Path Loss Prediction Using Machine Learning Models for in-vivo Wireless Nanosensor Networks in Cardiac Health Monitoring
Conference paper, Lecture Notes in Networks and Systems, 2025, DOI Link
View abstract ⏷
Accurate path loss predictions are paramount for the optimization of future 6-G and 7-G in-vivo Wireless Nanosensor networks iWNSN. In this research, we focus on the critical aspect of in-vivo channel path loss prediction using the machine-learning approach for cardiac health monitoring. We initially present the theoretical model used for predicting and calculating the total path loss due to absorption and spreading loss. Using these models we present the numerical simulation results using the data collected from the online repository. We then use machine learning models such as Artificial Neural Network, Polynomial Regression, and Gradient Boosting algorithms to predict the total path loss in in-vivo communication channels and compare the performance of these models with the analytical models. The inference is that the gradient boosting model is outperforming compared to ANN and polynomial regression models with R2 scores of 0.9996, 0.9817, and 0.9547 respectively. We conclude from this work that machine learning models could be used for predicting path loss prediction in iWNSNs.
A Terahertz Split Ring Resonator Nanosensor for Cardiac Biomarker Detection
Sharan B., Elayan H., Ghosh A., Datta R., Jornet J.M., Manjula R.
Article, IEEE Sensors Journal, 2025, DOI Link
View abstract ⏷
This article presents a terahertz (THz) metamaterial-based nanosensor employing a split ring resonator (SRR) for the detection of N-terminal pro–B-type natriuretic peptide (NT-proBNP), a cardiac biomarker released in response to increased myocardial pressure and volume overload in the heart. The sensor is designed and simulated in CST Studio to enable real-time detection via changes in the refractive index of NT-proBNP associated with cardiac abnormalities. Validation is performed through equivalent circuit modeling (ECM) using the Advanced Design System (ADS). The nanosensor achieves a sensitivity of 1460 GHz/RIU, a Q-factor of 22.06, and a figure of merit (FOM) of 41.71. Assuming minimally invasive placement in the pericardium, signal attenuation is modeled using a path-loss framework that accounts for the serous and fibrous pericardial layers. Transmission line theory is applied to evaluate the intrinsic impedance, reflection coefficients, and attenuation characteristics of THz waves propagating through cardiac tissue. The model estimates the received power at a nanocontroller located at the fibrous layer and is validated using COMSOL Multiphysics. By leveraging refractive index variations induced by NT-proBNP, this nanosensor enables intrabody THz communication as a diagnostic modality. The platform is particularly suited for detecting conditions such as pericarditis, where biomarker fluctuations and pericardial thickening jointly modulate the THz signal.
Analysis of THz Signal in Multi-layered Biological Tissues for in-Vivo Communications: Heart Monitoring
Sharan B., Manjula R., Ghosh A., Avinash M.V., P K.L., Teja P.V.S.P.
Conference paper, Lecture Notes in Networks and Systems, 2025, DOI Link
View abstract ⏷
In this article, we aim to investigate the electromagnetic (EM) THz (0.1 THz to 10 THz) signal loss by considering the reflections across the different layers of the human heart wall. We model the human heart wall as a multi-layer heterogeneous dielectric media and apply the theory of transmission laws from EM theory to study the phenomena of multiple reflections. On evaluating the intrinsic impedance of each layer, reflection coefficient across each interface, and overall reflection coefficient we observe that the epicardium layer provides maximum impedance to signal propagation in the THz band and hence the interfaces sharing the epicardium layer are more prone to reflect signals due to a higher reflection coefficient. Finally, the power reflected and transmitted from the final interface compared to the transmitted power from the initial layer is calculated using the overall reflection coefficient which is estimated via a black box concept. The results reveal that although up to a maximum of 25% of the initial transmitted power can be lost due to reflection when the frequency of operation belongs to the lower end of the considered spectrum, the value reduces to a mean of 2.5% as the frequency of operation increases.
Terahertz and Microwave Signal Behavior in Heart Tissues: Toward Nano-Biomedical Diagnostic Systems
Manjula R., Sai Kusum Sarayu N., Sai Sruthi N., Samaya D., Tarun Teja K.
Conference paper, 2025 IEEE 20th Nanotechnology Materials and Devices Conference, NMDC 2025, 2025, DOI Link
View abstract ⏷
This paper investigates the propagation characteristics of terahertz (THz, 0.1-10 THz) and microwave (500 MHz-20 GHz) signals through cardiac tissues to enable nanoscale biomedical diagnostic systems. Using the Double Debye model with parameters extracted from ovine heart tissue datasets, we analyzed spreading, absorption, and total path loss across six representative tissues. Results show that microwave signals experience path loss below 60 dB for 1 mm tissue thickness at 5 GHz, supporting reliable implant-to-skin communication, while THz signals - despite higher attenuation - retain transmittance up to 90% enabling high-resolution, near-field cardiac sensing and communication. These findings establish the complementary use of both frequency bands for future hybrid cardiac monitoring systems.
The effect of hop-count modification attack on random walk-based SLP schemes developed for WSNs: a study
Raja M., Ghosh A., Kumar C.P., Samba S., Shariff C.N.
Article, International Journal of Information Security, 2025, DOI Link
View abstract ⏷
Source location privacy (SLP) has been of great concern in WSNs when deployed for habitat monitoring applications and is addressed by employing privacy-preserving routing schemes. However, in the existing works, the attacker is assumed to be passive in nature and backtracks to the source node by eavesdropping on the transmitted messages. The effectiveness of such SLP solutions when faced with an active attack is not yet known and is the purview of the current study. In this regard, we initially introduce a novel hybrid attacker model and then assess the impact of such a model on the location privacy performance of three existing time-to-live (TTL)-based random walk solutions—phantom routing scheme (PRS), source location privacy using randomized routes (SLP-R), and position-independent section-based scheme (PSSLP). The location privacy performance in terms of privacy metrics such as capture ratio, safety period, and entropy. It is observed that PSSLP is affected most by the proposed hybrid model with a 125% increase in capture ratio, 83.58%, and 17.36% respective reduction in safety period and entropy. The results indicate the importance and relevance of such attacks.
Gold-based nanoantenna design using golden ratio optimization for in-vivo communication at terahertz frequency
Sharan B., Manjula R., Gupta S.H., Rajawat A., Ghosh A., Datta R.
Article, Nano Communication Networks, 2025, DOI Link
View abstract ⏷
In this article, a novel microstrip patch antenna of size 210 × 205 × 22 μm3 operating in the terahertz band is proposed. We then perform optimization of the proposed antenna using the Golden Ratio technique to realize an antenna with reduced dimensions and better performance. The optimized nanoantenna has reduced dimensions of 120 × 160 × 14 μm3 (≈ 71.61 % reduction in volume); improved return loss S11 (< -45.43 dB); gain (> 5.29 dBi), and bandwidth (156.9 GHz i.e., 45% more). The results are validated through an equivalent circuit model (ECM) in Advanced Design System (ADS), demonstrating good agreement with the CST Studio results. Next, a human heart-phantom model has been created and tested for each designed scenario. It examines the interactions between the heart tissues and the proposed antenna, and it identifies the substrate material that performs the best. The results show that polytetrafluoroethylene (PTFE) material performs better than other substrates. Additionally, the research includes an analysis of the link budget of terahertz channels in the intrabody nanocommunication networks—a bio-medical application. The findings indicate the feasibility of using nanoantennas for practical in-vivo nanocommunications.
High-performance multiband terahertz nanoantenna for advanced wireless nanocommunications
Article, Engineering Research Express, 2025, DOI Link
View abstract ⏷
This article presents a novel multiband, biocompatible MIMO nanoantenna for terahertz applications, designed to address the growing demand for faster data transfer rates in future wireless nanocommunications systems. The design process began with a foundational single-element antenna (157.12 × 184.40 × 11 μm3) constructed from a gold patch and ground on a PTFE substrate. This initial element, resonating at 1.041, 1.602, 2.199, and 2.814 THz, was subsequently expanded into a two-port MIMO structure (157.12 × 276.60 × 11 μm3) to enhance channel capacity. The proposed MIMO nanoantenna operates nearly at the same frequencies as the single-element nanoantenna. Still, it delivers better performance, achieving a significantly higher channel capacity (up to 1.163 Tbps at 1.044 THz) and gain (up to 9.06 dBi at 2.214 THz). Furthermore, the MIMO system demonstrates excellent diversity performance, with an ECC close to zero and a consistently high DG of 9.999 dB—indicating effective signal fading mitigation and enhanced reliability. The proposed nanoantennas prove highly effective for multiband operations up to 3 THz, demonstrating significant potential for various advanced applications. These include low-power 6G communications, high-resolution terahertz imaging, in-vivo biomedical sensing, and other high-speed nanoscale communication systems.
Characterization of Heart-Centric Nanoscale Communication at Terahertz and Optical Bands
Manjula R., Ghosh A., Jayaswi Prasad M., Mettu S., Bacchala B.A., Bhojanapalli S.T., Ankalla G.W., Bethala S.
Conference paper, Lecture Notes in Networks and Systems, 2024, DOI Link
View abstract ⏷
This article presents an analytical model and characterization of path loss in a human heart-centric nanoscale network. The frequencies under investigation include the terahertz (THz) band (0.1–10 THz) and the optical band (400–750 THz). The path loss models incorporate the impact of spreading loss from signal propagation and absorption loss caused by different layers in the human heart. The results indicate a dependence of the path loss not only on the thickness of the different layers but also on the frequency of operations. The useful insights obtained from the path loss trends at the explored frequencies can facilitate the deployment of nanosensor networks for heart-centric communications.
Increasing Source Privacy and Network Lifetime without Affecting Latency: A Strategic Random Walk for WSNs∗
Mukamanzi F., Raja M., Datta R., Koduru T., Hanyurwimfura D.
Conference paper, 2023 8th International Conference on Computer and Communication Systems, ICCCS 2023, 2023, DOI Link
View abstract ⏷
Remote monitoring in wireless sensor networks (WSNs) requires enhanced privacy and long-term monitoring of objects or events without escalating delay. To address this problem, a strategic random walk routing for protecting source location privacy (SRWSLP) in wireless sensor networks (WSNs) is proposed in this article. The proposed technique routes the packets from the source node to the base station (BS) using three phases of routing, namely: i) adaptive backward random walk (A-BRW), ii) adaptive equal depth routing (A-EDR), and iii) forward random walk (FRW). In order to give an impression to a backtracking attacker that the routing pathways are dynamic, the A-BRW and A-EDR phases are designed to carefully route the packets away from the source node in the first two phases of routing. In the third phase, the packets are sent to the base station (BS) using the forward random walk. The objective of the solution is to achieve improved privacy and network lifetime without affecting delay. Simulation results have demonstrated that the proposed technique performs better than the existing random walk class of SLP techniques.
A Total Randomized SLP-Preserving Technique with Improved Privacy and Lifetime in WSNs for IoT and the Impact of Radio Range on SLP
Mukamanzi F., Manjula R., Datta R., Koduru T., Hanyurwimfura D., Didacienne M.
Article, Sensors, 2023, DOI Link
View abstract ⏷
Enhanced source location privacy and prolonged network lifetime are imperative for WSNs—the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by a walk on annular rings, to create divergent routing paths in the network, and finally, min-hop routing together with the walk on dynamic rings to send the packets to the base station (BS). The existing random walk-based SLP approaches have either focused on enhancing only privacy at the cost of network lifetime (NLT) or have aimed at improving the amount of privacy without degrading the network lifetime performance. Unlike these schemes, the objectives of the proposed work are to simultaneously improve the safety period and network lifetime along with achieving uniform privacy. This combination of improvements has not been considered so far in a single SLP random walk-based scheme. Additionally, this study investigates for the first time the impact of the sensors’ radio range on both privacy strength and network lifetime metrics in the context of SLP within WSNs. The performance measurements conducted using the proposed analytical models and the simulation results indicate an improvement in the safety period and network lifespan. The safety period in SLP-E increased by 26.5%, 97%, 123%, and 15.7% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. Similarly, the network lifetime of SLP-E increased by 17.36%, 0.2%, 83.41%, and 13.42% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. No matter where a source node is located within a network, the SLP-E provides uniform and improved privacy and network lifetime. Further, the simulation results demonstrate that the sensors’ radio range has an impact on the safety period, capture ratio, and the network lifetime.
Source location privacy in wireless sensor networks: What is the right choice of privacy metric?
Article, Wireless Networks, 2023, DOI Link
View abstract ⏷
Today, communication between objects, machines, objects to machines and to humans is possible due to the Internet of Things (IoT). However, their applicability is restricted mostly to areas that are inhabited by humans. Monitoring and tracking in wilderness areas is still a challenging task to date, if not impossible. To bridge this gap, IoT networks are instrumented with Wireless Sensor Networks that are capable of providing remote services through multi-hop communication paradigm. Since these networks are deployed in deserted places, it becomes very crucial to protect the privacy of the location information of critical events or sources that these networks are monitoring. To this end, we propose a new random-walk based routing protocol namely BLS (Backward walk, L-walk, Shortest path walk) to protect the location of critical sources/events. The aim is to break the correlations between the network traffic and render the traffic-analysis efforts of the attacker, in locating the source of information, useless. In addition, we also evaluate the performance of the proposed technique by comparing it with the existing techniques using different privacy metrics such as safety period, entropy and capture ratio. Through this research work, we observed that the performance of source location privacy (SLP) preservation techniques is giving differing results for different privacy metrics. Although the proposed solution outperforms in terms of entropy metric by 104.59-folds improvements compared to Forward Random Walk technique, its performance in terms of safety period and capture ratio metrics are very poor with an improvement of just 0.65-folds and 0.1-fold respectively. Therefore, there is a dire need to come up with a right choice of metric for SLP preservation techniques.
Position-Independent and Section-Based Source Location Privacy Protection in WSN
Mukamanzi F., Raja M., Koduru T., Datta R.
Article, IEEE Transactions on Industrial Informatics, 2023, DOI Link
View abstract ⏷
Privacy of critical locations (or events) is essential when monitored by wireless sensor networks. To mitigate such issues, in this article, a new privacy protection technique named position-independent and section-based source location privacy (PSSLP) is developed. A biased random walk and greedy walk using a three- or four-phase routing strategy is employed here, where the number of phases depends on the network segment in which the source is situated. The biased random walk is intended to send packets away from the source of information and make routing paths appear dynamic to the eavesdropper, whereas, the greedy routing ensures that the packets converge at the base station. The objective of the solution is to achieve a uniform amount of privacy irrespective of the position of the asset in the network without compromising the network lifetime. Performance evaluation is done using developed analytical models and simulation results reveal that PSSLP achieves 8247.06- and 33.0- folds improvement in terms safety period and network lifetime, respectively, compared to no SLP protection technique (i.e., shortest path routing technique).
Cluster-Head Selection Protocol for Improving the Network Lifetime of Wireless Sensor Network
Conference paper, 2023 9th International Conference on Signal Processing and Communication, ICSC 2023, 2023, DOI Link
View abstract ⏷
Prolonged network operations are crucial for any Wireless Sensor Network (WSN) based applications such as health care, military, industrial, etc. The fixed energy and the restrained communication range of the sensor nodes (SNs) make it challenging to achieve a longer life of network operations. Generally, the data collection and direct message transmissions to the base station (BS) consume most of the SNs' energy resulting in a shorter network lifetime. Reduction in the energy expended on these data transmissions leads to an improvement in the WSNs' network lifetime. Clustering approaches are employed to mitigate this issue. The cluster head (CH) in each cluster collects the data from the SNs and forwards it to the BS. Proper CH node selection is vital in achieving enhanced network lifetime. Therefore, in this work, a new clustering technique is proposed that probabilistically selects the best CHs for each cluster by considering the residual energy of each SN. The suggested technique has been compared with the existing LEACH scheme and the experimental outcomes show the suggested technique has improved the network lifetime by an average of 8.32% and 14.23% in terms of the first and the last node dead respectively.
Protecting Source Location Privacy in IoT-Enabled Wireless Sensor Networks: The Case of Multiple Assets
Manjula R., Koduru T., Datta R.
Article, IEEE Internet of Things Journal, 2022, DOI Link
View abstract ⏷
A major limitation to the use of wireless sensor networks (WSNs) in asset monitoring applications is security and privacy, particularly the privacy of source location information. In this article, we develop two phantom routing-based solutions to provide source location privacy for the case of multisource/asset scenario - the case that has received very little attention in the literature. The idea of phantom routing is to relay the packets to a distant node in a random fashion to obfuscate the traffic flows and confuse the attacker. The first technique phantom routing-based backward random walk (PRBRW) uses a combination of backward random walk (RW) and a greedy forwarding approach to route the packets to the base station (BS). Although the first approach has better performance improvements in terms of capture ratio and safety period it hampers the lifetime of the network and has a poor entropy metric. To better this problem, an improved phantom routing scheme phantom routing-based L-path RW (PRLPRW) is proposed. The second technique has three phases: 1) pure RW; 2) L-walk; and 3) greedy walk. This technique performs well in terms of capture ratio, safety period, and entropy metrics. The improvement in network lifetime is 10-folds and entropy is 477-folds when compared with PRBRW. The performance is evaluated using the developed analytical models and compared with the baseline protection-less scheme shortest path routing (SPR). It is observed that PRBRW and PRLPRW, respectively, have 60- and 73-fold improvements in terms of capture ratio when compared with SPR, whereas existing phantom routing-based pure RW and forward RW techniques, respectively, have only 54- and 34-fold improvements.
Artificial Olfaction for Detection and Classification of Gases Using e-Nose and Machine Learning for Industrial Application
Manjula R., Narasamma B., Shruthi G., Nagarathna K., Kumar G.
Book chapter, Studies in Computational Intelligence, 2021, DOI Link
View abstract ⏷
An artificial electronic nose (e-nose) is developed, that mimics the human olfactory system, as an alternative to the human nose. In this work, we aim to develop a mini prototype of e-nose and use it for the detection of various types of gases present in the atmosphere. We then use existing machine learning models to carry out the classification task. Our study shows that the proposed e-nose system can find its potential application in various fields such as medical health care to detect chemical gas leakage, industries to detect hazardous gases, a substitute to the human nose when people are suffering from anosmia disorder, etc. We use k-Nearest Neighbours (kNN), Support Vector Classifier (SVC), Linear Regression (LR), Decision Tree (DT) and Random Forest (RF) algorithms to test the classification accuracy. Through the experimentation results we found that random forest model performs better with 97.77% classification accuracy compared to other models such as kNN, SVC, logistic regression and decision tree, whose classification accuracy are 93.33%, 62.22%, 71.11%, and 91.11% respectively. In future, we intend to extend this pilot work to automate the entire task where detected gaseous information by the e-nose is sent directly to the user to its mobile phone via Internet, instantly in real time fashion. We also aim to study using Deep Learning Techniques.
Measuring web content credibility using predictive models
Book chapter, Lecture Notes in Networks and Systems, 2020, DOI Link
View abstract ⏷
Web content credibility is a measure of believable and trustworthy of the web content that is perceived. Content can turn out to be unreliable if it is not up-to-date and it is not measured for quality or accuracy and therefore, web content credibility is important for the individuals to access the content or information. The analysis of content credibility is an important and challenging task as the content credibility is expressed on essential factors. This paper focus on building predictive models to discover and evaluate credibility of a web page content through machine learning technique. A corpus of 300 web page contents have been developed and the factors like Readability, Freshness, Duplicate Content are defined and captured to model the credibility of web content. Two different labeling such as binary labeling and numeric labeling are used for defining credibility. In case of binary labeling, the high and low credibility of web content are represented by 1 and 0, respectively, whereas in case of numeric labeling five-point scale rating is used to mark the content credibility. Accordingly, two independent datasets have been developed. Different regression algorithms such as Linear Regression, Logistic Regression, Support Vector Regression (SVR) are employed for building the predictive models. Various experiments have been carried out using two different datasets and the performance analysis shows that the Logistic Regression model outperforms well when compared to other prediction algorithms.
Deep neural network for evaluating web content credibility using keras sequential model
Conference paper, Lecture Notes in Electrical Engineering, 2020, DOI Link
View abstract ⏷
Web content credibility determines the measure of acceptable and reliable of the web content that is observed. Content will prove to be unreliable if it is not updated, and it is not controlled for remarkable, and therefore, web content credibility is considerably essential for the people to assess the content. The analysis of content credibility is a vital and challenging task as the content credibility is outlined on crucial factors. This paper focuses on building deep neural network (DNN)-based predictive model using sequential model API to evaluate credibility of a webpage content. Deep neural network (DNN) is considered as an extremely promising decision-making architecture, and it performs feature extraction and transformation with the use of refined statistical modeling. A corpus of 400 webpage contents has been developed, and the factors like readability, freshness, and duplicate content are defined and captured from the webpage content. These features are redefined, and a new set of features is self-learned through the deep layers of neural network. Numeric labeling is used for defining credibility, wherein five-point Likert scale rating is used to denote the content credibility. By using sequential model, KerasRegressor with ADAM optimizer and a multilayer network is generated for building DNN-based predictive model and discovered that deep neural network outperforms other general regression algorithms in prediction scores.
A novel source location privacy preservation technique to achieve enhanced privacy and network lifetime in WSNs
Article, Pervasive and Mobile Computing, 2018, DOI Link
View abstract ⏷
In this paper, we propose a two-phase routing technique using multiple virtual sources to provide enhanced source location privacy in Wireless Sensor Networks (WSNs). We use the concept of escape-angle and random walks that is based on potential energy. The proposed method routes packets to the base station via different virtual sources located at various positions in the network. The key idea of this work is to exploit the excess energy available in the non-hotspot areas of the network to generate dispersive routes between source node and the virtual sources. This approach maximizes safety-period without hampering the network lifetime. We present mathematical models to estimate the overall energy expenditure that incurs at each node during Min Hop Routing phase (phase two). We then determine the remaining amount of energy which could be used for Stochastic and Diffusive Routing phase (phase one). Simulation results show that the proposed technique achieves improved safety-period without hampering the network lifetime.
Efficient aggregation technique for data privacy in wireless sensor networks
Article, IET Networks, 2018, DOI Link
View abstract ⏷
In wireless sensor networks (WSNs), the existing cluster-based private data aggregation techniques are energyintensive due to high message transmission complexity. Reliable data transmissions are also vital for resource constraint WSNs. To address these issues, the authors propose a reliability enabled private data aggregation technique that has message transmission complexity of O(N). Every node in the cluster cleaves its data into n integrants using simple modular arithmetic with suitable prime moduli and transmits to the cluster heads (CHs) for intermediate aggregation. The CHs, in turn, forward the partial aggregate data to the base station where the final aggregate is recovered using an elegant Chinese remainder theorem. The authors use data privacy, communication overhead, and reliability metrics to gauge the performance of the proposed work. Numerical and simulation results demonstrate that the proposed solution outperforms the existing schemes having O(N2) communication complexity.
An Enhanced Source Location Privacy Protection Technique for Wireless Sensor Networks using Randomized Routes
Article, IETE Journal of Research, 2018, DOI Link
View abstract ⏷
Asset monitoring and tracking is an important application of wireless sensor networks (WSNs). Tiny sensors collect information about the assets and convey this message to the base station using multi-hop routing techniques. For instance, in habitat monitoring application, the nodes collect details of the endangered species and report to the central controller, i.e., the base station. Preserving the privacy of these assets from the attackers is imperative. An attacker may backtrack the message flow and eventually capture the asset. In this work, we aim to improve the source location privacy, which is measured by the safety period, by designing a new routing technique where randomized routes in the whole network are generated distributively between the node of origin and the base station. The diversity of the routing paths will lengthen the backtracking period of the attacker and thus increase the safety period. The key feature of the solution is that it achieves improved source location privacy without hampering the network lifetime. Unlike the existing solutions, the proposed technique does not employ any fake sources that decreases the network lifetime due to generation of large number of dummy packets. The solution performs quite well even when the asset is near the base station. The proposed method is analysed and compared with forward random walk and phantom single path routing schemes. Simulation results demonstrate that the proposed method achieves improved privacy level with more uncertainty in the routing paths than the current techniques.
Application of the Chinese remainder theorem for source location privacy in wireless sensor networks
Conference paper, 2016 IEEE Students' Technology Symposium, TechSym 2016, 2017, DOI Link
View abstract ⏷
In this paper, we investigate an application of the Chinese Remainder Theorem (CRT) for a novel privacy preserving routing technique in wireless sensor networks (WSNs). The distinctive nature of the proposed technique is gathering the data, aggregating and then slicing the aggregate using t-out-of-n secret sharing scheme (based on CRT) and then route the packets to the sink via multiple paths. The sink will recover the final aggregate with just t shares using CRT. The multi-objective of the proposed technique is to provide data privacy, identity privacy, source location privacy, and route privacy. The solution also mitigates the problem of packet losses associated with the unreliable wireless communication medium by improving the reliability. We also propose an enhanced privacy preserving routing technique that achieves network wide routing paths, and overcomes the drawback of existing phantom single-path routing (PSPR) and forward random walk-based routing (FRW) schemes. Finally, we present the details of the proposed technique with a numerical example and give its possible application for secret and dynamic routing in WSNs. We also show the simulation results to validate the proposed method and comparison with PSPR and FRW techniques demonstrates that our scheme outperforms the other two solutions.
An energy-efficient routing technique for privacy preservation of assets monitored with WSN
Conference paper, IEEE TechSym 2014 - 2014 IEEE Students' Technology Symposium, 2014, DOI Link
View abstract ⏷
Wireless Sensor Networks (WSNs) are deployed to monitor the assets (endangered species) and report the locations of these assets to the Base Station (BS) also known as Sink. The hunter (adversary) attacks the network at one or two hops away from the Sink, eavesdrops the wireless communication links and traces back to the location of the asset to capture them. The existing solutions proposed to preserve the privacy of the assets lack in energy efficiency as they rely on random walk routing technique and fake packet injection technique so as to obfuscate the hunter from locating the assets. In this paper we present an energy efficient privacy preserved routing algorithm where the event (i.e., asset) detected nodes called as source nodes report the events' location information to the Base Station using phantom source (also known as phantom node) concept and a-angle anonymity concept. Routing is done using existing greedy routing protocol. Comparison through simulations shows that our solution reduces the energy consumption and delay while maintaining the same level of privacy as that of two existing popular techniques. © 2014 IEEE.
