Internet DRAFT - draft-pei-nodeselection
draft-pei-nodeselection
Mobile Ad-hoc Networks working group Errong Pei
Internet Draft School of Communication and
Information Engineering
Chongqing University
of Postsand Telecommu.
December 2017
Intended status: Informational
Expires: June 2018
Energy efficient node selection framework in cooperative spectrum
sensing
draft-pei-nodeselection-00.txt
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Abstract
Based on the hybrid spectrum sensing method, this paper proposes a
SENS node selection algorithm which effectively reduces the number
of nodes participated in spectrum sensing. The algorithm reduces the
loads and energy consumption of the cognitive wireless sensor
networks. It conforms to the development trend of current cognitive
wireless sensor networks. At the same time, This method is also
suitable for traditional wireless sensor networks. This algorithm
which considers the perception of energy consumption and performance
parameters of nodes forms a node priority function, The network
selects the nodes according to the priority of nodes, It reduces
energy consumption and improves the spectrum sensing performance. In
the cooperative spectrum sensing, sensor nodes transmit the sensing
results to the fusion center. Moreover, this paper uses the "OR"
standard, the node whose local sensing decision is "1" transmits the
local sensing result to the fusion center. So it can reduce the
energy consumption in the process of spectrum sensing and achieve
the purpose of energy saving.
Table of Contents
1. Introduction ................................................ 2
2. Conventions used in this document............................ 4
3. The System Model and node selection algorithm ................ 4
3.1. The Energy framework of sensor users .................... 4
3.2. node selection algorithm................................ 5
4. Formal Syntax ............................................... 6
5. Security Considerations .................................. 6
6. IANA Considerations ......................................... 6
7. Conclusions ................................................. 6
8. References .................................................. 7
8.1. Normative References................................... 7
8.2. Informative References.................................. 7
1. Introduction
It is commonly believed that there is a spectrum scarcity at
frequencies that can be economically used for wireless
communications. This concern has arisen from the intense competition
for use of spectra at frequencies below 3 GHz. The Federal
Communications Commission's (FCC) frequency allocation chart
indicates overlapping allocations over all of the frequency bands,
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which reinforces the scarcity mindset. On the other hand, actual
measurements taken in downtown Berkeley are believed to be typical
and indicate low utilization, especially in the 3-6 MHz bands. the
power spectral density (PSD) of the received 6 GHz wide signal
collected for a span of 50s sampled at 20 GS/s.This view is
supported by recent studies of the FCC's .Spectrum Policy Task Force
who reported vast temporal and geographic variations in the usage of
allocated spectrum with utilization ranging from 15% to 85%. In
order to utilize these spectrum 'white spaces', the FCC has issued a
Notice of advancing Cognitive Radio (CR) technology as a candidate
to implement negotiated or opportunistic spectrum sharing.
Wireless systems today are characterized by wasteful static spectrum
allocations, fixed radio functions, and limited network coordination.
Some systems in unlicensed frequency bands have achieved great
spectrum efficiency, but are faced with increasing interference that
limits network capacity and scalability. Cognitive radio systems
offer the opportunity to use dynamic spectrum management techniques
to help prevent interference, adapt to immediate local spectrum
availability by creating time and location dependent in "virtual
unlicensed bands", i.e. bands that are shared with primary users.
Unique to cognitive radio operation is the requirement that the
radio is able to sense the environment over huge swaths of spectrum
and adapt to it since the radio does not have primary rights to any
pre-assigned frequencies. This new radio functionality will involve
the design of various analog, digital, and network processing
techniques in order to meet challenging radio sensitivity
requirements and wideband frequency agility.
In CRSN, not only cooperative spectrum sensing enhances the accuracy
of sensing, but also there are some shortcomings. For example, the
participation of all nodes in spectrum sensing will increase network
overhead and computational complexity. In summary, we propose a SENS
node selection algorithm. Under the constraints of detection rate
and false alarm rate, the energy-saving problem of cooperative
spectrum sensing is transformed into 0-1 integer linear programming
problem through mathematical analysis. Based on mathematical
analysis, an energy efficient node selection algorithm by adjusting
the energy consumption of node and the weight coefficient of node
performance in the priority function is formed, some nodes are
selected to perform spectrum sensing and the sensing result is
delivered to the fusion center to reduce the energy consumption
while ensuring that the system constraints are met. Since the OR
criterion is adopted in this paper, the node with the decision
result of 0 will not affect the final decision of the fusion center.
Therefore, in the process of transmitting the local perception
result, only the node with the decision result of 1 performs the
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result transmission, so it can effectively reduce the energy
consumption in the transmission of results.
2. Conventions used in this document
"CSS" indicates Cooperative Spectrum Sensing.
"RSSA" indicates Random Sensor Selection Algorithm.
"Cognitive users" also indicates the sensor nodes
"Detect" also indicates sense
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
In this document, these words will appear with that interpretation
only when in ALL CAPS. Lower case uses of these words are not to be
interpreted as carrying significance described in RFC 2119.
In this document, the characters ">>" preceding an indented line(s)
indicates a statement using the key words listed above. This
convention aids reviewers in quickly identifying or finding the
portions of this RFC covered by these keywords.
3. The System Model and node selection algorithm
3.1.The Energy framework of sensor users
In order to minimize the energy consumption, we have to calculate
the energy consumption in the cooperative spectrum sensing. The
total energy consumption includes. The first part is the energy
consumed to sense the channel and to process the signal. The second
part is the energy consumed to transmit reliable information to the
fusion center, assuming that all the nodes have the same perceived
energy. So the total energy is calculated as follows:
E_total=SUM (E_c+E_t)
E_t=k*E_elec+k*e_fs*(d_i)^2
In the traditional literature on spectrum sensing, it is stipulated
that all nodes participating in sensing transmit the local sensing
results to the fusion center. Because this article adopts the "OR"
fusion rule, the node whose decision result is "0" does not affect
the fusion result. Therefore, in order to reduce the energy
consumption of node transmission, only the local decision result of
the node judged as "1" Fusion Center. Let the probability of node
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with local decision result "1" be Pd-1, then Pd-1 is calculated as
follows:
P-d-1=P(H_0)Pf-i+P(H_1)Pd-i
Therefore, the calculation of energy becomes
E_total=SUM (X_i*E_c+X_i*E_t-i*Pd-i)
The energy minimization problem convert into the following questions:
P1:Min|E_total|
s.t.1-product(1-X_i*Pd-i^fc)>=alpha
1-product(1-X_i*Pf-i^fc)<=beta
The problem P1 is a 0-1 nonlinear programming problem. The 0-1
nonlinear programming problem is more complex and difficult to solve,
so the constraints under the model can be reasonably transformed and
the problem can be carried out Simplify.
The optimal solution to the 0-1 integer linear programming problem
can use the more mature algorithms such as branch and bound method
or Gomory cut plane method. Branch and bound method is a search and
iterative method. Gomory cut plane method In the process of solution,
it is necessary to calculate the fraction in the rotation iteration,
so the computational complexity is very high. The complexity of time
and space of these algorithms is high, especially the complexity of
n increases. Therefore, heuristic algorithm can be used to solve the
optimization problem under the inequality constraint under the
condition of satisfying certain accuracy. The complexity of the
algorithm can be reduced by solving the optimal solution instead of
the optimal solution under the linear programming problem. It can be
known from the analysis that the nodes selected for spectrum sensing
should have smaller E_i,smaller ln(1-Pd-i^fc),and larger ln(1-Pf-
i^fc).
Therefore, a function c(i)that represents the priority of a node can
be constructed according to these factors, and according to the size
of c(i)Node prioritization.
3.2.node selection algorithm
[step1] k-min=0,k-max=c(C is less than 1 and relatively large)
[step2]while(|(k-min)-(k-max)|)>eps,k=(k-max+k-min)/2
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[step3]calculate c(i) in ascending order, Choose n nodes at random
Calculate Pd
[step4] Decree Pd-temp=Pd
[step5] if(Pd>=alpha),while(Pd-temp>=alpha),n=n-1,update Pd-temp,end
n=n+1,calculate Pf
if(Pf<=beta),Get the minimum number of nodes n,and decree
k_max=k,else Pf>beta, Can not get the right n,and decree
k_min=k,end.
Else (Pd<alpha),while(Pd-temp<alpha),n=n+1,update Pd-temp,end.
Calculate Pf, if(Pf<=beta),Get the minimum number of nodes
n,and decree k_max=k, else Pf>beta, Can not get the right
n,and decree k_min=k,end.e
After many iterations, the optimal K value and the minimum
number of nodes n are obtained
4. Formal Syntax
The following syntax specification uses the augmented Backus-Naur
Form (BNF) as described in RFC-2234 [RFC2234].
5. Security Considerations
This specification forms a node selection algorithm based on the
constraints for Cognitive sensor networks
6. IANA Considerations
This document has no actions for IANA.
7. Conclusions
This proposal proposes an energy efficient node selection algorithm
whose goal is to reduce energy consumption in the spectrum sensing
process by minimizing the number of nodes involved in sensing. By
analyzing the factors that affect the spectrum sensing performance
(detection rate and false alarm rate), a priority formula of
spectrum sensing nodes is formed, and then nodes are selected
through the node selection algorithm. SENS algorithm can effectively
select fewer nodes for spectrum sensing. While improving the sensing
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accuracy, it can effectively save the energy consumption of spectrum
sensing and improve the performance of cognitive sensor networks.
8. References
8.1. Normative References
REN Ju, ZHANG Yaoxue, and ZHANG Ning,et al. Dynamic channel access
to improve energy efficiency in cognitive radio sensor networks[J].
IEEE Transactions on Wireless Communications, 2016, 15(5): 3143-3156.
MUCHANDI N,KHANAI R.Cognitive radio spectrum sensing: A
survey[C]//Electrical, Electronics, and Optimization Techniques
(ICEEOT), International Conference on. IEEE, 2016: 3233-3237.
BALAJI V, Nagendra T, Hota C, et al. Cooperative spectrum sensing in
Cognitive Radio: An Archetypal Clustering approach[C]//Wireless
Communications, Signal Processing and Networking (WiSPNET),
International Conference on. IEEE, 2016: 1137-1143.
8.2. Informative References
T. Zhang, R. Safavi-Naini, and Z. Li, "ReDiSen: Reputation-based
securecooperative sensing in distributed cognitive radio networks"
inProc. IEEE ICC, Budapest, Hungary, Jun. 9-13, 2013, pp. 2601-2605.
This document was prepared using 2-Word-v2.0.template.dot.
Authors' Addresses
Errong Pei
School of Communication and Information Engineering
Chongqing University of Posts and Telecommunications
Nanan Dist., Chongqing, China
<Address>
Phone: 008613638323589
Email: peier@cqupt.edu.cn
Pei Expires June, 2018 [Page 7]
