Efficient distributed consensus in wireless sensor networks
Bibliographic record
Abstract
Many applications of wireless sensor networks can be formulated as instances of the distributed average consensus problem. This problem involves reaching a network state where each node has the same value---the average of the initial values. Reaching a consensus can be challenging in practical scenarios where the network topology varies in time due to node mobility or unreliable wireless communication links. Randomized gossip algorithms are attractive methods for such scenarios because they do not require specialized routes; they rely on asynchronous updates between random pairs of nodes. However, the communication overhead of gossip is high on topologies that are generally used for modeling wireless sensor networks. Here we propose novel gossip algorithms that reach the consensus with fewer wireless transmissions compared to randomized gossip.We first propose greedy gossip with eavesdropping. This algorithm takes advantage of the broadcast nature of wireless transmissions such that nodes eavesdrop on the updates in their neighborhood. Consequently, when a node wakes up for gossip update, instead of choosing a neighbor randomly, it chooses the neighbor which has the most different value than its own. We prove that greedy updates in this fashion are guaranteed to converge faster than randomized gossip and the communication savings can be expressed as a function of the maximum number of neighbors in the network.Then we move on to studying the problem of reaching consensus on a high-dimensional vector. Although consensus on the entries of a vector can be achieved by running gossip in parallel for each entry, this can be wasteful when only few entries of the vector are significant. This thesis presents threshold and top-m selective gossip algorithms which aim to reach a consensus only on the significant entries of the consensus vector. Both algorithms focus communication resources at each update on exchanging only the significant entries of the local vectors. We prove that such myopic updates identify the significant entries of the consensus vector successfully. Using these algorithms, we propose novel approaches to decentralized compression and distributed particle filtering in wireless sensor networks. Numerical experiments demonstrate communication savings over existing methods.The methods proposed in this thesis are appropriate alternatives to randomized gossip because they do not require additional information to be transmitted beyond local neighborhoods. Taken together our results indicate that it is possible to decrease the communication overhead of randomized gossip while preserving its attractive properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".