Two players wishing to communicate are placed each in a room with N telephones connecting the two rooms. The players do not know how the telephones are interconnected. In each round, each player picks up a phone and says “hello” until when they hear each other. The problem is to devise an algorithm minimising the delay to establish communication. The above problem, called the Telephone Coordination Game, also termed as the Telephone Problem, is of fundamental importance in distributed algorithm design. In this paper, we investigate a generalised version where among N telephones, only a subset can establish communication between the two players. We are interested in devising the deterministic strategy achieving bounded rendezvous delay and minimising the worst-case rendezvous delay. Specifically, we first establish the lower-bound of worst-case rendezvous delay. We then characterise the structure of the phone pick sequences that can guarantee rendezvous without any prior coordination. Assuming each player has a globally unique ID, we further devise a deterministic strategy that (1) guarantees rendezvous between the players regardless of their telephone labeling functions and their relative time difference and (2) approaches the performance bound within a constant factor proportional to the ID length.
The detection of face-screen distance on smartphone (i.e., the distance between the user face and the smartphone screen) is of paramount importance for many mobile applications, including dynamic adjustment of screen on-off, screen resolution, screen luminance, font size, with the purposes of power saving, protection of human eyesight, etc. Existing detection techniques for face-screen distance depend on external or internal hardware, e.g., an accessory plug-in sensor (e.g., infrared or ultrasonic sensors) to measure the face-screen distance, a built-in proximity sensor that usually outputs a coarse-grained, two-valued, proximity index (for the purpose of powering on/off the screen), etc. In this paper, we present a fine-grained detection method, called "Look Into My Eyes (LIME)", that utilizes the front camera and inertial accelerometer of the smartphone to estimate the facescreen distance. Specifically, LIME captures the photo of the user's face only when the accelerometer detects certain motion patterns of mobile phones, and then estimates the face-screen distance by looking at the distance between the user's eyes. Besides, LIME is able to take care of the user experience when multiple users are facing the phone screen. The experimental results show that LIME can achieve a mean squared error smaller than 2.4 cm in all of experimented scenarios, and it incurs a small cost on battery life when integrated into an SMS application for enabling dynamic font size by detecting the face-screen distance.
In cognitive radio (CR) networks, a pair of CR nodes have to ``rendezvous'' on a common channel for link establishment. Channel hopping (CH) protocols have been proposed for creating rendezvous over multiple channels to reduce the possibility of rendezvous failures caused by the detection of primary user signals. Rendezvous within a minimal bounded time over multiple channels is a challenging problem in heterogeneous CR networks where two CR nodes may have asynchronous clocks, different sensing capabilities, no common universal channel set, and heterogeneous channel index systems. In this paper, we present a systematic approach using group theory for designing CH protocols that guarantee the maximum number of rendezvous channels and the minimal time-to-rendezvous (TTR) in heterogeneous environments. We derive the minimum upper bound of TTR, and propose two types of rendezvous protocols that are independent of environmental heterogeneity. Analytical and simulation results show that these protocols are resistant to rendezvous failures under various network conditions.
The received signal strength (RSS) fingerprinting based localization is a widely used technique to locate mobile devices in cellular networks. However, traditional RSS finger-printing based techniques require a set of RSS values from multiple base stations (BSs) at each location, and the RSS from remote BSs is weak and easily affected by the noise and other variations of the propagation channel, which limits the localization accuracy. In this paper, we propose to utilize a reconfigurable intelligent surface (RIS) to improve the accuracy for the RSS fingerprinting based multi-user outdoor localization, which requires the RSS from only one BS. By modifying phase shifts of signals reflected by the RIS, we can create different RSS values at the same location using the signal from the nearest BS for localization. To optimize the RIS phase shifts, we formulate the localization error minimization (LEM) problem and propose an LEM algorithm. Simulation results validate the effectiveness of the proposed scheme.
In this paper, we study the platoon cooperation in the multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything (V2X) communication. The platoon cooperation is important for the interference management and communication reliability enhancement, yet it is difficult, since the platoon formation, subchannel allocation, and power control of each platoon interact with each other and influence those of other platoons. To increase the number of vehicles in the platoon and reduce the power consumption, we propose a two-step resource allocation strategy in consideration of platoon formation, i.e., the resource allocation at the base station (BS) and within each platoon. A branch and bound algorithm is utilized for the resource allocation at the BS. We then design a distributed dynamic programming-based subchannel allocation and power control algorithm for the joint optimization of platoon formation, subchannel allocation, and power control. The simulation results evaluate the impact of the penalty factor and the latency requirement on the system performance.
The meta-material sensor has been regarded as a next-generation sensing technology for the battery-free Internet of Things (IoT) due to its battery-free characteristic and improved sensing performance. The meta-material sensors function as backscatter tags that change their reflection coefficients with the conditions of sensing targets such as temperature and gas concentration, allowing transceivers to perform sensing by analyzing the reflected signals from the sensors. Simultaneously, the sensors also function as environmental scatterers, creating additional signal paths to enhance communication performance. Therefore, the meta-material sensor potentially provides a new paradigm of Integrated Sensing and Communication (ISAC) for the battery-free IoT system. In this article, we first propose a Meta-Backscatter system that utilizes meta-material sensors to achieve diverse sensing functionalities and improved communication performance. We begin with the introduction of the metamaterial sensor and further elaborate on the Meta-Backscatter system. Subsequently, we present optimization strategies for meta-material sensors, transmitters, and receivers to strike a balance between sensing and communication. Furthermore, this article provides a case study of the system and examines the feasibility and trade-off through the simulation results. Finally, potential extensions of the system and their related research challenges are addressed.
In urban areas, WiFi is the most widely-deployed portal for users to acquire the broadband access. Meanwhile, phishing AP (access point)-a rogue AP that falsifies the SSID (or even the BSSID) of a legitimate corporate AP-has caused many security problems in commodity WiFi networks. Existing research on the phishing AP detection can be divided into two categories: (1) the hardware-based approach usually deploys sensors (sniffers and/or USB-based wireless adapters) and conducts radio frequency (RF) sensing at a large scale to detect the anomaly at link and physical layers; and (2) the measurement-based approach enables a laptop to determine the legitimacy of a given AP by monitoring the RTT (round trip time) of data and/or control messages. However, these approaches require the additional cost on either the hardware deployment, or periodic statistical measurements. In this paper, we present Trident, a context-based reverse authentication method for detecting phishing AP in commodity WiFi networks, which requires no extra hardware deployment or periodic statistical measurements. Specifically, Trident employs a challenge-response protocol that allows a user to (reversely) authenticate an AP by two steps: (1) sending the AP a few questions regarding three user-context features (time, location, traffic) during the user-AP interaction procedure, and (2) examining the answers returned by the AP to determine its legitimacy. Our experimental results reveal that Trident achieves a high reliability rate of 95% and a detection rate of 98% when users are connecting rogue APs in the commodity WiFi network on campus.
Group signatures (GSs) is an elegant approach for providing privacy-preserving authentication. Unfortunately, modern GS schemes have limited practical value for use in large networks due to the high computational complexity of their revocation check procedures. We propose a novel GS scheme called the Group Signatures with Probabilistic Revocation (GSPR), which significantly improves scalability with regard to revocation. GSPR employs the novel notion of probabilistic revocation, which enables the verifier to check the revocation status of the private key of a given signature very efficiently. However, GSPR's revocation check procedure produces probabilistic results, which may include false positive results but no false negative results. GSPR includes a procedure that can be used to iteratively decrease the probability of false positives. GSPR makes an advantageous tradeoff between computational complexity and communication overhead, resulting in a GS scheme that offers a number of practical advantages over the prior art. We provide a proof of security for GSPR in the random oracle model using the decisional linear assumption and the bilinear strong Diffie-Hellman assumption.
Recently, mobile devices have been used to carry sensors to monitor air quality index (AQI), and help construct an AQI map in 2-dimensional (2D) areas. In this paper, we design a novel 3-dimensional (3D) AQI monitoring system, called Arms (AQI realtime monitoring system), to efficiently build realtime fine-grained 3D AQI maps, with the help of unmanned-aerial- vehicles (UAVs). Based on the data monitored by Arms, a novel dispersion model, namely Adaptive Gaussian Plume Model (AGPM) is proposed to predict the distribution of AQI. Moreover, the adaptive monitoring techniques, i.e., complete and optimized monitoring, are designed to effectively produce and maintain realtime AQI maps, while greatly reducing the measurement efforts. Experimental results verify that Arms can provide higher predicting accuracy of AQI with the proposed AGPM than other existing models. In addition, the whole system's battery consumption can be greatly reduced.
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