Efficient allocation of resources in multiple heterogeneous Wireless Sensor Networks

Li, Wei; Delicato, Flávia C.; Pires, Paulo F.; Lee, Young Choon; Zomaya, Albert Y.; Miceli, Claudio; Pìrmez, Luci

doi:10.1016/j.jpdc.2013.09.012

Cited by 92 publications

(39 citation statements)

References 22 publications

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“…An application defines a set of quality-of-service (QoS) requirements, described in terms of maximum end-to-end delay, maximum percentage of packet loss, and energy consumption. 11 Moreover, applications require a set of services provided by the physical WSAN nodes that are described in terms of the following provided services: data collection, processing, decision, routing, and actuation. Such capabilities must be published within the cloud in a central repository through a publish/subscribe mechanism.…”

Section: Olympus Frameworkmentioning

confidence: 99%

Olympus: The Cloud of Sensors

et al. 2015

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A decentralized wireless sensor and actuator network (WSAN) virtualization model leverages the cloud of sensors paradigm to make the best use of the cloud and physical WSAN environments.n the last few years, we've witnessed the emergence of a new paradigm, the cloud of things (CoT), 1 a combination of cloud computing 2 and the Internet of Things (IoT). 3 A cloud is a large-scale (ideally unlimited) set of userfriendly virtualized computing resources that can be dynamically reconfi gured to serve a variable load, seeking optimum resource utilization. 2 The IoT paradigm envisions a global network infrastructure linking a wide variety of physical and virtual devices-the "smart things" that provide identifi cation, data processing, sensing, and connection capabilities to support the development of cooperative services and applications. 3 Essentially, in the CoT paradigm, the cloud acts as an intermediate layer between smart things and applications. Such an intermediate layer hides the complexity of smart things necessary to implement applications. 1 The IoT can benefi t from the cloud's virtually unlimited resources to implement service management and composition for utilizing smart things and the data they produce, 1 whereas the cloud can benefi t from the IoT by extending its scope to deal with real-world objects (smart things) in a distributed and dynamic way.Smart sensors play an important role in the CoT paradigm. 1 These sensors are tiny battery-powered devices endowed with processing, storage, sensing, actuation, and wireless communication capabilities. Such capabilities enable smart sensors to be grouped together to monitor variables, forming a wireless sensor and actuator network (WSAN). 3 WSANs include sink nodes, which are nodes at the edge of the WSAN that don't have the computational, energy, or communication resource constraints of smart sensors. Sink nodes serve as entry points for application requests, as well as points for collecting data from the smart sensors. In WSANs, the data acquired by the smart sensors can be processed and interpreted locally and/or sent to one or more of the sink nodes. Actuators can perform actions on the physical environments in response to the smart sensors' decisions.Given these smart sensor capabilities, WSANs show an advantage within the CoT paradigm in their support of the development of myriad novel coop-

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Section: Olympus Frameworkmentioning

confidence: 99%

Olympus: The Cloud of Sensors

et al. 2015

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“…UMADE [14] is an integrated system for allocating and deploying applications in shared sensor networks based on the concept of Quality of Monitoring (QoM). Fok et al [15] introduce middleware abstractions to represent multiple QoM requirements from multiple applications, whereas a service-oriented middleware is presented in [16] to address the challenges faced by running multiple applications onto heterogeneous WSNs.…”

Section: Related Workmentioning

confidence: 99%

An Optimization Framework for Resource Allocation in Virtual Sensor Networks

Delgado

Gállego

Canales

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

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Abstract-We propose an optimization framework to perform resource allocation in virtual sensor networks. Sensor network virtualization is a promising paradigm to improve flexibility of wireless sensor networks which allows to dynamically assign physical resources to multiple stakeholder applications. The proposed optimization framework aims at maximizing the total number of applications which can share a common physical network, while accounting for the distinguishing characteristics and limitations of the wireless sensor environment (limited storage, limited processing power, limited bandwidth, tight energy consumption requirements). The proposed framework is finally applied to realistic network topologies to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-network vertical design approaches.

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“…Meanwhile, the computational capacity may be another reason for heterogeneity. For example, the sink node and cluster head nodes have more powerful computational capacity when clustering algorithms are used in the network [20]. However, additional still images, audio and video streams compared with the scalar data make WMSNs kind of stronger heterogeneous networks.…”

Section: B Heterogeneitymentioning

confidence: 99%

Modeling the lifetime of Wireless Multimedia Sensor Networks

Wang

2014

2014 9th International Symposium on Communication Systems, Networks &Amp; Digital Sign (CSNDSP)

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This paper reviews the existing lifetime models of Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs) as well as the additional factors influencing the lifetime of WMSNs compared with conventional WSNs. Considering the relationship between individual sensors and the whole network, the importance of each sensor based on its position and the number of alive nodes is given. Then we propose a novel model to formally define the lifetime of WMSNs as the number of data packets the network has handled before its death based on energy by taking into consideration the heterogeneity, network latency, data rate, and data accuracy. Our purpose is to contribute a more general and accurate lifetime model of WMSNs.

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Efficient allocation of resources in multiple heterogeneous Wireless Sensor Networks

Cited by 92 publications

References 22 publications

Olympus: The Cloud of Sensors

Olympus: The Cloud of Sensors

An Optimization Framework for Resource Allocation in Virtual Sensor Networks

Modeling the lifetime of Wireless Multimedia Sensor Networks

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