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Mukamanzi, F.; Manjula, R.; Datta, R.; Koduru, T.; Hanyurwimfura, D.; Didacienne, M. Source Location Privacy Protection Techniques. Encyclopedia. Available online: https://encyclopedia.pub/entry/53612 (accessed on 16 November 2024).
Mukamanzi F, Manjula R, Datta R, Koduru T, Hanyurwimfura D, Didacienne M. Source Location Privacy Protection Techniques. Encyclopedia. Available at: https://encyclopedia.pub/entry/53612. Accessed November 16, 2024.
Mukamanzi, Florence, Raja Manjula, Raja Datta, Tejodbhav Koduru, Damien Hanyurwimfura, Mukanyiligira Didacienne. "Source Location Privacy Protection Techniques" Encyclopedia, https://encyclopedia.pub/entry/53612 (accessed November 16, 2024).
Mukamanzi, F., Manjula, R., Datta, R., Koduru, T., Hanyurwimfura, D., & Didacienne, M. (2024, January 09). Source Location Privacy Protection Techniques. In Encyclopedia. https://encyclopedia.pub/entry/53612
Mukamanzi, Florence, et al. "Source Location Privacy Protection Techniques." Encyclopedia. Web. 09 January, 2024.
Source Location Privacy Protection Techniques
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This entry is adapted from the peer-reviewed paper 10.3390/s23249623

Internet of Things (IoT) research has been considered as a paramount domain while enhancing current technologies such as wireless sensor networks (WSNs). Enhanced source location privacy and prolonged network lifetime are imperative for WSNs.

radio range network lifetime SLP safety period uniform privacy WSN

1. Introduction

Internet of Things (IoT) research has been considered as a paramount domain while enhancing current technologies such as wireless sensor networks (WSNs) [1]. To assist individuals in finding information in various fields, IoT deployment intends to collect data via a wireless connection. IoT is easily deployed in locations with widespread internet access. However, in wilderness areas such as wildlife sanctuaries, military battlefields, polar regions of the earth, etc., internet connectivity is inadequate or unreliable. In such cases, WSNs play a key role in mitigating this issue. The sensor nodes are primarily used to monitor and track priceless assets, such as reporting in-the-moment military data from a battleground, tracking the whereabouts of threatened species in their habitats [2][3][4][5][6][7], etc. In particular, the sensors form a mesh network and send the collected data wirelessly to single or multiple central controllers known as base stations (BSs).
It is observed that establishing a direct communication link between IoT nodes and the sink node leads to a noteworthy decrease in the remaining energy levels of the individual nodes [8]. Therefore, within the context of IoT applications, ensuring an adequate battery charge within the IoT nodes holds significant importance. Various research initiatives are dedicated to addressing challenges associated with energy consumption and the prolonged lifetime of networks. Singh et al. in [9] endeavored to address concerns regarding the optimal placement of RF-energy transmitters by introducing a network-aware scheme for positioning these transmitters. This approach becomes pivotal, as radio frequency (RF) stands out as one of the primary ambient energy sources viable for energy harvesting. Zheng et al. conducted an in-depth study on the RF-powered AB-assisted hybrid underlay cognitive radio network (ABHU-CRN), with the aim of enhancing the long-term secondary throughput in [10]. This research endeavor was undertaken to tackle the critical challenges posed by energy and spectrum constraints within wireless sensor networks. Numerous approaches have been introduced by various researchers to enhance the operational lifespan of networks. These methods encompass optimal power schemes, as outlined in Reference [11], which facilitate a systematic utilization of the nodes’ remaining energy. Additionally, the clustering of IoT nodes, as discussed in Reference [12], has been employed. Furthermore, intelligent energy utilization approaches, such as innovative routing techniques and the application of wake-up radio protocols detailed in References [13][14], have also contributed to enhancing network lifetime. Since these WSNs are deployed in internet-devoid zones, the BS is supposed to be connected to an IoT gateway that is situated in common places where an internet facility is available. Data collected from the WSN can then be forwarded to the operator, via IoT for live tracking and monitoring purposes.
However, alongside network lifetime, there are other undesirable issues associated with these networks. The nature of wireless communication channels makes security and privacy crucial considerations for these networks. The attacker may employ active attacks such as packet dropping or passive attacks such as eavesdropping on the signals. Several solutions have been proposed in the literature by various researchers to mitigate such attacks [6][15][16][17][18][19][20][21][22]. Source location privacy (SLP) protection techniques are the name given to these solutions.
The current SLP solutions in the literature can be categorized broadly into two distinct categories, namely, phantom routing (random walk)-based SLP and fake packets/fake source-based SLP techniques [23][24]. In the former case, random walk techniques are employed to randomize the routing paths, and in the later scenarios, dummy packets or fake sources are employed to introduce anonymity and unobservability features to the original traffic. These approaches aim to obfuscate the attacker.

2. Source Location Privacy Protection Techniques

Researchers have put forth a variety of routing strategies to address the WSN privacy issue with source nodes. In [15][19][22][25][26][27][28][29], fake packet-based SLP techniques were put out. For energy-constrained WSNs, such schemes (fake packet/fake sources-based SLP) would be energy-expensive, so they are not the focus here. This text describes only random walk- or phantom routing-based techniques since the proposed scheme falls under this category, as it has been noticed that those techniques are more effective for energy-constrained WSNs [18][21][30][31]. Conti et al. in [23] provide an in-depth review of the source location privacy (SLP) preservation methods suggested for WSN. The fundamental goal of employing random walk-based approaches is to make a packet’s journey appear entirely random to an adversary in order to defend against hop-by-hop and traffic analysis attacks. As relaying or forwarding nodes are picked at random from a sending node’s neighbor set, the packet’s journey takes on a random pattern [32].
For the first time, a random walk-based SLP solution was proposed by Ozturk et al. in [32]. Based on the panda hunter game as the baseline, in order to provide SLP in a WSN, their work introduced the phantom flooding scheme (PFS), which is based on conventional random walks. Every message goes through two phases such as a directed walk phase, followed by a flooding phase that sends the packet to the BS. In the directed walk phase, the packet is relayed for up to H hops in a random manner. Baseline flooding is used to flood the data packet once the hop count H reaches zero. The phantom node (PN) is the node at which the hop count H is zero. This solution showed some degree of privacy. Nevertheless, due to the uneven distribution of packet forwarding probability among neighboring nodes, the attainment of a purely random walk was unsuccessful, resulting in compromised privacy. Additionally, the study failed to consider the improvement in the network lifetime (NLT).
Different routing techniques were proposed to improve PFS, including [33][34][35]. In [34], the authors proposed a new SLP routing technique, namely, phantom routing with a locational angle (PRLA). The key idea is to forward a packet to the neighbor node that has the greatest inclination angle with reference to the base station. Each node in the network determines the angle of inclination between itself and its neighbors with respect to the base station. Each node then calculates the forwarding probability using that inclination angle. The chance is greatest for the node with the highest angle of inclination. When an event is detected, a node chooses its neighbor with the highest inclination angle and sends the packet to that node. The above procedure continues until the hop count H reaches zero, or if a node cannot transfer the packet to a neighbor node with the required inclination angle, it converts into a phantom node and forwards the packet via shortest path routing to reach the BS. It was observed that this scheme has an enhanced safety period compared to PSF. This is due to the fact that in PSF, tracing back the source of information can be easy since the packet forwarding probability is less evenly distributed among the neighboring nodes of the source of information, as shown in [35][36][37]. However, the routing path in this scheme lacks the requisite level of randomness necessary to ensure robust privacy for the source of information, and it also neglects the crucial factor of assessing and improving the network’s lifetime.
Li and Ren developed a scheme that uses a three-phase routing strategy [38]. In the first phase, the data packet is sent by the message source to the randomly selected intermediate node (RRIN) in the sensor domain, which subsequently directs it to a ring node. This stage’s goal was to offer local source location privacy. It was projected that the intermediate node would be far from the actual source node, making it difficult for the attackers to learn about the real source from the intermediate node chosen. To provide source location privacy at the network (global) level, the information in the packet is subsequently combined with that of other packets using a network mixing ring (NMR) in the following routing phase. In the end, selected nodes on the mixing ring forward the data packet to the SINK node. This scheme shows the same latency and power consumption as PFS, but its privacy level is higher. Additional routing strategies were proposed by Li and Ren [39][40] to improve this routing scheme for enhancing privacy.
More random walk-based SLP solutions have been proposed by different researchers with the aim of improving existing ones in terms of privacy. To safeguard both the source node and the sink, a source location privacy (SLP) mechanism was created by Chen et al. in [25]. Even though the article proposed four solutions, we consider only the forward random walk (FRW) strategy since the other three strategies are based on fake packets or fake sources, which belong to the different categories of SLP-preserving approaches. Each node in the forward random walk technique randomly chooses a neighbor node that is nearer to the BS when relaying a packet. This guarantees packet convergence at the BS. Since every packet’s destination is the BS, the nodes located in its sensing range send packets straight there. A forward random walk appears to provide a certain degree of privacy when the source node is situated far from the base station. However, significant privacy concerns emerge when the source node is in close proximity to the BS (base station).
For improving existing random walk-based routing protocols, directed random walk was proposed by Gu et al. in [41]. In a directed random walk, each node splits its neighbors into two groups that are opposite one another. Instead of employing a pure random walk, the next hop is chosen randomly from two groups to reach an intermediate node. Then, from the intermediate node, the next hop is chosen from the opposite group. It seems that the suggested scheme may offer some enhancements in the security period. However, the routing path lacks the necessary diversity to confound potential adversaries, and there is a notable absence of consideration for the network’s overall lifetime.
The research in [30] segregates the network into sectors. The selection of the sector is made randomly for each new packet that is sent from the source node. Once the packets reach that intended sector, they travel toward the BS. Sectors that are closest to the source are given lower priority than those that are away from the source. However, this technique exhibits weaker privacy due to its inadequately varied and unpredictable routing path, which fails to effectively mislead potential adversaries. Furthermore, the relationship between privacy and network lifespan has not been investigated.
Lilian et al. proposed another routing scheme in [18] that divides a network into quadrants with the aim of hiding the real source of information from a local adversary. When an asset is detected, a source node sends a packet at random to a predetermined proxy node that is chosen and situated in the quadrant next to its own. The proxy node then uses a forward random walk to deliver the packet to the BS. As the predefined proxy nodes in this work are found in only two quadrants, it may be easier for an adversary to find them as the packets are coming from only those quadrants. Furthermore, since the same set of nodes (from those quadrants) are utilized to deliver the packets to the BS, the NLT may also be impacted.
The article [42] developed a phantom node-based scheme. In this article, the source node chooses a limited number of network nodes to behave as phantom nodes that are not inside a circle encircling the source node whenever an asset is detected. The packets are then transferred via shortest path routing from the source node to the phantom nodes. From there, they are sent in either a clockwise or counterclockwise direction to reach the ring nodes and finally to the BS. In contrast to existing methods, this approach attains the highest degree of anonymity. Nonetheless, it is worth noting that the utilization of the same set of intermediate nodes for packet transmission may have implications for the network lifetime (NLT).
In [43], two solutions with the names PRBRW and PRLPRW were put forth. In PRBRW, packets are sent oppositely initially, and then the greedy technique is used to get to the BS. The limitation of this approach is that there is no improvement in network lifespan. To deal with this limitation, a second strategy, namely, PRLPRW, was suggested. In this technique, the packets are relayed randomly initially and then sent to the BS using min-hop routing. The random walk consists of a vertically up-or-down walk followed by a horizontally left-or-right walk. These two phases sought to increase traffic uncertainty and diversify routing paths. However, the equilibrium between privacy, i.e., the safety period, and the network lifetime problem was not solved using these techniques.
Li et al. proposed a new SLP scheme in [44] to enhance the privacy level of the source of information. In their scheme, the source node sets two candidate domains, and the proxy node that will serve as the source node is chosen from one of the candidate domains. Then, min-hop routing is used by the proxy node to forward the packets to the base station. Considering that the proxy node must be located within one of the candidate domains, all of which are established in close proximity to the source node in the same general direction, there is a potential vulnerability for the attack to discern the source node’s location. This vulnerability arises from the lack of diversity in the routing path. Notably, there was no effort to enhance the network lifetime (NLT), despite the fact that the next hop for packet relay was chosen based on the remaining residual energy.
The authors of [21] proposed an SLP scheme with the aim of protecting path location privacy and congestion avoidance by employing a jellyfish structure. Also, a network is divided into various subdivisions, and sensor nodes placed in these subdivisions select the target area by computing the transmission distance. A virtual ring and radial line are used to protect the routing path from a particular node to the sink, and congestion is prevented by the proposed alternate path routing. To streamline the communication channel between nodes and the sink, the network’s configuration involves the simultaneous selection of the number of radial lines and the virtual ring radius. In this arrangement, bell nodes within the virtual ring are routed probabilistically with varying angular orientations, whereas radial line pathways maintain a fixed routing direction. While this setup appears to offer a notable level of privacy, the recurrent use of the same radial line pathways for packet transmission to the sink raises concerns about potential implications for the network lifetime (NLT).
A bidirectional location-based SLP scheme was proposed in [20] by Zhou et al. for protecting both sink and source node. The scheme’s objectives include improving source and sink node privacy and balancing the quality of end-to-end communication. However, the network lifetime was not taken into account by the authors. An enhanced solution, namely, PSSLP, was proposed in [16]. The PSSLP technique aims to achieve uniform privacy. The notion of dividing a network into different sections was adopted so that the packets, based on distinct phases, are transmitted to the base station. This protocol excels at elevating the privacy of the source node when compared to existing ones. However, it encounters a challenge regarding network lifetime (NLT) due to the utilization of a constant ring, which was intended to enhance routing path diversity, inadvertently resulting in reduced NLT.
It is observed that the majority of these existing techniques do not prioritize the simultaneous improvement of both the safety period and network lifetime (NLT). Although these protocols exhibit a certain degree of improved privacy, they still show some degree of privacy vulnerabilities. Notably, there are shortcomings in terms of routing path randomization and diversity. Furthermore, it is evident that the majority of these solutions aimed at enhancing privacy did not consider the specific position of the source node within the network; consequently, this results in a privacy model that is depending on the source node’s position within the network. The proposed scheme (SLP-E) utilizes a reverse random walk, followed by the walk on annular rings to establish a completely random and diverse routing path to confuse passive attackers. It then employs min-hop routing in conjunction with the walk on dynamic rings to relay the packets to the base station (BS). The routing path in each phase is strategically designed with the aim of enhancing both privacy and network lifetime (NLT) while simultaneously achieving uniform privacy across the network. Furthermore, there has been no research aimed at SLP which investigates the impact of sensor nodes’ radio range on SLP schemes’ important performance metrics. These performance metrics typically encompass the safety period, network lifetime (NLT), entropy, capture percentage, energy consumption, and delay.

References

  1. Shah, S.H.; Yaqoob, I. A survey: Internet of things (IoT) technologies, applications, and challenges. In Proceedings of the 2016 IEEE Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 21–24 August 2016; pp. 381–385.
  2. Egala, B.S.; Pradhan, A.K.; Badarla, V.; Mohanty, S.P. Fortified-chain: A blockchain-based framework for security and privacy-assured internet of medical things with effective access control. IEEE Internet Things J. 2021, 8, 11717–11731.
  3. Li, M.; Chen, Y.; Kumar, N.; Lal, C.; Conti, M.; Alazab, M. Quantifying Location Privacy for Navigation Services in Sustainable Vehicular Networks. IEEE Trans. Green Commun. Netw. 2022, 6, 1267–1275.
  4. Sharma, N.; Bhatt, R. Source location privacy preservation in IoT-enabled event-driven WSNs. Int. J. Pervasive Comput. Commun. 2022.
  5. Awotunde, J.B.; Jimoh, R.G.; Folorunso, S.O.; Adeniyi, E.A.; Abiodun, K.M.; Banjo, O.O. Privacy and security concerns in IoT-based healthcare systems. In The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care; Springer: Berlin/Heidelberg, Germany, 2021; pp. 105–134.
  6. Bradbury, M.; Jhumka, A. Quantifying Source Location Privacy Routing Performance via Divergence and Information Loss. IEEE Trans. Inf. Forensics Secur. 2022, 17, 3890–3905.
  7. Pai, S.; Meingast, M.; Roosta, T.; Bermudez, S.; Wicker, S.B.; Mulligan, D.K.; Sastry, S. Transactional confidentiality in sensor networks. IEEE Secur. Priv. 2008, 6, 28–35.
  8. Sudevalayam, S.; Kulkarni, P. Energy harvesting sensor nodes: Survey and implications. IEEE Commun. Surv. Tutor. 2010, 13, 443–461.
  9. Singh, A.; Redhu, S.; Beferull-Lozano, B.; Hegde, R.M. Network-aware RF-energy harvesting for designing energy-efficient IoT networks. Internet Things 2023, 22, 100770.
  10. Zheng, K.; Jia, X.; Chi, K.; Liu, X. DDPG-Based Joint Time and Energy Management in Ambient Backscatter-Assisted Hybrid Underlay CRNs. IEEE Trans. Commun. 2023, 71, 441–456.
  11. Arms, S.W.; Townsend, C.P.; Churchill, D.L.; Galbreath, J.H.; Mundell, S.W. Power management for energy harvesting wireless sensors. In Smart Structures and Materials. Smart Electronics, MEMS, BioMEMS, and Nanotechnology; SPIE: Bellingham, WA, USA, 2005; Volume 5763, pp. 267–275.
  12. Leu, J.S.; Chiang, T.H.; Yu, M.C.; Su, K.W. Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor Network With Isolated Nodes. IEEE Commun. Lett. 2015, 19, 259–262.
  13. Akkaya, K.; Younis, M. A survey on routing protocols for wireless sensor networks. Ad Hoc Netw. 2005, 3, 325–349.
  14. Redhu, S.; Mahavar, R.; Hegde, R.M. Energy-efficient wake-up radio protocol using optimal sensor selection for IoT. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15–18 April 2018; pp. 1–6.
  15. Wang, N.; Fu, J.; Li, J.; Bhargava, B.K. Source-location privacy protection based on anonymity cloud in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 2019, 15, 100–114.
  16. Mukamanzi, F.; Raja, M.; Koduru, T.; Datta, R. Position-Independent and Section-Based Source Location Privacy Protection in WSN. IEEE Trans. Ind. Inform. 2023, 19, 6636–6646.
  17. Bradbury, M.; Jhumka, A.; Maple, C. A spatial source location privacy-aware duty cycle for the internet of Things sensor networks. Acm Trans. Internet Things 2021, 2, 1–32.
  18. Mutalemwa, L.C.; Shin, S. Achieving Source Location Privacy Protection in Monitoring Wireless Sensor Networks through Proxy Node Routing. Sensors 2019, 19, 1037.
  19. Wang, H.; Wu, L.; Zhao, Q.; Wei, Y.; Jiang, H. Energy Balanced Source Location Privacy Scheme Using Multibranch Path in WSNs for IoT. Wirel. Commun. Mob. Comput. 2021, 2021, 6654427.
  20. Zhou, Z.; Wang, Y.; Li, P.; Chang, X.; Luo, J. Node Location Privacy Protection in Unattended Wireless Sensor Networks. Math. Probl. Eng. 2021, 2021, 5539382.
  21. Christopher, V.B.; Jasper, J. Jellyfish dynamic routing protocol with mobile sink for location privacy and congestion avoidance in wireless sensor networks. J. Syst. Archit. 2021, 112, 101840.
  22. Long, J.; Dong, M.; Ota, K.; Liu, A. Achieving source location privacy and network lifetime maximization through tree-based diversionary routing in wireless sensor networks. IEEE Access 2014, 2, 633–651.
  23. Conti, M.; Willemsen, J.; Crispo, B. Providing source location privacy in wireless sensor networks: A survey. IEEE Commun. Surv. Tutor. 2013, 15, 1238–1280.
  24. Jiang, J.; Han, G.; Wang, H.; Guizani, M. A survey on location privacy protection in wireless sensor networks. J. Netw. Comput. Appl. 2019, 125, 93–114.
  25. Chen, H.; Lou, W. On protecting end-to-end location privacy against local eavesdroppers in wireless sensor networks. Pervasive Mob. Comput. 2015, 16, 36–50.
  26. Wang, H.; Han, G.; Zhang, W.; Guizani, M.; Chan, S. A probabilistic source location privacy protection scheme in wireless sensor networks. IEEE Trans. Veh. Technol. 2019, 68, 5917–5927.
  27. Han, G.; Wang, H.; Miao, X.; Liu, L.; Jiang, J.; Peng, Y. A dynamic multipath scheme for protecting source-location privacy using multiple sinks in WSNs intended for IIoT. IEEE Trans. Ind. Inform. 2019, 16, 5527–5538.
  28. Han, G.; Miao, X.; Wang, H.; Guizani, M.; Zhang, W. Cpslp: A cloud-based scheme for protecting source location privacy in wireless sensor networks using multi-sinks. IEEE Trans. Veh. Technol. 2019, 68, 2739–2750.
  29. Han, G.; Liu, Y.; Wang, H.; Zhang, Y. A collision-free-transmission-based source location privacy protection scheme in UASNs under time slot allocation. IEEE Internet Things J. 2023, 10, 1546–1557.
  30. He, Y.; Han, G.; Wang, H.; Ansere, J.A.; Zhang, W. A sector-based random routing scheme for protecting the source location privacy in wsns for the Internet of Things. Future Gener. Comput. Syst. 2019, 96, 438–448.
  31. Raja, M.; Datta, R. An enhanced source location privacy protection technique for wireless sensor networks using randomized routes. Iete J. Res. 2018, 64, 764–776.
  32. Ozturk, C.; Zhang, Y.; Trappe, W. Source-location privacy in energy-constrained sensor network routing. In Proceedings of the 2nd ACM workshop on Security of Ad hoc and Sensor Networks, Washington, DC, USA, 25 October 2004; pp. 88–93.
  33. Kamat, P.; Zhang, Y.; Trappe, W.; Ozturk, C. Enhancing source-location privacy in sensor network routing. In Proceedings of the 25th IEEE International Conference on distributed computing systems (ICDCS’05), Columbus, OH, USA, 6–10 June 2005; pp. 599–608.
  34. Wang, W.-P.; Chen, L.; Wang, J.-X. A source-location privacy protocol in WSN based on locational angle. In Proceedings of the 2008 IEEE International Conference on Communications, Beijing, China, 19–23 May 2008; pp. 1630–1634.
  35. Wang, H.; Sheng, B.; Li, Q. Privacy-aware routing in sensor networks. Comput. Netw. 2009, 53, 1512–1529.
  36. Xi, Y.; Schwiebert, L.; Shi, W. Preserving source location privacy in monitoring-based wireless sensor networks. In Proceedings of the 20th IEEE International Parallel and Distributed Processing Symposium, Rhodes Island, Greece, 25–29 April 2006; p. 425.
  37. Zhang, L. A self-adjusting directed random walk approach for enhancing source-location privacy in sensor network routing. In Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, Amman, Jordan, 17–20 September 2006; pp. 33–38.
  38. Li, Y.; Ren, J. Preserving Source-Location Privacy in Wireless Sensor Networks. In Proceedings of the 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, Rome, Italy, 22–26 June 2009; pp. 1–9.
  39. Li, Y.; Ren, J.; Wu, J. Quantitative measurement and design of source-location privacy schemes for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 2011, 23, 1302–1311.
  40. Li, Y.; Ren, J. Providing source-location privacy in wireless sensor networks. In International Conference on Wireless Algorithms, Systems, and Applications; Springer: Berlin/Heidelberg, Germany, 2009; pp. 338–347.
  41. Gu, C.; Bradbury, M.; Jhumka, A.; Leeke, M. Assessing the performance of phantom routing on source location privacy in wireless sensor networks. In Proceedings of the 2015 IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC), Zhangjiajie, China, 18–20 November 2015; pp. 99–108.
  42. Wang, H.; Han, G.; Zhu, C.; Chan, S.; Zhang, W. Tcslp: A trace cost based source location privacy protection scheme in wsns for smart cities. Future Gener. Comput. Syst. 2020, 107, 965–974.
  43. Manjula, R.; Koduru, T.; Datta, R. Protecting Source Location Privacy in IoT-Enabled Wireless Sensor Networks: The Case of Multiple Assets. IEEE Internet Things J. 2021, 9, 10807–10820.
  44. Li, F.; Ren, P.; Yang, G.; Sun, Y.; Wang, Y.; Wang, Y.; Li, S.; Zhou, H. An efficient anonymous communication scheme to protect the privacy of the source node location in the Internet of Things. Secur. Commun. Netw. 2021, 2021, 6670847.
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Subjects: Computer Science, Theory & Methods
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Florence Mukamanzi
,
Raja Manjula
,
Raja Datta
,
Tejodbhav Koduru
,
Damien Hanyurwimfura
,
Mukanyiligira Didacienne
View Times: 249
Revisions: 2 times (View History)
Update Date: 10 Jan 2024
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