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Record W1500921800 · doi:10.5539/cis.v8n3p13

Collision Detection in Wireless Sensor Networks Through Pseudo-Coded ON-OFF Pilot Periods per Packet: A Novel Low-Complexity and Low-Power Design Technique

2015· article· en· W1500921800 on OpenAlex
Walid Ahmed, Mohsen Sarraf, Victor B. Lawrence

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
FundersTaif University
KeywordsComputer scienceNetwork packetWireless sensor networkDecoding methodsComputer networkCollisionDemodulationCollision problemWirelessKey distribution in wireless sensor networksReal-time computingWireless networkTelecommunicationsComputer securityChannel (broadcasting)

Abstract

fetched live from OpenAlex

Sensor nodes in Wireless Sensor Networks (WSNs) operate with limited power resources such as small batterieswhich are difficult to be either recharged or replaced in some environments when depleted. Power consumptionrepresents one of the most constraints impact the design of WSNs, leading to various protocols and algorithmsaimed at minimizing the power consumption and extending batteries' lifetime. Sensor nodes in WSNs transmittheir periodic packets continuously to central nodes (receivers) which are responsible for decoding packets andtransmitting them to other communication networks. In addition, sensors usually follow various MAC strategieswhich allow accessing to wireless communication channels. However, sensors may attempt to access thewireless channels at the same time, potentially, leading to collisions among multiple nodes. In fact, central nodesin WSNs most often consume a large amount of power due to the necessity to decode every received packetregardless of the fact that the transmission may suffer from packets collision which impede the networkperformance. Therefore, in the receiver side of WSNs current collision detection mechanisms have largely beenrevolving around direct demodulation and decoding of received packets and deciding on a collision based onsome form of parity bits in each packet for error control. From information theoretic prospective full decoding ofreceived packets with error control bits at central nodes can achieve an efficient usage of network capacity,however, such an approach represents a major burden on power-constrained sensors. This drawback comes fromthe need to expend a significant amount of energy and processing complexity at sink nodes in order tofully-decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a morepractical power saving approaches which achieve a significant power saving with low-complexity at the expenseof low throughput losses. Based on studying the statistics of received packets, central nodes can make a fastdecision to detect a collision without the need for full-decoding of the whole received packets. Our novelapproaches not only reduces processing complexity and hence power consumption, but it also reduces the delayincurred to detect a collision since it operates on only a small number of IQ samples in the beginning of areceived packet. In such a paradigm, our approaches operate directly at the output of the receiver’sAnalog-to-Digital-Converter (ADC) and eliminate the need to pass the corrupted packets through the entiredemodulator/decoder line-up. The performance gain of our proposed approach is illustrated through thecomparison between the computational complexity of our Statistical Discrimination (SD) approaches and someexisting Full Decoding (FD) algorithms(note 1). Our results show that the SD approaches has significant powersavings and low computational complexities over existing FD algorithms with low False-Alarm and Missprobabilities, which qualify our SD approaches to be considered as reliable collision detection mechanisms inWSNs. We also show how to tune various design parameters in order to allow a system designer multipledegrees of freedom for design trade-offs and optimization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0010.006
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.255
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it