Summary Traditional feature‐based semantic similarity (SS) approaches exploit the Wikipedia features in term of sets. They evaluate the similarity of concepts based on the commonalities among their feature sets. However, these feature‐based approaches treat all the features equally in similarity evaluation. Therefore, they ignore the underlying statistics of the features and consequently lose the essential semantic details about them. One solution is that each feature can be assigned a specific weight using its statistics. This weight will reflect the relative importance of a feature in similarity evaluation. Therefore, in this paper, based on two statistical models, ie, information content and TFIDF, we propose some hybrid semantic similarity measurement methods. Firstly, we propose some new methods called weighting functions to compute the weights of the features and feature sets in Wikipedia. Secondly, based on the weighting functions, we propose some new weighted feature‐based SS approaches for Wikipedia concepts. Thirdly, we evaluate the proposed methods on well‐known benchmarks for English, German, French, and Spanish languages. Finally, we compare the performance of our methods with the traditional feature‐based and some state‐of‐the‐art SS approaches. The experimental evaluation shows that our weighted methods perform better than the traditional feature‐based and some state‐of‐the‐art approaches in similarity evaluation.

Over last two decades, Speaker Recognition has primarily been focused on source, system, and prosodic features of the speech. The breath, however, has either been treated as a trivial part of the speech, or considered a noise entity. Our observation reveals that breath is a unique fingerprint of human respiratory system which offers overwhelming results for Speaker Recognition. Moreover, its passive nature, short-duration, fewer occurrences and simple processing results to a light-weight, text-independent and transparent system, which we articulate as BreathID. The breath features are extracted and classified by Mel Frequency Cepstral Coefficients, MFCC, based template matching technique. The verification is performed by a similarity based scheme, whose efficiency competes with classification algorithms. We process a data set collected from 50 users. Our system offers a 0.04 percent False Identification Rate, FIR, for Speaker Identification, and 0.12 percent False Acceptance Rate, FAR, and 0.15 percent False Rejection Rate, FRR, for Speaker Verification. We further evaluate our scheme under various practical modalities, like text in-dependence, replay scenario, users' motion status (sitting and walking), recording equipment (03 smartphones and 02 microphones), recording period (08 months), and bilingual contents (English and Chinese). Though we use Matlab to formulate a fine-grained approach, we foresee breath biometric as a viable security measure for practical realizations.

As a result of the limited available energy, radio frequency (RF)-powered devices must be capable of efficiently utilizing scarce energy by planning task execution according to the current harvested energy. However, the energy utilization efficiency is challenging to be improved in RF-powered devices, since sensing the harvested energy consumes a significant amount of energy that should be used for task execution. In this paper, we propose Sentinel, a novel low power method to sense the harvested energy. Sentinel is fully delegated to detect the energy for the device, while the device does not participate in the energy sensing. By this means, the computing overhead of the device is reduced. Sentinel works with low energy consumption, and functions as a trigger to activate the device when, and only when, the energy reaches an expected energy threshold. We also present a lightweight scheme to set the desired thresholds so that Sentinel achieves detecting any expected thresholds. We implement Sentinel by off-the-shelf components and conduct experiments to show that Sentinel consumes only 5.2% of energy overhead of the general energy sensing technique. With Sentinel, we show that the energy utilization efficiency can be improved up to 94.9%, outperforming the best existing works at 64.7% in the WISP platform.

Computational Radio Frequency Identification (CRFID) tags operate solely on harvested energy and have emerged as viable platforms for a variety of ubiquitous sensing and computation applications. Due to their battery-less nature, these tags can be permanently deployed in hard-to-reach places where the possibility of tag access is eliminated. In such scenarios, maintaining and upgrading the tag’s firmware becomes infeasible because programming tools, including wired interface and PC-based software, are required to erase, modify, or reprogram the microcontroller unit’s memory. Such limitations necessitate the demand for an over-the-air (OTA) scheme, which can wirelessly reprogram or upgrade the firmware in CRFID tags. In this article, we present R 3 —a reliable OTA reprogramming scheme that is compliant with EPC protocol and requires no hardware upgrade to RFID reader or CRFID tag. We demonstrate our scheme on three platforms, which include both software-defined as well as chip-based CRFID tags, that is, WISP5.1 and Optimized WISP (Opt-WISP), and Spider tag, respectively. The selection also includes both the FLASH- and FRAM-based microcontrollers. We extensively evaluate our scheme in terms of several metrics, including overall system delay, time and energy overhead, and success rate in line with interrogation range. We foresee our endeavor to offer the viability of OTA reprogramming and firmware upgrade for CRFID tokens under practical situations.

We present the design, implementation, and evaluation of an RFID-based cellphone anti-lost and anti-theft measure. The cellphone owner is given an audio-visual alert at the very instant of the lost event, and the cellphone deduces the fact that it is away from its owner and executes the safety measures. This is realized by equipping the cellphone with a low-power RFID Reader and tagging the owner with a passive RFID token to determine a private space around him, which spans within 2-10 feet. We prototype an RFID Reader from discrete components under $60 which can transmit 30 dBm with -78 dBm sensitivity, and can also serve as an educational tool for academic learning. Our system works on automatic, timed, or accelerometer based thresholds. We interface our system with Samsung Galaxy Note2 and develop an Android User Interface. We carry out extensive indoor and outdoor experiments under static and dynamic scenarios to ascertain the Frontal and Angular ranges, energy and power consumption, and memory and computational overheads. Our salient contribution is a twofold probing scheme-a duty cycle approach that economizes battery overhead, mitigates false alarms and scans the tag for multiple times by leveraging the interrogation time and power. We argue that though our design is costly in power budget, it is highly economical on battery energy because of short interrogation cycles. We show that for 17 tag interrogations from 20-24 dBm, our scheme consumes 72.1 to 52.4 percent lower energy than a single Bluetooth device scan. For a fully embedded design, we propose System-on-Chip RFID solutions. We foresee our endeavor as a viable proximity absence detection scheme for short range applications and scenarios.

We present a physical layer solution to RF distance bounding, which reduces the prover's processing delay virtually to zero by employing the concepts of frequency modulated continuous wave (FMCW) and secondary radars, and equipping it with the capability of cryptographic communication over unintelligent radar signals. To realize, we encode the verifier's challenges in waveform slope of signals while the prover communicates its cryptographic replies in shape of backscatter modulation. For resilience to multipath, the prover introduces analog frequency modulation using the principle of secondary radar. Our protocol is based on pre-commitment distance bounding scheme. Our paper primarily focuses on design intricacies of a realizable system while we briefly present the simulation results. We critically analyze the security of our protocol against attacks most concerned in distance bounding and thoroughly investigate its robustness under noise and multipath environments. A shortcoming of our design is low range resolution at lower spectral bandwidths, which can be foretoken as system tolerance. We foresee our endeavor to present a viable zero-delay RF distance bounding scheme while minimizing the hardware, energy, and computational overhead for passive and semipassive tokens.

As distance bounding can be leveraged for solving numerous security issues, extensive studies have been carried out for its implementation. Realizing RF distance bounding in battery-less or constrained devices is quite challenging because of inadequate harvested energy and large signal processing delays. We present TIGHT, an RF distance bounding scheme based on Signal Conditioning and Polarization Selection, with which a prover codes and reflects the incident challenges as a polarization function at analog RF at 1 nsec. In addition, our focus lies in designing TIGHT as a full duplex energy optimized system while considering the device synchronization in passive hardware. Security analysis shows that TIGHT is resilient to attacks most concerned in distance bounding. We demonstrate our scheme through prototype implementation and practical evaluation for delay measurements and calculate bit error rate while considering channel interference at ten outdoor and indoor places. Dealing with noise, we estimate the protocol failure, false-acceptance and false-rejection probabilities for TIGHT. Our results show that TIGHT is an effective RF distance bounding approach for passive wireless devices, especially the RFID tokens.

Wormhole attack is one of the severe threats to wireless sensor and ad hoc networks. Most of the existing countermeasures either require specialized hardware or demand high network overheads in order to capture the specific symptoms induced by the wormholes, which in result, limits their applicability. In this paper, we exploit an inevitable symptom of wormholes and present Pworm, a passive wormhole detection and localization system based upon the key observation that a large amount of network traffic will be attracted by the wormholes. The proposed passive and real-time scheme silently observes the variations in network topology to infer the wormhole existence. Our approach relies solely on network routing information and does not necessitate specialized hardware or poses rigorous assumptions on network features. We evaluate our system performance through extensive simulations of 100 to 500 nodes for various network scales and show that Pworm is well suited for false alarms, scalability and time delay.