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 Status of this Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet- Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." 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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, Pei Expires June, 2018 [Page 2] Internet-Draft Nodes selection framework December 2017 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 Pei Expires June, 2018 [Page 3] Internet-Draft Nodes selection framework December 2017 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 Pei Expires June, 2018 [Page 4] Internet-Draft Nodes selection framework December 2017 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 Pei Expires June, 2018 [Page 5] Internet-Draft Nodes selection framework December 2017 [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 (Pdbeta, 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 Pei Expires June, 2018 [Page 6] Internet-Draft Nodes selection framework December 2017 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
Phone: 008613638323589 Email: peier@cqupt.edu.cn Pei Expires June, 2018 [Page 7]