A Research to Data Management in Savant Based on Data Grid

Traditional Complex Event systems (CEP) did not consider the computation requirements of continuous dynamic behavior such as differential equations. In addition, the event composition rules were predefined before the CEP engine began working. The rule defining task is error-prone and cumbersome. In this paper, therefore, a hybrid complex event service is proposed, which deals with not only discrete events but also continuous behavior computation based on IoT (Internet of Things) resource models. In order to satisfy the real-time constraints of processing IoT events, a divide-and-conquer principle is adopted, where we give a combination theorem such that different events can be processed on different IoT resources and then these processed results can be combined to derive complex events. Based on the formal IoT resources and event knowledge, we define interest goals to direct event composition without enumerating event relation to define event composition rules. We finally present event composition algorithms and evaluation to show our idea.

The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.

Cloud of Things (CoT) consists of heterogeneous Cloud and Internet of Things (IoT) resources. CoT increasingly requires adaptive run-time management due to the CoT dynamism, environmental uncertainties and unpredictable changes in IoT resources. Adapting to these changes benefits particularly trading of CoT resources where the adaptability of traded resources and applications remains a challenge. Run-time changes in CoT trading environments can impact vital aspects including resource allocation, resource utilisation and application performance. This paper adopts monitoring, analysis, planning and execution (MAPE) model from autonomic computing to support adaptations when trading CoT resources. This is achieved by applying the MAPE model to systematically capture and identify changes in CoT environment. Based on the identified adaptations, an adaptive model is proposed to react to these changes.

The number of devices connected together in the Internet of Things (IoT) are growing and therefore the demand of an efficient and secure method for discovering the resources in IoT is increasing. In most of IoT schemes, the resources are discovered based on their properties (i.e. their location, types, etc.) and the clients are able to discover the resources that are registered in the network. This discovery ability can be restricted to a number of clients by encrypting the discoverable address. However, any client with the key can decrypt and access a private resource regardless of the attributes of the client. In this paper, a decentralized resource discovery model using Cipher Policy Attribute Based Encryption (CP-ABE) and Distributed Hash Table (DHT) is introduced that allows secure and private discovering of the resources in IoT network. We integrated the CP-ABE that allows clients only by users' inherent attributes, to discover the resources in the network. Our proposed model provides a higher level of security for resource discovery by allowing the resources during registration in addition to their properties to define the attributes of the clients that are able to discover those resources. The model we proposed uses multi-authority ABE, which as a public key encryption can fit the decentralized environment.

A knowledge-based resource discovery for Internet of Things

Internet of things (IoT) is considered as the next evolution of the Internet. IoT is considered as a global network of things, having a distinct identity, and these are interconnected via a wide range of the network to exchange data and present it into vital information. IoT is an emerging technology that shaping the development of the ICT sector at large. The Internet of Things can also be considered as a uniquely recognizable smart object and their virtual representation in an Internet-like structure. To interchange data, IoT resources using the internet makes use of multiple interconnected technologies like Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID). Also, integration of IoT with cloud provides improved services to end-user. For beneficence to the growing body of research on IoT, authors have presented the four-layered architecture of IoT, services offered by IoT, current IoT applications as well as issues and challenges of IoT technology in detail in this paper.

Internet of things (IoT) is a smart technology that connects all the things anywhere at any time. Such multiuse nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research topic currently. This chapter presents an energy-efficient architecture for IoT, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design of IoT allows the system to ensure the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for any particular application. The obtained value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources it has when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources in nature. Wireless sensor network (WSN) and low-power smart device network IoT are fast-growing technologies used in a wide range of applications. In IoT, nodes are defined as sensor/actuators, which are highly constrained in nature (limited in energy and CPU usage). Designing and analyzing energy-efficient network architecture and routing mechanism is a major challenge in IoT networks. The network is said as energy-efficient based on node survivability. Energy-efficient network architecture is used to prolong the network lifetime. Sensor nodes and relay nodes are placed in a hierarchical manner to avoid uneven energy drainage. Given the architecture, sensors do the sensing and relay nodes handle the communication (data transmission from the sensor to sink), which reduces the complexity of device. Relay node is placed based on data traffic of the network. Energy-efficient routing mechanism is implemented in a given architecture to achieve balanced energy consumption.

Massive diffusion of constrained devices communicating through low-power wireless technologies in the future Internet of Things (IoT) will require in many scenarios the deployment of IoT gateways to allow applications to discover and access IoT resources. In this context, Fog/Edge computing will represent an opportunity to deploy IoT gateways in proximity of IoT domains, meeting the requirements of applications needing low-latency interactions with devices. In this paper, we present an edge-centric distributed architecture to provide resource discovery and access services to IoT applications. The proposed approach leverages the CoRE Resource Directory interface and the Constrained Application Protocol (CoAP) to expose a standard interface for global discovery and access. Multiple IoT gateways are federated through a peer-to-peer overlay implemented by a distributed hash table, which is exploited to share the information on IoT resources available across multiple domains. The proposed solution is validated by means of a small-scale prototype, and extensively evaluated through emulation in large scale deployment in comparison to a centralized Cloud-based approach. Experimental results demonstrated that the proposed approach guarantees lower latencies than a centralized solution and can ensure scalability for small to medium sized deployments.

Complex Event Processing (CEP) systems extract interest situations from event flows based on event detection patterns. However, local event processing for distributed Internet of Things (IoT) has not been discussed yet. Besides, it is complex or impossible to discover such patterns in some applications of IoT. In this article, we design a complex event service to process event flows based on IoT resource models, which does not depend on existing patterns, and deals with both discrete events and continuous variables. To improve the CEP performance, local IoT resources are used for local event processing, and a lazy exchange method is designed to realize the collaborated event processing between network edges and a data center. Our evaluation shows that our solution is feasible and effective.

The Internet of Things (IoT) is becoming increasingly ubiquitous, acting as an important source of real-time data for various applications. By allowing data exchange between various parties along the IoT devices-Edge-Cloud computing continuum, the larger societal benefits of the IoT can be achieved. Assuring security and fostering confidence for IoT data sharing, however, is one of the biggest obstacles. Sharing real-time data originating from connected devices is crucial to real-world intelligent IoT applications, i.e., based on artificial intelligence/machine learning. Such IoT data sharing involves multiple parties for different purposes and is usually based on data contracts that might depend on the dynamic change of IoT data variety and velocity. We aim to support multiple parties (aka tenants) with dynamic contracts based on the data value for their specific contextual purposes. This work addresses these challenges by introducing a novel dynamic context-based policy enforcement framework to support IoT data sharing (on-Edge) based on dynamic contracts. Our enforcement framework allows IoT Data Hub owners to define extensible rules and metrics to govern the tenants accessing the shared data on the Edge based on policies defined with static and dynamic contexts. We have created an edge-centered architecture that enables multi-tenant use cases with tenant-specific application deployment and IoT-context-based data sharing on edge servers. Our proof-of-concept prototype for sharing sensitive data such as surveillance camera videos has illustrated our proposed framework. The experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time. We have addressed the need to enable multi-parties IoT (data) resources to be shared based on contracts, especially with dynamic IoT contexts, for tenant applications on the edge to allow their closer access to data.

Prolonging battery lifetime, enhancing computation capability and improving spectral efficiency have been the key design challenges in Internet of Things (IoT) era. This paper provides a novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer (WET), Mobile-Edge Computing (MEC) and Full-Duplex (FD). Specifically, the Hybrid Access-Point (HAP) (integrated with a MEC server) operates in FD mode to simultaneously broadcast energy and receive computation tasks to/from the mobile devices in the same band. Each mobile relies on the harvested energy to accomplish computation tasks by locally executing or (partial) offloading to the HAP. We concentrate on max-min energy efficiency optimization problem (MMEP) with the joint the optimization of the transmission power at the HAP, computation energy consumption and offloaded bits at each mobile device, time slots for energy transfer and computation offloading. We study the cases with perfect and imperfect self-interference cancellation at the HAP. To solve the non-convex MMEP, we apply the fractional programming theory and Block Coordinate Descent (BCD) method to design the algorithms with low complexity. Numerical results demonstrate that the proposed solutions outperform the baseline scheme in terms of the worst-case mobile EE. Moreover, the proposed algorithms can converge to the optimal solution through a few iterations.

Routing protocols plays a key role in coherent use of resources in Internet of Things (IoT). The more efficient routing scheme is, the more efficiently will nodes communicate with each other. In this paper, we present presents an efficient cluster based routing protocol for IoT. The protocol aims at increasing lifetime and stability of networks. The paper introduces a new routing scheme Modified-Percentage LEACH Protocol based on existing protocol namely Percentage LEACH. Extra amount of energy wastage is alleviated by reducing communication between Cluster Heads (CHs) and sink. Thus, it increases the lifetime of sensor nodes in IoT communication networks. It is achieved by introducing threshold calculation on each CH, which is used as basic criteria for CH election and selection. The protocol also takes into account distance parameter of nodes from sink. This protocol helps plummeting energy wastage which results in more lifetime and high throughput. The depicted protocol is compared with its parent protocol and simulation results shows high throughput and outperforms its parent protocols.

Threats, Detection and Mitigation of Rank Attack: A Survey

With The advent of the Internet of Things (IoT) and its use cases there is a necessity for improved latency which has led to edgecomputing technologies. IoT applications need a cloud environment and appropriate scheduling based on the underlying requirements of a given workload. Due to the mobility nature of IoT devices and resource constraints and resource heterogeneity, IoT application tasks need more efficient scheduling which is a challenging problem. The existing conventional and deep learning scheduling techniques have limitations such as lack of adaptability, issues with synchronous nature and inability to deal with temporal patterns in the workloads. To address these issues, we proposed a learning-based framework known as the Deep Reinforcement Learning Framework (DRLF). This is designed in such a way that it exploits Deep Reinforcement Learning (DRL) with underlying mechanisms and enhanced deep network architecture based on Recurrent Neural Network (RNN). We also proposed an algorithm named Reinforcement Learning Dynamic Scheduling (RLbDS) which exploits different hyperparameters and DRL-based decision-making for efficient scheduling. Real-time traces of edge-cloud infrastructure are used for empirical study. We implemented our framework by defining new classes for CloudSim and iFogSim simulation frameworks. Our empirical study has revealed that RLbDS out performs many existing scheduling methods.

Extending the current Internet and providing connection, communication, and internetworking between devices and physical objects, or things, is a growing trend that's often referred to as the Internet of Things (IoT). Integrating real-world data into the Web, with its large repositories of data, and providing Web-based interactions between humans and IoT resources is what the Web of Things (WoT) stands for. Here, the guest editors describe the Big Data issues in the WoT, discuss the challenges of extracting actionable knowledge and insights from raw sensor data, and introduce the theme articles in this special issue.