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Data Dissemination is an essential transmitting method for a sensor network to the end-users across any set of interconnected frameworks. WSN is often used within an IoT system, in other words. As in a mesh network, a wide collection of sensors can collect data individually and send data to the web via an IoT system through a router. The conventional defined solution for data dissemination in Wireless Sensor Networks (WSN) does not include the wide range of new applications built on the Internet of Things (IoT)systems. Hence, it is observed that searching for an appropriate transmission link while distributing data with optimized utilization of energy is a significant challenge in the IoT communication infrastructure. Therefore, in this paper, an Optimized Energy Management Model for Data Dissemination (OEM-DD) framework has been proposed to optimize energy during data transmission efficiently across all sensor network nodes in the IoT system. The efficiency of the data dissemination across an interconnected network has been achieved by introducing a Non-adaptive routing approach in which data is distributed effectively from a single source to various points. Besides, Non-adaptive routing involves the dispersed collaboration system and the priority task planning principle combined with an integer framework for the efficient energy processing and grouping of data in the sensor’s network. Optimization of the energy management model through Non-adaptive routing allows low power consumption and minimal energy usage for each sensor node in the IoT system to improve the transfer and handling of data in severe interruption. The experimental results show that the suggested model enhances the data transmission rate of 96.33% with less energy consumption of 20.11% in WSN, which is the subset of IoT systems.

Wireless Sensor Networks (WSNs) is a collection of tiny distributed sensor nodes that have been used to sense the physical parameters of the environment where it has been deployed. Data dissemination is an important activity performed in WSNs in order to administer and manage them. Gossiping makes the network to transmit the same data item multiple times by multiple sensor nodes to their neighbors until they reach the required nodes which are in need of them. These multiple transmissions result in a problem called a Redundant Broadcast Storm Problem (RBSP). Moreover, the RBSP results in too many senders’ problem and also leads to the consumption of more energy in the network. In data dissemination, providing energy efficiency and security are the two major challenging issues. In such a scenario, the attackers may make use of the weakness in security provisions available in the network and they can perform unauthorized activities to disrupt the process of data dissemination. Hence, it is necessary to address the issues of RBSP, energy consumption, security and too many senders problem in order to enhance the reliability and security of communication in WSN for data dissemination. In this paper, a novel protocol named Cluster based Secured Data dissemination Protocol (CSDP) has been proposed for providing energy efficient and secured dissemination of data. The proposed protocol is a distributed protocol which considers the route discovery process, cluster formation, cluster head selection, cluster based routing and security through the design of a new digital signature based authentication algorithm, trust based security enhancement and encryption techniques for effective key management. The major contributions of the proposed work include the proposal of cryptography based public key and private key generation algorithms, techniques for trust score computation and malicious node identification and finally the effective prevention of malicious activities for enhancing the security of the network. Moreover, this work considers node identification techniques for effective clustering of nodes and performs optimal route discovery and secured transmission of packets. This work is novel with respect to multicast based data dissemination protocol, proposal of combined signature generation and verification schemes, encryption based key management and distributed data collection and communication techniques. In addition, an Intelligent Fuzzy based Unequal Clustering algorithm is used to perform effective clustering process and the traffic analyzer to identify the intruders by monitoring the node’s behaviors and their trust values. The proposed protocol has been extensively tested with realistic simulation parameters using NS2 simulator. The simulation results obtained from this work have proved that the proposed protocol improves the level of security through the proposal of a time efficient encryption and decryption algorithm with increase in packet delivery ratio and network throughput and at the same time it reduces the energy consumption as well as delay in data dissemination.

Recent scholarship in business ethics has revealed the importance of privacy expectations as they relate to implicit privacy norms and the business practices that may violate these expectations. Yet, it is unclear how and when businesses may violate these expectations, factors that form or influence privacy expectations, or whether or not expectations have in fact been violated by company actions. This article reports the findings of three studies exploring how and when the corporate dissemination of consumer data violates privacy expectations. The results indicate that consumer sentiment is more negative following intentional releases of sensitive consumer data, but the effect of data dissemination is more complex than that of company intentionality and data sensitivity alone. Companies can effectively set, and re-affirm, privacy expectations via consent procedures preceding and succeeding data dissemination notifications. Although implied consent has become more widely used in practice, we show how explicit consent outperforms implied consent in these regards. Importantly, this research provides process evidence that identifies perceived violation of privacy expectations as the underlying mechanism to explain the deleterious effects, on consumer sentiment, when company actions are misaligned with consumers' privacy expectations. Ethical implications for companies collecting and disseminating consumer information are offered.

Since the IMF launched the data standards initiatives a decade ago, 145 of its 184 member countries have participated. This 80 percent participation rate reaffirms the importance countries place on data transparency in the globalized economy, which the initiatives promote. The wide participation can be attributed to the consultative process that has allowed for the development of a coherent program that takes account of countries' capabilities, delineates clear responsibilities between the IMF and participating countries, and establishes effective monitoring procedures to ensure the credibility of the standards for policymakers, capital markets, and the general public. The approach has also provided checks and balances and fostered accountability. The initiatives may provide insights for the promotion of similar international standards.

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

Blockchain technology can solve current interoperability challenges in health information systems and provide the technical standard which ensures a secure sharing of electronic health data between individuals, healthcare providers, medical care institutions, and medical experts. In healthcare management, IoMT devices can deliver real-time sensory data from patients to be analyzed and processed. In IoMT devices within a range of product areas, the confidentiality of patients’ health data transmission and privacy is still a big problem. In this paper, the Multi-Modal Secure Data Dissemination Framework (MMSDDF) has been proposed based on blockchain in IoMT for secure patient data access and control. To meet the optimized security and privacy requirements of healthcare data management in IoMT devices, the proposed framework is effectively employed. Blockchain’s key has been used to a healthcare application network where the patient’s health data can create warnings that are significant to authenticated healthcare providers securely. The simulation outcomes show that the suggested MMSDDF method achieves a high accuracy ratio of 95.8%, a prediction ratio of 92.3%, less delay of 0.48 s, latency range of 0.5, and response time of 1.5% to other existing methods.

The Data Dissemination Initiative was launched in the mid-1990s as part of a broader internationally-agreed-upon initiative to strengthen transparency and promote good governance practices by establishing standards and codes. Ten years later, the initiative is viewed as an integral part of the international financial architecture, and is considered to have improved the functioning of international financial markets and contributed to global financial stability. This volume reviews certain aspects of the development of and experience with the initiative over the past decade, and concludes by reflecting on potential challenges ahead and possible enhancements.

The persistent mild-explosive activity of Stromboli volcano (Aeolian Archipelago, Southern Italy) is episodically interrupted by much more energetic and dangerous episodes called paroxysms. The Istituto Nazionale di Geofisica e Vulcanologia (Italy) set up the interdisciplinary UNO Project (UNderstanding the Ordinary to forecast the extraordinary), aimed at understanding when the Stromboli volcano is about to switch from the ordinary to the extraordinary (dangerous) activity. The UNO Project includes an outstanding variety of research activities, including the collection of new data within the timeframe of the Project (2019–2025). Key to the success of the Project is the collection of integrated high spatial and temporal resolution data and their joint analyses in a shared relational geo-spatial database. Here, we present the development of this database, focusing on the preparatory work and the design of the logical model. Several hints and examples are provided to bridge the gap between the standard perspective of field volcanologists and the technical issues and caveats governing the domain of relational geo-spatial databases.

The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an 88.9% delivery ratio with minimal energy consumption and the highest trust accuracy (86.5%), demonstrating its robustness and scalability in real-world-inspired IoT environments.