=Paper=
{{Paper
|id=Vol-2590/paper1
|storemode=property
|title=Metric Properties of Building a List of Trusted Nodes during Selection of Data Transfer Routes in Wireless Sensor Networks
|pdfUrl=https://ceur-ws.org/Vol-2590/paper1.pdf
|volume=Vol-2590
|authors=Boris Sovetov,Tatiana Tatarnikova
|dblpUrl=https://dblp.org/rec/conf/micsecs/SovetovT19
}}
==Metric Properties of Building a List of Trusted Nodes during Selection of Data Transfer Routes in Wireless Sensor Networks==
https://ceur-ws.org/Vol-2590/paper1.pdf
Metric Properties of Building a List of Trusted
Nodes during Selection of Data Transfer Routes
in Wireless Sensor Networks
Boris Sovetov1[0000−0003−3116−8810] and Tatiana
Tatarnikova2[0000−0002−6419−0072]
1
Saint Petersburg Electrotechnical University ”LETI”, ul. Professora Popova 5,
197376 St. Petersburg, Russia
bysovetov@mail.ru
2
Russian State Hydrometeorological University, ul. Voronezhskaya, 79, 192007 St.
Petersburg, Russia
tm-tatarn@yandex.ru
Abstract. Known attacks aimed at routing a wireless sensor network
are considered. An algorithm is proposed for generating a list of trusted
nodes involved in the construction of logical data transfer routes be-
tween the sensor device and the nearest base station. The algorithm
works independently on each head node of the clusters that make up
the wireless sensor network. Mandatory and selective metrics that are
involved in determining the list of trusted nodes are proposed. The sim-
ulation of a wireless sensor network consisting of the same sensor nodes
and a base station located on the territory of a given size is performed.
The model provides for the possibility of mobility (movement) of nodes
and self-organization of the network. As a result of the model’s work,
text log files are generated that contain a list of events that occurred in
the network — the appearance of data, the transfer of data packets, the
exchange of reputation between nodes, as well as the network’s function-
ing characteristics — the life cycle of the wireless sensor network and
the percentage of lost packets. The results of a simulation experiment
for a wireless sensor network with and without malicious nodes are pre-
sented, which showed the advantage of the proposed method of protection
against routing attacks in terms of network operation characteristics.
Keywords: Wireless Sensor Network · Routing Attacks · Metric Char-
acteristics · Node Reputation · Node Trust · Network Life Cycle · Mali-
cious Node.
1 Introduction
Wireless sensor networks (WSN) are considered one of the most promising
information and communication technologies of the 21st century. Inexpensive
Copyright © 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 B. Sovetov, T. Tatarnikova
and “smart” sensors integrated into a wireless network connected to the Internet
provide a wide range of services for monitoring and controlling bodies, houses,
enterprises, cars, etc. [1].
An important component of the installation and use of WSN is to ensure the
information security of the transmitted data. WSNs are particularly vulnerable
to routing attacks due to self-organization and limited resources of sensor nodes.
A self-organizing wireless network does not have a specific structure, and the
functions of the nodes are not fixed. Each time a new device is connected to
the network, the functions are redistributed between the network nodes and the
characteristics of the communication channels change. This feature contributes
to the development of attacks aimed at compromising the WSN nodes.
The limited resources of WSN sensor nodes, such as a small amount of com-
puting power and memory, and battery energy reserve, contribute to the devel-
opment of attacks aimed at shortening the WSN life cycle [2].
The lifetime of the WSN depends on the energy consumption of the sensor
devices [3]. For this reason, methods of transmitting data to the WSN are aimed
at reducing the number of operations performed by nodes. Energy consumption
occurs during data transmission, processing, calculation of the route, etc. In
connection with this, a common option is the hierarchical structure of building
the WSN in which multi-step interaction is realized (Fig. 1) [4].
Fig. 1. Hierarchical structure of a WSN
Lower-level sensor devices first transmit data to the head nodes around which
the devices are clustered. The head node is a sensory device that for some time
performs the functions of a hub. These features include data aggregation, pack-
etization of the sensor network, and relay to the nearest router. Routers form a
mesh network topology through which packets are transported.
After several hops (jumps) data will be transmitted to the gateway – the
output of the sensor network to the global network.
The time diagram of the life cycle of a clustered WSN is shown in Fig. 2.
The head node can be selected in the process of self-organization by chance
or predetermined [5]. In the first case, each sensor device can become the head
node with an equal chance, in the second case the head node is assigned based
Metric Properties of Building a List of Trusted Nodes WSN 3
on the central characteristics of the location of the sensor device in Euclidean
distance or residual energy. The following head node selection algorithms can be
used: LEACH, TEEN, PEGASIS and their subsequent modifications. A random
selection of head nodes is preferable, since this allows you to create clusters of
various sizes [6].
Fig. 2. WSN Life Cycle Diagram
2 Attacks aimed at routing in the WSN traffic
Consider known attacks aimed at routing in the WSN.
Wormhole attacks – its implementation requires at least two compromised
nodes in different parts of the network. Packets intercepted on one compromised
node Si are transmitted to another compromised node Sj outside the channel
band. All nodes receiving a packet passing through the “wormhole” of node
Sj will consider node Si as their neighbor. Thus, with further data transfer,
the routes will be built incorrectly. A wormhole attack is considered difficult to
detect.
Assembly point attack – compromised node reports incorrect information
about itself, for example, about a high energy reserve of the battery, thereby
forcing neighboring nodes to refer to themselves as to the head node of a cluster.
Packet ejection attacks – there are two types of this attack: “black hole” and
“gray hole”. In a black hole attack, a compromised node destroys all received
data. In the “gray hole” attack, packets are discarded selectively, which compli-
cates its detection. These attacks are especially effective if the attacking node is
the main one in the cluster.
Sibyl attack – a compromised node appears to other WSN nodes as multiple
virtual nodes. When transmitting data through virtual nodes, the throughput
of the WSN is significantly reduced.
Loop attack – using the features of the routing protocol, several compromised
nodes can create a loop in the route, which leads to reduced bandwidth, data
loss and increased power consumption.
Rush attack – lies in the propagation of service messages used in routing,
which creates a decrease in throughput and, as a result, a reduction in the life
cycle of the WSN.
4 B. Sovetov, T. Tatarnikova
3 Description of the proposed solution
The proposed solution is an algorithm for compiling a list of trusted nodes that
can participate in the construction of logical data transfer routes from sensor
devices to the nearest base station [8]. The algorithm works independently on
each head node and consists of the following steps:
1. Initialization – each node determines its neighbors – nodes located at the
distance of one hop and adds them to the list of trusted nodes. Initialization is
performed once at the beginning of the deployment of the WSN.
2. Control – each node evaluates the behavior of its neighbors according to
special metrics, according to which it determines its level of trust in them. The
intrinsic trust of the node p to the neighbor node q is estimated as
n
X
Cp,q = mi wi , (1)
i=0
where Cp,q is the level of trust the node p to the node q;
n is the quantity of metrics;
mi is the value of the i-th metric;
wi is the weight of the i-th metric.
3. Estimation of the reputation the neighboring node. The reputation of the
node p to the node q is calculated as
PL
i=1 Cp,i Ci,q
Rp,q = PL (2)
i=1 Cp,i
where L is the list of nodes exchanging trust.
4. Comprehensive estimation of the trust of the node to its neighbors based
on their own trust and reputation gained from a third party. The confidence of
the node p to the neighboring node q is estimated as
z z
D = Cp,q + Rp,q 1 − (3)
r r
where r is the quantity of interactions with the node;
z is the quantity of reputation exchanges with a node.
5. The node p makes a decision on interaction with the neighboring node q
according to a logical rule: D > P ?, where P is the limit level of trust.
6. The creation of the list of trusted nodes. Node p makes a decision about
addition / exclusion of a node q to its list of trusted nodes.
The list of trusted nodes will be considered as a list of available nodes for
further operation of the routing protocol.
4 Metrics Used
The metrics involved in determining the list of trusted sites are given in Table 1.
Metric Properties of Building a List of Trusted Nodes WSN 5
Table 1. Mandatory and selective metrics to define a list of trusted sites.
№ Metric Name Estimation
Necessary metrics
m1 = 1/N ,
1 The intensity of the packets where N is the number of packets
transmitted by the node in one round
m2 = r∗ /r,
where r∗ is the number of packets
2 The share of relayed packets transmitted through the node in one round;
r is the number of interactions with a node
in one round.
m3 = r∗∗ /r∗ ,
3 The proportion of correctly relayed where r∗∗ is the number of packets correctly
packets relayed through the node in one round;
m4 = z/r,
4 The reputation exchange where z is the number of exchanges of
reputation with a node in one round
m5 = z ∗ /z,
5 The correctness of the reputation where z ∗ is the number of correct reputation
exchange exchanges with the node in one round
m6 = Ej /E0 ,
where Ej is the energy remaining
6 The level of residual energy on the j-th round of working WSN;
E0 is a node energy at the moment
initialization WSN.
Sample metrics
PL
Ri,q
1, if Cp,q − Pi=1 ≥ Rlim
m7 = L
L
Ri,q
0, if Cp,q − i=1
< Rlim
L
7 Correctness reputation where Rlim is the limit for the number of
mismatches of trust levels with trust levels
received from other nodes.
m8 = T (i − 1),
where T (i − 1) is the value of the level of
8 Historical trust
trust to a node in the previous round of
the working
of the WSN.
1, if the node has been authenticated,
9 Authentication m9 =
0, if the node failed authentication.
m10 = r+ /r∗ ,
where r+ is the number of packets that
10 Transmission Confirmation
received confirmation of their receipt
for one
round.
1, if CRC = CRC ∗
m11 =
0, if CRC 6= CRC ∗
where CRC is data field checksum written
11 Data integrity
in packet format;
CRC ∗ is checksum computed by the node
based on the packet data field.
6 B. Sovetov, T. Tatarnikova
The list of metrics is not limited to those listed in Table 1. The contents of
this list, as well as values of the weighting factors are determined by experts.
5 Wireless Network Model Description
The simulated network is a collection of 100 identical sensor nodes and a base
station, located on an area of 200 to 200 meters in size. The nodes are randomly
distributed over this territory and can move randomly in a radius of 2 meters
in one iteration, if the protocol under study supports the mobility of the nodes.
The range of the nodes is 25 meters.
WSN simulation model is created on the Java software platform. In the pro-
cess of development, the principles of object-oriented programming were used,
which made it possible to identify individual entities that describe the state and
behavior of each of the WSN objects. List of the main programming interfaces
that describe the components of the model:
Protocol – a software interface used to describe the clustering of the WSN
and the initialization phase of the WSN.
Node – host. The touch node is characterized by the protocol with which it
works, an identifier, a list of neighboring nodes, energy reserve, as well as other
data used by the routing protocol, for example, the number of intermediate nodes
to the base station. Also, an attacking effect on a network can be simulated on
a node.
BaseStation – network gateway. Used to collect data from all network nodes.
The gateway is located in the center of the sensor field, and unlike the WSN
nodes, it has an unlimited supply of energy and cannot be attacked. Also, in the
modeling process, the gateway is used to count correctly delivered packets.
Network – the entire sensor network, the totality of all nodes and the base
station. It is used to create network components, initial placement of nodes, to
simulate the appearance of data on nodes and to initialize attacking nodes. It is
also used to collect data on the state of network nodes.
Attack – a software interface that describes the behavior of nodes simulating
an attack.
TrustManager – software implementation of the proposed solution.
The software implementation of the model allows us to simulate the operation
of the WSN with a different number of nodes and topologies. The network model
allows you to use various protocols for routing, simulate attacks on the network
by configuring nodes for attacking behavior. Also, the network model will allow
a comparison of the network with nodes using the proposed solution to protect
a network with the same network that does not use protection [7–10].
As a result of the program, text log files are generated containing a list
of events that occurred in the WSN, for example, the appearance of data, the
transfer of packets or the exchange of reputation between nodes. In addition, it is
possible to add instructions that calculate other information about the network
depending on the task, for example, the percentage of lost packets.
Metric Properties of Building a List of Trusted Nodes WSN 7
6 Experimental evaluation of the effectiveness of the
developed solution on a software network model
To evaluate the effectiveness of the developed solution against energy depletion
attacks, a comparison was made of the number of functioning nodes in the pres-
ence of a network of malicious nodes that depleted the energy of the WSN. The
comparison was made between a network that does not use the proposed method
of protection, a network without malicious nodes and a network that uses the
proposed method. To assess the maximum possible efficiency of network protec-
tion, a network with malicious nodes was used as the upper boundary, but all
malicious nodes were ignored by the network. In Fig. 3 shows the results of the
experiment.
Fig. 3. Comparison of the life cycle duration of the WSN with malicious nodes
As can be seen from the graphs of Fig. 3, the proposed solution significantly
increases the operating time of a network prone to energy depletion attacks. The
difference from the upper boundary is due to the fact that the WSN nodes take
time to collect statistics and detect malicious nodes. Metrics responsible for the
transmission and integrity of data require additional energy costs.
An assessment was also made of the effectiveness of the proposed solution
from the release or damage of data packets. A comparison was made of the
number of lost or damaged packets between the network that uses the proposed
solution and the network without protection, depending on the number of ma-
licious nodes that damage data packets or attack a black and gray hole. In Fig.
4 shows the results of comparison.
8 B. Sovetov, T. Tatarnikova
Fig. 4. Proportion of lost data packets depending on the number of malicious nodes
A similar experiment was carried out for networks with random attacks car-
ried out by malicious nodes. The results are shown in Fig. 5.
As can be seen from the graphs of Fig. 3–5, the proposed solution significantly
reduces the proportion of lost packets in the presence of malicious nodes in the
WSN. The nonzero proportion of lost packets when using protection is explained
by the fact that the network needs time to detect malicious nodes.
Thus, it was experimentally shown that the proposed solution is able to
effectively protect the network from various attacks, reducing the number of lost
data packets and increasing the operating time of the WSN.
7 Conclusion
A wireless sensor network has a number of features compared to classic wired
networks, namely decentralization, limited resources and features of physical
access to nodes. Attacks using the features of the WSN and directed to the
process of routing data packets can lead to denial of service for the WSN nodes
and, as a result, a reduction of the WSN life cycle duration.
Various metrics obtained on the basis of statistics on the interaction of WSN
nodes and proposed in the article can help to identify malicious nodes and reduce
the negative impact of the attacks they implement.
The results of the simulation experiment for the WSN with and without
malicious nodes showed the advantage of the proposed method of protection
against routing attacks over the WSN life cycle and the percentage of lost data
packets.
Metric Properties of Building a List of Trusted Nodes WSN 9
Fig. 5. Proportion of lost data packets depending on the number of malicious nodes
(random attacks)
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