Skin cancer involves abnormal growth of skin cells, typically caused by ultraviolet radiation exposure. Timely and accurate detection is essential to mitigate significant health risks and ensure effective treatment. This paper proposes a nanoantenna to enhance diagnostic and therapeutic capabilities for skin cancer detection. These antennas, emitting electromagnetic waves in the terahertz band (0.1–10 THz), improve integration for miniaturized wireless systems and serve as a foundation for the Internet of Medical Things (IoMT). We design a miniaturized, metamaterial-inspired gold-patch axe-shaped nanoantenna (), implemented in CST Studio Software. The antenna resonates at 1.152 THz, with a very low return loss (dB), a gain of 2.42 dBi, and a bandwidth of 40 GHz. The proposed antenna can be used as a sensor, considering the S11 spectra as a key parameter to differentiate between normal and cancerous skin (i.e., basal cell carcinoma). The simulation demonstrates significant and quantifiable differences between normal and cancerous skin and also highlights the proposed antenna’s suitability for applications such as radar systems, satellite communications, and advanced measurement technologies.

The rate of tetracycline (TC) production and consumption has tremendously increased lately for the treatment of infectious diseases in humans and animals. Their release into the environment through overuse and improper disposal practices has raised serious concerns for the ecosystem deliberating the significance of easy detection approaches towards TC. By virtue of the excellent physical and chemical attributes of Metal-Organic frameworks (MOFs), a Tb-doped MOF was developed for the selective and accurate detection of TC. Remarkably, the strong green luminescence of Tb3+ is quenched efficiently with TC even in the presence of other antibiotics. The sensor material exhibits a highly sensitive turn-off response with an exceptionally low limit of detection of 0.07 ?M and a quenching constant value of 1.68 × 104 M-1. The mechanisms for selective quenching of fluorescence towards TC are investigated in detail using theoretical calculations and simulations to demonstrate the occurrence of electron transfer within the system. The detection behavior is also tested with river water samples collected from Chennai rivers which exhibited an excellent recovery of results qualifying Tb@ZB as a promising candidate to be developed into a prototype device to enable facile rapid analysis in real-time samples. The sensing approach provides a crucial ground for monitoring the presence of TC with 87–101 % reliability in wastewater systems

A novel microstrip patch antenna of size 210 × 205 × 22 ?m3 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 ?m3 ( ? 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

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

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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).

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.

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.

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.

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.

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.

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.