Pith. sign in

REVIEW 6 major objections 6 minor 42 references

Partially Synchronous BFT Consensus Made Practical in Wireless Networks

T0 review · 6 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read ReduceCatch is a two-phase communication protocol that claims O(log N) reliability for wireless BFT consensus in partially synchronous ad hoc networks.

desk verdict A valuable wireless-consensus testbed and a plausible protocol, but the O(log N) complexity claim rests on a factor-of-two error in the active-node calculation and the evaluation lacks error bars. read the letter →

arxiv 2412.05512 v1 pith:YSTB3XWL submitted 2024-12-07 cs.NI cs.CRcs.DC

Shuo Liu , Minghui Xu , Yuezhou Zheng , Yifei Zou , Wangjie Qiu , Gang Qu , Xiuzhen Cheng This is my paper · ORCID
classification cs.NIcs.CRcs.DC
keywords ReduceCatchwirelessBFTconsensuspartiallysynchronousmodelreliablecommunicationpatternsLoRatestbedCSMA/TDMANACKadhocnetworks
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes ReduceCatch, a two-phase wireless communication protocol that gives reliable 1-to-N, N-to-1, and N-to-N message delivery under a partially synchronous network model. The claim is that by first using TDMA to shrink the set of nodes that still need retransmission (the reduce phase) and then using CSMA with NACK for the few stragglers (the catch phase), message complexity drops to O(log N) for one-to-many, O(N log N + beta n) for many-to-one, and O(N log N) for all-to-all, where n is the small number of active nodes left after reducing. The paper then ports PBFT, Tendermint, and HotStuff onto this communication layer and evaluates them on a moving LoRa-based smart-car testbed with 20 protocol combinations. If the complexity bounds hold, partially synchronous BFT consensus becomes practical for dynamic ad hoc wireless networks such as drone swarms and robot teams.

What carries the argument

The central object is the ReduceCatch protocol itself, with two phases. The reduce phase allocates one TDMA slot per node per round and repeats for NTX rounds, spacing a node's repetitions apart so a burst of channel losses does not swallow all copies; after NTX rounds each node counts how many others it has heard and how many have heard it, and a node is considered inactive once it has both received all messages and been received by all others. The probability that a given node becomes inactive is (1 - $\alpha$^NTX)^(2N-2), so with NTX = O(log N) the number n = N - N(1 - $\alpha$^NTX)^(2N-2) of active nodes is O(1). The catch phase then switches to CSMA with NACK: active nodes that missed a packet send a NACK within a bound $\Delta$, the sender retransmits, and $\Delta$ prevents Byzantine nodes from forcing endless resends. This two-stage design is what converts a broadcast-heavy consensus pattern from O($N^{2}$) or O(N log N) overhead down to the O(log N) or O(N log N) bounds.

What would settle it

Measure packet delivery on a LoRa link under real interference and count active nodes after running the reduce phase with NTX = O(log N) at the paper's target alpha. If the number of nodes still missing messages remains significantly above O(1) across repeated trials—because losses are correlated in time—the stated complexity bounds for the catch phase and the consensus protocol no longer follow.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that the obstacle to partially synchronous BFT consensus in wireless networks is not the consensus logic but the communication layer: standard CSMA and TDMA with ACK/NACK scale poorly because CSMA suffers collisions and TDMA wastes slots on inactive nodes. ReduceCatch combines the two: a reduce phase uses TDMA to broadcast each sender's packet NTX times in non-consecutive slots so that, under independent packet loss with rate alpha, the expected number of nodes still missing a message becomes O(1) once NTX = O(log N); a catch phase then uses CSMA with NACK so only those few nodes request retransmission. The paper claims this yields the message and time complexities in its comparison table, and that on its testbed the ReduceCatch-enabled consensus protocols achieve lower latency and higher throughput than the four CSMA/TDMA plus ACK/NACK combinations, with the largest gains when packet loss exceeds 10%.

Load-bearing premise

The core reduction assumes that every packet loss is independent with a fixed probability alpha, so that after NTX = O(log N) repetitions the expected number of nodes needing retransmission becomes O(1); on real wireless channels, losses come in bursts, so this expected-count calculation may severely underestimate how many active nodes remain.

Editorial extensions

If this is right

  • Reliable 1-to-N communication (leader proposal) has message and time complexity O(log N) instead of O(N) or O(N log N) under previous schemes.
  • N-to-N vote collection in PBFT and Tendermint drops to O(N log N) message and time complexity, removing the quadratic bottleneck of ACK/NACK schemes.
  • N-to-1 collection in HotStuff and view-change phases costs O(N log N + beta n) time, where n is the O(1) active nodes after reduction.
  • Merging catch phases of consecutive communication patterns further cuts total latency in consensus rounds.
  • On the LoRa testbed, ReduceCatch-based consensus improves latency by up to 44.28 seconds and throughput by up to 14.8 TPM over the best-performing non-ReduceCatch baselines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: The independent-loss assumption used to set NTX is likely the fragile point; on real LoRa channels with bursty losses, the active-node count after the reduce phase may stay well above O(1), so a practical deployment would need an adaptive NTX or a fallback that expands the catch phase.
  • Inference: The complexity gains depend on the bound Delta in the catch phase being tight; a Byzantine node can force extra retransmissions within Delta, so the safety/performance trade-off deserves a dedicated analysis.
  • Inference: The cluster-based multi-hop extension suggests a sharding-like design; one could test whether the per-cluster reduction still pays off when inter-cluster routing overhead dominates.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 6 minor

Summary. The paper proposes ReduceCatch, a two-phase (reduce and catch) wireless communication protocol that combines TDMA, CSMA, and NACK to implement reliable 1-to-N, N-to-1, and N-to-N communication patterns for partially synchronous BFT consensus in ad hoc wireless networks. The authors claim O(log N) message and time complexity for 1-to-N communication, O(N log N + beta n) for N-to-1, and O(N log N) for N-to-N, assuming NTX = O(log N) transmissions per sender. They adapt PBFT, Tendermint, and HotStuff to single-hop and multi-hop networks, and build a LoRa-based smart-car testbed on which they measure latency and throughput of 20 protocol combinations, reporting that ReduceCatch-based protocols reduce latency and increase throughput relative to CSMA/TDMA with ACK/NACK baselines.

Significance. If the theoretical complexity claims and experimental results held, the paper would be a valuable systems contribution: it ships an open-source, three-layer wireless consensus testbed, implements a broad set of 20 consensus-protocol/MAC-protocol combinations, and directly addresses practical issues in adapting partially synchronous BFT to dynamic wireless networks. The experimental comparison is extensive for a testbed study, and the code release is a concrete strength. However, the core theoretical guarantee of O(1) active-node reduction is based on a miscalculated constant in the stated NTX threshold, and the reliability claim is weakened by the fixed-length catch phase. The experimental evaluation also uses NTX values below the corrected threshold, so the observed gains cannot be attributed to the claimed active-node reduction. These issues are load-bearing for the paper's central claims, though they appear repairable with revised analysis, corrected thresholds, and more careful statistical reporting.

major comments (6)
  1. [Section IV-B and Table I] The claim that O(n) = O(1) when NTX = log N does not follow from the formula n = N - N(1 - alpha^{NTX})^{(2N-2)}. If NTX = log_{1/alpha} N, then alpha^{NTX} = 1/N and n is approximately N(1 - e^{-2}) = Theta(N), not O(1). To obtain n = O(1) one needs alpha^{NTX} = O(1/N^2), i.e., NTX >= 2 log_{1/alpha} N plus an additive constant. The missing factor of 2 arises from the (2N-2) exponent. This invalidates the stated O(log N) message/time complexity for 1-to-N and the O(N log N) complexity for N-to-N as derived, and the Table I footnote is incorrect as written.
  2. [Section VII-A and IV-B] The evaluation uses NTX = 5 for proposals and NTX = 3 for votes with N = 10 and packet loss rates up to alpha = 0.5. At alpha = 0.5, NTX = 3 gives alpha^{NTX} = 1/8, so n = 10 - 10(1 - 1/8)^{18} is approximately 9.08; almost every node remains active after the reduce phase. Thus the experimental configuration does not realize the O(1) active-node reduction that the paper claims, and the latency/throughput improvements in Figures 5 and 6 cannot be attributed to the reduce phase reducing the number of active nodes to a constant.
  3. [Section IV-C and Table I] The catch phase has a fixed Delta-slot window, and the text states that 'Failing to complete communications within the Delta time limit is considered a failure.' This means a lost NACK or a lost retransmission is simply counted as a failure, not retried. Consequently, ReduceCatch does not provide the deterministic reliability indicated by the check marks in Table I or by the abstract's 'reliable 1-to-N, N-to-1, and N-to-N communications.' At most it provides probabilistic delivery under favorable loss conditions; the paper should state this explicitly and give the failure probability as a function of Delta, alpha, and N, or else modify the catch phase to provide an eventual-delivery guarantee.
  4. [Section IV-A vs. IV-B] The active-node calculation in Section IV-B assumes independent packet losses with a fixed rate alpha, but Section IV-A and Figure 1 document bursty, correlated losses on the same LoRa hardware. Under correlated losses, repeated transmissions separated by TDMA cycles may still fail together, so the expected number of active nodes after NTX rounds can be much larger than the formula predicts. The paper should either model bursty losses, present measurements of the actual number of active nodes after the reduce phase, or justify why independence is a reasonable approximation for the evaluated settings.
  5. [Section VII] The experimental claims rest on the mean of only five trials per sample point, with no confidence intervals, standard deviations, or statistical tests. Given that the differences reported are sometimes modest (e.g., average improvements of 10.77 s and 3.82 TPM), the reader cannot assess whether the observed advantages are significant or robust. Additionally, NTX and Delta are hand-tuned with no sensitivity analysis, so it is unclear how the results depend on these key parameters. The paper should report variance and ideally show performance as a function of NTX and Delta.
  6. [Section V-B] The multi-hop design's safety and liveness argument is only sketched. The text asserts that cluster-local consensus plus global leader consensus 'ultimately ensures' safety and liveness, but it does not specify the inter-cluster communication model, the failure assumptions on routing, or the exact interaction between local consensus and global ordering. Since the multi-hop evaluation is a claimed contribution, this argument needs to be made precise or explicitly deferred with a pointer to a full proof.
minor comments (6)
  1. [Section III] The term 'global standardization time' should be 'global stabilization time (GST)' to match the standard partial-synchrony terminology.
  2. [Section VII-A] The notation 'alpha = 0 : 10% : 50%' is unclear; please write the set of evaluated loss rates explicitly.
  3. [Section V-A] Tendermint1 and Tendermint2 are used without a clear definition; the paper should state which communication-pattern variant each refers to before the evaluation section.
  4. [Section IV-C] The examples in Figure 2 use NTX = 2 or 3 and Delta = 5 or 6, but the relationship between these illustrative values and the theoretical NTX threshold is not discussed; please clarify that the figures are only illustrative.
  5. [Table I] beta is introduced only in a footnote; please define it in the main text before Table I and explain how it is measured or bounded in the experiments.
  6. [Section VI-B] The text says 'FreeRTOS ... occupies only 9 KB of memory' without specifying which memory region or configuration; this is a minor clarity issue.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ReduceCatch's complexity claims follow from an explicit probabilistic model and are benchmarked against independent baselines; the O(1) active-node caveat is a correctness gap rather than a circular reduction.

full rationale

The paper's central derivation is self-contained. Section IV-B defines the active-node count after the reduce phase as n = N - N(1 - alpha^NTX)^(2N-2) and then treats O(n) = O(1) as following from NTX = O(log N); this is an algebraic consequence of the stated model, not a reuse of the conclusion being proved. The complexity entries in Table I are then obtained by counting TDMA reduce transmissions and CSMA/NACK catch transmissions, with the O(n) term carried through; no equation is identical to an input by construction. NTX and Delta are explicit protocol parameters chosen by the implementer, not fitted to the measured outcome, and the latency/throughput comparisons are external measurements against CSMA-ACK, CSMA-NACK, TDMA-ACK, and TDMA-NACK baselines. The self-citations ([20], [21]) appear only in related-work discussion and are not load-bearing for the ReduceCatch design or its complexity proofs; there is no imported uniqueness theorem or ansatz smuggled in by citation. The genuine weakness is quantitative: the claim that NTX = O(log N) yields O(1) active nodes requires a sufficiently large constant, and because of the (2N-2) exponent, actually needs a factor of 2 in the logarithm; the experimental choice NTX = 3 votes at N = 10 with alpha = 0.5 does not satisfy that threshold. That is a correctness/model-validation concern, not circularity: the conclusion is not assumed as an input; it is derived from a model that may be under-parameterized. Similarly, the catch phase's fixed Delta window means reliability is conditioned on completing within Delta, which is a liveness/scoping issue rather than a self-referential derivation.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on one probabilistic model assumption (independent losses) and on hand-chosen parameters NTX and Delta. The multi-hop composition is asserted rather than proved. No new physical entities are introduced.

free parameters (3)
  • NTX (proposal) = 5
    Number of repeated transmissions in the reduce phase for the leader proposal; set manually in experiments (Section VII-A) and assumed O(log N) in the complexity analysis.
  • NTX (vote) = 3
    Same mechanism for vote transmissions in experiments; set lower than the proposal NTX.
  • Delta (catch-phase window) = 5 or 6 time slots
    Length of the NACK window in the catch phase, set to bound Byzantine NACK traffic; chosen per pattern in Section IV-C.
assumptions (5)
  • domain assumption Each packet loss is independent and identically distributed with rate alpha.
    Used in Section IV-B to derive active-node count n = N - N(1 - alpha^{NTX})^{2N-2}; contradicts the bursty-loss evidence in Section IV-A Figure 1.
  • standard math Partially synchronous model with global stabilization time (Dwork-Lynch-Stockmeyer).
    Borrowed from [30]; underpins the protocol adaptation and the claim that ReduceCatch works in partially synchronous networks.
  • domain assumption Byzantine behavior is confined above the network layer; MAC-layer and network-layer attacks are out of scope.
    Stated in Section III; means ReduceCatch need not withstand jamming, collision, or other physical-layer attacks.
  • standard math N = 3f + 1 and cryptographic signatures are secure.
    Standard BFT threshold taken from [5]-[7]; the paper explicitly relies on prior security proofs.
  • ad hoc to paper Multi-hop cluster-local consensus plus global leader consensus preserves safety and liveness.
    Section V-B asserts this without proof; no formal argument is provided for why local honest majorities guarantee global correctness.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Partially Synchronous BFT Consensus Made Practical in Wireless Networks." pith.science (2026). https://pith.science/paper/YSTB3XWL

@misc{pith2026241205512,
  author       = {Pith},
  title        = {Pith review of: Partially Synchronous BFT Consensus Made Practical in Wireless Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YSTB3XWL}},
  note         = {Machine review of arXiv:2412.05512}
}
read the original abstract

Consensus is becoming increasingly important in wireless networks. Partially synchronous BFT consensus, a significant branch of consensus, has made considerable progress in wired networks. However, its implementation in wireless networks, especially in dynamic ad hoc wireless networks, remains challenging. Existing wireless synchronous consensus protocols, despite being well-developed, are not readily adaptable to partially synchronous settings. Additionally, reliable communication, a cornerstone of BFT consensus, can lead to high message and time complexity in wireless networks. To address these challenges, we propose a wireless communication protocol called ReduceCatch (Reduce and Catch) that supports reliable 1-to-N, N-to-1, and N-to-N communications. We employ ReduceCatch to tailor three partially synchronous BFT consensus protocols (PBFT, Tendermint, and HotStuff) for seamless adaptation from wired to ad hoc wireless networks. To evaluate the performance of the ReduceCatch-enabled consensus protocols, we develop a three-layer wireless consensus testbed, based on which we implement 20 distinct consensus protocols and measure their latency and throughput. The experimental results demonstrate the superiority of the ReduceCatch-based consensus protocol in terms of latency and throughput.

Figures

Figures reproduced from arXiv: 2412.05512 by the authors.

Figure 1
Figure 1. , where a transmitter continuously sends messages to a receiver. The two nodes are about 300 meters apart. The 𝑥-axis denotes the number of message transmissions, while the 𝑦-axis indicates the delay between the receptions of two consecutive messages by the receiver. A delay that surpasses line 1 or is below line 2 indicates a message loss. Successive packet losses occur during periods 1, 2, and 3. Assigning these c… view at source ↗
Figure 2
Figure 2. Communication patterns based on ReduceCatch. S denotes the sender and R denotes a receiver. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. demonstrates a multi-hop topology that enables an efficient application of ReduceCatch. This solution approach adopts a parallelized design, similar to sharding in blockchain [35], [36], in which many local consensus processes parallelly agree on different proposals, and rely on a global consensus to finally determine the order of these proposals. To proceed, the network is divided into multiple clusters, and each c… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Wireless consensus testbed. (a) Wireless consensus testbed archi [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 7
Figure 7. Figure 7: The latency or throughput of four consensus algorithms under multi-hop scenarios [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 38 canonical work pages

  1. [1]

    A fast distributed auction and consensus process using parallel task allocation and execution,

    G. P. Das, T. M. McGinnity, S. A. Coleman, and L. Behera, “A fast distributed auction and consensus process using parallel task allocation and execution,” in 2011 IEEE/RSJ International Conference on Intelli- gent Robots and Systems . IEEE, 2011, pp. 4716–4721

    work page 2011

  2. [2]

    Distributed consensus on robot networks for dynamically merging feature-based maps,

    R. Aragues, J. Cortes, and C. Sagues, “Distributed consensus on robot networks for dynamically merging feature-based maps,” IEEE Transac- tions on Robotics , vol. 28, no. 4, pp. 840–854, 2012

    work page 2012

  3. [3]

    A framework for the collective movement of mobile robots based on distributed decisions,

    I. Navarro and F. Mat ´ıa, “A framework for the collective movement of mobile robots based on distributed decisions,” Robotics and Autonomous Systems, vol. 59, no. 10, pp. 685–697, 2011

    work page 2011

  4. [4]

    Drones for cooperative search and rescue in post-disaster situation,

    J. Q. Cui, S. K. Phang, K. Z. Ang, F. Wang, X. Dong, Y . Ke, S. Lai, K. Li, X. Li, F. Lin et al. , “Drones for cooperative search and rescue in post-disaster situation,” in 2015 IEEE 7th international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM) . IEEE, 2015, pp. 167– 174

    work page 2015

  5. [5]

    Practical byzantine fault tolerance,

    M. Castro, B. Liskov et al. , “Practical byzantine fault tolerance,” in OsDI, vol. 99, 1999, pp. 173–186

    work page 1999

  6. [6]

    The latest gossip on bft consensus,

    E. Buchman, J. Kwon, and Z. Milosevic, “The latest gossip on bft consensus,” arXiv preprint arXiv:1807.04938 , 2018

    arXiv 2018

  7. [7]

    Hot- stuff: Bft consensus with linearity and responsiveness,

    M. Yin, D. Malkhi, M. K. Reiter, G. G. Gueta, and I. Abraham, “Hot- stuff: Bft consensus with linearity and responsiveness,” in Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing , 2019, pp. 347–356

    work page 2019

  8. [8]

    Reli: Real-time lightweight byzantine consen- sus in low-power iot-systems,

    H. Goyal, S. Saha et al., “Reli: Real-time lightweight byzantine consen- sus in low-power iot-systems,” in 2022 18th International Conference on Network and Service Management (CNSM) . IEEE, 2022, pp. 275–281

    work page 2022

Show all 42 references
  1. [9]

    Network-wide con- sensus utilizing the capture effect in low-power wireless networks,

    B. Al Nahas, S. Duquennoy, and O. Landsiedel, “Network-wide con- sensus utilizing the capture effect in low-power wireless networks,” in Proceedings of the 15th ACM conference on embedded network sensor systems, 2017, pp. 1–14

    2017

  2. [10]

    Paxos made wireless: Consensus in the air

    V . Poirot, B. Al Nahas, and O. Landsiedel, “Paxos made wireless: Consensus in the air.” in EWSN, 2019, pp. 1–12

    2019

  3. [11]

    Efficient network flooding and time synchronization with glossy,

    F. Ferrari, M. Zimmerling, L. Thiele, and O. Saukh, “Efficient network flooding and time synchronization with glossy,” in Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE, 2011, pp. 73–84

    2011

  4. [12]

    Chaos: Versatile and efficient all-to-all data sharing and in-network processing at scale,

    O. Landsiedel, F. Ferrari, and M. Zimmerling, “Chaos: Versatile and efficient all-to-all data sharing and in-network processing at scale,” in Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013, pp. 1–14

    2013

  5. [13]

    Wirelesshart: Applying wireless technology in real-time industrial process control,

    J. Song, S. Han, A. Mok, D. Chen, M. Lucas, M. Nixon, and W. Pratt, “Wirelesshart: Applying wireless technology in real-time industrial process control,” in 2008 IEEE Real-Time and Embedded Technology and Applications Symposium . IEEE, 2008, pp. 377–386

    2008

  6. [14]

    Speed: A stateless protocol for real-time communication in sensor networks,

    T. He, J. A. Stankovic, C. Lu, and T. Abdelzaher, “Speed: A stateless protocol for real-time communication in sensor networks,” in 23rd International Conference on Distributed Computing Systems, 2003. Proceedings. IEEE, 2003, pp. 46–55

    2003

  7. [15]

    Mac essentials for wireless sensor networks,

    A. Bachir, M. Dohler, T. Watteyne, and K. K. Leung, “Mac essentials for wireless sensor networks,” IEEE communications surveys & tutorials , vol. 12, no. 2, pp. 222–248, 2010

    2010

  8. [16]

    To nack or not to nack? negative acknowledgments in information-centric networking,

    A. Compagno, M. Conti, C. Ghali, and G. Tsudik, “To nack or not to nack? negative acknowledgments in information-centric networking,” in 2015 24th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2015, pp. 1–10

    2015

  9. [17]

    Distributed and centralized hybrid csma/ca-tdma schemes for single-hop wireless networks,

    B. Shrestha, E. Hossain, and K. W. Choi, “Distributed and centralized hybrid csma/ca-tdma schemes for single-hop wireless networks,” IEEE Transactions on Wireless Communications , vol. 13, no. 7, pp. 4050– 4065, 2014

    2014

  10. [18]

    Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks,

    G. Ahmed, J. Zou, M. M. S. Fareed, and M. Zeeshan, “Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks,” Computers & Electrical Engineering , vol. 56, pp. 385–398, 2016

    2016

  11. [19]

    Performance analysis of wireless practical byzantine fault tolerance networks using ieee 802.11,

    Z. Zhou, O. Onireti, L. Zhang, and M. A. Imran, “Performance analysis of wireless practical byzantine fault tolerance networks using ieee 802.11,” in 2021 IEEE Globecom Workshops (GC Wkshps) . IEEE, 2021, pp. 1–6

    2021

  12. [20]

    Blown: a blockchain protocol for single-hop wireless networks under adversarial sinr,

    M. Xu, F. Zhao, Y . Zou, C. Liu, X. Cheng, and F. Dressler, “Blown: a blockchain protocol for single-hop wireless networks under adversarial sinr,” IEEE Transactions on Mobile computing , 2022

    2022

  13. [21]

    wchain: a fast fault-tolerant blockchain protocol for multihop wireless networks,

    M. Xu, C. Liu, Y . Zou, F. Zhao, J. Yu, and X. Cheng, “wchain: a fast fault-tolerant blockchain protocol for multihop wireless networks,” IEEE Transactions on Wireless Communications , vol. 20, no. 10, pp. 6915– 6926, 2021

    2021

  14. [22]

    Ratcheta: Memory-bounded hybrid byzantine consensus for cooperative embedded systems,

    W. Xu and R. Kapitza, “Ratcheta: Memory-bounded hybrid byzantine consensus for cooperative embedded systems,” in 2018 IEEE 37th Symposium on Reliable Distributed Systems (SRDS) . IEEE, 2018, pp. 103–112

    2018

  15. [23]

    Byzantine agreement service for cooperative wireless embedded systems,

    W. Xu, M. Wegner, L. Wolf, and R. Kapitza, “Byzantine agreement service for cooperative wireless embedded systems,” in 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE, 2017, pp. 10–15

    2017

  16. [24]

    Turquois: Byzantine consensus in wireless ad hoc networks,

    H. Moniz, N. F. Neves, and M. Correia, “Turquois: Byzantine consensus in wireless ad hoc networks,” in 2010 IEEE/IFIP International Confer- ence on Dependable Systems & Networks (DSN) . IEEE, 2010, pp. 537–546

    2010

  17. [25]

    Tinybft: Byzantine fault- tolerant replication for highly resource-constrained embedded systems,

    H. B ¨ohm, T. Distler, and P. W ¨agemann, “Tinybft: Byzantine fault- tolerant replication for highly resource-constrained embedded systems,” in Proceedings of the 30th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’24) , 2024

    2024

  18. [26]

    Versatile low power media access for wireless sensor networks,

    J. Polastre, J. Hill, and D. Culler, “Versatile low power media access for wireless sensor networks,” in Proceedings of the 2nd international conference on Embedded networked sensor systems , 2004, pp. 95–107

    2004

  19. [27]

    An energy-efficient mac protocol for wireless sensor networks,

    W. Ye, J. Heidemann, and D. Estrin, “An energy-efficient mac protocol for wireless sensor networks,” in Proceedings. Twenty-first annual joint conference of the IEEE computer and communications societies , vol. 3. IEEE, 2002, pp. 1567–1576

    2002

  20. [28]

    FlockLab 2: Multi-Modal Testing and Validation for Wireless IoT,

    R. Tr ¨ub, R. Da Forno, L. Sigrist, L. M ¨uhlebach, A. Biri, J. Beutel, and L. Thiele, “FlockLab 2: Multi-Modal Testing and Validation for Wireless IoT,” in 3rd Workshop on Benchmarking Cyber-Physical Systems and Internet of Things (CPS-IoTBench 2020). OpenReview.net,

    2020

  21. [29]

    Fit iot-lab: A large scale open experimental iot testbed,

    C. Adjih, E. Baccelli, E. Fleury, G. Harter, N. Mitton, T. Noel, R. Pissard-Gibollet, F. Saint-Marcel, G. Schreiner, J. Vandaele et al. , “Fit iot-lab: A large scale open experimental iot testbed,” in 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT) . IEEE, 2015, pp. 459–464

    2015

  22. [30]

    Consensus in the presence of partial synchrony,

    C. Dwork, N. Lynch, and L. Stockmeyer, “Consensus in the presence of partial synchrony,” Journal of the ACM (JACM) , vol. 35, no. 2, pp. 288–323, 1988

    1988

  23. [31]

    Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey,

    H. Pirayesh and H. Zeng, “Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey,” IEEE communications surveys & tutorials , vol. 24, no. 2, pp. 767–809, 2022

    2022

  24. [32]

    Security issues of low power wide area networks in the context of lora networks,

    D. Basu, T. Gu, and P. Mohapatra, “Security issues of low power wide area networks in the context of lora networks,” arXiv preprint arXiv:2006.16554, 2020

    2006 arXiv

  25. [33]

    A study of lora: Long range & low power networks for the internet of things,

    A. Augustin, J. Yi, T. Clausen, and W. M. Townsley, “A study of lora: Long range & low power networks for the internet of things,” Sensors, vol. 16, no. 9, p. 1466, 2016

    2016

  26. [34]

    A survey on lora networking: Research problems, current solutions, and open issues,

    J. P. S. Sundaram, W. Du, and Z. Zhao, “A survey on lora networking: Research problems, current solutions, and open issues,” IEEE Commu- nications Surveys & Tutorials , vol. 22, no. 1, pp. 371–388, 2019

    2019

  27. [35]

    The ripple protocol consensus algorithm,

    D. Schwartz, N. Youngs, A. Britto et al., “The ripple protocol consensus algorithm,” Ripple Labs Inc White Paper , vol. 5, no. 8, p. 151, 2014

    2014

  28. [36]

    Centrally banked cryptocurrencies,

    G. Danezis and S. Meiklejohn, “Centrally banked cryptocurrencies,” arXiv preprint arXiv:1505.06895 , 2015

    2015 arXiv

  29. [37]

    Algorand: Scaling byzantine agreements for cryptocurrencies,

    Y . Gilad, R. Hemo, S. Micali, G. Vlachos, and N. Zeldovich, “Algorand: Scaling byzantine agreements for cryptocurrencies,” in Proceedings of the 26th symposium on operating systems principles , 2017, pp. 51–68

    2017

  30. [38]

    A secure sharding protocol for open blockchains,

    L. Luu, V . Narayanan, C. Zheng, K. Baweja, S. Gilbert, and P. Saxena, “A secure sharding protocol for open blockchains,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 17–30

    2016

  31. [39]

    A survey on opportunistic routing in wireless commu- nication networks,

    N. Chakchouk, “A survey on opportunistic routing in wireless commu- nication networks,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2214–2241, 2015

    2015

  32. [40]

    Energy- efficient routing protocols in wireless sensor networks: A survey,

    N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy- efficient routing protocols in wireless sensor networks: A survey,” IEEE Communications surveys & tutorials , vol. 15, no. 2, pp. 551–591, 2012

    2012

  33. [41]

    Swarm robotics: a review from the swarm engineering perspective,

    M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, vol. 7, pp. 1–41, 2013

    2013

  34. [2020]

    Available: https://doi.org/10.3929/ethz-b-000442038

    [Online]. Available: https://doi.org/10.3929/ethz-b-000442038

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.