On the construction of energy-efficient broadcast and multicast trees in wireless networks

Wieselthier, Jeffrey E.; Nguyen, Gam D.; Ephremides, Anthony

doi:10.1109/infcom.2000.832232

Cited by 877 publications

(1,131 citation statements)

References 9 publications

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“…We first compare the energy consumption of PNC with Non-NC. We use an energy consumption model of E = d 4 [27], where d is the transmission range. The field will generate events which are of interest in an hourly basis and the sensors will record these events.…”

Section: B Comparison Of Energy Consumptionmentioning

confidence: 99%

Partial Network Coding: Theory and Application for Continuous Sensor Data Collection

Wang

Zhang

Liu

2006

200614th IEEE International Workshop on Quality of Service

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Abstract-Wireless sensor networks have been widely used for surveillance in harsh environments. In many such applications, the environmental data are continuously sensed, and data collection by a server is only performed occasionally. Hence, the sensor nodes have to temporarily store the data, and provide easy and on-hand access for the most updated data when the server approaches. Given the expensive server-to-sensor communications, the large amount of sensors and the limited storage space at each tiny sensor, continuous data collection becomes a challenging problem.In this paper, we present partial network coding (PNC) as a generic tool for the above applications. PNC generalizes the existing network coding (NC) paradigm, an elegant solution for ubiquitous data distribution and collection. Yet, PNC enables efficient storage replacement for continuous data, which is a major deficiency of the conventional NC. We prove that the performance of PNC is quite close to NC, except for a sublinear overhead on storage and communications. We then address a set of practical concerns toward PNC-based continuous data collection in sensor networks. Its feasibility and superiority are further demonstrated through simulation results.

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Section: B Comparison Of Energy Consumptionmentioning

confidence: 99%

Partial Network Coding: Theory and Application for Continuous Sensor Data Collection

Wang

Zhang

Liu

2006

200614th IEEE International Workshop on Quality of Service

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“…MAC broadcast messages are used to minimize transmissions for data to multiple children, and overheard data packets provide free acknowledgements using the "wireless broadcast advantage" ( [14]) to parent nodes, with onyl leaf nodes sending an explicit acknowledgement. Link weight is expected transmissions to the root node, calculated from collected link-quality data.…”

Section: Minimum Spanning Tree Algorithmmentioning

confidence: 99%

Software Update Recovery for Wireless Sensor Networks

Brown

Sreenan

2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Updating software over the network is important for Wireless Sensor Networks in support of scale, remote deployment, feature upgrades, and fixes. The risk of a fault in the updated code causing system failure is a serious problem. In this paper, we identify a single, critical, symptom loss-of-control, that complements exception-based schemes, and supports failsafe recovery from faults in software updates. We present a new software update recovery mechanism that uses loss-ofcontrol to provide high-reliability, low energy, software updates, including a comparison of optimised-flooding against spanning-tree for determining loss-of-control in a multi-path environment. The solution presented supports a trial phase (with lower latency), and an operational phase (with lower energy). The energy/latency tradeoff of this is shown, and the high-reliability of this update recovery is demonstrated by analysis and simulation. The results presented control the risk in existing WSN software update mechanisms.

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“…Broadcast oriented protocols achieve the objective but considers the broadcast process from a given source node. For example, the well-known centralized algorithm is a greedy heuristics called BIP [5] (Broadcast Incremental Power). It is a variant of the Prim's algorithm that takes advantage of the broadcast nature of wireless transmissions.…”

Section: Related Workmentioning

confidence: 99%

Effect of Realistic Physical Layer on Energy Efficient Broadcast Protocols for Wireless Ad Hoc Networks

Jeon

Cho

et al. 2006

Next Generation Teletraffic and Wired/Wireless Advanced Networking

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Abstract. Previous work on energy efficient broadcast protocols for wireless ad hoc networks are based a commonly used physical layer model called "Pathloss model" which assume two nodes can communicate if and only if they exist within their transmission radius. In this paper, we analyze the effect of realistic physical layer on energy efficient broadcast protocols. We employ a more realistic log-normal shadowing model for physical layer and consider two link layer operating models: EER (end-to-end retransmission) and HHR (hop-byhop retransmission). Networks with omni-antennas and directional antennas are dealt with separately. Based on above models, we analyze how to adjust actual transmission radius for transmission nodes and relay nodes to get the trade-off between maximizing probability of delivery and minimizing energy consumption. From our analysis based on shadowing model, we have derived the appropriate transmission range. The results presented in this paper are expected to improve the performance of broadcast protocols under realistic physical layer.

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On the construction of energy-efficient broadcast and multicast trees in wireless networks

Cited by 877 publications

References 9 publications

Partial Network Coding: Theory and Application for Continuous Sensor Data Collection

Partial Network Coding: Theory and Application for Continuous Sensor Data Collection

Software Update Recovery for Wireless Sensor Networks

Effect of Realistic Physical Layer on Energy Efficient Broadcast Protocols for Wireless Ad Hoc Networks

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