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Demo: Radio Spectrum Data Collection with Distributed-Proof-of-Sense Blockchain Network

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Dynamic Spectrum Access (DSA) addresses the underutilized spectrum allocation issues associated with static spectrum allocation. Blockchain is a critical enabler for imple- menting a DSA system because it allows untrusted parties to conduct business, such as spectrum buying and selling, without the involvement of a trusted third party. A blockchain system is implemented using the Proof-of-Sense consensus algorithm, which is designed to facilitate DSA while also providing several additional benefits, such as spectrum data collection.
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Demo: Radio Spectrum Data Collection with
Distributed-Proof-of-Sense Blockchain Network
Pramitha Fernando, Madusanka Liyanage
∗† School of Computer Science, University College Dublin, Ireland
Email: pramitha.fernando@ucd.ie, madhusanka@ucd.ie
Abstract—Dynamic Spectrum Access (DSA) addresses the
underutilized spectrum allocation issues associated with static
spectrum allocation. Blockchain is a critical enabler for imple-
menting a DSA system because it allows untrusted parties to
conduct business, such as spectrum buying and selling, without
the involvement of a trusted third party. A blockchain system
is implemented using the Proof-of-Sense consensus algorithm,
which is designed to facilitate DSA while also providing several
additional benefits, such as spectrum data collection.
Index Terms—Dynamic Spectrum Access (DSA), Blockchain,
Consensus
I. INTRODUCTION
The exponential growth in wireless devices needs a more
flexible and robust spectrum access mechanism [1], which
is not available in the current fixed spectrum allocation. In
fixed spectrum allocation, a government spectrum regulatory
body sells this scarce resource to Mobile Network Operators
(MNOs), providing the MNO exclusive rights to a particular
band. Given that the electromagnetic radio spectrum is a scarce
natural resource, some MNOs barely use some frequency
bands or use them sporadically, resulting in spectrum holes [2],
which highlights the underutilisation issue that comes with the
fixed allocation model. To address these problems, the research
communities introduce Dynamic Spectrum Allocation/Access
(DSA) concepts. In the literature, there are several blockchain-
based and non-blockchain-based solutions to implement a
spectrum management system.
Nevertheless, blockchain-based solutions have several key
advantages, such as the elimination of a trusted third party to
conduct deals (reduced operational cost), guaranteed integrity
(accurate auditing), and transparency (better visibility into
spectrum usage). However, one major shortcoming of such
designs is the blockchain network’s high resource consumption
(e.g., energy and computational power). Additionally, existing
DSA systems still need to address how to detect unauthorised
spectrum usage in the shared model and how to collect
spectrum data.
The Distributed-Proof-of-Sense (DPoS) consensus algo-
rithm was introduced in [3] to address these identified short-
comings. DPoS is a tailored consensus algorithm created
specifically to provide a more appropriate consensus algorithm
for blockchain-based DSA systems. DPoS incentivises nodes
to collect spectrum data as part of its consensus mechanism.
Spectrum data collection is an important component of any
DAS system because it detects which bands are not being used
and when someone is accessing the spectrum in an unautho-
rised manner (also known as spectrum misuse, spectrum fraud,
or spectrum violation).
Unauthorised access poses the risk of causing interference
to subscribers of a wireless network. Spectrum misuse in DSA
includes activities such as accessing the spectrum beyond the
allocated time, transmitting within unauthorised bands, access-
ing restricted frequencies, breaching allowed energy levels,
and contravening permitted modulation techniques and stan-
dards. Fraudulent activities in a DSA system can potentially
diminish service quality and may lead to substantial financial
losses. Therefore, the collection and analysis of spectrum data
are indispensable for identifying and mitigating such issues.
II. SY ST EM ARCHITECTURE
In this demonstration, we implement the DPoS algorithm
in a prototype network and present how spectrum-sensing-
enabled nodes collect data and share it with the network
using a DPoS-based blockchain network. As shown in Fig.
1, HackRF One Software Defined Radio (SDR) is used as the
spectrum sensor, and a Raspberry Pi 4 device is used as the
node, which runs the blockchain client and handles spectrum
data processing. The blockchain network is built using the
Substrate blockchain framework, with DPoS running as the
consensus mechanism.
Fig. 1. A Node in the Blockchain Network
A. Consensus Mechanism
The DPoS consensus mechanism this platform utilises is
a decentralised key generation and verification process based
on non-interactive Elliptic Curve Cryptography (ECC) based
Zero-Knowledge Proof (ZKP) following the Schnorr scheme
[4]. Here, some basic terminologies and operations are de-
scribed to explain the demonstration.
B. Key Generation
Each node generates a point Bi,j (i.e., Bi,j =ai,j G) on the
elliptic curve (E). Here, Bi,j is considered the public key, and
ai,j as the private key of the ith node for the jth session.
C. Key Sharing
A node signs and transmits its private key (ai) in a random
frequency band. Additionally, the node shares Biwith the
network to become shared knowledge. Also, E,Gand P
(another point on the curve) are considered shared knowledge.
D. Final Key Construction
A node must collect at least t-out-of-n keys (where t=
threshold, n= total nodes) to become the winner and create the
next block. The final key is a combination of keys transmitted
by other nodes (a=a1+a2+.... +an).
E. Verification
Other nodes can indirectly check the final key to confirm
the prover’s claim to be correct by utilising the ECC-based
ZKP. The prover must also generate additional parameters, as
illustrated in Fig. 2.
9) Send for Verification
13) Checks if s.G = A + c.B
14) Checks if s.P = r.P + ca.P
8) Broadcast the block
7) Create & sign the block
Block has id, s, a.P, r.P, A
Prover Verifier(s)
2) Generate node ID list
id = {node IDs of final key a}
1) Calculate a
a = a1 + a2 +...+ ar +...+ an
3) Generate random r and compute A
such that A = rG
5) Compute s = r + ca
4) Compute c = Hash (aP|rP|A|id)
6) Compute a.P and r.P
11) Compute B
B = B1 + B2 +...+ Br +...+ Bn
12) Compute c = Hash (aP|rP|A|id)
10) Identify corresponding Bis from id list
Fig. 2. Messages Exchanged in the Verification Process
The operations device performed in this blockchain-based
DSA system can be explained as follows.
Step 1 A node register with the DSA system.
Step 2 Node generates and transmits a session key in a
random frequency band.
Step 3 Node scans the radio spectrum to collect session
keys from other nodes. Nodes collect valuable spec-
trum data while scanning for the keys.
Step 4 Nodes that collect enough keys -to create the final
key- will create a new block. The node stores the
sensed data in an off-chain storage and puts the hash
address of that data in the new block. The node then
broadcasts the new block to the network.
Step 5 Other nodes verify the block using ECC ZKPs and
add it to the local blockchain. This marks the end
of a session and nodes go to Step 2 again.
III. DEM ON ST RATI ON
The described system is set up in a prototype network
of five nodes. Each node corresponds to a Mobile Network
Operator (MNO) responsible of gathering radio spectrum
data. These nodes run on a custom Substrate core, which
includes a modified consensus library optimized for the DPoS
consensus algorithm. The demonstration includes several key
steps. Initially, a node is registered in the system and in-
tegrated into the network. The node then starts collecting
radio spectrum data while playing the consensus game to add
the next block to the network. After accumulating enough
keys to generate the final key, the node can append the
next block, along with the collected spectrum data. Following
that, the block is verified by other nodes via ECC-based
ZKPs. After successful verification, the block is added to each
node’s local chain. Furthermore, the demo showcases the real-
time collection and sharing of spectrum data using HackRF
One devices. It illustrates how this data seamlessly integrates
into the blockchain. Thus, the demonstration highlights key
components of the proposed system and its novel blockchain
network. The collected data serves as the foundation for future
work, which will include analysis to detect available spectrum
and unauthorized access.
IV. CONCLUSION
This demo paper describes a DSA system that allows
network users to share radio spectrum efficiently and au-
tonomously. The novel consensus mechanism encourages users
to collect spectrum data, which will then be analysed to detect
unauthorised spectrum access. The demo provides insight into
how the DSA system operates. It demonstrates how the nodes
compete to add the next block to the chain while also gathering
valuable spectrum data. Future work will include analysing
the collected spectrum data to detect violations and designing
rentable tokens that can be used to purchase and sell radio
spectrum chunks.
ACK NOW LE DG EM EN T
This work is partly supported by the European Commis-
sion under CONFIDENTIAL-6G (Grant:101096435) and SFI
under CONNECT P2 (Grant no. 13/RC/2077 P2) projects.
REFERENCES
[1] V.-D. Nguyen and O.-S. Shin, “Cooperative prediction-and-sensing-based
spectrum sharing in cognitive radio networks, IEEE Transactions on
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2018.
[2] V. Valenta, R. Marˇ
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alek, G. Baudoin, M. Villegas, M. Suarez, and
F. Robert, “Survey on spectrum utilization in europe: Measurements,
analyses and observations,” in 2010 Proceedings of the Fifth Interna-
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Communications, 2010, pp. 1–5.
[3] P. Fernando, K. Dadallage, T. Gamage, C. Seneviratne, A. Braeken,
A. Madanayake, and M. Liyanage, “Distributed-Proof-of-Sense:
Blockchain Consensus Mechanisms for Detecting Spectrum Access
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Communications and Networking, 2023.
[4] C. P. Schnorr, “Efficient Signature Generation by Smart Cards,” Journal
of Cryptology, vol. 4, no. 3, pp. 161–174, Jan 1991. [Online]. Available:
https://doi.org/10.1007/BF00196725
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