School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing100044, China.
Department of Computer Science, Bowling Green State University, Bowling Green, OH43403, USA.
Show Author Information
Hide Author Information
Abstract
In this paper, we design a friendly jammer selection scheme for the social Internet of Things (IoT). A typical social IoT is composed of a cellular network with underlaying Device-to-Device (D2D) communications. In our scheme, we consider signal characteristics over a physical layer and social attribute information of an application layer simultaneously. Using signal characteristics, one of the D2D gadgets is selected as a friendly jammer to improve the secrecy performance of a cellular device. In return, the selected D2D gadget is allowed to reuse spectrum resources of the cellular device. Using social relationship, we analyze and quantify the social intimacy degree among the nodes in IoT to design an adaptive communication time threshold. Applying an artificial intelligence forecasting model, we further forecast and update the intimacy degree, and then screen and filter potential devices to effectively reduce the detection and calculation costs. Finally, we propose an optimal scheme to integrate the virtual social relationship with actual communication systems. To select the optimal D2D gadget as a friendly jammer, we apply Kuhn-Munkres (KM) algorithm to solve the maximization problem of social intimacy and cooperative jamming. Comprehensive numerical results are presented to validate the performance of our scheme.
No abstract is available for this article. Click the button above to view the PDF directly.
References
[1]
B.Khalfi, B.Hamdaoui, and M.Guizani, Extracting and exploiting inherent sparsity for efficient IoT support in 5G: Challenges and potential solutions, IEEE Wireless Communications, vol. 24, no. 5, pp. 68-73, 2017.
Z.Cai and X.Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Transactions on Network Science and Engineering, vol. 7, no. 2, pp. 766-775, 2020.
J.Mao, Y.Zhang, P.Li, T.Li, Q.Wu, and J.Liu, A position-aware merkle tree for dynamic cloud data integrity verification, Soft Computing, vol. 21, no. 8, pp. 2151-2164, 2017.
X.Zheng and Z.Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE Journal on Selected Areas in Communications, vol. 38, no. 5, pp. 968-979, 2020.
F.Jameel, Z.Hamid, F.Jabeen, S.Zeadally, and M. A.Javed, A survey of device-to-device communications: Research issues and challenges, IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2133-2168, 2018.
J. A.Stine and C. E. C.Bastidas, Enabling spectrum sharing via spectrum consumption models, IEEE Journal on Selected Areas in Communications, vol. 33, no. 4, pp. 725-735, 2015.
X.Zheng, Z.Cai, and Y.Li, Data linkage in smart internet of things systems: A consideration from a privacy perspective, IEEE Communications Magazine, vol. 56, no. 9, pp. 55-61, 2018.
Z.Cai and Z.He, Trading private range counting over big IoT data, in Proc. of IEEE 39th International Conference on Distributed Computing Systems, Dallas, TX, USA, 2019, pp. 144-153.
[13]
Y.Jia, Y.Chen, X.Dong, P.Saxena, J.Mao, and Z.Liang, Man-in-the-browser-cache: Persisting https attacks via browser cache poisoning, Computers & Security, vol. 55, pp. 62-80, 2015.
T.Qiu, B.Chen, A. K.Sangaiah, J.Ma, and R.Huang, A survey of mobile social networks: Applications, social characteristics, and challenges, IEEE Systems Journal, vol. 12, no. 4, pp. 3932-3947, 2018.
C.Kong, G.Luo, L.Tian, and X.Cao, Disseminating authorized content via data analysis in opportunistic social networks, Big Data Mining and Analytics, vol. 2, no. 1, pp. 12-24, 2019.
J.Mao, W.Tian, Y.Yang, and J.Liu, An efficient social attribute inference scheme based on social links and attribute relevance, IEEE Access, vol. 7, pp. 153074-153085, 2019.
Z.Cai, Z.He, X.Guan, and Y.Li, Collective data sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 4, pp. 577-590, 2018.
J. S.He, M.Han, S.Ji, T.Du, and Z.Li, Spreading social influence with both positive and negative opinions in online networks, Big Data Mining and Analytics, vol. 2, no. 2, pp. 100-117, 2019.
X.Meng, G.Xu, T.Guo, Y.Yang, W.Shen, and K.Zhao, A novel routing method for social delay-tolerant networks, Tsinghua Science and Technology, vol. 24, no. 1, pp. 44-51, 2019.
E.Tekin and A.Yener, The general gaussian multipleaccess and two-way wiretap channels: Achievable rates and cooperative jamming, IEEE Transactions on Information Theory, vol. 54, no. 6, pp. 2735-2751, 2008.
Y.Choi and J. H.Lee, A new cooperative jamming technique for a two-hop amplify-and-forward relay network with an eavesdropper, IEEE Transactions on Vehicular Technology, vol. 67, no. 12, pp. 12447-12451, 2018.
G.Chen, Y.Gong, P.Xiao, and J. A.Chambers, Physical layer network security in the full-duplex relay system, IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, pp. 574-583, 2015.
M.Nafea and A.Yener, Secure degrees of freedom for the MIMO wiretap channel with a multi-antenna cooperative jammer, IEEE Transactions on Information Theory, vol. 63, no. 11, pp. 7420-7441, 2017.
Z.Chu, H. X.Nguyen, T. A.Le, M.Karamanoglu, E.Ever, and A.Yazici, Secure wireless powered and cooperative jamming D2D communications, IEEE Transactions on Green Communications and Networking, vol. 2, no. 1, pp. 1-13, 2018.
T.Shi, Z.Cai, J.Li, and H.Gao, CROSS: A crowdsourcing based sub-servers selection framework in D2D enhanced MEC architecture, in Proc. of IEEE 40th International Conference on Distributed Computing Systems, Singapore, 2020, pp. 1-11.
[30]
R.Zhang, X.Cheng, and L.Yang, Cooperation via spectrum sharing for physical layer security in device-to-device communications underlaying cellular networks, IEEE Transactions on Wireless Communications, vol. 15, no. 8, pp. 5651-5663, 2016.
H.Wang, B.Zhao, and T.Zheng, Adaptive full-duplex jamming receiver for secure D2D links in random networks, IEEE Transactions on Communications, vol. 67, no. 2, pp. 1254-1267, 2019.
Q.Li, P.Ren, Q.Du, D.Xu, and Y.Xie, Safeguarding NOMA enhanced cooperative D2D communications via friendly jamming, in Proc. of IEEE 90th Vehicular Technology Conference, Honolulu, HI, USA, 2019, pp. 1-5.
[33]
S.Zhu, W.Li, H.Li, L.Tian, G.Luo, and Z.Cai, Coin hopping attack in blockchain-based IoT, IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4614-4626, 2019.
L.Wang, H.Wu, L.Liu, M.Song, and Y.Cheng, Secrecy-oriented partner selection based on social trust in device-to-device communications, in Proc. of IEEE International Conference on Communications, London, UK, 2015, pp. 7275-7279.
[35]
Y.Wen, Y.Huo, L.Ma, T.Jing, and Q.Gao, A scheme for trustworthy friendly jammer selection in cooperative cognitive radio networks, IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3500-3512, 2019.
H.Wang, Y.Xu, K.Huang, Z.Han, and T. A.Tsiftsis, Cooperative secure transmission by exploiting social ties in random networks, IEEE Transactions on Communications, vol. 66, no. 8, pp. 3610-3622, 2018.
Y.Zhao and W.Song, Energy-aware incentivized data dissemination via wireless D2D communications with weighted social communities, IEEE Transactions on Green Communications and Networking, vol. 2, no. 4, pp. 945-957, 2018.
Z.He, Z.Cai, and J.Yu, Latent-data privacy preserving with customized data utility for social network data, IEEE Transactions on Vehicular Technology, vol. 67, no. 1, pp. 665-673, 2018.
X.Zheng, Z.Cai, J.Yu, C.Wang, and Y.Li, Follow but no track: Privacy preserved profile publishing in cyber-physical social systems, IEEE Internet of Things Journal, vol. 4, no. 6, pp. 1868-1878, 2017.
C.Yi, S.Huang, and J.Cai, An incentive mechanism integrating joint power, channel and link management for social-aware D2D content sharing and proactive caching, IEEE Transactions on Mobile Computing, vol. 17, no. 4, pp. 789-802, 2018.
Y.Sun, T.Wang, L.Song, and Z.Han, Efficient resource allocation for mobile social networks in D2D communication underlaying cellular networks, in Proc. of IEEE International Conference on Communications, Sydney, Australia, 2014, pp. 2466-2471.
[46]
M.Alwakeel and V. A.Aalo, A teletraffic performance study of mobile LEO-satellite cellular networks with Gamma distributed call duration, IEEE Transactions on Vehicular Technology, vol. 55, no. 2, pp. 583-596, 2006.
Huo Y, Fan J, Wen Y, et al. A Cross-Layer Cooperative Jamming Scheme for Social Internet of Things. Tsinghua Science and Technology, 2021, 26(4): 523-535. https://doi.org/10.26599/TST.2020.9010020
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
10.26599/TST.2020.9010020.F001
Heterogeneous IoT system supporting D2D communication.
10.26599/TST.2020.9010020.F002
Framework of proposed scheme.
10.26599/TST.2020.9010020.F003
Framework to update the optimal scheme.
4.2 A cross-layer cooperative jamming scheme
Considering all cooperative devices in the heterogeneous IoT system with cellular and D2D communications, we embody a transmission rate influence factor. The factor is the weight of physical layer, i.e., , by a normalization process. In this case, we maximize the achievable rate of and while satisfying several constraints. Specifically, for or , we have a similar objective function in the optimization problem, which is expressed as follows:
The optimization problem comes with five constraints:
where C1 specifies the transmission power constraint. C2 and C3 indicate that each cellular device can be only selected at most one D2D pair and each D2D pair can reuse the spectrum resource from at most one cellular device. C4 and C5 guarantee the performance requirements of cellular links and D2D links, respectively. Note that denotes the minimum secrecy rate threshold for cellular devices and denotes the minimum data rate threshold of D2D communications. Then, we introduce two lemmas to compute the optimal value of and .
Lemma 1 For the achievable secrecy rate in Eq. (
7
), we define the following equations:
where the related discriminant is . The optimal solution of transmission power for Eq. (
7
), , in different cases is as follows:
• When and ,
where
• When and , or and , .
• In other cases, .
Then, we can obtain the maximum achievable rate .
Proof See Appendix A.
Lemma 2 In the constraint of C4, the equation has , , or positive solutions. If zero solutions exist or , . In other cases, . Thus, we can obtain .
Proof See Appendix B.
According to Lemmas 1 and 2, we construct an evaluation framework with coupling of social relationships and physical entities, and take and as two performance indices. In general, we should select D2D gadgets with close social relationships with cellular devices and excellent cooperative jamming capabilities as possible. Consequently, our objective is to maximize the social-physical utility with respect to the binary matching variables , while guaranteeing the requirements of cellular devices and D2D gadgets. The whole optimization problem can be formulated as follows:
Next, we propose a social property-based cooperative jamming algorithm to solve Eq. (
21
). Based on the social relationships sorted in descending order, we develop a social group to further satisfy requirements of the physical layer. is composed of D2D gadgets with the highest intimacy degree. In this way, we do not need to traverse all devices to calculate the social-physical utility. Then, we can temporarily match each cellular device with each D2D pair , i.e., , to obtain the corresponding social-physical utility. The utility is the weight for all possible matching between cellular devices and D2D gadgets . The implementation detail of the proposed algorithm is described in Algorithm 1.
The entire problem involves the spectrum resource allocation belonging to the domain in application of matching theory, and can be modeled as the optimal two-dimensional matching problem of the weighted bipartite graph. Cellular devices and D2D gadgets are disjoint sets of vertices and the set of weights is defined as . We aim to find a matching result to optimize the sum of based on graph theory. An equality subgraph is defined as a graph where the sum of labeling is equal to the weight . The neighbor of a vertex is defined as a set of all vertices adjacent to the vertex. An augmenting path starts and ends at unmatched points and alternately passes through unmatched and matched edges. Here, we utilize KM algorithm to obtain the optimal management and control scheme and calculate the total utility. The detail of the KM algorithm is described in Algorithm 2.
4.2 A cross-layer cooperative jamming scheme
Considering all cooperative devices in the heterogeneous IoT system with cellular and D2D communications, we embody a transmission rate influence factor. The factor is the weight of physical layer, i.e., , by a normalization process. In this case, we maximize the achievable rate of and while satisfying several constraints. Specifically, for or , we have a similar objective function in the optimization problem, which is expressed as follows:
The optimization problem comes with five constraints:
where C1 specifies the transmission power constraint. C2 and C3 indicate that each cellular device can be only selected at most one D2D pair and each D2D pair can reuse the spectrum resource from at most one cellular device. C4 and C5 guarantee the performance requirements of cellular links and D2D links, respectively. Note that denotes the minimum secrecy rate threshold for cellular devices and denotes the minimum data rate threshold of D2D communications. Then, we introduce two lemmas to compute the optimal value of and .
Lemma 1 For the achievable secrecy rate in Eq. (
7
), we define the following equations:
where the related discriminant is . The optimal solution of transmission power for Eq. (
7
), , in different cases is as follows:
• When and ,
where
• When and , or and , .
• In other cases, .
Then, we can obtain the maximum achievable rate .
Proof See Appendix A.
Lemma 2 In the constraint of C4, the equation has , , or positive solutions. If zero solutions exist or , . In other cases, . Thus, we can obtain .
Proof See Appendix B.
According to Lemmas 1 and 2, we construct an evaluation framework with coupling of social relationships and physical entities, and take and as two performance indices. In general, we should select D2D gadgets with close social relationships with cellular devices and excellent cooperative jamming capabilities as possible. Consequently, our objective is to maximize the social-physical utility with respect to the binary matching variables , while guaranteeing the requirements of cellular devices and D2D gadgets. The whole optimization problem can be formulated as follows:
Next, we propose a social property-based cooperative jamming algorithm to solve Eq. (
21
). Based on the social relationships sorted in descending order, we develop a social group to further satisfy requirements of the physical layer. is composed of D2D gadgets with the highest intimacy degree. In this way, we do not need to traverse all devices to calculate the social-physical utility. Then, we can temporarily match each cellular device with each D2D pair , i.e., , to obtain the corresponding social-physical utility. The utility is the weight for all possible matching between cellular devices and D2D gadgets . The implementation detail of the proposed algorithm is described in Algorithm 1.
The entire problem involves the spectrum resource allocation belonging to the domain in application of matching theory, and can be modeled as the optimal two-dimensional matching problem of the weighted bipartite graph. Cellular devices and D2D gadgets are disjoint sets of vertices and the set of weights is defined as . We aim to find a matching result to optimize the sum of based on graph theory. An equality subgraph is defined as a graph where the sum of labeling is equal to the weight . The neighbor of a vertex is defined as a set of all vertices adjacent to the vertex. An augmenting path starts and ends at unmatched points and alternately passes through unmatched and matched edges. Here, we utilize KM algorithm to obtain the optimal management and control scheme and calculate the total utility. The detail of the KM algorithm is described in Algorithm 2.
10.26599/TST.2020.9010020.F004
Influence of the number of cellular users on the sum utility.
10.26599/TST.2020.9010020.F005
Effect of the number of cellular users on the sum rate.
10.26599/TST.2020.9010020.F006
Effect of the power of cellular users on the sum utility.
10.26599/TST.2020.9010020.F007
Effect of the power of cellular users on the sum rate.