We modeled the packet-forwarding procedure as a Markov decision process thereafter. We developed an appropriate reward function for the dueling DQN algorithm, incorporating penalties for additional hops, total waiting time, and link quality to enhance its learning. Our proposed routing protocol, based on simulation results, displayed a superior packet delivery ratio and average end-to-end delay compared to competing protocols.
Our investigation concerns the in-network processing of a skyline join query, situated within the context of wireless sensor networks (WSNs). While considerable effort has been invested in the study of skyline queries within wireless sensor networks, skyline join queries have been largely confined to conventional centralized or distributed database systems. However, these approaches are not translatable to the context of wireless sensor networks. The feasibility of implementing both join filtering and skyline filtering techniques in Wireless Sensor Networks (WSNs) is undermined by the limited memory resources of sensor nodes and the substantial energy demands of wireless communication protocols. This document describes a protocol, aimed at energy-efficient skyline join query processing in Wireless Sensor Networks, while keeping memory usage low per sensor node. The very compact data structure, the synopsis of skyline attribute value ranges, is what it uses. The range synopsis is applied to locate anchor points within skyline filtering and, simultaneously, to 2-way semijoins for join filtering. This document explores the structure of a range synopsis and introduces our protocol. With the aim of improving our protocol, we find solutions to optimization problems. Through practical implementation and a suite of detailed simulations, our protocol's effectiveness is evident. The compact range synopsis has been validated as being sufficiently small to enable our protocol to function effectively within the energy and memory constraints of each sensor node. Our protocol's substantial performance gain over alternative protocols is evident for correlated and random distributions, showcasing the power of in-network skyline and join filtering.
This paper's contribution is a high-gain, low-noise current signal detection system designed specifically for biosensors. The biosensor's interaction with the biomaterial causes a modification in the current flowing through the bias voltage, enabling the detection of the biomaterial. The resistive feedback transimpedance amplifier (TIA) is implemented for the biosensor, a device needing a bias voltage. The self-designed graphical user interface (GUI) displays the current biosensor readings in real time. Variations in bias voltage do not affect the input voltage of the analog-to-digital converter (ADC), guaranteeing reliable and accurate plotting of the biosensor's current. Multi-biosensor arrays employ a method for automatically calibrating current flow between individual biosensors via a controlled gate bias voltage approach. Input-referred noise is decreased with the aid of a high-gain TIA and chopper technique. Using a TSMC 130 nm CMOS process, the proposed circuit achieves an input-referred noise of 18 pArms, and its gain reaches 160 dB. Simultaneously, the power consumption of the current sensing system is 12 milliwatts; the chip area, on the other hand, occupies 23 square millimeters.
Smart home controllers (SHCs) are capable of managing residential load schedules, thereby maximizing both financial savings and user comfort. For this determination, the electricity company's tariff variations, the lowest cost plans, user preferences, and the comfort level that each appliance brings to the household are taken into account. Current user comfort models, referenced in the literature, do not account for the user's individual comfort experiences, concentrating solely on user-defined load on-time preferences that are recorded in the SHC. The user's comfort perceptions are in a continual state of change, unlike their consistent comfort preferences. This paper thus proposes a comfort function model that integrates user perceptions into its design, leveraging fuzzy logic. Chronic immune activation To achieve multiple objectives, economy and user comfort, the proposed function is integrated into an SHC that utilizes PSO for scheduling residential loads. The proposed function's assessment and confirmation require consideration of multifaceted scenarios. These include comparing economy and comfort, examining load-shifting, considering variable energy costs, incorporating user preferences, and factoring in user perceptions. The proposed comfort function method is demonstrably more advantageous when prioritizing comfort over financial savings, as dictated by the user's SHC requirements. Instead of relying on user perceptions, a comfort function focused solely on the user's comfort preferences offers a superior approach.
Artificial intelligence (AI) development heavily depends on the quality and quantity of data. click here Moreover, AI requires the data users voluntarily share to go beyond rudimentary tasks and understand them. 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. This research further analyzes the influence of multi-robot situations, with a focus on their moderating effect. For empirical investigation of these effects and expanding the reach of research implications, a field experiment employing prototypes was performed in the context of children utilizing smart speakers. Both robot types' self-disclosures proved successful in drawing out children's personal disclosures. The effect of the disclosing robot and the involved user's participation demonstrated a shift in direction, dictated by the sub-dimension of the user's self-revelation. Conditions involving multiple robots contribute to a partial moderation of the effects stemming from the two types of robot self-disclosures.
The importance of cybersecurity information sharing (CIS) in ensuring secure data transmission across diverse business processes is undeniable, as it encompasses Internet of Things (IoT) connectivity, workflow automation, collaboration, and seamless communication. Influenced by intermediate users, the shared information loses its distinctive qualities. While a cyber defense system mitigates risks like data confidentiality and privacy, current methods still hinge on a centralized system vulnerable to damage in the event of an accident. In parallel, the distribution of private information presents difficulties in relation to rights when utilizing sensitive data. The research issues generate considerable uncertainty and affect trust, privacy, and security in a third-party environment. In conclusion, this project utilizes the Access Control Enabled Blockchain (ACE-BC) framework to strengthen data security overall in the CIS infrastructure. caractéristiques biologiques Within the ACE-BC framework, attribute encryption ensures data security, alongside access control measures that prevent unauthorized users from accessing the data. Data privacy and security are guaranteed by the effective application of blockchain techniques. 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.
Contemporary times have witnessed the emergence of numerous data-driven services, encompassing cloud services and big data-focused services. Data storage and value derivation are performed by these services. Ensuring the data's trustworthiness and completeness is essential. Unfortunately, hackers have made valuable data unavailable, demanding payment in attacks labeled ransomware. The encrypted files within ransomware-infected systems prevent the retrieval of original data, requiring decryption keys for access. Cloud services offer data backup solutions; nonetheless, encrypted files are synchronized to the cloud service. Subsequently, the cloud storage becomes useless for retrieving the original file once the systems are compromised. In this work, we propose a procedure for the reliable detection of ransomware within cloud infrastructures. Infected files are identified by the proposed method, which synchronizes files based on entropy estimations, leveraging the consistent nature of encrypted files. In the experiment, files containing sensitive user data and system operation files were chosen. This research definitively identified 100% of all infected files, encompassing all file types, free from any false positives or false negatives. When compared to prevailing ransomware detection methods, our proposed technique showcased a marked degree of effectiveness. This study's results predict that the detection technique's synchronization with a cloud server will fail, even when the infected files are identified, due to the presence of ransomware on victim systems. Also, the restoration of the original files is planned by utilizing cloud server backups.
Investigating the actions of sensors, particularly the specifications within multi-sensor systems, poses complex issues. The application domain, sensor usage, and architectural designs are among the variables requiring consideration. A multitude of models, algorithms, and technologies have been developed to accomplish this objective. This paper presents a novel interval logic, Duration Calculus for Functions (DC4F), for the precise specification of signals from sensors, particularly those used in heart rhythm monitoring, including the analysis of electrocardiograms. Precision is indispensable for constructing robust and dependable specifications of safety-critical systems. DC4F represents a natural evolution of Duration Calculus, an interval temporal logic, specifically designed to articulate the duration of a process. To represent intricate, interval-dependent behaviors, this is applicable. This method enables the definition of temporal series, the illustration of intricate interval-dependent behaviors, and the assessment of the associated data within a consistent logical system.