A network of sensors and surveillance devices creates a critical infrastructure in smart cities, enabling civic authorities to render faster and more efficient services. Notably, many deployed sensor applications form linear topologies, such as road monitoring sensors (e.g., intelligent lighting, road traffic, pedestrians, etc.), railway track sensors, and pipeline sensors (e.g., sensing leaks, pressure, etc.). These linear sensors convey data to a central control station by daisy-chaining through neighbor sensors. However, it causes critical energy inefficiency and imbalance issues and creates data transmission delay, which provokes complex application and service management hassles. In this paper, we propose an Energy-Efficient Linear Sensor Architecture (ELSA) for building sensor network infrastructure of Smart Cities. We design and develop an energy-efficient Wake-up Receiving (WuRx) method on sensor nodes by using a dual-channel, an infrared (IR) based WuRx channel over a WiFi data channel to enhance the duty cycle efficiency. In addition, we build a pipelined relay algorithm to avoid any network delay performance degradation. Furthermore, we design a centrality-based algorithm to identify linear sensor networks. The experimental results show that IR WuRx ELSA performed nearly ten times better energy efficiency per node and achieved a constant network delay regardless of the number of relaying nodes in a network.

The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user’s physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users’ active responses.

Providing coherent summarization for the deaf community can be challenging, particularly in capturing the details relationships between spoken content and user queries. To our knowledge, no prior attempts have been made to develop a graph-based framework to address this challenge. The current approaches often concentrate on extracting semantically relevant information from audio signals, which fails to consider the contextual coherence and logical flow between sentences. As a result, users are frequently presented with disjointed summaries that lack a structured narrative, making it difficult for them to follow the intended storyline. This paper presents GAS-Saudi, a proof-of-concept novel graph-based framework designed to enhance summarization for the deaf community by leveraging complex relationships within acoustic signals. The framework consist of several key components: (1) an audio extractor that transforms audio signals into textual representations, (2) a query-answer module that aligns audio content with user inquiries, and (3) a story learning module that constructs and analyzes sentence relationships through graph-based learning. We conduct empirical evaluations on a real-world dataset, demonstrating the effectiveness of GAS-Saudi in generating coherent and meaningful summaries. The empirical results indicate that the GAS-Saudi model outperforms state-of-the-art methods, achieving an accuracy of 91%. The experiment’s code can be found here: https://github.com/raed19/GAS-Saudi.