The packet-forwarding process was subsequently modeled by using a Markov decision process. We implemented a reward function tailored for the dueling DQN algorithm, penalizing each additional hop, total waiting time, and link quality to enhance its learning process. Ultimately, the simulation outcomes demonstrated that our proposed routing protocol exhibited superior performance compared to alternative protocols, as evidenced by its higher packet delivery ratio and lower average end-to-end delay.
Our investigation concerns the in-network processing of a skyline join query, situated within the context of wireless sensor networks (WSNs). Extensive research on skyline queries in wireless sensor networks contrasts sharply with the limited attention given to skyline join queries, which have predominantly been addressed within centralized or distributed database systems. In contrast, these methods are not deployable in wireless sensor network environments. Carrying out join filtering and skyline filtering simultaneously within wireless sensor networks is not feasible, due to the limitations of memory in sensor nodes and the large energy consumption in wireless transmissions. For energy-efficient processing of skyline join queries in wireless sensor networks, this paper details a protocol that conserves memory at each sensor node. It relies upon a synopsis of skyline attribute value ranges, a data structure which is remarkably compact. The range synopsis serves a dual role, supporting the search for anchor points in skyline filtering and participating in 2-way semijoins for join filtering. We elucidate the structure of a range synopsis and present our established protocol. In pursuit of improving our protocol, we work through various optimization problems. Our protocol's effectiveness is demonstrated through detailed simulations and practical implementation. The range synopsis's compact design is confirmed to allow our protocol to function properly given the limited memory and energy capacity of each sensor node. In comparison to other protocols, our protocol exhibits a significant advantage for correlated and random distributions, validating the efficacy of our in-network skyline and join filtering capabilities.
A high-gain, low-noise current signal detection system, especially suited for biosensors, is the topic of this paper. When the biomaterial is affixed to the biosensor, a shift is observed in the current that is passing through the bias voltage, facilitating the sensing of the biomaterial. A bias voltage is a necessary component in the biosensor's operation, leading to the implementation of a resistive feedback transimpedance amplifier (TIA). Real-time biosensor current data can be observed on a self-constructed graphical user interface (GUI). No matter how the bias voltage fluctuates, the analog-to-digital converter (ADC) maintains a constant input voltage, producing a reliable and accurate plot of the biosensor's current. For multi-biosensor arrays, an automatic current calibration method is introduced, manipulating gate bias voltage to control the current between biosensors. A high-gain TIA and chopper technique are employed to minimize input-referred noise. The 18 pArms input-referred noise, coupled with a 160 dB gain, is a hallmark of the proposed circuit, which was fabricated using a 130 nm TSMC CMOS process. The current sensing system's power consumption is 12 milliwatts, while the chip area measures 23 square millimeters.
Smart home controllers (SHCs) are capable of managing residential load schedules, thereby maximizing both financial savings and user comfort. Considering the electricity provider's price fluctuations, the least expensive tariff plans, user choices, and the level of comfort associated with each appliance in the household, this evaluation is conducted. Nevertheless, the comfort modeling, documented in existing literature, overlooks the subjective comfort experiences of the user, relying solely on the user's predefined loading preferences, registered only when logged in the SHC. The user's shifting perceptions of comfort contrast with the static nature of their comfort preferences. Subsequently, this paper suggests a comfort function model that accounts for user perceptions using the principles of fuzzy logic. infant infection An SHC, employing PSO for residential load scheduling, integrates the proposed function, aiming for both economical operation and user comfort. Validating the suggested function necessitates exploring different scenarios, including the optimization of economy and comfort, load shifting techniques, consideration of fluctuating energy rates, understanding user preferences, and incorporating user feedback about their perceptions. For achieving optimal comfort outcomes as determined by user-defined SHC parameters, the proposed comfort function method surpasses other strategies that prioritize financial savings. To maximize benefits, it is more effective to use a comfort function that concentrates solely on the user's comfort preferences, irrespective of their perceptions.
Artificial intelligence (AI) development heavily depends on the quality and quantity of data. Selleckchem 5-Fluorouracil Furthermore, user self-disclosure is essential for AI to transcend its role as a mere machine and grasp the user's intent. This investigation introduces two strategies for robot self-disclosure, involving robot communication and user input, aiming to inspire higher levels of self-disclosure from artificial intelligence users. Additionally, this research investigates the impact of multi-robot contexts on observed effects, acting as moderators. In order to gain empirical understanding of these effects and expand the implications of the research, a field experiment was carried out using prototypes, focusing on the use of smart speakers by children. The self-disclosures of robots of two distinct types were efficient in getting children to disclose their personal experiences. Depending on the nuanced level of a user's self-disclosure, the interplay between the disclosing robot and the involved user exhibited a different directional influence. The presence of multiple robots partially moderates the consequences of the two types of robot self-revelations.
Different business processes necessitate secure data transmission, which is facilitated by cybersecurity information sharing (CIS), encompassing Internet of Things (IoT) connectivity, workflow automation, collaborative environments, and communication networks. Intermediate users' input shapes the shared information, diminishing its original character. Although a cyber defense system minimizes concerns regarding data confidentiality and privacy, the underlying techniques often utilize a centralized architecture that is susceptible to harm during incidents. Separately, the disclosure of personal information incurs legal implications when accessing sensitive data. The research questions at stake have repercussions for the trustworthiness, privacy, and security of external environments. Thus, this investigation implements the Access Control Enabled Blockchain (ACE-BC) framework to advance data security protocols within CIS. Passive immunity To manage data security, the ACE-BC framework uses attribute encryption, whereas access control procedures prohibit unauthorized user entry. Implementing blockchain technology ensures the protection of data privacy and security holistically. Experimental results assessed the introduced framework's efficacy, revealing that the ACE-BC framework, as recommended, amplified data confidentiality by 989%, throughput by 982%, efficiency by 974%, and reduced latency by 109% compared to prevailing models.
In recent years, a diverse array of data-dependent services, including cloud services and big data-related services, have emerged. Data is retained and its value is calculated by these services. The dependability and integrity of the provided data must be unquestionable. Regrettably, attackers have seized valuable data, demanding ransom in cyberattacks known as ransomware. Files within ransomware-infected systems are encrypted, making it hard to recover original data, as access is restricted without the decryption keys. While cloud services provide data backup, encrypted files are concurrently synchronized with the service. In consequence, the infected victim systems prevent retrieval of the original file, even from the cloud. For this reason, we introduce in this paper a technique for the unambiguous recognition of ransomware specifically designed for cloud computing services. Infected files are identified by the proposed method, which synchronizes files based on entropy estimations, leveraging the consistent nature of encrypted files. For the experimental process, files holding sensitive user information and system files required for system operation were selected. A complete analysis of all file formats revealed 100% detection of infected files, with no errors in classification, avoiding both false positives and false negatives. Our findings highlight the superior performance of our proposed ransomware detection method relative to existing approaches. This paper's data indicate that synchronization with the cloud server by this detection method will not occur when infected files are found, even if the victim systems are compromised by ransomware. On top of that, the original files are anticipated to be restored by utilizing a cloud server backup.
The intricacy of sensor behavior, especially when considering multi-sensor system specifications, is substantial. The application's scope, the strategic uses of sensors, and the architectures of these sensors constitute important variables requiring assessment. Many models, algorithms, and technologies have been specifically designed to realize this purpose. In this study, we introduce Duration Calculus for Functions (DC4F), a novel interval logic, that aims to precisely specify signals from sensors, especially those used in heart rhythm monitoring procedures, such as electrocardiograms. The paramount concern in the specification of safety-critical systems is precision. DC4F, a natural outgrowth of the well-established Duration Calculus, an interval temporal logic, is employed to specify the duration of a process. Complex, interval-dependent behaviors are aptly described by this. This strategy permits the delineation of time-based series, the characterization of intricate behaviors contingent upon intervals, and the appraisal of associated data within a unified theoretical framework.