An Active Defense Model with Low Power Consumption and Deviation for Wireless Sensor Networks Utilizing Evolutionary Game Theory
<p>The architecture of rational nodes without choosing defense.</p> "> Figure 2
<p>The architecture for half of the rational nodes choosing defense.</p> "> Figure 3
<p>The architecture for all rational nodes choosing defense.</p> "> Figure 4
<p>The rational nodes not choosing defense.</p> "> Figure 5
<p>Half the rational nodes choosing defense.</p> "> Figure 6
<p>Analyses of rational nodes without defender strategies.</p> "> Figure 7
<p>Analyses half of rational nodes when choosing defender strategies.</p> "> Figure 8
<p>All rational nodes choosing defender strategies and <span class="html-italic">R<sub>c</sub></span> = 0.</p> "> Figure 9
<p>Analyses of when all rational nodes choose defender strategies.</p> "> Figure 10
<p>All rational nodes choosing defender strategies and <span class="html-italic">R<sub>c</sub></span> ≠ 0.</p> "> Figure 11
<p>Analyses all of rational nodes when choosing defender strategies.</p> ">
Abstract
:1. Introduction
2. Development and Key Knowledge of Game Theory
3. Active Defense Model Based on Evolutionary Game Theory
- Assumption 1: Infinite sensor network nodes are limited, and each node has a routing forwarding function. The parameters of the defender and the attacker can be defined as follows: R denotes the reward for forwarding one message, denotes the cost of the attacker, denotes the cost of the defender, denotes half the rational nodes choosing defense, and denotes all the rational nodes choosing defense. In this current effort, we assumed that the value of half the rational nodes choosing defense was 0.33, and the value of all the rational nodes choosing the defenders was 1. Normally, .
- Assumption 2: is widely used to design defensive strategies for rational sensor nodes and protocol implementation in WSNs [17]. There are two kinds of nodes in the network: (1) defender nodes (rational nodes), and (2) attacker nodes (malicious nodes). The latter type refers to nodes in the network after capture with all packets having the same size as the malicious node. The node will successfully forward a packet, and the benefits are the same as those for the proceeds of the R units.
- Participants: According to the features of the security of a wireless sensor network, the participants in the game can be divided into two different populations: (1) a population composed of rational nodes, mainly through forwarding packets to obtain benefits (the rational nodes); and (2) a population composed of malicious nodes (denoted the malicious nodes), so that the population system is a group of strategic behaviors. These populations can gain revenue by launching attacks or by forwarding packets, with the former having a larger payoff than the latter.
- Strategy Space: The rational node has two kinds of cooperation strategies: (1) rational node cooperation , and (2) rational node non-cooperation . Cooperation is the behavior of the node when it is forwarding a packet, which is the behavior of a node loss packet; then, its strategy set is recorded as . A malicious node has three different strategies: (1) malicious node cooperation ; (2) attacker ; and (3) malicious node non-cooperation . This strategy set is recorded as .
- Profit Matrix: According to the characteristics of wireless sensor networks, represents the resource (e.g., energy or bandwidth) being consumed by the malicious node to attack the behavior, and indicates that the node is forwarding the resource consumed by a forwarded packet, normally . This profit matrix is divided into two kinds of nodes as shown in Table 1.
4. Modeling Simulation and Analysis
4.1. Rational Nodes Not Choosing Defense and Half the Rational Nodes Choosing Defense
4.2. All Rational Nodes Choosing Defense
5. Discussion
6. Conclusions
Authors Contributions
Acknowledgments
Conflicts of Interest
References
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Malicious Node | Cooperative | Attacker | Non-Cooperative |
---|---|---|---|
Rational Node | |||
Cooperative | R − CR, R − CR | −CR, R − CA | −CR, 0 |
Non-cooperative | 0, −CR | 0, −CA | 0, 0 |
Malicious Node | Cooperative | Attacker | Non-Cooperative |
---|---|---|---|
Rational Node | |||
Cooperative | R − CR, R − CR | −CR, R − CA | −CR, 0 |
Non-cooperative | 0, −CR | 0, −CA | 0, 0 |
Defender | R − CD, R − CR | R − CD, −CA | −CD, 0 |
X | Rc | Rnc | Df | Mc | At | Mnc | |
---|---|---|---|---|---|---|---|
Minimum Value | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.22 |
Maximum Value | 300 | 0.50 | 0.50 | 0.00 | 0.33 | 0.36 | 0.41 |
Average Value | 116.3 | 0.05 | 0.04 | 0.00 | 0.04 | 0.04 | 0.39 |
Medium Value | 95.03 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.41 |
Mode | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.22 |
Deviation | 94.09 | 0.13 | 0.08 | 0.00 | 0.09 | 0.11 | 0.05 |
Range | 300.0 | 0.50 | 0.48 | 0.00 | 0.33 | 0.36 | 0.19 |
X | Rc | Rnc | Df | Mc | At | Mnc | |
---|---|---|---|---|---|---|---|
Minimum Value | 0.00 | 0.28 | 0.00 | 0.00 | 0.33 | 0.00 | 0.00 |
Maximum Value | 600 | 1.00 | 0.33 | 0.56 | 1.00 | 0.33 | 0.33 |
Average Value | 294.7 | 0.96 | 0.00 | 0.03 | 0.99 | 0.00 | 0.00 |
Medium Value | 294.5 | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 |
Mode | 0.00 | 1.00 | 0.00 | 0.00 | 0.33 | 0.00 | 0.00 |
Deviation | 177.1 | 0.13 | 0.02 | 0.11 | 0.06 | 0.03 | 0.03 |
Range | 600.0 | 0.72 | 0.33 | 0.56 | 0.67 | 0.33 | 0.33 |
X | Rc | Rnc | Df | Mc | At | Mnc | |
---|---|---|---|---|---|---|---|
Minimum Value | 0.00 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 |
Maximum Value | 1000 | 0.00 | 0.00 | 1.00 | 1.00 | 0.50 | 0.75 |
Average Value | 482.7 | 0.00 | 0.00 | 1.00 | 0.97 | 0.01 | 0.02 |
Medium Value | 481.4 | 0.00 | 0.00 | 1.00 | 1.00 | 0.00 | 0.00 |
Mode | 0.00 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 |
Deviation | 298.5 | 0.00 | 0.00 | 0.00 | 0.14 | 0.04 | 0.10 |
Range | 1000 | 0.00 | 0.00 | 0.00 | 1.00 | 0.50 | 0.75 |
X | Rc | Rnc | Df | Mc | At | Mnc | |
---|---|---|---|---|---|---|---|
Minimum Value | 0.00 | 0.00 | 0.00 | 9.881 × 10−3 | 0.00 | 0.00 | 0.00 |
Maximum Value | 10,000 | 1.00 | 0.00 | 1.01 | 1.00 | 0.50 | 0.74 |
Average Value | 4952.0 | 0.98 | 0.00 | 0.02 | 1.00 | 0.00 | 0.00 |
Medium Value | 4951.0 | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 |
Mode | 0.00 | 1.00 | 0.00 | 9.881 × 10−3 | 0.00 | 0.00 | 0.00 |
Deviation | 2917.0 | 0.13 | 0.00 | 0.13 | 0.06 | 0.02 | 0.04 |
Range | 10,000 | 1.00 | 0.00 | 1.01 | 1.00 | 0.50 | 0.74 |
Al-Jaoufi et al. [15] | This Paper | |
---|---|---|
Number of Strategies | 3 | Increased up to 6 |
Rang Number | 1000 | Increased up to 10,000 |
Equilibrium State | The average medium values in the non-cooperative nodes strategy were 0.003944 and 4.44 × 10−58, and nodes strategy conditional cooperative were 0.7485 and 0.7529. | The average medium values in malicious node cooperation for each case were only 1, and in the attacker nodes strategies and malicious node non-cooperation were each only zero. |
Power Consumption | Higher power consumption because the model always chooses the conditional cooperative nodes strategy. | Lower power consumption, only after malicious nodes cooperation, attacker, and malicious nodes non-cooperation equilibrium state, the defender strategies are close to zero. |
Deviations | When the deviation X = 294.7, the result of the deviation was small in the non-cooperative nodes, given that the deviation value was 0.031. | When the deviation X = 2917 in Figure 11 occurred, the result of that deviation was zero in the attacker nodes strategy, which is a better result given that the deviation value was 0.02. |
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Al-Jaoufi, M.A.A.; Liu, Y.; Zhang, Z. An Active Defense Model with Low Power Consumption and Deviation for Wireless Sensor Networks Utilizing Evolutionary Game Theory. Energies 2018, 11, 1281. https://doi.org/10.3390/en11051281
Al-Jaoufi MAA, Liu Y, Zhang Z. An Active Defense Model with Low Power Consumption and Deviation for Wireless Sensor Networks Utilizing Evolutionary Game Theory. Energies. 2018; 11(5):1281. https://doi.org/10.3390/en11051281
Chicago/Turabian StyleAl-Jaoufi, Mohammed Ahmed Ahmed, Yun Liu, and Zhenjiang Zhang. 2018. "An Active Defense Model with Low Power Consumption and Deviation for Wireless Sensor Networks Utilizing Evolutionary Game Theory" Energies 11, no. 5: 1281. https://doi.org/10.3390/en11051281