A UAV-assisted CH election framework for secure data collection in wireless sensor networks

Journal Pre-proof A UAV-assisted CH election framework for secure data collection in wireless sensor networks G. Wang, B. Lee, J. Ahn, G. Cho

PII: DOI: Reference:

S0167-739X(18)32505-6 https://doi.org/10.1016/j.future.2019.07.076 FUTURE 5129

To appear in:

Future Generation Computer Systems

Received date : 15 October 2018 Revised date : 4 June 2019 Accepted date : 31 July 2019 Please cite this article as: G. Wang, B. Lee, J. Ahn et al., A UAV-assisted CH election framework for secure data collection in wireless sensor networks, Future Generation Computer Systems (2019), doi: https://doi.org/10.1016/j.future.2019.07.076. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier B.V.

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A UAV-assisted CH Election Framework for Secure Data Collection in Wireless Sensor Networks G. Wanga,∗, B. Leea , J. Ahna , G. Chob b 567

a 128 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea

Abstract

With the advance of UAV-related technologies, UAV-assisted wireless communications such as UAV-assisted coverage extension, UAV-assisted relaying and

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UAV-assisted data distribution and collection are gathering a lot of interests from government and industry fields. More specifically, the UAV-assisted data distribution and collection is implemented as a UAV-based WSN (Wireless Sensor Network). In the UAV-based WSN, a CH (Cluster Head) plays a crucial

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role such as data collection from members, data transfer toward a UAV and data distribution from the UAV to its members. Due to the role of a CH, many attackers try to make their compromised nodes CHs. In the general CH election framework, since each node determines a CH role by itself, compromised nodes declare themselves as a CH regardless of their disqualification. Generally, a com-

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promised CH consumes much more energy than a normal CH because it keeps fulfilling the CH role to deliver corrupted messages to the sink greedily. Inspired by this phenomenon, we propose a UAV-assisted CH election framework which collects residual energy of nodes, and employs them for electing new CHs and excluding the lowest energy nodes from CH candidates. Simulation results show that our framework outperforms the general CH election framework even when the number of compromised nodes is large. Concerning the CH election period,

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our framework provides better security and performance than the general CH ∗ Corresponding

author Email addresses: [email protected] (G. Wang), [email protected] (B. Lee), [email protected] (J. Ahn), [email protected] (G. Cho)

Preprint submitted to Journal of LATEX Templates

June 4, 2019

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election framework with a short CH election period. Besides, the variation of node compromise time had no impact on our framework’s superiority to the

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general CH election framework. Another simulation results show that the increase of UAV tour frequency enhances both security and performance of our framework. Our CH election framework is easily applied to a network of IoT devices because the IoT network also demands clustering for energy and data management efficiency. Our future work item is applying as many other CH election schemes as possible to our framework and comparing their security and performance.

Keywords: Unmanned Aerial System, Wireless Sensor Network, Cluster Head

2010 MSC: 00-01, 99-00

1. Introduction

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Election, Unmanned Aerial Vehicle, Security

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Recently, UAS (Unmanned Aerial System) has drawn a lot of interests from the general public as well as government agency and UAV-related industry. A UAV (Unmanned Aerial Vehicle) with some mission devices, a GCS (Ground 5

Control Station) maneuvering the UAV, and a communication link connecting the UAV with the GCS constitute a UAS[1].

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As UAS-related technologies advance greatly, UAV-assisted wireless communications such as UAV-assisted coverage extension, UAV-assisted relaying and UAV-assisted data collection and dissemination are emerging [2]. The UAV10

assisted data collection and dissemination is substantiated as a UAV-based wireless sensor network. In conventional wireless sensor networks, data of sensors are delivered from one sensor node to the other until they arrived at the sink. During the delivery process, nodes expend energy both for delivering their own

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data and for relaying data from other sensor nodes. So, the delivery process

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is an energy-intensive task to nodes, and the situation gets worse when node population grows up. In addition, when the sink disseminates some data to sensors, the energy-intensive task happens again in the opposite direction. If a

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UAV was employed for the delivery process, it could speed up the data collection and dissemination and save the precious energy resource of nodes. Fig. 1 shows the data collection and dissemination process using a UAV. As depicted in Fig.

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1, the UAV visits CHs of clusters 1 through 4 and collects data from the CHs which gathered the data from their members in advance. If any data sould be disseminated to members of cluster 5, the UAV visits the CH of cluster 5 and

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disseminates the data to the members through the CH.

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Figure 1: Data collection and dissemination in a UAV-based WSN

In the UAV-based WSN, grouping nodes into clusters and allowing only a cluster leader to communicate with the UAV is preferred for reducing energy consumption of nodes, and the cluster leader is called the CH (Cluster Head). Note that the process of grouping nodes into clusters is called the cluster formation. Contrarily, selecting a data collector or disseminator in a cluster is called the CH election. Any CH election can be performed in two ways; CH-

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first election and cluster-first election. In the CH-first election, CHs are first determined, and clusters are formed later through membership confirmation between the CHs and their members. In the cluster-first election, nodes first

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form clusters by exchanging messages with neighbors, and they select CHs of 35

the clusters later.

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In a UAV-based WSN, a CH transfers collected data to a UAV and disseminates information from the UAV to its members as illustrated in Fig. 1. Due to this attractive role, attackers try to make a compromised node a CH. In the UAV-based WSN, if a compromised node becomes a CH, this node can forge 40

data delivered to a UAV. Even worse, if all CHs were occupied by compromised nodes, attackers would control the whole network through the compromised CHs [3].

Even though the LEACH (Low-Energy Adaptive Clustering Hierarchy) [4]

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was employed for a UAV-based WSN in [5], the UAV is not employed for protecting CH elections in the network. In the LEACH, since each node determines its CH role by itself, a compromised node can become a CH in spite of disqualification. Even worse, this malfunctioning self-election cannot be stopped by other nodes. If a UAV is employed for CH elections, the UAV can determine

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appropriate and inappropriate candidates of CHs by gathering remained energy of nodes. In addition, the UAV can distribute the selection result to all nodes of the network. Even if a compromised node elects itself as a CH, other nodes can negate the self-election thanks to the appropriate or the inappropri-

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ate candidates of CHs which were broadcasted by the UAV. As a result, this UAV-enabled CH election shortens the work hour of any compromised CH. To 55

this end, we propose a UAV-assisted CH election framework in this paper, and apply the the framework into the well-known LEACH protocol. Then, we compare performance and security of our framework with those of the general CH election framework. Our paper’s organization is as follows. In Section 2, related work concerning UAV designs and applications, multi-UAV network, UAV-based WSN and flight route planning are reviewed. Section 3 describes the network and the threat model of our UAV-assisted CH election framework. In Section 4,

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the UAV-assisted CH election procedure is described in detail. In Section 5, we compare the UAV-assisted CH election framework with the general CH election framework through various simulations. In Section 6, we deal with the issues of 4

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extending a UAV’s flight time and applying our framework to an IoT netowrk.

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We conclude this paper in Section 7.

2. Related Work

Recently, a lot of prospective UAV applications have been introduced. To support those applications, customized UAV designs and their prototypes also 70

have been rolled out. A fixed-wing UAV perching on walls [6] and a hybird UAV switching between hovering and curising [7] are two examples of them. Another intersting UAV-related work include UAV-navigation based on machine learning [8], UAV-based military cargo delivery [9], GPS spoofing detection based on

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UAV’s camera and IMU [10] and development of realistic UAV simulator [11]. As UAV-related technologies develop greatly, multi-UAV applications have been preferred by their users. This is because multi-UAV applications enhance service reliability and availability preventing a single point of failures

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[12]. Menouar et al. proposed three multi-UAV applications for intelligent transportation system [13]; traffic accident report, flying road side unit and fly80

ing ploice eye. In the traffic accident report, UAVs detect a trafic accident and quickly advertise it to the following vehicles. In the flying road side unit, UAVs detect an emergent situation on a road and advertise it to the following vehicles.

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In the flying police eye, UAVs detect illegally running vehicles and provide their information to the police. 85

Generally, a multi-UAV application requires a network of UAVs which is called a FANET (Flying Ad Hoc Network). The FANET exposes a lot of challenges including secure and reliable routing, delay and disruption tolerance and energy consumption optimization [12]. Among the challenges, the secure and reliable routing is the most important issue because its malfunction disables the operation of FANET [14]. Some survey papers [14, 15] have investigated advan-

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tages and weaknesses of existing FANET routing protocols systematically. Note that the above challegnes require modifications of the MAC (Medium Access Control) and the network layer at each UAV node.

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Zeng et al. introduced three prominent applications of UAV-based wireless 95

communications [2]. In the first application, UAVs recover wireless commu-

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nication of mobile phones when base stations are overloaded or unavailable. Mekikis et al. presented detailed examples of the UAV-based communication recovery and their performance benefits in [16, 17]. In the second application, UAVs indirectly connect any two distant users through their communication 100

relay. The last one is UAV-assisted data distribution and gathering where a UAV distributes (or gathers) data to (from) sensors deployed in a duty field. As shown in Fig. 1, clustering a network makes the data distribution and gathering efficient.

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Ho et al. presented a UAV’s fligth route selection scheme which optimizes energy consumption of nodes for a UAV-based WSN [18]. As an improved scheme, the PSO (Particle Swarm Optimization) scheme was proposed in [19]. The scheme tries to reduce the following three metrics; energy consumption of nodes, BER (Bit Error Rate) and UAV travel time. Then, the PSO scheme was

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compared with the LEACH-C [20] using the three metrics, and its superiority was proved through simulations.

An agent-based data collection scheme for a UAV-based WSN was proposed in [21]. In the scheme, a UAV dispatches a mobile agent to each cluster of the

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network, and the mobile agent gathers data from all nodes in the cluster. After gathering all data from the cluster, the mobile agent goes back to the UAV 115

carrying the data, and the UAV reacts to the network using the gathered data. This process is repeated until all clusters are visited by the UAV. However, it is unclear whether this scheme is superior to the LEACH or the LEACH-C with regard to security and performance. Say et al. proposed a novel data collection framework for a UAV-based WSN in [5]. First, the UAV’s coverage area is divided into different frames in line with nodes’ location, and each frame has a different transmission priority. Next, a

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lower contention window is assigned to a frame with a higher priority to reduce packet losses and collisions of nodes under the frame. A routing protocol which optimizes the energy consumption of nodes on the framework was also proposed 6

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in [5]. Zhan et al. proposed a data collection scheme for UAV-enabled WSNs suf-

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fering under fading channels [22]. The scheme jointly optimizes nodes’ wake-up schedule and UAV’s trajectory to minimize energy consumption and communication falures of nodes in the fading channels.

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3. Network and Threat Model 3.1. Assumptions

Before describing network and threat model of UAV-assisted CH election framework, some assumptions are defined as follows in order to facilitate under-

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standing.

First, we employ a cluster formation scheme to create initial clusters having no CHs in them. We need initial clusters to save energy consumption for key establishments between a CH and its members whenever a new CH election is

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required. After initial clusters are formed, cluster membership of the clusters is fixed and CH roles are rotated among the members. Therefore, if pairwise 140

keys are established for all pairs of members in an initial cluster, the pairwise keys are still valid after a new CH is elected. We employ a connectivity-based cluster formation protocol where each node compares its connectivity with other

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neighbors and declares itself as a CH when it has the highest connectivity among neighbors. 145

Second, some keys are pre-assigned from the sink to each node to enable the node to establish pairwise keys and a group key using the pre-assigned keys. The group key is employed for encrypted communication between a cluster and the UAV. Pairwise keys are employed for encrypted communications between a CH and its members along with encrypted communications between CHs and the UAV. If a node’s ID is known to other nodes, the IDs of pre-assigned keys

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of the node are also known to them as in [23]. So, if any two members share

same pre-assigned keys, they establish a pairwise key using the common preassigned keys. Up to now, many key establishment schemes for WSNs have

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been proposed, and some of them can be employed for the pairwise and group 155

key establishments.

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Third, we assume that all nodes are trustworthy at network boot-up time. The trustworthiness are not corrupted during the initial cluster formation and the pairwise and group key establishments. Since the preliminary two steps occur only once and finish within a very short time, this assumption is reasonable. Fourth, the location of all sensor nodes are known to the sink and its UAV.

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Fifth, the time of all nodes is synchronized, and the CH election step of all nodes starts at the same time as in the LEACH. We can achieve the synchronization by making the sink broadcast synchronization messages periodically

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[4].

Last, we assume that each node can measure its residual energy. However, it is difficult to measure a sensor’s residual energy [24]. Contrarily, we can easily know the amount of energy which is consumed for sensing, processing, and communication [25]. That is, we can roughly estimate the residual energy

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3.2. Network Model

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of a sensor node by counting the number of the above energy-consuming actions.

Fig. 2 depicts the network operation of a UAV-based WSN. As shown in Fig. 2, The network operation is divided into three steps; initial cluster formation,

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pairwise and group key establishments and UAV-assisted CH election. Zooming in the UAV-assisted CH election, its timeline consists of multiple rounds as 175

shown in Fig. 3. One CH election time and multiple data frames constitute a round. In one data frame, there are multiple slot time, one UAV tour time for data collection and broadcast of new CHs or blacklists. Here, a blacklist means a list of nodes whose residual energy is lowest in their cluster. We explain the reason why the lowest enegry nodes should be included in a blacklist for secure CH elections in Subsection 3.3. One slot time is assigned to each member

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according to a TDMA schedule created by the CH. Before the UAV-assisted CH election begins, the sink calculates the UAV tour time for data collection and broadcast of new CHs or blacklists, and broadcasts the time to nodes. The 8

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UAV tour time means the time interval during which the UAV collects data from 185

all CHs and advertises new CHs or blacklists by visiting the clusters according to

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the UAV’s flight route. Because there are multiple frames in a round, the UAV broadcasts a new CH for each cluster at non-last frames to support the next frame’s data transmission. Contrarily, at the last frame, the UAV broadcasts a

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blacklist for each cluster to support the next round’s CH election.

Figure 2: Network operation of a UAV-based WSN

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3.3. Threat Model

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The primaty goal of attackers is to transform as many compromised nodes as possible into CHs. Since we assumed that the LEACH is employed for CH elections, compromised nodes can elect themselves as CHs in spite of their disqualification. In our therat model, a compromised node keeps declaring itself 195

as a CH until it is removed from a new CH list or it is shown in the blacklist. We explain the reason why a compromised node should keep declaring itself as a CH in the following. First, since pairwise keys were established between a CH

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and its members, a non-CH member cannot obtain data from other members. Second, if a compromised node wants to replace collected data of its cluster with

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fabricated data and convince the sink to accept the fabricated data, it should be a CH. Because a CH and the UAV communicate with each other using a shared 9

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Figure 3: Timeline of UAV-assisted CH election

pairwise key, a non-CH member cannot fabricate the collected data and deliver the fabricated data to the UAV. Therefore, if a compromised node intentionally avoids becoming a CH, it cannot achieve its instrinsic goal such as illegal 205

data collection and corruption in spite of its extended lifespan. Meanwhile, it is not desirable that multiple compromised nodes declare themselves as CHs in a cluster. This is because multiple CH declarations separate the cluster and

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the separation reduces influence range of the compromised CHs. So, we make only one compromised node declare as a CH in a cluster by comparing the CH

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winning frequency among the compromised nodes. By doing so, we can get two advantages. First, a cluster is not separated into multiple ones by preventing

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multiple CH declarations. Second, the compromised non-CH nodes can save their precious energy to extend their lifespan.

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After a compromised node becomes a CH, it fabricates all data collected from its members and delivers the fabricated data to an UAV. When a compromised node fails in becoming a CH, it still fabricates its own data and delivers it to the CH. Contrarily, a compromised node does not fabricate its residual energy reported to the UAV because the fabrication is easily detected by the UAV. At network boot-up time, initial energy of all nodes are equal, and residual 220

energy of nodes are reported to the sink and the UAV continuously. If a node is suspected to fabricate its residual energy, it will be removed from new CH

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cadidates or even shown in the blacklist so that it cannot become a CH any

4. UAV-Assisted CH Election Procedure 4.1. Initial Cluster Formation

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At network boot-up time, each node exchanges its ID and neighbor list with its neighbors. After these exchanges, each node can detect other nodes sharing common pre-assigned keys within its two-hop range. After exchanging the ID and the neighbor list, each node performs the extended HCCP (Highest Connectivity Clustering Protocol) [26] to generate initial clusters. In the HCCP

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[27], a member with the highest connectivity among neighbors declares itself as a CH and the receivers become the members of the CH. In the extended HCCP [26], a single cluster which consists of only one member joins into one of neighboring clusters because those single clusters deteriorates efficiency of 235

clustering. We employ the extended HCCP not only for removing single clusters but also for efficiency of key establishments between members. We deal with the

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efficiency in Subsection 4.2. After the initial cluster formation, each CH registers itself into the sink so as to assign a unique spreading code to its cluster. The code assignment method is very simple. The first code on a predefined list is

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assigned to the first CH which registers itself into the sink. The second code from the same list is assigned to the second CH registering into the sink.

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After all CHs register themselves into the sink, both the sink and the UAV recognize the initial CHs as nodes with which the UAV will contact during any UAV tour time. Hereafter, we refer to the initial CHs as the contact nodes to 245

avoid confusion. The UAV produces an optimal flight route which visits all the contact nodes and returns to the sink’s position. However, producing a UAV’s optimal flight route is beyond the scope of this paper, and it is covered in [28, 29]. If new CHs are elected, they should be notified to the UAV through long distant communications between the CHs and the UAV. Otherwise, the UAV makes an extra visit to all clusters to know the newly elected CHs. Because this is a very

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time- and energy-consuming work, we never change the optimal flight route once it has been determined. In case of election of new CHs, the UAV first approaches a contact node in a cluster and advertises that it is ready to receive collected data from the CH using the shared key between the UAV and the cluster. A shared key between the UAV and a cluster is referred to as a gorup

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key, and details about the group key generation are explained in Subsection 4.2. Then, the newly elected CH delivers its collected data to the UAV using the shared pairiwse key between the UAV and the CH. Details about the pairwise

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key generation between the UAV and a CH are also explained in Subsection 4.2. Next, the sink computes the UAV tour time during which the UAV collects data and broadcasts a new CH or a blacklist for all clusters. Note that the frequency of UAV tour is already known to nodes as a system parameter. After the registration of CHs, the sink notifies all nodes of the UAV tour time. The notified time enables all nodes to recognize the next data transmission time, the next UAV tour time and the next CH election time. 4.2. Pairwise and Group Key Establishments

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Each CH establishes pairwise keys with all members using shared keys or

a proxy node. Here, the shared keys mean common pre-assigned keys shared between any two members. If any two members share no common pre-assigned 12

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keys, they can find a proxy node which shares a common pre-assigned key(s) with each member exclusively. The proxy node can establish a pairwise key for

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the two members using those exclusive pre-assigned keys. If there is no proxy between a CH and a member, the CH requests the sink to create a pairwise key and distribute it to them. This action requires a lot of energy consumption 275

because sometimes the sink is far away from a CH. So, the case in which there is no proxy node among a CH and any member should be minimized. Because, the extended HCCP minimizes the case as compared with other schemes under the same condition [26], we chose the extended HCCP as the initial cluster formation scheme. Then, each member in a cluster establishes pairwise keys with other members using common pre-assigned keys. If a member shares no pre-assigned

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keys with any other member, it requests its CH to distribute a common key for the two members. The CH creates a pairwise key and distributes it to the two members securely. Note that the CH role as a helper of key establishment is tentative, and it turns to the normal node again after the key establishment. Group keys are also established for communication between clusters and the

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UAV using common pre-assigned keys. In other words, all members in a cluster establish the group key with the UAV by employing all common pre-assigned keys among the members. The UAV also can easily generate the same group key

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because it has all pre-assigned keys of nodes and knows which keys are assigned to any node. The group key is employed for the UAV to broadcast a new CH or a blacklist to all members of the cluster. 4.3. UAV-Assisted CH Election 4.3.1. CH Election

During a CH election time, each node elects itself as a CH based on its CH winning probability as in the LEACH. If a node’s CH winning probability is higher than a threshold, it becomes a CH by broadcasting a CH declaration

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message. Note that the CH declaration message is propagated within only the cluster thanks to employment of different spreading codes. After election of a CH, the CH generates the pairwise key with the UAV using all pre-assigned 13

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keys of the CH. The UAV can easily get the same key because it has all preassigned keys of nodes and knows which keys are pre-assigned to a specific

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node. Before flight, the UAV can download all pre-assigned keys of nodes and the pseudo random number generator which generates a set of key numbers from the sink. Then, before the UAV communicates with a CH, the UAV picks 305

up the pre-assigned keys of the CH using the pseudo random number generator and generates the pairwise key using all pre-assigned keys of the CH. 4.3.2. Data Transmissions from Sensors to CH

Each CH creates a TDMA transmission schedule, and distributes the schedule to members of the cluster. Due to the TDMA schedule, transmission of each member is separated from that of other memebres, and each member can

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turn off its transceiver except its assigned time slot. Each member transmits its data and residual energy to its CH during its assigned time slot. Note that the transmission of the data and the residual energy is encrypted by a member,

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and it is decrypted by its CH using the shared pairwise key. Note that each CH delivers the gathered data and residual energy of members to the UAV. Gathering residual energy of members enables the UAV to select a maximum energy member as the CH which will serve during the next frame.

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4.3.3. UAV’s Visit to All Clusters and the Sink Before visiting to all clusters, the UAV stores the latest residual energy of 320

nodes into its memory to check if a compromised node fabricates its residual energy. During each UAV tour time, the UAV visits each contact node and broadcasts a message advertising its readiness of data collection. When the CH delivers data and residual energy of all members to the UAV, the message is encrypted and decrypted using the pairwise key between the CH and the UAV. Then, the UAV selects the highest energy node as the new CH and broadcasts

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the message containing the new CH’s ID which is encrypted with the cluster’s group key. All members of the cluster get the new CH’s ID after decrypting the message with the same group key. After visiting all CHs, the UAV hands over

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the collected pairs of data and residual energy to the sink. 330

During the last UAV tour time at a given round, the UAV first selects the

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lowest energy members in each cluster and encrypts their IDs with the cluster’s group key. Recall that a list of the lowest energy members has been called a blacklist in this paper. The UAV boradcasts the encrypted blacklist to all mambers in each cluster. Upon receiving the encrypted blacklist, each member 335

in a cluster recovers the plaintext blacklist by decrypting with the cluster’s group key. Each member removes the members on the blacklist from CH candidates for the next CH election.

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5. Simulations

We evaluated the security and performance of our framework using the well340

known ns-2 simulator. In the simulation environment, 100 nodes were randomly deployed in an area of 100 meters by 100 meters. The sink was laid in the

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location of (50 meters, 175 meters), and takeoff and landing location of the UAV was eqaul to the location of the sink. The energy consumption model of [4] was applied to these simulations. Two different clustering frameworks were 345

performed 20 times for each number of compromised nodes, and the results were averaged to get a representative value. Note that we changed the locations of

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nodes for each simulation run to give the randomness to each simulation. In addition, compromised nodes and their compromise time were selected randomly for each simulation run. All simulation results have 95% confidence intervals. 350

We evaluated the impact of increasing the CH election period and the node compromise time on security and performance of two frameworks by executing additional simulations. Last, for the UAV-assisted CH election framework, we executed extra simulations to evaluate the impact of increasing the UAV tour

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frequency on the security and the performance. Table 1 shows the simulation 355

parameters and their values. In this paper, our goal is creating a UAV-assisted CH election framework

which houses all CH election schemes and enables secure communications among

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Table 1: Parameters for simulations

Parameter value

Simulation time

1800 sec.[3]

Initial energy of nodes

2 Joules[4]

Data bit rate

1 Mbps[4]

Data packet size

500 bytes[4]

Pakcet header size

25 bytes[4]

Number of compromised nodes

10∼50[3]

Compromise time of nodes

<180, <300, <450, <900

CH election period

20 sec., 30 sec., 40 sec., 50sec.

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Simulation parameter

Frequency of UAV tours per round

2, 3, 4, 5

Initial cluster formation protocol

Extended HCCP[26]

CH election protocol

Cluster-first LEACH LEACH[4]

Radio propagation model

Two ray ground model(sensor node, sink),

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Energy consumption model of nodes

Free space model(UAV)[30]

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nodes. A cluster-first scheme which first generates clusters and later elects CHs suits well to the framework. However, a CH-first scheme like the LEACH which 360

first elects new CHs and generates membership of the new CHs cannot support the framework. That is, whenever any cluster membership changes, keys between the members should be also reestablished, and this key establishment overhead is quite burdensome to sensor nodes. For this reason, we modified the LEACH to make it operate as if it were a cluster-first protocol, and to support secure communication not only within a cluster but also between the

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UAV and sensor nodes. However, the cluster-first LEACH still makes all nodes autonomously elect a CH in their cluster boundary. The general CH election framework employs the cluster-first LEACH as the CH election protocol. Next,

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we implemented our UAV-assisted CH election framework which employs the 370

cluster-first LEACH as well. We employed the extended HCCP [26] as an ini-

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tial cluster formation protocol because it succeeds in all key establishments for members without increasing energy consumption [26]. Our UAV-assisted CH election framework is compared with the general CH election framework in terms of security and performance. To evaluate the security and performance, 375

we introduce the following metrics.

• Number of corrupted messages: it means the number of corrupted messages which are received by the UAV during network operation.

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• CH winning frequency of compromised nodes: it means the frequency that a compromised node is elected as a CH. 380

• Energy consumption: it means the total amount of energy that a framework consumes per unit time.

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• Average node lifetime: it means the average lifespan of nodes, and it is represented as a ratio of node lifetime-to-network lifetime. 5.1. Security and Performance with Fixed CH Election Period and Latest Com385

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Fig. 4 through 7 show the security and performance of two frameworks (that is, the general CH election framework and the UAV-assisted CH election framework) as the number of compromised nodes increases. Note that the CH election period is set to 20 seconds and the latest compromise time of nodes is 390

set to 450 seconds in these simulations. Fig. 4 depicts the number of corrupted messages as the number of compromised nodes rises up. The increase in the number of compromised nodes

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correspondingly increases the corrupted messages at the UAV. However, our scheme greatly reduces the increase rate as shown in Fig. 4.

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Fig. 5 depicts how the increase of compromised nodes affects the CH win-

ning frequency of compromised nodes. As shown in Fig. 5, both frameworks 17

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Figure 4: Number of corrupted messages vs. compromised nodes

Figure 5: CH winning frequency of compromised nodes vs. compromised nodes

make more compromised nodes CHs as the number of compromised nodes increases. However, our framework reduces the increase rate of compromised CHs as compared with the general CH election framework. Fig. 6 depicts the energy consumption of nodes as the number of compromised nodes increases. As shown in Fig. 6, the increase of compromised nodes

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does not affect the energy consumption of nodes in both frameworks. Nevertheless, the UAV-assisted CH election framework causes less energy consumption

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Figure 6: Energy consumption vs. compromised nodes

than the general CH election framework. 405

Fig. 7 depicts the relative node lifetime as the number of compromised nodes rises up. Because both frameworks cause different network lifetime for

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each number of compromised nodes, direct comparison of their node lifetime is meaningless. To address this problem, the relative node lifetime is introduced as a ratio of node lifetime-to-network lifetime. In the general CH election 410

framework, compromised nodes elect themselves as CHs regardless of their qualification, and keep serving as CHs until they fail in becoming a new CH or they

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are included in a blacklist. For this reason, the increase of compromised nodes slightly reduces the relative node lifetime. In the UAV-assisted CH election framework, even if a compromised node elects itself as a CH, the malicious be415

havior cannot be repeated in the next election. This is because the compromised node is prone to be excluded from a CH candidate in the next election by the UAV which gathers remained energy of members in the cluster and selects CH candidates using them. As a result, the UAV-assisted CH election framework

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extends the node lifetime significantly as compared with the general CH election

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framework as shown in Fig. 7.

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Figure 7: Node lifetime vs. compromised nodes

5.2. Security and Performance Under Variation of CH Election Period Fig. 8 through 11 show the security and performance of two frameworks as the CH election period increases. Note that the number of compromised

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respectively.

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nodes and the latest compromise time of nodes are set to 30 and 180 seconds

Fig. 8 shows the number of corrupted messages as CH election period increases. As shown in Fig. 8, the UAV-assisted CH election framework gurantees

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lower corrupted messages than the general CH election framework up to 30 seconds of CH election period. As the CH election period increases further, the 430

UAV-assisted CH election framework shows almost equal or worse security than the general CH election framework. Fig. 9 shows the CH winning frequency of compromised nodes as the CH election period increases. As shown in Fig. 9, the UAV-assisted CH election framework sharply cuts the the CH winnig frequency of compromised nodes when the CH election period is less than or equal to 30 seconds. However, the difference between two frameworks becomes very small when the CH election

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period is longer than 30 seconds. This is because the UAV-assistd CH election framework enlarges energy consumption of nodes as the CH election period increases. Namely, energy consumption of non-CH nodes increases as the CH 20

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Figure 8: Number of corrupted messages vs. CH election period

Figure 9: CH winning frequency of compromised nodes vs. CH election period

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election period increases. This evens the energy consumption among nodes, and a compromised node gets an opportunity of being a CH more frequently. Fig. 10 shows the energy consumption of nodes as CH election period increases. As shown in Fig. 10, the general CH election framework makes the

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energy consumption of nodes almost constant regardless of variation of CH

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election period. The UAV-assisted CH election framework consumes more energy than the general CH election framework except the CH election period of

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Figure 10: Energy consumption of nodes vs. CH election period

20 seconds. In the general CH election framework, as the CH election period increases, the transmission frequency of members and CHs increases accordingly while the control overhead for CH reelection decreases. As a result, the energy consumption of nodes keep constant in the general CH election framework. How-

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ever, since the UAV-assisted CH election framework removes the long-distance transmissions of CHs, the increase of CH election period greatly impacts on growth of the transmission freqeuncy of members. That is the reason why the UAV-assisted CH election framework induces more energy consumption than the general CH election framework.

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Fig. 11 shows the relative node lifetime as the CH election period increases. As shown in Fig. 11, both frameworks decrease the relative node lifetime when the CH election period increases. In all cases of CH election period, the UAVassisted CH election framework extends the relative lifetime of nodes greatly. 460

Considering the above simulation results with the variation of CH election

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period, we can draw the following insight. The UAV-assisted CH election framework provides better security when the CH election period is equal to or less than 30 seconds. The general CH election framework consumes less energy than

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the UAV-assisted CH election framework when the CH election period is higher 22

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Figure 11: Node lifetime vs. CH election period

than 20 seconds. However, the UAV-assisted CH election framework evens the energy consumption among nodes so that it makes the lifetime of nodes much

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longer than the general CH election framework.

5.3. Security and Performance Under Variaton of Node Compromise Time 470

Fig. 12 through 15 show the variation of security and performance of two frameworks as compromise time of nodes is delayed. Note that the CH election period and the number of compromised nodes are set to 20 seconds and 30 each.

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Fig. 12 depicts the the number of corrupted messages as the compromise time of nodes is delayed. As the compromise of nodes slows down, the UAV475

assisted CH election framework shrinks the corrupted messages while the general CH election framework fluctuates the number of corrupted messages. The UAV-assisted CH election framework greatly reduces the number of corrupted messages as compared with the general CH election framework. Fig. 13 depicts the CH winning frquency of compromised nodes as the compromise time of nodes is delayed. Naturally, the CH winnig frequency of

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compromised nodes decreases in both frameworks as the compromise of nodes slows down. The UAV-assisted CH election framework allows smaller compromised nodes to be a CH than the general CH election framework under the same

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Figure 12: Number of corrupted messages vs. Compromise time of nodes

Figure 13: CH winning frquency of compromised nodes vs. Compromise time of nodes

condition. As shown in Fig. 13, the earlier nodes are compromised, the larger 485

benefit the UAV-assisted CH election framework induces. Fig. 14 depicts the energy consumption of nodes as the compromise time of nodes is delayed. Both frameworks maintain the energy consumption of nodes

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evenly regardless of compromise time of nodes. However, the UAV-assisted CH election framework greatly reduces the energy consumption of nodes as

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compared with the general CH election framework.

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Figure 14: Energy consumption of nodes vs. Compromise time of nodes

Figure 15: Node lifetime vs. Compromise time of nodes

Fig. 15 depicts the relative node lifetime as the compromise time of nodes is delayed. As suggested in the result of Fig. 14, both frameworks maintain the node lifetime evenly regardless of compromise time of nodes. However, the UAV-assisted CH election framework extends the node lifetime greatly as compared with the general CH election framework.

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Considering the above simulation results with the variation of compromise

time of nodes, we can draw the following insight. The UAV-assisted CH elec-

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Figure 16: Number of corrupted messages vs. UAV tour frequency per round

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tion framework provides better security and performance than the general CH election framework regardless of compromise time of nodes. 500

5.4. Security and Performance Under Variation of UAV Tour Frequency Fig. 16 through 19 show the variation of security and performance of our

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framework as UAV tour frequency per round increases. Note that the CH election period and the latest compromise time of nodes are set to 20 seconds and 900 each. 505

Fig. 16 illustrates the number of corrupted messages as the UAV tour fre-

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quency per round increases. The UAV-assisted CH election framework removes more compromised nodes from a CH candidate list as the UAV tour frequency rises up. This is because the UAV maintains the most recent data of residual energy and changes CH roles in line with the data. 510

Fig. 17 illustrates the CH winning frquency of compromised nodes as the UAV tour frequency per round rises up. The UAV-assisted CH election framework increases the CH winning frequency of compromised nodes with the increase of the UAV tour frequency. The increase of UAV tour frequency rotates

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CH roles among nodes more frequently, and limits working hour of compromised

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CHs. Since this extends the lifetime of compromised nodes significantly, their CH winning frequency also increases during the network lifetime.

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Figure 17: CH winning frquency of compromised nodes vs. UAV tour frequency per round

Figure 18: Energy consumption of nodes vs. UAV tour frequency per round

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Fig. 18 illustrates the energy consumption of nodes as the UAV tour frequency per round rises up. The UAV-assisted CH election framework reduces the energy consumption of nodes as the UAV tour frequency increases. The in520

crease of UAV tour frequency changes CH nodes more frequently so that energy consumption among nodes becomes even. The even distribution of energy consumption among nodes extends lifetime of nodes, and consequently the energy consumption of nodes decreases accordingly.

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Fig. 19 illustrates the relative node lifetime as the UAV tour frequency per

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round rises up. The UAV-assisted CH election framework extends the lifetime of nodes with the increase of UAV tour frequency. The increase of UAV tour frequency changes CH nodes more fequently and reduces the energy consump-

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Figure 19: Node lifetime vs. UAV tour frequency per round

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tion of nodes as shown in Fig. 18. This saving of energy consumption at each node extends the node’s lifetime as shown in Fig. 19. 530

Considering the above simulation results with the variation of UAV tour frequency, we can draw the following insight. Even though the increase of UAV

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tour frequency raises the CH winning frequency of compromised nodes, their CH role duration time is too short to deliver corrupted messages enough, as shown in Fig. 16. In terms of performance, the increase of UAV tour frequency 535

evens the energy consumption of nodes and consequently extends the lifetime

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of network.

6. Discussion

6.1. Extending the UAV’s Flight Time Generally, flight time of a UAV is shorter than 60 minutes because its bat540

tery power is limited, and a UAV consumes a lot of energy for its flight only. Therefore, it is very important that we make the UAV’s flight time as long as possible. The first solution is making an optimal flight route for a UAV so as

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to save its precious energy resource. This approach is covered in [19, 28]. The second solution is making more than one UAV serve for a UAV-assisted WSN at

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the same time. Since the service area is distributed to each UAV, its flight time and energy consumption are also reduced. Employment of multiple UAVs and 28

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the impact of their mobility pattern on the performance are covered in [31]. The third solution is deploying some recharging stations in the network and making

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them recharge UAVs on a request basis. Tseng et al. proposed a scheme which deploys recharging stations in a UAV-assisted WSN and makes an optimal flight route including the recharging stations [29]. Note that employing the recharging stations is also helpful when UAVs are struggling from bad weather conditions. 6.2. Applying the UAV-assisted CH election framework to an IoT network

Now, we examine applying the UAV-assisted CH election framework to an 555

IoT network. WSNs are considered as the technological predecessor and foundation for IoT networks [24]. Besides, dynamics, diversities of devices, and

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deployment scales of the IoT network are much higher than those of WSN. Neverthless, clustering techniques are also required in the IoT network to provide energy and data management efficiency in the IoT network [24]. In the same 560

context, the UAV-assisted CH election framework can be applied to an IoT

7. Conclusions

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network, and it brings many benefits to the IoT network.

In this paper, we proposed a UAV-assisted CH election framework for a

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UAV-based WSN. Even though we applied the well-known LEACH protocol into our framework in this paper, almost all CH election protocols can be applied into our framework with some modifications. Simulation results show that the UAV-assisted CH election framework significantly lowers the number of corrupted messages and the CH winning frequency of compromised nodes. Besides, another simulation results show that the framework provides less energy con570

sumption and longer network lifetime than the general CH election framework.

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Concerning the CH election period, the UAV-assisted CH election framework provides better security and performance than the general CH election framework with a short CH election period. Besides, the variation of node compromise time had no impact on our framework’s superiority to the general CH election

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framework. Last, the increase of UAV tour frequency enhances both security and performance of our framework. Our future work item is to apply as many

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existing CH election protocols as possible to our framework, and to reveal their security and performance.

Aknowledgement 580

This work was supported by Korea government (MSIT) 19ZR1110, (Development of Fundamental Technology for Hyper-Realistic Media Space).

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Gicheol Wang received the B.S. degree from Gwangju University, Gwangju, Korea, in 1997, and the M.S. degree from Mokpo Nat’l University, Mokpo, Korea, in 2000, in computer science and statistics. He received Ph. D degree in computer science and statistics from Chonbuk Nat’l University, Jeonju, Korea, in 2005. He worked for CAIIT(Center for Advanced Image and Information Technology) at Chonbuk Nat’l University, Jeonju, Korea, as a Postdoctoral Research Fellow from Jan. 2006 to Dec. 2007, for the Research Center for Ubiquitous Information Appliances at Chonnam Nat’l University, Gwangju, Korea, as a Postdoctoral Research Fellow from Jan. 2008 to Dec. 2008. From Jan. 2009 to Nov. 2013, he worked for KISTI(Korea Institute of Science and Technology Information), Daejeon, Korea. From Dec. 2013 to Jan. 2016, he worked for ADD(Agency for Defense Development). and he is currently serving as a principal research scientist. From Feb. 2016, he joined to Autonomous Unmanned Vehicle Research Div. at ETRI(Electronics and Telecommunications Research Institute) as a principal research scientist. His current research interests include UAS, sensor networks, FANET, security of wireless networks, and mobile computing. Byoung-Sun Lee

Jae Young Ahn

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Gihwan Cho received the B.S. degree in Computer Science and Statistics from Chonnam University, Gwangju, Korea in 1985 and the M.S. degree in Computer Science and Statistics from Seoul National University, Seoul, Korea in 1987. He received Ph. D degree in Computer Science from Newcastle upon Tyne, England in 1996. He worked for ETRI(Electronics and Telecommunications Research Institute), Daejeon, S. Korea, as a senior member of technical stuff, and from Sep. 1997 to Feb. 1999, for the Dept. of Computer Science at Mokpo National University, Mokpo, S. Korea, as a full time lecturer. From Mar. 1999, he joined to the Division of Electronic & Information Engineering at Chonbuk National University, Chonju, S. Korea, as a professor. His current research interests include mobile computing, computer communication, security of wireless networks, sensor networks, and distributed computing system.

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Gicheol Wang

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Byoung-Sun Lee

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Gihwan Cho

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Jae Young Ahn

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 None

Journal Pre-proof  UAV-assisted CH election framework houses almost all CH election protocols  UAV-assisted CH election outperforms the general CH election framework in terms of security enhancement and energy savings  Increase of CH election period makes security and energy consumption of the UAV-assisted CH election framework worse than the general CH election framework  Delay of node compromise time has no impact on the superiority of the UAVassisted CH election framework

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 Increase of UAV tour frequency enhances both the security and performance of UAV-assisted CH election