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Device Management

Device Management

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Device Management is the process of administering, monitoring, and maintaining devices within an organization’s network. It encompasses the deployment, configuration, security, and lifecycle management of hardware and software assets to ensure optimal performance, compliance, and security across various platforms and environments.
lightbulbAbout this topic
Device Management is the process of administering, monitoring, and maintaining devices within an organization’s network. It encompasses the deployment, configuration, security, and lifecycle management of hardware and software assets to ensure optimal performance, compliance, and security across various platforms and environments.

Key research themes

1. How can distributed and software-based architectures enhance secure device management against unauthorized USB activities?

This theme focuses on leveraging distributed architectures, multi-agent systems, and software-based blocking mechanisms to detect, assess, and prevent unauthorized or malicious activities through USB devices such as BadUSB attacks. It examines system designs that provide effective threat assessment, user-informed decisions, and scalable protection for personal and corporate computing environments.

Distributed Architecture to Enhance Systems Protection against Unauthorized Activity via USB Devices
by Henrique Santos

2023, Journal of Sensor and Actuator Networks

Key finding: Proposed a distributed multi-agent architecture on Microsoft Windows that inspects USB devices upon connection, shares anonymous threat assessments across external databases derived from cumulative user choices, and provides... Read more
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Secure management of logs in internet of things
by Udit Gupta

2023, arXiv (Cornell University)

Key finding: Identified the necessity to categorize and securely store large volumes of real-time generated logs from IoT devices, including security-related logs crucial for identifying patterns like malware and intrusion attempts. The... Read more
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Object technology for the integration of the infrastructure, data manager, and tracker for command and control applications
by Mike Gates

2025, Proceedings Third International Workshop on Object-Oriented Real-Time Dependable Systems

Key finding: Implemented an object-oriented infrastructure integrating middleware, data management, and real-time tracking components to enable evolution of command and control systems into more modular, extensible architectures. The... Read more
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2. What methodologies enable efficient management and interaction of heterogeneous, resource-constrained devices in networks such as Home Area Networks or IoT?

Research under this theme explores proxy representations, middleware frameworks, and feature-based modular software architectures that support constrained devices unable to run full middleware stacks. It also investigates semantic models and fog computing architectures to address heterogeneity, device discovery, interaction, and unified management of diverse connected devices with minimal overhead.

Simpleware Device Surrogates: Enabling High-Level Description and Interaction with Resource Constrained Devices
by Victor Callaghan

2015

Key finding: Presented the Nexus middleware middleware framework that employs proxy 'simpleware' device surrogates to represent resource-constrained embedded devices within Home Area Networks, enabling dynamic device representation and... Read more
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Fog Services Provider Architecture for IoT
by Hoan Le

2023, 2020 11th International Conference on Network of the Future (NoF)

Key finding: Proposed the Fog Services Provider (FSP) microservice architecture leveraging fog computing and Semantic Web technologies to enable semantic annotation, unified data modeling, registration, and management of heterogeneous IoT... Read more
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3. How can middleware and software platforms support dynamic, distributed applications and device management across heterogeneous and mobile devices?

This theme addresses architectures and platforms that facilitate running distributed applications spanning multiple co-located or heterogeneous devices, including mobile and embedded ones. It highlights software component models supporting dynamic reconfiguration, service orchestration in medical or pervasive environments, and system designs enabling seamless interaction, dynamic remote control, or migration of applications based on device context and proximity.

Enabling the Development of Pervasive Multi-Device Applications
by Pedro Albuquerque Santos

2017, EICS '17 - Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Key finding: Identified proxemics (interpersonal distance) as key to designing applications that run seamlessly across multiple co-located devices, proposed a framework architecture integrating indoor positioning techniques and... Read more
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Dynamic remote control through service orchestration of point-of-care and surgical devices based on IEEE 11073 SDC
by Frank Golatowski

2023, 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT)

Key finding: Demonstrated a dynamic, vendor-independent remote control system for medical devices in operating rooms by leveraging IEEE 11073 SDC service-oriented architecture. Introduced compositional services that separate control... Read more
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Software Architecture for Limited Mobile Devices
by Philippe Roose

2022

Key finding: Presented Kalimucho, a software platform based on service-oriented component models and leveraging OSGi technology, which enables dynamic reconfiguration and deployment of distributed applications on heterogeneous,... Read more
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A Platform for Device and Computation Management
by Kuldeep Patel

2013

Key finding: Reported the design and implementation of the PDCM system, a secure, reliable application framework supporting multi-platform applications across heterogeneous mobile devices. It enables application mobility by allowing users... Read more
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All papers in Device Management

Meditrina
by Gaia Maselli

2024

Physi cal D evi ces Int ell i gent Cont rol Syst em Caregiver Cont rol Syst em Aut om ated Cont rol Syst em User Cont rol Syst em
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Secure management of logs in internet of things
by Udit Gupta

2023, arXiv (Cornell University)

Ever since the advent of computing, managing data has been of extreme importance. With innumerable devices getting added to network infrastructure, there has been a proportionate increase in the data which needs to be stored. With the... more
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Fog Services Provider Architecture for IoT
by Hoan Le

2023, 2020 11th International Conference on Network of the Future (NoF)

The prominence of Internet of Things (IoT) devices is characterized by a wide diversity of network access technologies, including Wi-Fi, Cellular, Lo-Ra, ZigBee, or Bluetooth. Dealing with such heterogeneity is still very challenging.... more
Fig. 1: The Internet of Things and Fog computing the fog computing services architecture managing the heterogeneous devices through the definition of their profiles and storing information based on seman- tic annotation service. The rest of the as follows: Section II presents related introduces our fog services provider (FSP) architecture for IoT and manages IoT devices through de properties and relationships between data objects with the help of ontology and a converting algorithm from relationa databases to a triplet. Section IV highlights its features thought real experimental Finally, section V, summarizes this paper and discusses some of our prospects. by controlling and paper is organized works. Section II fining concepts and
Fig. 2: Microservices architecture of Fog computing We propose a four-layer micro-services architecture based on fog computing and the two underlying system services with service providing capabilities for IoT environments to meet the above criteria. These include the IoT producer layer, the FSP infrastructure, the FSP management services, the FSP support services, the security/private sphere management and the analysis and data management. Based on the advantages o a micro-services model such as self-discovery, loose coupling and high cohesion, this architecture is proposed to integrate services that manage a kind of multi-network technology of heterogeneous IoT producers and components in the FSP and are ubiquitously located in Fog areas. Fig. 2 shows a simplified services stack for the fog architecture in our proposal.
Fig. 3: IoT producers management service
Fig. 4: The semantic data annotation service When converted to the RDF diagram, the classes (Class) correspond to each table, each row in the table is represented by a statement in the form of triples (S, P, O) to form the RDF diagram, where a subject (S) is the key attribute value, predicates (P) correspond to the labels of each column, object (O) is the value at the column corresponding to the predicate (P). Algorithm 1 represents a detailed transformation from a relational database to an RDF graph.
Fig. 5: IoT producers registration service Consumer entity as defined in section III. It is a software, an API, a service or an application that allow users to interact with it to obtain information from producers. We use the concept Consumer which defined in the ontology with attributes and the relationships with data objects. This concept enables to access resources in the FSP node through authenticating and authorizing mechanism. To register into the FSP architecture, we also use a profile of Consumer as basic information to become a member of the FSP, illustrated as in Table. III.
Fig. 6: Registration and access procedures for consumers the main functionalities of the proposed architecture such as register procedures of producer and consumers and manage its information in the core concepts using the ontology. The IoT producer network includes Raspberry Pis 3 Model B with an operating system called Hypriot and built-in WiFi, GPIO ports, MicroSD card slot, memory, video/audio outputs. Hypriot [10] enables the execution of Docker on a Raspberry Pi and deploys services in Docker runtime. Those are also reasons why we use Docker technology to deploy distributed services. The RasPis will run a Python script to collect data from a DHT11 sensor (i.e., temperature, humidity) and send it to the FSP via CoAP protocol.
We also show the example result of mapping from RDB to RDF graph through Algorithm I with related tables in a producer management relational database. The primary key of relational tables is the bolded properties, as described in the following:
TABLE I: Comparison of the related fog computing architecture architecture to achieve accurate results by minimizing overall delay and with high reliability by using edge services close to the endpoints; Energy Efficiency (EFf): provides the flexibility, interoperability, connectivity, privacy and real-time service required for energy efficiency management; Semantic Data Annotation (SDA): is the ability to map meaning to raw data and obtain knowledgeable information. Besides, to ensure semantic interoperability in heterogeneous IoT environments, semantic annotation of sensory raw data using ontology is an appropriate method. their native protocols. The two most important services are producer service and consumer service. The producer service plays a role as connectors that interact with the IoT producers. It can simultaneously serve one or more producers, including sensors, actuators, etc. In order to ensure semantic interop- erability, this service provides a semantic methodology that focuses on interoperable IoT provisioning by converting the raw data produced and transmitted by the IoT producers into a common data structure called TripleStore or RDF storage and sending this converted data to the storage services at the FSP infrastructure layer and to other micro-services at other layers of the FSP. The consumer services are the means of extracting, processing/converting event/read data from FSP and sending it to an endpoint or process of the end-users choice. Besides, these services provide APIs or SDKs that can be used to build applications by assembling triggers, built-in functions, and custom functions for end-user decision making on specific services.
TABLE II: Producer profile [he producer register procedure is performed and illustrated in Fig.5 by following steps: (1) the producer joins to the FSP node by asking information about the domain of FSP via a DNS service; (2,3) The DNS service responses based on the received information with an IP address of the FSP node; (4) the producer, then send their profile to register with the FSP node via this IP address; (5) The FSP node forwards this profile to the semantic data annotation service to process and update the producer’s profile into a corresponding format; (6) the FSP node confirms the successful registration and wait for the producer send data to the FSP node. 3) Producers access service: To manage a heterogeneous set of producers and their different native protocols. In the first step of the service, the producers need to be registered are defined by their profiles. Each producer profile contains a unique identifier of the producer to be registered. Detailed information of producers is not necessary, because they are contained in the producer ontology. We define and describe a profile of producer shown in Table IL.
TABLE III: Consumer profile Fig. 6. illustrates a sequence diagram related to the registra- tion procedure and request resources with FSP. This procedure is authenticated and granted through two Authentication and Authorization mechanisms after it has been successfully reg- istered with consumer’s profile information.
TABLE IV: The RDF store triplet Table. V and Table. VI are API endpoints between the FSP node and microservices. The former depicts the endpoint to be used for processing the register producer’s profile in the FSP. The later is the endpoint to be applied to access the resource by an external user. The data converted from an RDF graph into the data model for semantical annotation is stored as a_ Triple- Store, as shown in Table. IV. The namespace fsp = http://www.fsp.com/fspdb/ indicates that the ele- ments with the fsp prefix belong to a triplet (S, P, O). The execution of the query data in the RDF store is supported by SPARQL language.
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Secure management of logs in internet of things
by Udit Gupta

2022, ArXiv

Ever since the advent of computing, managing data has been of extreme importance. With innumerable devices getting added to network infrastructure, there has been a proportionate increase in the data which needs to be stored. With the... more
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Unified Device Management via Java-enabled Network Devices
by Tal Lavian

2020

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Open Java-Based Intelligent Agent Architecture for Adaptive Networking Devices
by Tal Lavian

2020

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Unified Device Management via Java-enabled Network Devices
by Tal Lavian

2020

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