Cost-Aware Capacity Optimization in Dynamic Multi-Hop WSNs

Suhonen,; Kohvakka,; Kuorilehto,; Hannikainen,; Hamalainen,

doi:10.1109/date.2007.364670

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Each time slot is further divide into two subslots, first subslot is for data frame and the following subslot is for acknowledgment. The use of contention free slots is preferred as it eliminates collisions and thus increases reliability [ 29 ]. The ALOHA based CAP is used only for joining a cluster and requesting reservations on CFP.…”

Section: Zigbee and Tutwsn Technologiesmentioning

confidence: 99%

Availability and End-to-end Reliability in Low Duty Cycle MultihopWireless Sensor Networks

Suhonen

Hämäläinen

Hännikäinen

2009

Sensors

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A wireless sensor network (WSN) is an ad-hoc technology that may even consist of thousands of nodes, which necessitates autonomic, self-organizing and multihop operations. A typical WSN node is battery powered, which makes the network lifetime the primary concern. The highest energy efficiency is achieved with low duty cycle operation, however, this alone is not enough. WSNs are deployed for different uses, each requiring acceptable Quality of Service (QoS). Due to the unique characteristics of WSNs, such as dynamic wireless multihop routing and resource constraints, the legacy QoS metrics are not feasible as such. We give a new definition to measure and implement QoS in low duty cycle WSNs, namely availability and reliability. Then, we analyze the effect of duty cycling for reaching the availability and reliability. The results are obtained by simulations with ZigBee and proprietary TUTWSN protocols. Based on the results, we also propose a data forwarding algorithm suitable for resource constrained WSNs that guarantees end-to-end reliability while adding a small overhead that is relative to the packet error rate (PER). The forwarding algorithm guarantees reliability up to 30% PER.

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Section: Zigbee and Tutwsn Technologiesmentioning

confidence: 99%

Availability and End-to-end Reliability in Low Duty Cycle MultihopWireless Sensor Networks

Suhonen

Hämäläinen

Hännikäinen

2009

Sensors

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“…1) Capacity Efficiency: The efficiency of the network capacity is an important metric for estimating the performance of network optimization [28]. It is defined as the percentage of the utilized capacity in the total network capacity.…”

Section: B Application Examplesmentioning

confidence: 99%

On real-time capacity of event-driven data-gathering sensor networks

Jiang¹,

Ravindran²,

Cho³

2009

Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Network capacity is a critical feature of wireless ad hoc and sensor networks. It is particularly challenging to determine network capacity when combined with other performance objectives such as timeliness. This paper investigates real-time capacity for event-driven data-gathering sensor networks with the unbalanced many-to-one traffic pattern. First, we compute the average allowable throughputs of nodes for a given event distribution, based on which we then leverage results of queuing theory to estimate the per-hop delays. We develop a new slack time distribution scheme for the unbalanced many-to-one traffic pattern, and prove it optimal in terms of the per-hop success probability. Here the per-hop success probability is defined as the probability for a packet to meet its sub-deadlines at each hop. Finally, we define the network-wide real-time capacity, i.e., given a threshold for the per-hop success probability, how much data (in bit per second) can be delivered to the sink node meeting their deadlines. For these research results, we provide some application scenarios, including configuring packet deadlines or verifying a specific deadline configuration, setting a packet's priority for dynamic scheduling, and trading the reliability of real-time data delivery for capacity efficiency etc. We also study two special cases of WSNs, the chain model and the continuous model. Our slack distribution scheme yields consistent or similar results for these two special cases as that of past works, but is more adaptive by supporting more generic cases.

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