensus mechanism, a new se-
curity layer has been added to the IOTA consensus protocol to
overcome the issue of conflicting tips. This security measure is
a voting-based mechanism called as a shimmer. The traditional
5
voting mechanism cannot automatically scale if the number of
nodes in the network increases. Also, each node is required
to know the other nodes in the network in case of traditional
voting-based algorithms. In the shimmer algorithm, the peers
tend to change their state based on the state of other peers and
do not need to know the state of all the nodes in the network.
D. Transaction Procedure
Every charging station and the UAV has its own pseudo-
anonymous, virtual, and private wallet, which stores IOTA
tokens used for making transactions [51], [52]. The user has
to create a secret password called seed (a string of 81 trytes)
for using IOTA as a network [53]. IOTA is based on trinary
or ternary computing. Trit is a digit in a base 3 (0 or 1 or 2).
Tryte consists of 3 trit. It can be in one of 27 states consisting
of 26 uppercase alphabets or digit 9. Each seed can create 957
addresses and private keys using the IOTA address generation
algorithm. Since it is public, users can send messages and
tokens to other users using the address field in the transaction.
Bundles are signed using unique private keys, for withdrawing
IOTA tokens from address.
V. PROP OS ED SY ST EM MODEL
In this section, the complete system model is discussed,
along with the roles of different components present in the
IOTA network. The purpose of the proposed model is to
balance the supply and demand of the energy required to
charge the UAVs. The model focuses on increasing the overall
flight time of the UAVs. A game-theoretic model is used to
perform energy trading in a cost-optimal way.
A. Components of Proposed Energy Trading Model
1. Energy nodes: A node can be either of UAV (buyer) or a
charging station (seller). The UAV must have a minimum
amount of IOTA tokens to buy energy from the charging
station. If the UAV does not have the minimum amount
of tokens, then it can borrow tokens from the charging
station. The borrowed tokens need to be repaid back to
charging stations with interest or late fees imposed by
the charging station.
2. Energy Aggregator: The smart contract in the IOTA
network acts as an energy aggregator. The work of the
smart contract is that of a broker or a mediator between
a buyer and a seller node. It helps in setting up a
communication between two parties who want to share
energy. The buyer and the seller interact directly via
a smart contract without the presence of a third-party
broker.
3. Smart meters: Smart meters will incorporate the pro-
posed pricing algorithm by considering the details such
as energy already present in the account, amount of
energy being traded and the charging price. The charging
price and amount of energy being traded between UAV
and charging station is according the the values calcu-
lated by the smart meter, as an intermediate broker.
B. Working of Proposed Energy Trading Model
In this section, various steps involved in the energy trading
process are discussed step by step.
1. System framework and entry of nodes in a network:
The users are registered on the IOTA network, and each
user after registration becomes a unique entity node.
Each node with a unique ID gets its public and private
keys. Every node obtains a set of wallet addresses, and
the distributed ledger stores all the information regarding
energy requests and trading in mapping lists. Wallet ad-
dresses contain energy coins, and IOTA tangle stores all
the transaction records of UAVs and charging stations.
As discussed above, the wallet addresses are given to
the nodes by the IOTA network as soon as they enter
the network and desire to perform transactions. There
are charging stations that act as energy suppliers for the
UAVs. The UAV’s are allowed to enter the network to
trade energy with the charging station in exchange of
IOTA tokens. If the UAV does not have sufficient tokens,
then it can borrow tokens from the charging station to
buy the required energy. The borrowed tokens are later
repaid to the charging station with interest or late fees
imposed by the charging station. The UAV that is neither
willing to purchase the energy nor willing to borrow
the tokens and does not have the minimum threshold
amount of energy is not allowed to enter the network.
This will act as a checkpoint to ensure that only the
nodes satisfying the above-mentioned criteria enter the
network, and the network is not flooded by more number
of nodes.
2. Different Roles in energy trading Network: There are
many nodes in the network which act either as a buyer
node (UAVs) or a seller node (charging station). The
energy-deficient UAVs buy the energy from the charging
station in exchange for IOTA tokens. The token deficient
UAVs can first borrow the tokens from the charging
station and can then buy the energy in exchange for
the borrowed tokens. The borrowed tokens need to be
repaid back to the charging station with interest or late
fees.
3. Exchange of energy between buyers and sellers: A
smart contract’s exchange function is responsible for all
the transactions in IOTA. It takes a few parameters, like
the address of the buyer and the amount of energy it
wants to buy. The smart contract matches the buyer’s
(UAVs) requirements with the sellers present in the seller
pool based on the charging price and late fees imposed
by the seller (charging station).
4. Payment Security plus Incentives and Rewards: The
UAVs in the network after each transaction get the
updated data of new seller nodes and information about
the energy available with the new seller. Each transaction
needs to be signed with a digital signature of the
initiator.
5. Hashing in energy trading: For verification, each event
or transaction carries a hash of the previous transaction
as is done in the traditional blockchain [54]. This
6
TABLE II: List of Acronyms
Notation Meaning
XTransaction
WXweight of transaction X
CWXCumulative Weight of the transaction X
ITotal number of UAVs in IOTA network
JTotal number of charging stations in network
Diith UAV in the network
Cjjth charging station in the network
IDiAccount address of UAV Di
Hi,kK
k=1 Transaction history of UAV Di
amountiNumber of tokens requested by Difrom Cj
creditiAvailable number of tokens with UAV Di
requestiRequest message from Dito Cj
SWtl Shared wallet for IOTA tokens between Diand Cj
MiT okenRequestS uccess Message form Cj
Msign Message signature form Cjto Di
T imestamp Time at which Cjsends message to Di
responsejResponse message form Cjto Di
statusiStatus of current wallet SWtl
P RiPrevious records of repayment related to Di
qPredefined constant
AiEnergy given by charging station to UAV Di
piCost of energy requested by UAV Di
Qmin
iMinimum energy demand for UAV Di
φiPredefined factor for UAV Di
diPredefined factor for UAV Di
uiUtility value of UAV Di
σiRepayment Capacity of UAV Di
tiRepayment time given to UAV Di
yjInterest rate at which Diborrowed tokens for time ti
xjLate fee given by UAV Diin delay of repayment
sNumber of times that UAV repayed tokens successfully
fNumber of times that UAV failed in repaying the tokens
ηiPredefined constant
TiTime at which Dimakes repayment to Cj
ucs
jUtility value of Charging Station Cj
zjPredefined credit grade factor relied upon UAV Di
ΨjOverhead of charging station Cj
GStackelberg game
makes the transactions in the network tamper-free and
immutable.
VI. GAME TH EO RY IN ENERGY TRADING MO DE L
In the proposed energy trading model, UAVs act as buyers,
and charging stations act as the sellers. The role of the charging
station is to feed the energy in the UAVs. Each UAV has
access to these charging stations, which have enough IOTA
tokens. UAVs need to have sufficient IOTA tokens to request
for energy. The charging stations provide IOTA tokens to the
UAVs, which makes them capable of purchasing the energy
from the charging station [55]. Energy trading between UAVs
and charging stations is done depending on the present balance
of tokens with the UAV and its previous transaction history.
On-demand of UAVs, sufficient tokens are transferred from the
charging station to the UAV’s wallet address. UAVs can also
request for the energy from the charging station in exchange
for the tokens that they already have. In this section, the
various steps and scenarios in energy and token trading among
the nodes in the network are discussed.
A. Request for Tokens from Charging Station
Initially, a UAV Di(an IOTA node that is in need of
tokens) sends a request to the charging station and waits for an
acknowledgment from the charging station. The detailed steps
followed in the process of getting the tokens are discussed as
follows.
1. Disends a request message along with other information
about its own account address IDi, all previously used
transaction history Hi,kK
k=1, number of tokens requested
amountiand available number of tokens creditito the
charging station Cj.
DiCj:requesti=I DikHi,k K
k=1 kamountik
crediti
2. After charging station gets the requestifrom UAV,
it verifies the UAV’s identity and previous transaction
history from Hi,kK
k=1 to check the UAV’s account status.
3. The message ”T okenS haringSuccess” is obtained
only to that UAV Diwho is able to fulfill certain
following necessary requirements:
a) There is a sufficient amount of crediti, which must
be a positive amount.
b) The account must be active and should have success-
fully completed the recent transactions with charging
stations. The request is rejected if the UAV fails to
complete the previous transactions.
4. A shared wallet SWtl is created between charging
station Cjand UAV Di. The public and private keys
are sent to UAV Di. The charging station and UAV both
have access to the shared wallet SWtl. The shared wallet
can be further reused for other transactions between
charging station Cjand UAV Di.
5. If all the requirements are fulfilled from UAV’s side,
then Direceives a ”T okenRequestS uccess” message
Mialong with message signature MSign as a reply from
charging station Cjwhich indicates that the UAV Diis
eligible for tokens.
CjDi:responsej=SWtl kMikMSign k
T imestamp
where, Mi=amountjkstatusiktikP Ri
Here, Miincludes some information like amount
amounti, current wallet status statusi, repay time dura-
tion tiin which UAV has to repay the tokens to charging
station and otherwise it has to pay a late fee xi, and
previous records of repayment P Ri.
B. Energy Trading using Borrowed Tokens
UAV Dican now obtain the tokens from the shared wallet
SWtl for energy trading. All payments made via SWtl wallet
will get verified and recorded by the charging station Cj. The
encrypted value of the token data is also added in P Riby
the charging stations. Following steps further, elaborate on the
procedure:
1. The UAV Disends the acknowledgment of received
tokens along with the ”T okenReceivedS uccess” mes-
sage Mj, message signature MSign to charging station
Cj. Then charging station Cjverifies the certification as
7
well as validates the duration of the wallet SWtl used
for payment.
2. The charging station Cjrecords information attached
with a digital signature of this trade in the network such
as the bill, the ”T okenReq uestSuccess” message Mi,
address of wallet which is to receive the tokens.
3. The charging station Cjwill compare the received
success message Mjwith the original success message
in its record for verification via decryption technique.
The charging station then checks the status statusi
of this Mj. If the UAV Dihas sufficient funds, then
required tokens are transferred through the shared
wallet SWpl to charging station Cj. If it is not the
case, a message is sent to charging station Cjstating
N otEnoughF unds.”
C. Repayment of IOTA Tokens
After a certain duration of IOTA tokens shared by Cj, UAV
Diis encountered with a new message Mnew
iwith repayment
information based on the following possibilities.
1) First possibility: If the UAV Direpays its tokens within
repayment time ti, then Diis charged with a certain
amount of interest yialong with the principal amount.
2) Second possibility: If UAV Diis not able to repay the
charging station Cjwithin time ti, then credit-status of
UAV will further degrade. The new credit-status value
for the UAV will be updated as:
crediti
n+1 =crediti
n(qamounti)(2)
Where crediti
ndenotes nth transaction’s value of the
credit, and qis a predefined constant greater than 0. The
charging station generates a transaction record about this
process, and adds to the address pool and appeared to
the network. So, even if the UAV Difinally finishes
paying the tokens, it still experiences a fine amount.
3) Third Possibility: In the case when UAV, Diis not able
to repay the charging station’s tokens for a considerably
long period, then the charging station will put that
particular UAV into the blacklist. This ensures that nodes
in the future will not cooperate with this UAV for energy
trading.
VII. OPTIMAL PRI CE FO RM UL ATIO N
In this section, an optimal energy trading algorithm is
proposed to increase the token revenue of both the UAVs
and the charging stations. The proposed formulation also
encourages energy trading among UAVs and charging stations
by optimizing the various parameters such as rate of interest yi,
late fee xi, and amount of tokens shared [56]. The UAVs with
insufficient tokens, borrow the required tokens from charging
stations. UAVs likewise need to boost their profitability by
asking the appropriate token amount. Similarly, the charging
stations trade IOTA tokens and charge with interest rate of yi
and late fees xiin a way that enhances their revenue.
A. Problem Formulation
The energy given by a charging station Cjto UAV Diis
denoted as Ai. The minimum energy demand for UAV Diis
denoted as Qmin
iand piis a given cost of the energy requested
by UAV Di. The fulfillment capacity of UAV Diis indicated
as:
uf=diln Ai
pi
Qmin
i+φi(3)
where, di>0and φi>0are the factors predefined for UAV
Di. The utility of Diis defined as:
ui=σi(ufyjAiti)(1 σi)xjAi(4)
Where σiis repayment capacity of a UAV Di, i.e., its ability
to repay the tokens in assigned time ti. The records of previous
transactions comprise of token repayment record denoted by
RPi(s, f ), where sdenotes the number of times that UAV Di
repaid tokens successfully within repayment time, and fis
the number of failures in repaying the tokens. The repayment
capacity σiof a UAV Dican be defined as the number of times
that UAV repaid tokens successfully (s) upon total number of
transactions between UAV Diand charging station Ci(s+
f). The value of σican be calculated with the help of token
repayment record RPi(s, f )for UAV Dias follows.
0< σi= ( s
s+f)1(5)
The value of yjrefers to the extra tokens charged by the
charging station Cjas interest based on the time tifor which
the UAV was allowed to use the tokens. The xjdenotes the
fine amount, i.e., late fee given by the UAV Diin case of
delay in repayment. The late fee xjgiven by the UAV Di
is defined as the difference between the time at which UAV
Dimade repayment and time given by charging station Cjto
UAV Dito repay the tokens, multiplied with the interest rate
yjat which UAV Diborrowed tokens form charging station
Cjfor time ti. The correlation between the value of yjand
xjis given as follows.
xj=ηi(Titi)yj(6)
where Tiis the time at which the UAV Dimade token
repayment to charging station Cjand ηi>1is a predefined
constant.
The utility of the charging station comprises of the extra
tokens charged from UAV Dias interest, and fine amount if
Dican’t repay the requested tokens in time ti. The overhead
of charging station is given by
Ψj=Aitici(7)
Here, ciis the unit cost of tokens requested by UAV Difrom
the charging station. Accordingly, the monetary advantages of
the charging station, Cjare characterized as follows.
ucs
j=zj(yjAitiΨj) + (1 zj)xjAi(8)
where, zjis the credit score for UAV Digiven by the charging
station Cjand its value should lie in the range from 0to 1.
8
The value of zjis determined from the previous completed
transactions by UAV Diin the network. The higher credit
score brings the higher value of zj.
Algorithm 1 Optimal Energy Trading Algorithm
Let x
j=0,ucs
j=0,A
i=0,u
i=0
for i= 1 : Ido
for x
j=0:xmax
jdo
if li>0then
x
j= 0, A
i= 0
break
else
Charging Station Cjadjusts energy Aiaccording:
A
i=σidi
σiyjti+(1σi)xj+li
end if
UAV Diupdates their utility according:
u
i=σi(ufyjA
iti)(1 σi)x
jA
i
Charging station Cjupdates its utility according:
ucs
j=zj(yjA
itiΨj) + (1 zj)x
jA
i
if ucs
jucs
jthen
Records maximum utility and optimal late fee
ucs
j=ucs
j, ui=u
i, Ai=A
i, xj=x
j
break
else
ucs
j=ucs
j, ui=u
i, Ai=A
i, xj=x
j
end if
end for
end for
Stackelberg Equilibrium is achieved
Behavior or action of one entity affects the decision of oth-
ers as both charging stations, and the UAVs want to maximize
their economic benefits and profitability, respectively.
In this paper, a Stackelberg game approach is used to
maximize the economic benefit of both UAVs and charging
stations. The Stackelberg game formally tells us about the
staggered basic leadership procedures of various independent
decision-makers (i.e., followers) in light of the choice taken
by the main player (leader) of the Stackelberg game. We, at
that point, determine the Stackelberg balance of the planned
game [57]. Here, the charging station is the seller energy node,
and the UAV is the buyer energy node. The charging station
Cjsets its decision of interest rate yj, and fine amount xjfor
every UAV Diseparately. The UAVs observe the decision of
the charging station and react with the best outcome of the
energy Aias per the late fee xjgiven by the charging station.
The overall structure of the game Gis defined as follows.
G=n(D∪ {C}),{ui}iI,ucs
jjJ, Ai, xjo(9)
The target functions for the charging stations and UAVs are
denoted as follows:
Charging Station: maxxjPJ
j=1 ucs
j(xj)
s.t. xj0
UAV: maxAiui(Ai)
s.t., Ai> Qmin
ipiφipi
(10)
B. Problem Solution
Backward induction methodology is used to get the equi-
librium of game defined in Eqn.9. By differentiating uifrom
Eqn.4 with Ai, we get:
∂ui
∂Ai
=σidi
AiQmin
ipi+φipi
σiyjti(1 σi)xj(11)
Further differentiating uiwith respect to Ai, we have:
2ui
∂A2
i
=σidi
AiQmin
ipi+φipi2<0(12)
As the second derivative of uiis negative, we will obtain a
strictly concave function. We acquire the optimal methodology
using the following equation:
∂ui
∂Ai
= 0 (13)
By solving Eqn.13 the relation between UAV Di’s favorable
amount of tokens Ai, and late fee xjgiven by charging station
Cjis expressed as follows.
Ai=σidi
σiyjti+ (1 σi)xj
+li(14)
where,
li=Qmin
ipiφipi(15)
Substituting Eqn.14 into Eqn.8 utility of charging station
ucs
jis changed as follows:
ucs
j=σidi[zjyjtizjtici+ (1 zj)xj]
σiyjti+ (1 σi)xj
+
li[zjyjtizjtici+ (1 zj)xj]
(16)
Further we simplify the Eqn.16 as follows:
ucs
j=r1yjr2+r3xj
σiyjti+ (1 σi)xj
+r4yjr5+r6xj(17)
where,
r1=σidizjti
r2=σidizjtici
r3=σidi(1 zj)
r4=lizjti
r5=lizjtici
r6=li(1 zj)(18)
By double differentiating ucs
jwith respect to xj,we have
2ucs
j
∂x2
j
=2r2ηi
(σi+ηiσiηi)x3
j
<0(19)
If li<0, then we have:
lim
xj0ucs
j=−∞
lim
xj+ucs
j=−∞ (20)
9
When li<0, for
0< xj<r2η2
iti
(r4+r6ηiti) (σi+ηiσiηi)1/2
we have, ∂ucs
j
∂xj
>0
and for,
xi>r2η2
iti
(r4+r6ηiti) (σi+ηiσiηi)1/2
(21)
we have, ∂ucs
j
∂xj
<0(22)
respectively.
The utility function ucs
jis found to first increase to certain
maxima, and then it starts decreasing with the increase in the
value of xj. This proves that the utility function ucs
jis convex
in nature. The following equation gives the optimal pricing for
energy sharing.
∂ui
∂xj
= 0 (23)
By solving Eqn.23, the late fee xjgiven by the charging
station is changed as follows.
xj=r2η2
iti
(r4+r6ηiti) (σi+ηiσiηi)1/2
(24)
If li>0then xj<0. Therefore, we have xj= 0. For
simplicity, optimal strategy of the charging station can be
rewritten as:
xj
=
0, li>0
min r2η2
iti
(r4+r6ηiti)(σi+ηiσiηi)1/2
, xmax
j!
, li0
(25)
and also the value of extra tokens charged as interest i.e.,
yjby charging station Cjis as follows:
yj=xj
ηi(Titi)(26)
To accomplish the Stackelberg Equilibrium (SE), the charg-
ing station needs to communicate with UAVs [58]. Algorithm 1
is introduced to give a distributed path to all the UAVs and the
charging station to obtain the unique Stackelberg Equilibrium
iteratively.
VIII. NUMERICAL ANALYSIS
A. Simulation Settings
For assessing the performance of P2P energy trading be-
tween charging stations and UAVs, the simulation results are
presented in this section. In our model, we have considered
a network consisting of jcharging stations and iUAVs. The
predefined factors diand φifor UAV Dilie in an interval of
[1,5] and [5,7] respectively. The token repayment time, i.e.,
tifor any UAV, lies in the interval [5,9] months. The cost
of the energy requested by UAV Diis pilies in the interval
[6,15] dollars. The minimum energy demand for UAV Diis
Qmin
iand lies in an interval of [50,60] kJ. The predefined
credit grade factor relies upon Di’s credit grade denoted by
zjand lies in the interval of [0,1]. The entire code for network
creation is written in python.
Late Fee (in % of borrowed tokens)
12345678910
Utility of Charging Station
0
50
100
150
200
250
300
350
400
Drone1
Drone2
Drone3
Drone4
Fig. 2: Utility of charging station over change in late fee to
UAVs.
Late Fee (in % of borrowed tokens)
12345678910
Utility of Drones
75
80
85
90
95
100
105
110
115
120
Drone1
Drone2
Drone3
Drone4
Fig. 3: Utility of UAVs over the change in a late fee.
B. Performance Evaluation
Results generated by the simulation of the model are com-
pared and evaluated in this section. Consider in a network
there is 1charging station and 4UAVs that need the energy
to complete their task. The UAVs which do not have enough
tokens to buy energy will borrow the IOTA tokens from
charging station over late fee xjwith repayment time ti. The
late fee of xiis calculated in proportion to the borrowed
tokens. Figure. 2 shows the change in the value of utility of
charging station ucs
jover change in late fee xj. The value
of utility of charging station ucs
jincreases initially and then
decreases with the increase in a late fee. This is so because as
10
the value of xjincreases over some threshold, the UAVs are
less motivated to accept the tokens. Therefore, the utility of
the charging station starts decreasing after a certain increase
in the value of xj. Finally, the appropriate value of late fee
xishould be imposed on UAV Diaccording to Algorithm 1,
which results in maximizing the utility of charging stations.
Figure 3 shows the change in the value of utility of UAVs
uiover the change in the value of late fee xjto UAVs. From
Eqn.4, it is observed that with the increase in the value of late
fee xj, there will be a decrease in the value of utility of UAVs.
So optimal value of late fee xjis negotiated with charging
station Cjthrough Algorithm 1, which results in maximizing
the utilities of both UAVs and charging stations. Figure. 4
refers to the change in energy given by the charging station
to UAVs over the increase in the value of late fee xj. This is
so because the UAVs expect less energy Aias the value of xj
increases. From the Eqn.14, it is interpreted that the relation
between energy given by charging station Aiand late fee are
inversely proportional to each other. In order to acquire the
minimum energy required by UAV Di, i.e., Qmin
i, it should
negotiate the energy Aiat optimal late fee value xjthrough
Algorithm 1.
Late Fee (in % of borrowed tokens)
1 2 3 4 5 6 7 8 9 10
Energy given by Charging Station
80
85
90
95
100
105
110
115
120
Drone1
Drone2
Drone3
Drone4
Fig. 4: Energy given by charging station over change in late
fee.
Figure. 5 shows a change in the utility value of charging
station and energy given by charging station to UAVs with
the increase in the value of late fee xj. The utility value of
the charging station increases initially and then decreases with
the increase in the value of the late fee. The energy Aigiven
by charging station to UAVs will be inversely proportional
to late fees. So with an increase in the value of late fee xj,
there will decrease in energy given by the charging station.
This finally results in a decrease in the utility of the charging
station. Therefore, the optimal late fee value xjis charged by
charging station from UAVs, in order to maximize its utility
based on Algorithm 1.
Figure. 6 shows the comparison of utility value of charging
station ucs
jin random and proposed model. In the random
model, the value of late fee xjand energy given by charging
station Aito UAV are selected randomly and are not changed,
246810
90
95
100
105
110
100
200
300
400
Late Fee
Energy given by Charging Station
Utility of Charging Station
150
200
250
300
350
Fig. 5: Utility and energy given by charging station over
change in late fee.
which results in a lower utility value than the value through
proposed model. However, in the proposed model, the value
of late fee xjchanges with the change in energy given by
charging station Aito UAVs. This continues until the values
of the utility of charging stations and UAVs are maximum.
Similarly, Figure. 7 presents the comparison of the utility value
of UAVs uiin the random and proposed model.
Fig. 6: Comparison of utility of charging stations in Random
and Proposed Model.
IX. CONCLUSION
In this paper, we have proposed a distributed framework for
energy trading between UAVs and charging stations. We have
used an IOTA based tangle data structure to create a distributed
network. IOTA tokens are shared between the different entities
in the network. The UAVs can borrow the tokens from the
charging station and can purchase energy using those tokens.
UAVs need to return the tokens to the charging station in a
predefined time. UAVs also need to return some extra tokens
as interest for using the tokens. If the UAVs fail to repay the
tokens in a predefined time, a fine is charged by the charging
station. The fine rate is the motivating factor for the charging
11
Fig. 7: Comparison of utility of UAVs in Random and Pro-
posed Model.
station to give tokens to the UAVs. UAVs expect more tokens
in less fine, and the charging stations expect high fine. A game-
theoretic approach is applied to this scenario to maximize the
profit of both UAVs and charging stations. Numerical results
prove that the proposed model to give better revenue to the
UAVs and charging stations as compared to its counterparts.
The overall utility of the drones and the charging stations is
enhanced based on the numerical analysis. We expect that
the proposed scheme can be generalized to accommodate IoT
devices. In particular those devices that are used in critical
applications such as healthcare systems.
X. AC KN OWLEDGEMENT
This research was made possible by NPRP10-1205-160012
grant from the Qatar National Research Fund (a member of
The Qatar Foundation). The statements made herein are solely
the responsibility of the authors.
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Vikas Hassija received the B.Tech. degree from
Maharshi Dayanand University, Rohtak, India, in
2010, and the M.S. degree in telecommunications
and software engineering from the Birla Institute
of Technology and Science (BITS), Pilani, India, in
2014. He is currently pursuing the Ph.D. degree in
IoT security and blockchain with the Jaypee Institute
of Information and Technology (JIIT), Noida, where
he is currently an Assistant Professor. He has eight
years of industrial experience and has worked with
various telecommunication companies, such as Tech
Mahindra and Accenture. His research interests include the IoT security,
network security, blockchain, and distributed computing.
Vinay Chamola received the B.E. degree in elec-
trical and electronics engineering and master’s de-
gree in communication engineering from the Birla
Institute of Technology and Science, Pilani, India, in
2010 and 2013, respectively, and the Ph.D. degree
in electrical and computer engineering from the Na-
tional University of Singapore, Singapore, in 2016.
In 2015, he was a Visiting Researcher with the Au-
tonomous Networks Research Group, University of
Southern California, Los Angeles, CA, USA. He is
currently an Assistant Professor with the Department
of Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus. His
research interests include Green communications and networking, 5G network
management, Internet of Things and Blockchain.
Dara Nanda Gopala Krishna is currently pursuing
the B.Tech. degree with the Jaypee Institute of Infor-
mation and Technology. He is currently a Summer
Intern with the Birla Institute of Technology and Sci-
ence (BITS), Pilani. He has completed few projects
on blockchain applications and machine leaning. His
research interests include distributed computing, the
IoT, and data analytics.
Mohsein Guizani (S’85–M’89–SM’99–F’09) re-
ceived the B.S. (with distinction) and M.S. degrees
in electrical engineering, the M.S. and Ph.D. degrees
in computer engineering from Syracuse University,
Syracuse, NY, USA, in 1984, 1986, 1987, and 1990,
respectively. He is currently a Professor at the Com-
puter Science and Engineering Department in Qatar
University, Qatar. Previously, he served in different
academic and administrative positions at the Univer-
sity of Idaho, Western Michigan University, Univer-
sity of West Florida, University of MissouriKansas
City, University of Colorado-Boulder, and Syracuse University. His research
interests include wireless communications and mobile computing, computer
networks, mobile cloud computing, security, and smart grid. He is currently
the Editor-in-Chief of the IEEE Network Magazine, and the Founder and
Editor-in-Chief of Wireless Communications and Mobile Computing journal
(Wiley). He was the Chair of the IEEE Communications Society Wireless
Technical Committee and the Chair of the TAOS Technical Committee. He
served as the IEEE Computer Society Distinguished Speaker and is currently
the IEEE ComSoc Distinguished Lecturer. He is a Fellow of IEEE and a
Senior Member of ACM.
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