MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks

Mottola, Luca; Picco, Gian Pietro

doi:10.1109/tmc.2010.250

Cited by 102 publications

(52 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these systems, multiple sink nodes (actuators) must collect data generated by multiple source nodes. Tree-based routing designed for data collection at a single sink becomes insufficient, and several transport protocols are specifically targeted at these scenarios [20].…”

Section: Discussionmentioning

confidence: 99%

The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

Ferrari

Zimmerling

Thiele

et al. 2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

Self Cite

View full text Add to dashboard Cite

Abstract-We present the low-power wireless bus (LWB), a new communication paradigm for QoS-aware data collection in lowpower sensor networks. The LWB maps all communication onto network floods by using Glossy, an efficient flooding architecture for wireless sensor networks. Therefore, unlike current solutions, the LWB requires no information of the network topology, and inherently supports networks with mobile nodes and multiple data sinks. A LWB prototype implemented in Contiki guarantees bounded end-to-end communication delay and duplicate-free, inorder packet delivery-key QoS requirements in many control and mission-critical applications. Experiments on two testbeds demonstrate that the LWB prototype outperforms state-of-theart data collection and link layer protocols, in terms of reliability and energy efficiency. For instance, we measure an average radio duty cycle of 1.69 % and an overall data yield of 99.97 % in a typical data collection scenario with 85 sensor nodes on Twist. I. INTRODUCTIONProviding and analyzing Quality of Service (QoS) guarantees are key factors in making low-power wireless networks viable for a variety of applications, including safety-critical alarm systems, real-time control for factory automation, and mission-critical patient monitoring. These applications rely on a data collection service that provides bounded end-to-end delays, highly reliable packet delivery, and energy efficiency to achieve system lifetimes of several years. However, current solutions often fall short of these requirements, especially in multi-hop networks involving mobile nodes and multiple sinks.A major obstacle in current solutions is their strong dependency on the network topology. To cope with the multi-hop nature of low-power wireless networks, they let nodes continuously collect information about link qualities and neighboring nodes, serving, for example, as input to routing and TDMA scheduling algorithms [11], [15]. Keeping such network state up-to-date in a wireless environment, where channel conditions change frequently [27], consumes significant bandwidth and energy. Node mobility compounds the problem as network state becomes quickly outdated [9]. Moreover, many existing solutions use multiple protocols concurrently, which can lead to unintended interactions that impair system performance and cause failures difficult to find and fix [8], [25], or they employ cross-layer designs, which often increase complexity and thus hamper analysis of the provided QoS guarantees [26].To tackle the issues above, we adopt a clean-slate design and rethink the entire networking stack. In particular, we propose the low-power wireless bus (LWB), a novel communication paradigm for data collection with QoS guarantees.

show abstract

Section: Discussionmentioning

confidence: 99%

The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

Ferrari

Zimmerling

Thiele

et al. 2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

Self Cite

View full text Add to dashboard Cite

show abstract

“…We essentially wrap a simplified version of an existing data collection protocol [35] with the APIs necessary to make it usable as an instance of report. We call it STREAM abstraction, as it primarily supports periodic traffic.…”

Section: Meta-abstractionsmentioning

confidence: 99%

makeSense: Simplifying the Integration of Wireless Sensor Networks into Business Processes

Mottola

Picco

Oppermann³

et al. 2019

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Abstract-A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-also directly usable by WSN developers-and then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

show abstract

“…Each node attaches its normalized buffer size in awareness packet (AP) and broadcasts it in order to update its neighbors routing table. The receiver node compares APs received from all neighbors, selects the next destination with lowest potential field, and then calculates the average cumulative queue length in the direction of sink based on Equation 5. Afterwards, this node broadcasts AP that includes the average cumulative queue length.…”

Section: Average Cumulative Queue Lengthmentioning

confidence: 99%

“…Therefore, using multiple sinks is proposed as a more feasible scheme for such networks [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Hop-by-hop traffic-aware routing to congestion control in wireless sensor networks

Gholipour

Haghighat

Meybodi

2015

J Wireless Com Network

View full text Add to dashboard Cite

One of the major challenges in wireless sensor networks (WSNs) research is to prevent traffic congestion without compromising with the energy of the sensor nodes. Network congestion leads to packet loss, throughput impairment, and energy waste. To address this issue in this paper, a distributed traffic-aware routing scheme with a capacity of adjusting the data transmission rate of nodes is proposed for multi-sink wireless sensor networks that effectively distribute traffic from the source to sink nodes. Our algorithm is designed through constructing a hybrid virtual gradient field using depth and normalized traffic loading to routing and providing a balance between optimal paths and possible congestion on routes toward those sinks. The simulation results indicate that the proposed solution can improve the utilization of network resources, reduce unnecessary packet retransmission, and significantly improve the performance of WSNs.

show abstract

MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks

Cited by 102 publications

References 45 publications

The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

makeSense: Simplifying the Integration of Wireless Sensor Networks into Business Processes

Hop-by-hop traffic-aware routing to congestion control in wireless sensor networks

Contact Info

Product

Resources

About