Understanding optimal data gathering in the energy and latency domains of a wireless sensor network

Monaco, U.; Cuomo, Francesca; Melodia, Tommaso; Ricciato, Fabio; Borghini, Maria Chiara

doi:10.1016/j.comnet.2006.02.003

Cited by 28 publications

(15 citation statements)

References 8 publications

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“…Others consider latency constraints to meet time sensitive requirements [4] [5]. An aggregation with both of the factors is more general but has not been considered yet.…”

Section: Introductionmentioning

confidence: 99%

Event Aggregation with Different Latency Constraints and Aggregation Functions in Wireless Sensor Networks

Zhu

Cao

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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Event aggregation in WSNs is a process of combining several low-level events into a high-level event to eliminate redundant information to be transmitted and thus save energy. Existing works on event aggregation consider either latency constraint or aggregation function, but not both. Moreover, existing works only consider optimal aggregation for single highlevel event, but many applications are composed of multiple high-level events. This paper studies the problem of aggregating multiple high-level events in WSNs with different latency constraints and aggregation functions. We propose relation matrix to define aggregation function, which describes the similarity among limited number of primitive events rather than the growing number of high-level events. Based on it, we propose an event aggregation algorithm jointly considering the two issues for single high-level event. This algorithm supports partial aggregation which is more general than fully aggregation. Through selection the optimal base events, the work is extended to multiple highlevel events and consider the practical reliable constraint. The simulation results show that our algorithm outperforms existing approaches and saves significant amount of energy (up to 35% in our system).

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“…Others consider latency constraints to meet time sensitive requirements [4] [5]. An aggregation with both of the factors is more general but has not been considered yet.…”

Section: Introductionmentioning

confidence: 99%

Event Aggregation with Different Latency Constraints and Aggregation Functions in Wireless Sensor Networks

Zhu

Cao

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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“…Maximizing network lifetime can also be expressed similarly as maximizing the amount of data transmitted from sensor nodes to sinks [6][7][8] . In [9], the routing problem is formulated as multiple mathematical programming models from simple to complex based on different network performance. In [10], the routing problem is formulated as a nonlinear programming model and lagrangian relaxation is used to achieve the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Max-min Based Optimal Routing Algorithm for Wireless Sensor Networks

Jiang

2010

2010 Fourth International Conference on Genetic and Evolutionary Computing

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Wireless sensor networks are composed of energyconstrained nodes. Therefore, it is crucial to design routing algorithms to optimize the energy usage of nodes. Aimed at maximizing the network lifetime, we introduced an optimal routing algorithm based on max-min model. In this algorithm, the data transmission matrix and the energy consumption of nodes for data transmitting and receiving are defined. Based on the energy consumption for sending and receiving data and the available residual energy of nodes, the max-min based mathematical programming model is designed to find optimal routing. The routing paths and data volume are determined according to the optimization of parameters in the model. Simulation results show that the algorithm balances the energy consumption of nodes effectively and extends the network lifetime.

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“…There are essential drawbacks of geographical approaches which can be listed as follows: requiring complex calculations at nodes, depending on the MAC-layer (e.g., IEEE 802.11), being susceptible to local-minima, possessing inability to go around voids, and reliability. Moreover, frequently changing topologies harden the problem mentioned above [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

SWR: Smartness Provided by Simple, Efficient, and Self-Adaptive Algorithm

Soytürk

Altılar

2012

International Journal of Distributed Sensor Networks

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Resource limitations of sensor nodes in wireless sensor networks (WSN) bound the performance on its implementations. Main concern becomes utilizing these limited resources (CPU, memory, bandwidth, battery) as efficient as possible. Their efficiency is mostly affected by the applied routing algorithm, which carries gathered data to inclined/intended destinations. In this paper, a novel routing algorithm, stateless weight routing (SWR), is proposed. The SWR differs from other protocols in many ways. Major feature of the SWR is its simplicity. It is a completely stateless protocol without requiring any network or neighborhood information for routing. This feature decreases packet transmissions and energy consumption dramatically. For reliability, data flows to the sink node over multiple paths. Moreover, nodes have the ability of recovering from voids. Nodes process each packet independently and apply an adaptive approach according to the current conditions. These mechanisms are part of the applied simple routing algorithm, the SWR. The resultant of these features assures flexibility and smartness at nodes and in the network. Therefore, topological changes have a little effect on data packet transmissions. Performance evaluation of the proposed approach shows that the SWR is scalable for WSNs whose topology change instantly and frequently as well as remain stationary.

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Understanding optimal data gathering in the energy and latency domains of a wireless sensor network

Cited by 28 publications

References 8 publications

Event Aggregation with Different Latency Constraints and Aggregation Functions in Wireless Sensor Networks

Event Aggregation with Different Latency Constraints and Aggregation Functions in Wireless Sensor Networks

Max-min Based Optimal Routing Algorithm for Wireless Sensor Networks

SWR: Smartness Provided by Simple, Efficient, and Self-Adaptive Algorithm

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