Publish/Subscribe Middleware for Resource Discovery in MANET

Saghian, Malihe; Ravanmehr, Reza

doi:10.1109/ccgrid.2015.39

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Early middleware-based solutions [35][36][37] support prioritization or bandwidth allocation based on available system capacity, data relevance, and data importance. More recent solutions assign priorities on the basis of the validity span of published data and subscriptions [38], or on the basis of delay and reliability requirements [39]. Currently, standardized message brokers such as RabbitMQ and ActiveMQ support the assignment of priorities at the publisher side prior to the emission of a message.…”

Section: Related Workmentioning

confidence: 99%

“…Lastly, to represent IoT systems that consider the data recipients' preferences [32][33][34] to deliver different types of data (message sizes, important, etc.) [35,36,38,39], multiclass and priority queues are introduced where different classes of messages can be assigned with different priorities. To summarize, application designers are able to analyze and configure certain system aspects (middleware QoS delivery modes, network and user connectivity, message dropping rates, system resources, priority levels, message sizes) by combining the provided queueing models in order to tune and guarantee the appropriate response time and delivery success rate between IoT devices.…”

Section: Related Workmentioning

confidence: 99%

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Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

et al. 2021

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Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

et al. 2021

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“…Early middleware-based solutions [18,39,65] support prioritization or bandwidth allocation based on the available system capacity, data relevance and data importance. More recent middleware solutions assign priorities based on validity span of published data and subscriptions [50] or based on delay and reliability requirements [58]. Currently, standardized message brokers such as RabbitMQ, ActiveMQ, etc, support the assignment of priorities at the publisher side prior to the emission of a message.…”

Section: Related Workmentioning

confidence: 99%

“…Given the heterogeneity of this information and the limited networking resources for delivering notifications, we believe event prioritization is necessary in such mission-critical settings. Existing data exchange systems [6,38,50,54,58,65] provide mechanisms for event prioritization either by manually assigning priorities to specific data flows or by dynamically assigning them based on the application-level data flows/types (e.g., video data) or even QoS-specific requirements (e.g., delay-sensitive apps). However, such systems cannot be customized to support mission-critical applications with dynamic changes of the app requirements, interested data recipients, data flows/types and the networking conditions.…”

Section: Introductionmentioning

confidence: 99%

PrioDeX

Bouloukakis

Benson

Scalzotto³

et al. 2021

ACM Trans. Internet Things

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Real-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc.) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this article, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

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“…To locate and discover resources in a mobile ad hoc network, Saghian and Ravanmehr 66 use DHT and fault‐tolerant technology; this architecture can manage resources, Quality of Service, load balancing, and prioritization requests 67 . However, resource sharing in disconnected topology is not deeply addressed.…”

Section: Service Discovery and Cluster‐based Routing Protocolsmentioning

confidence: 99%

Service redundancy and cluster‐based routing protocols for wireless sensor and mobile ad hoc networks: A survey

Yagoub

Rodrigues

Khalifa

et al. 2020

Int J Communication

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SummaryService discovery (SD) is the cornerstone of wireless and mobile systems. The discovery of effective service is the key to achieve a higher level of ubiquity, which ensures the availability of services to users and applications significantly, and the high use of services. The research in this field is limited, with a few SD protocols available for specific purposes specified in the 6LoWPAN region. This paper discusses the various issues and challenges facing the design and selection of the appropriate service discovery and cluster‐based mechanism. Moreover, attention has been drawn to the cross‐layer solution by discussing different clustering techniques used to solve the redundancy problems. Furthermore, the service discovery protocols and cluster‐based routing protocols are critically investigated considering the redundancy problem, methods, mechanisms, and architectures by categorizing them into different categories, comparing them with fundamental parameters in the WSNs and MANETs environments. In addition, a modern form is proposed to show the life cycle of the service discovery process, and a new figure has been designed to clarify to the researchers various of the open issues related to the service discovery and cluster‐based routing protocols, corresponding objectives and the techniques of solving problems. Through the discussion of the scientific papers under survey, important results and valuable recommendations were reached. The survey is concluded with a summary, open research issues, outlook, and directions for future research on the topic.

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Publish/Subscribe Middleware for Resource Discovery in MANET

Cited by 7 publications

References 12 publications

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

PrioDeX

Service redundancy and cluster‐based routing protocols for wireless sensor and mobile ad hoc networks: A survey

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