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- Elsts, Atis, et al.
(författare)
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Enabling design of performance-controlled sensor network applications through task allocation and reallocation
- 2015
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Ingår i: Proc. 11th International Conference on Distributed Computing in Sensor Systems. - : IEEE Computer Society. - 9781479988563 ; , s. 248-253
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Konferensbidrag (refereegranskat)abstract
- Task Graph (ATaG) is a sensor network application development paradigm where the application is visually described by a graph where the nodes correspond to application-level tasks and edges correspond to dataflows. We extend ATaG with the option to add nonfunctional requirements: constraints on end-to-end delay and packet delivery rate. Setting up these constraints at the design phase naturally leads to enabling run-time assurance at the deployment phase, when the conditions of the constraints are used as network's performance goals. We provide both run-time middleware that checks the conditions of these constraints and a central management unit that dynamically adapts the system by doing task reallocation and putting task copies on redundant nodes. Through extensive simulations we show that the system is efficient enough to enable adaptations within tens of seconds even in large networks.
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| 2. |
- Elsts, Atis, et al.
(författare)
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ProFuN TG : A tool for programming and managing performance-aware sensor network applications
- 2015
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Ingår i: IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops). - : IEEE Computer Society. - 9781467367738 ; , s. 751-759
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Konferensbidrag (refereegranskat)abstract
- Sensor network macroprogramming methodologiessuch as the Abstract Task Graph hold the promise of enablinghigh-level sensor network application development. However,progress in this area is hampered by the scarcity of tools, andalso because of insufficient focus on developing tool support forprogramming applications aware of performance requirements.We present ProFuN TG (Task Graph), a tool for designing sen-sor network applications using task graphs. ProFuN TG providesautomated task mapping, sensor node firmware macrocompila-tion, application simulation, deployment, and runtime mainte-nance capabilities. It allows users to incorporate performancerequirements in the applications, expressed through constraintson task-to-task dataflows. The tool includes middleware that usesan efficient flooding-based protocol to set up tasks in the network,and also enables runtime assurance by keeping track of theconstraint conditions.We show that the adaptive task reallocation enabled by ourapproach can significantly increase application reliability whiledecreasing energy consumption: in a network with unreliablelinks, we achieve above 99.89 % task-to-task PDR while keepingthe maximal radio duty cycle around 2.0 %.
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| 3. |
- Elsts, Atis, et al.
(författare)
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ProFuN TG : Programming Sensornets with Task Graphs for Increased Reliability and Energy-Efficiency
- 2015
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Konferensbidrag (refereegranskat)abstract
- Sensor network macroprogramming methodologies such as the Abstract Task Graph hold the promise of enabling high-level sensor network application development. However, progress in this area is hampered by the scarcity of tools, and also because of insufficient focus on developing tool support for programming applications aware of performance requirements.In this demo we present ProFuN TG (Task Graph), a tool for designing sensor network applications using task graphs. ProFuN TG provides automated task mapping, sensor nodefirmware macrocompilation, application simulation, deployment, and runtime maintenance capabilities. It allows users to incorporate performance requirements in the applications, expressed through constraints on task-to-task dataflows. The tool includes middleware that uses an efficient flooding-based protocol to set up tasks in the network, and also enables runtime assurance by keeping track of the constraint conditions.Through task allocation in a way that optimizes an objective function in a model of the network, and adaptive task reallocation in case of link, node, or sensor failures the tool helps to make sensornet applications both more energy-efficient and reliable.
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| 4. |
- Elsts, Atis, et al.
(författare)
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ProFuN TG : A Tool Using Abstract Task Graphs to Facilitate the Development, Deployment and Maintenance of Wireless Sensor Network Applications
- 2015
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Ingår i: Proc. Poster/Demo Session. ; , s. 19-20
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Konferensbidrag (refereegranskat)abstract
- In this demo abstract we present ProFuN TG (Task Graph), a tool for sensor network application development using the data-flow programming paradigm. The tool has support for the whole lifecycle of WSN application: from the initial design of its task graph, task placement on network nodes, execution in a simulated environment, deployment on real hardware, to its automated maintenance through task remapping. ProFuN TG allows to program applications that incorporate quality-of-service requirements, expressed through constraints on task-to-task data flows.
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| 5. |
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| 7. |
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| 8. |
- Hassani Bijarbooneh, Farshid, 1981-
(författare)
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Cloud-Assisted Data Fusion and Sensor Selection for Internet-of-Things
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The Internet of Things (IoT) is connecting people and smart devices on a scale that once was unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimise energy consumption by selecting sensors for sampling and relaying data. We propose a multi-phase adaptive sensing algorithm with belief propagation protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We formulate the sensor selection problem and solve it using constraint programming (CP) and greedy search. We then use our message passing algorithm (belief propagation) for performing inference to reconstruct the missing sensing data. ASBP is evaluated based on the data collected from real sensors. The results show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP can provide load balancing among sensors successfully and preserves 80\% more energy compared with the case where all sensor nodes are actively involved.
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| 9. |
- Hassani Bijarbooneh, Farshid, 1981-
(författare)
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Constraint Programming for Wireless Sensor Networks
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment.WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment.In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques.
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