A novel cloud based self-adaptive cognitive radio network architecture

Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

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International Journal of Electronics and Communications (AEÜ) journal homepage: www.elsevier.com/locate/aeue

Regular paper

A novel cloud based self-adaptive cognitive radio network architecture Ashwini Kumar Varma ⇑, Debjani Mitra Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand 826004, India

a r t i c l e

i n f o

Article history: Received 26 December 2018 Accepted 19 April 2019

Keywords: Cognitive radio Network architecture Communication protocol Multi-hop relay Optimal multi-hop relay selection Outage probability

a b s t r a c t The constraints of battery, data processing and memory in mobile Secondary User (SU) in a Cognitive Radio Network (CRN) make it essential to move the spectrum sensing functionality from the SUs to the network. The cloud performing the role of the earlier defined ‘‘Spectrum Agent” or ‘‘Spectrum Monitoring Device” has a scope of taking up the computational tasks of spectrum sensing and path selection. The paper presents a novel cloud based self-adaptive network architecture for a multi-hop cognitive radio system including its communication protocol. A new method of optimal multi-hop relay selection based on Critical Path Method (CPM) is also introduced for execution in the cloud having network connectivity information. The proposed network architecture and relay selection scheme is analyzed and the performance tested with respect to the reported methods to show its strength in terms of end-to-end delay and reliability. A closed-form expression of outage probability of failed transmission for a generic multi-hop network is also analytically derived and used for testing the reliability of the proposed architecture. The results are encouraging to extend the use of CRN in dynamic environment of large deployment areas. Ó 2019 Elsevier GmbH. All rights reserved.

1. Introduction Opportunistic access with Cognitive Radio (CR) has emerged as a promising solution to the problem of underutilized spectrum [1]. CR aims to increase the efficiency of spectral utilization by allowing Secondary Users (SUs) to access the licensed band temporarily whenever and wherever the Primary User (PU) remain idle. Spectrum sensing is one of the essential blocks of a CR system which requires scanning a wide range of frequencies to determine the present state of PU [2–4]. However, scanning a wide frequency band requires good hardware capabilities such as a high-speed Analog to Digital Converter (ADC), a Radio Frequency (RF) unit and a high-speed signal processing board. Since SUs in Cognitive Radio Network (CRN) are mostly mobile nodes, battery life is a key factor and if they are embedded with powerful spectrum sensing algorithms, the resulting energy consumption of SUs will be exorbitantly large. Moreover, most of the sensing algorithms have a trade-off between accuracy and computational cost. Thus, a CRN architecture which can relieve SUs of spectrum sensing functionality should be highly efficient in terms of both power and complexity.

⇑ Corresponding author. E-mail addresses: [email protected] (A.K. Varma), [email protected]. in (D. Mitra). https://doi.org/10.1016/j.aeue.2019.04.016 1434-8411/Ó 2019 Elsevier GmbH. All rights reserved.

Architectures of CRN are typically infrastructure, ad-hoc, mesh or cluster-based [5,6]. Authors in [7] discuss the challenges and issues of such architecture where processing of massive sensed data is a critical issue. Hence, authors of [8,9] introduce a new functional entity in the network, so called ‘‘spectrum agents” and ‘‘spectrum monitoring devices” respectively. These entities perform spectrum sensing for SUs in order to reduce their design complexity, hardware cost, and high energy consumption. The introduction of a cloud has shown better utilization of network in peer-to-peer interaction of SUs [10]. To the best of our knowledge, no work has reported any standardized protocol of communication among SUs, in the wake of spectrum sensing shifting from SU nodes to independent entities such as ‘‘cloud”, ‘‘spectrum agents” or ‘‘spectrum monitoring devices”. Computing spectrum sensing outside the SU will save upon CRN resources, but there are several other issues which need to be explored while building CRN. They are network architecture, communication protocol, dynamic path planning, optimal multi-hop relay selection, and a few others. Motivated by the existing literature, the work here presents a new cloud based CRN and its communication protocol. It uses both the functionalities, spectrum agents/ spectrum monitoring devices (here referred as Secondary Access Point) and cloud. Cloud based CRN is an emerging area of research. However, existing literature has only a handful of papers over cloud based CRN. The authors in [9] propose a novel cloud based architecture where cloud acts

A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

as an intermediate entity between the SUs and the Spectrum Monitoring Devices (SMDs) which monitors the spectrum and determines the current state of the spectrum. Here, the cloud collects the current state of spectrum from various SMD and assist SUs with vacant spectrum information while data transmission. In [11], the authors propose to utilize the existing cellular network architecture while communicating among the SUs over unlicensed TV white space. However, both the papers [9,11] doesn’t focus over any protocol which needs to follow while communicating among SUs. In any multi-hop communication including CRN, there would be several intermediate nodes between source and destination. Optimal relay selection is therefore an integral part of the communication protocol. Also, in a dynamic environment, the network needs to be self-adaptive in selecting the most optimal end-toend path avoiding congestion and link failure. When spectrum sensing is being performed by the cloud or Secondary Access Point (SAP), the issue of optimal relay selection needs to be studied afresh. This aspect has not been addressed or analyzed in any reported work. In the existing literature approaches, optimal multi-hop relay selection is mostly performed on the basis of maximum SNR/ channel capacity [12,13], minimum delay/ trusted distance/ computed weights [14,5,15], maximum-minimum method [16] or by selecting channel with maximum instantaneous SNR at each hop [17]. The authors in [12,13] use SNR/ channel capacity as a parameter for relay selection, where relay with maximum SNR/ channel capacity is selected for data transmission from source to destination. In [14], an appropriate relay is selected based on Expected Path Delay (EPD) routing metrics. EPD metric reflects the expected time for a data packet to reach the destination over a given relay path. The relay path with least value of EPD metric is used for data transmission. Authors of [5,15] introduce new parameters, such as ‘Trusted Distance’ and ‘Weight’, to determine the appropriate relay path. A new Max-Min methodology of selecting the optimal relay path is elaborated in [16]. They have also derived an analytical expression for outage probability of the data packet failing to reach the destination over the selected relay. It is observed that in all the above works, in order to select an optimal relay, the respective parameter needs to be computed for all possible relay combinations. Hence, with an increase in the number of SUs in the network, the computational complexity will increase enormously. Goel et al. [17] have addressed this in their method which does not compute SNR for all the possible combination of relays to select the optimal relay. But this approach will not guarantee the selection of an end-to-end optimal relay. In their work, the relay is formulated by selecting a channel with maximum instantaneous SNR between the two adjacent nodes in each hop. Optimal path selection is not possible unless connectivity/ path loss/ delay etc of the entire network is known. The present work contributes in terms of a new cloud based self-adaptive network architecture and its communication protocol to address all the issues discussed above. We also include a new method of optimal multi-hop relay selection into the proposed CRN architecture. A slightly modified version of the Critical Path Method (CPM), conventionally used for project management has been used to identify the best relay that can transmit messages with least possible path loss or delay in a CRN. This algorithm is very effective in the cloud-based network architecture having the network connectivity information. To demonstrate the effectiveness of the proposed architecture, the protocol is analyzed and the performance comparison of the proposed relay selection scheme is carried out with respect to the approach presented in [17]. An analytical expression of outage probability of the data packet failing to reach the destination over the relay path has been derived as an additional contribution. The remaining paper continues as follows. In Section 2, the proposed network architecture and

33

the new method of optimal multi-hop relay selection are presented. Section 3 derives an analytical expression of outage probability for the relay path. Subsequently, in Section 4, total energy consumption incurred in a processor while communication and sensing have been compared. The results and corresponding discussions are outlined in Section 5. Finally, we conclude the paper in Section 6. 2. Proposed cloud based self-adaptive network architecture The proposed architecture is as shown in Fig. 1. The network consists of three essential entities, namely the Cloud, the Secondary Access Point (SAP) and the Secondary User (SU). The SUs in this work differs slightly from the SUs of conventional CRN. Here, SUs do not need to sense the spectrum and determine the state of the PU spectrum usage (Busy or Idle). Hence, when SUs need to access the available vacant spectrum, it sends a Spectrum State Request (SSR) to SAP, which responses with the current spectrum state. This reduces overall design complexity, hardware cost and high energy consumption of SUs. In this paper, SUs are considered as mobile while SAP and cloud are static over the area of deployment. The static nature of SAP and cloud enables predefined connectivity among them. However, while describing the proposed network, the section is divided into three parts namely Network Initialization, Connection Establishment and Optimal Multi-hop Relay Selection. The first two sub-section elaborately explain the initialization and connection establishment procedure of the network while the third sub-section explains the proposed optimal multi-hop relay selection over the proposed network architecture. 2.1. Network initialization The proposed network architecture divides a macrocellular area into a smaller region called subnet, each associated with corresponding SAP. An Asset Directory (AD) is installed in each SU as shown in Fig. 2. AD publishes the list of neighboring SAP along with their distances from the corresponding SU. For notational convenience, all the Q number of SAPs are represented by q ¼ f 1; 2; 3; . . . ; Q g, while all the P number of SUs are represented by p ¼ f 1; 2; 3; . . . ; P g. As the network is initiated, the SUs start retrieving information from the AD. AD assists the SU to determine the best possible SAP to which it can connect. The selection of SAP depends on its distance from the corresponding SU, where dq represents the distance of the corresponding SU from the qth SAP. In this manner, the SU selects the nearest SAP for establishing a wireless connection with it. The SAP may terminate the request of connection from a SU due to excessive architecture load, indicating it to choose an alternative SAP. As the connection between SAPs and SUs are finalized in the above manner, a parallel computation begins at SAP. The SAP periodically senses the spectrum and publishes its subnet information through a control data packet namely secondary user services (SUS), which is stored in the cloud. Every SAP has associated with it a SUS which provides two information; (i) the network map, and (ii) the cost map. Network map of a SUS denotes the connection details of the SUs and other SAPs connected to it. The connectivity of SUs and other SAPs are identified by their IP address (IP). Cost map provides the pair-wise path cost of all the interconnecting links of the corresponding network map. The path cost of each channel, between SAP and SUs, and SAP and other SAPs in the corresponding subnet is represented by its cost function (C) which depends on the distance between them. Hence, while formulating the cost map, the distance can be approximated through the strength of the incoming signal if a signal with known power is transmitted. Alternatively, the distance can also be obtained by

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A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

PU

PU PBS PU PU PU

PU Cloud

SAP

SU SU

SAP SU SAP

SU

SU SU

SU

SU PU SU PBS SAP

: : : :

Primary User Secondary User Primary Base Station Secondary Access Point

PU to PBS Connection SAP to Cloud Connection SAP to SAP Connection SU to SAP Connection

Fig. 1. Proposed cloud based self-adaptive network architecture.

2.2. Connection establishment

List of neighboring SAP’s

SAPq

D i st a n ce

SAP1 SAP2

d1 d2

M

M M

SAPQ

dQ

M

Assets Directory Fig. 2. Assets directory.

availing the locations of the associated entity using localization algorithms [18]. These approximated distance computed at SAP helps to build the associated cost map. Fig. 3 illustrates both of these maps. The network map is relatively stable and requires less update compared to the cost map which may change depending on the dynamic environment. These subnet information from various SAP in the network helps the cloud to build its own ‘‘network view” of the entire network. This completes the initialization process of the network.

Connection establishment procedure is initiated, as the SU S (source SU) proceeds to establish a connection with the SU D (destination SU). Index p = S and D respectively stands for source and destination SU. SU S first sends the SSR to the SAP associated with it, which in response sends the current state of the spectrum. The SU S then determines the optimal frequency band according to the required data rate, QoS (quality of service) and the current state of the spectrum. As the frequency band through which the data will be transmitted is finalized, the SU S sends a request of connection to the associated SAP. In response, the SAP asks the cloud to provide with the optimal relay path information to reach the destination. Since the cloud has information about the entire network, it is the best entity to provide this information. An optimization problem is formulated in the cloud, which provides information about the optimal multi-hop relay to reach the destination. This information is communicated to the SU through SAP as a response to SAP request, which is further used by the SU to reach its destination. Fig. 4 illustrates the timing flow diagram of the proposed network architecture.

SAP

SAP SA P → SU p

SAP → SAPq

SU p

Cost

SAPq

Cost

IPSU1 IPSU2

IPSAP1 IPSAP2

SU1

SAP1 SAP2

C1

SU 2

C1 C2

M

M

M

M

M

M

M

M

M

M

M

IPSAPQ

SU P

CP

SAPQ

IPSU

P

(i)

(ii) Fig. 3. (i) Network map and (ii) cost map.

C2 M

CQ

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A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

SU S

AD

SAP

SU D

Cloud

Request for information of closest SAPs Publishes SAPs information Establish connection Connection established

Network Initialization ⎛ Repeat itself on a ⎞ ⎜⎜ ⎟⎟ ⎝ regular ti me interval ⎠

Perform spectrum sensing

Publishes subnet information through SUS

SSR Information regarding the current spectrum state Request for connection to the destination

Connection Establisment

Request for optimal path information to reach the destination Perform optimal multihop relay selection Optimal path information (a group of intermediate SAPs combines to form an optimal path) Connection established through the optimal multi-hop relay

Fig. 4. Network flow (timing) diagram.

2.3. Optimal multi-hop relay selection

CapacitySUS SAPq ¼

Fig. 5 depicts a typical scenario of the network, where SU S associated with SAP1 needs to reach SU D associated with SAPQ through intermediate SAP in between. Let xSUS , DRSUS and PSUS respectively be the transmitted signal, required data rate and transmitted power of the SU S , while xPU , DRPU and PPU represent the transmitted signal, required data rate and transmitted power of the PU. Thus, the power of the received signal at qth SAP is represented as:

PSAPq ¼ hSU S SAPq

qffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffi PSUS xSUS þ hPUSAPq a PPU xPU þ nSAPq

ð1Þ

where hSUS SAPq and hPUSAPq represent Rayleigh fading coefficient of the SU S SAPq channel and that of PUSAPq channel respectively, nSAPq represents Additive White Gaussian Noise (AWGN) with zero mean and power spectral density N 0 and the parameter a is given by:

a¼



0;

H0

1;

H1

ð2Þ

where H0 and H1 represent the hypothesis of absence and presence of the PU respectively. Using Eq. (1), the capacity of the SU S -SAPq channel can be obtained as:

SUS

SAP1

!   hSU SAP 2 c 1 q SU S S log2 1 þ   N1 a hPUSAP 2 c þ 1 q

ð3Þ

PU

where cSUS ¼ PSUS =N 0 , cPU ¼ P PU =N 0 and N  1 represent the number

of hops required for SU 0S s signal xSUS to reach SU D via intermediate SAPs in between. All the ‘Q’ number of SAPs attempt to decode the signal transmitted by the SU S . But as per Shannon’s coding theorem, the receiver will fail to decode the transmitted signal whenever the channel capacity falls below the required data rate. If SU D succeeds in decoding the transmitted signal then relays will not be required in between. However, if SU D fails to decode the transmitted signal, the SAPq which decodes the transmitted signal retransmits it. The process is repeated until the SU D decodes the source signal. As per the proposed network architecture, each and every SU is associated with a SAP. Hence, SAPs connected to both the SU S and SU D remain constant while forming a different combination of the intermediate relays. Therefore, the sample space of possible combination of relays can be represented as R ¼ f u; R1 ; R2 ; . . . ; Rm ; . . . ; R2Q 2 1 g, where Rm represents the mth possible combination. Motivated from [19], the best combination of SAPs to form an optimal relay using ‘‘Optimal Multi-hop Relay Selection” is presented in Algorithm 1. The algorithm determines the relay with the

SAPs (SAP2 - SAPQ -1 ) to assist SAP 1 to reach SAPQ Fig. 5. Network view of multi-hop architecture.

SAPQ

SU D

36

A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

least C while transmitting over the wireless network. From the architecture point of view, the optimal relay selection algorithm is proposed to be executed in the cloud. This will eliminate the need to have high-speed signal processing tasks in the SUs. For a large deployment area, the availability of the network connectivity information and formulation of relay selection in the cloud will be a computationally efficient option resulting in the use of energysaving SUs. Algorithm 1: Optimal Multi-hop Relay Selection Parameters: Cost Function (C), Ahead Cost Function ðEÞ and Aback Cost Function (L) Returns: A set of SAPs, which combines to form a relay with least C 1: Start 2: Declare Eq while moving from the source towards the destination in a forward direction, where q 2 Q for Q number of SAP; C qr represent channel cost function as the transmitted signal travels from SAPq to the SAP at next hop, say SAPr 3: Declare Lr while moving from the destination towards the source in a backward direction, where r 2 Q for Q number of SAP; C rq represent channel cost function as the transmitted signal travels from SAPr to the SAP at next hop, say SAPq 4: The algorithm begins at SAP associated to the SU S with an initialization: Eq ¼ 0, where q = 1 5: While moving in forward direction, compute the parameter E at each SAP as: Er ¼ Eq þ C qr 6: If more than one SAP merges to the same rth SAP, than E can   be computed as: Er ¼ min Eq þ C qr

POutage ¼ PrðOutagejR ¼ /; H ¼ H0 Þ  PrðR ¼ /jH ¼ H0 Þ þPrðOutagejR ¼ Rm ; H ¼ H0 Þ  PrðR ¼ Rm jH ¼ H0 Þ þPrðOutagejR ¼ Rm ; H ¼ H1 Þ  PrðR ¼ Rm jH ¼ H1 Þ

An outage occurs only when the channel capacity falls below the required data rate. Hence, using Eq. (3) the occurrence of outage is represented as:

!   hSU SAP 2 c 1 q SU S S log2 1 þ  < DRSU S  N1 a hPUSAP 2 c þ 1

ð6Þ

  hSU SAP 2 < D; q S

ð7Þ

PU

q

H0 ;

    hSU SAP 2  hPUSAP 2 c D < D; q q PU S 

where D ¼ 2ðN1ÞDRSUS

H1 ;

ð8Þ

  1 =cSUS and N represents the number of

entities combined to form a relay. Using Eq. (7) and (8), (5) is written as:

  2 POutage ¼ Pr hSU S SU D  < DjR ¼ /;H ¼ H0  PrðR ¼ /jH ¼ H0 Þ N1  

 2 P þ Pr hnðnþ1Þ  < DjR ¼ Rm ; H ¼ H0  PrðR ¼ Rm jH ¼ H0 Þ n¼1   2  2 þPr hSU S SUD   hPUSUD  cPU D < DjR ¼ /; H ¼ H1 PrðR ¼ /jH ¼ H1 Þ N1  

 2  2 P Pr hnðnþ1Þ   hPUðnþ1Þ  cPU D < DjR ¼ Rm ; H ¼ H1 þ n¼1

PrðR ¼ Rm jH ¼ H1 Þ ð9Þ

q2Q

7: As the value of r reaches Q, it indicates that the algorithm arrived at the SAP associated with SU D . Hence, this returns the minimum E of the network 8: To compute the value of L we need to move in a reverse direction, the process begins at SAP associated to the SU D with an initialization: Lr ¼ Er , where r = Q 9: While moving in a backward direction, compute the parameter L at each SAP as: Lq ¼ Lr  C rq 10: If more than one SAP merges to the same qth SAP, than L   can be computed as: Lq ¼ max Lr  C rq

ð5Þ

þPrðOutagejR ¼ /; H ¼ H1 Þ  PrðR ¼ /jH ¼ H1 Þ

 2  2  2  2 As hSUS SUD  ; hPUSUD  ; hnðnþ1Þ  and hPUðnþ1Þ  are exponen-

tially distributed with parameter 1=r2SUS SUD ; 1=r2PUSUD ; 1=r2nðnþ1Þ

and 1=r2PUðnþ1Þ respectively, where

r

2 PUðnþ1Þ

r2SUS SUD ; r2PUSUD ; r2nðnþ1Þ and

represent the fading variances of the corresponding chan-

nel. Hence, we can obtain:

PrðR ¼ /jH ¼ H0 Þ ¼

Q Y

1  exp 

q¼1

!!

D

ð10Þ

r2SUS SAPq

r2Q

11: As the value of q reaches 1, it indicates that the algorithm arrived at the SAP associated with SU S . Hence, this finalizes the value of L of each SAP in the network 12: The SAPs with equal value of E and L are combined to form a multi-hop relay with least C 13: Stop

PrðR ¼ /jH ¼ H1 Þ ¼

Q Y q¼1

 exp 

D

!!

1

r

r2SUS SAPq þ r2PUSAPq cPU D

2 SU S SAPq

ð11Þ

r2SUS SAPq

  2 Pr hSU S SU D  < DjR ¼ /; H ¼ H0 ¼ 1  exp  3. Outage probability

D ðN  1Þ r2SUS SUD

!

ð12Þ

The section presents an analysis of outage probability to test the performance of the optimal multi-hop relay selected by the presented scheme. Outage probability is defined as the probability that SU D fails to receive the transmitted signal broadcasted by SU S . Hence the outage probability of the proposed model is calculated as:

POutage ¼ PrðOutage; R ¼ /jH ¼ H0 Þ þ PrðOutage; R ¼ Rm jH ¼ H0 Þ þPrðOutage; R ¼ /jH ¼ H1 Þ þ PrðOutage; R ¼ Rm jH ¼ H1 Þ ð4Þ

  2  2 Pr hSU S SUD   hPUSUD  cPU D < DjR ¼ /; H ¼ H1 ¼

ðN1Þ r2SU SU D D S 1  ðN1Þ r2  exp  þr2 c D ðN1Þ r2 SU S SU D

PUSU D PU

PrðR ¼ Rm jH ¼ H0 Þ ¼ 1  exp 

ð13Þ

SU S SU D

D

r2SUS SUD

!  1P

!!

Cm 2Q 2 1 Ck k¼1

ð14Þ

A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

PrðR ¼ Rm jH ¼ H1 Þ ¼ 1  r2 SU

r2SU S SU D

S SU D

þr2PUSU cPU D !!

 1  P2QCF2m1 k¼1

 exp  r2 D

D

SU S SU D

CF k

ð15Þ   2 Pr hnðnþ1Þ  < DjR ¼ Rm ; H ¼ H0 ¼ 1  exp 

!

D

5. Result and discussion

ð17Þ

Finally, by putting Eqs. (10)–(17) in (9), we get a closed-form expression of outage probability for the selected multi-hop relay which is as shown below: 1  exp  ðN1ÞrD2





SU S SU D

þ

N1 P n¼1

1  exp  r2



D

nðnþ1Þ



q¼1

1  r2

SU S SU D

c PUSU D PU

r

exp  r2

r

r2SU

S SU D 2 þ 2PUSU cPU D SUS SUD D

 1 r

r

!!!

D

exp  ðN1Þ rD2 D

2 nðnþ1Þ þ 2PUðnþ1Þ cPU D nðnþ1Þ

r

! D

SUS SAPq

q¼1

 exp  r2

S SAPq þ 2PUSAP cPU D SU S SAPq q



1  exp  r2

SU S SUD

r2SU

N1 P 1  r2 þ n¼1



 1  exp  r2

ðN1Þ r2SU SU S D þ 1  ðN1Þ r2 þr2 Q Q

Q Q

Cm  1  P2Q2 1



k¼1

Ck

SUS SUD

!

D

SU S SAPq

 exp  r2



D

nðnþ1Þ

!!!



D

SUS SU D

Cm  1  P2Q2 1 k¼1

Ck

ð18Þ

4. Energy consumption The section compares the total energy consumption incurred in a processor using the existing Energy Models. Both communication and sensing costs have been compared to show that the former is much less than the latter. Maximum Eigenvalue based Detection (MED) algorithm [20] is one of the accurate but computationally high-cost sensing algorithm, which has been selected for comparison. Hence, the total energy consumed while scanning a wide range of frequencies using MED is represented as:

Estotal ¼ Ecpu

instr

þ Ecpu

data

þ Emem

instr

þ Emem

data

cost, replacing it by communication cost Ectotal which depends mainly on the number of bits transmitted and received [22]. Thus Ectotal is f ðyÞ þ f ðZÞ where, y represents the y-bit SSR signal. Since y << Z;Ectotal  f ðZÞ: It is therefore established that Ectotal << Estotal : Because of this, computationally high spectrum sensing algorithms such as kernel-based online/ prediction learning [23] can be adopted for the sensing functionality in the cloud.

ð16Þ

r2nðnþ1Þ

  2  2 Pr hnðnþ1Þ   hPUðnþ1Þ  cPU D < DjR ¼ Rm ; H ¼ H1 !! r2nðnþ1Þ D ¼1 exp  r2nðnþ1Þ þ r2PUðnþ1Þ cPU D r2nðnþ1Þ

P Outage ¼

37

ð19Þ

Estotal

where represent the total energy consumed to execute the sensing algorithm while other parameters are described as referred in [21], (a) Ecpu instr represent instruction dependent cost inside the CPU (b) Ecpu data represent data dependent cost inside the CPU (c) Emem instr represent instruction dependent cost at instruction memory (d) Emem data represent data dependent cost at data memory Ecpu instr and Emem instr depend on number and nature of instructions in the algorithm which is proportional to the size of operational data Z In maximum eigenvalue based detection, autocorrelation of data is computed to form a covariance matrix from which the maximum eigenvalue obtained is compared with a threshold to determine spectrum occupancy. This makes Ecpui nstr and Ememi nstr proportional to Z3 The other two data dependent terms, Ecpud ata and Ememd ata are proportional to Z: Thus EStotal is 2ðf ðZ 3 Þ þ f ðZÞÞ: The proposed architecture eliminates this huge

The architecture was simulated in Network Simulator-3 (NS-3) environment where the SU S attempt to reach SU D through intermediate SAPs, whenever the state of the spectrum is vacant. A set of nine stationary SAPs were placed in such a way that they covered the entire area of deployment. A pair of mobile SUs (SU S and SU D ) moving in specific directions were also considered over the deployed area. The SUs are installed with ‘ConstantVelocity’ mobility model to offer mobility to the SUs with the velocity of 5 m/sec. In NS-3 the velocity parameter is considered as a 3-dimensional entity, which helps to define its velocity in a particular direction. However, the deployed entities were initially 200 m apart with the capability to connect over a wireless network. The WiFi standard 802.11b is considered for wireless connectivity with a range of 300 m. The network described above was mainly built to extract the C parameter of each channel in the network over the regular interval of time. In our work, these C parameters are interpreted as delay, which was further used to determine the optimal relay path using the algorithm presented in Section 2.3. The performance of this algorithm has been compared with that of the Minimum Instantaneous Delay (MID) method of [17]. To show the effectiveness of the presented algorithm over MID method, the results of one of the iterations of the simulation is as shown in Fig. 6. In this iteration, SU S and SU D are associated with SAP1 and SAP9 respectively, while other SAPs assist in establishing the connection. In the particular iteration shown, the method of [17] and the proposed method take up different relays having different C. The latter offers the connectivity at a lower propagation delay (392 :52 ls) as compared to the MID method (393:55 ls). Since the simulation considers a small area of deployment, the C parameter of the selected path by both the method remains almost the same. The effectiveness of the proposed algorithm will be seen through a practical realization of the method over a large area of deployment. Further, outage probability is computed for the relays selected by both the methods, where an additional attribute of transmitted SNR of the source SU ðcSUS Þ is added to this simulation. To make the calculation of outage simpler, we have considered the following parameters: r2SUS SUD ¼ r2SUS SAPq ¼ r2nðnþ1Þ ¼ 1, r2PUSUD ¼

r2PUSAPq ¼ r2PUðnþ1Þ ¼ 0:2, N = 7 and cPU ¼ 10 dB. Fig. 7 shows the outage probability versus cSUS plot obtain using both the methods. The characteristic of the outage graph follows the one shown because for a lower value of cSUS , the probability of outage remains relatively high as the transmitting SNR of the interfering signal ðcPU Þ is chosen as 10 dB which is high as compared to cSUS . The value of outage decreases rapidly as cSUS approaches cPU . For higher values of cSUS an outage floor is observed because cPU is now significantly low and does not have any effect over cSUS . The outage probability of the proposed method is seen to outperform the MID method over a wide range of SNR as shown in the figure. The significant improvement of outage was extensively studied and can be better explained through different situations as SU S and SU D changes its associated SAP while moving within the deployed area of simulation. For calculating the outage probability, for each cSUS , 1000 iterations were performed out of which five runs (out of

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A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

SAP3 CF23 = 99.370

SAP2 CF12 = 96.274

E=96.274 L=95.986

CF36 = 1 0 1.6 3 1

E=195.644 L=194.615

SAP6 CF25 = 100.89

CF56 = 99.370

E=296.534 L=296.246

CF69 = 9 6.2 7 4

SAP9

SAP5

SAP1

CF14 = 97.570

E=0 L=0

CF45 = 100.890

SAP4

SUS

E=97.570 L=97.570

E=197.164 L=196.876

CF58 = 97.570

CF89 = 99.010

E=392.520 L=392.520

SAP8

CF47 = 98.330

CF78 = 97.610

SU D

E=293.510 L=293.510

SAP7 E=195.900 L=195.900

Optimal Multi-hop Relay Selection Minimum Instantaneous Delay

Fig. 6. Simulation model in NS-3 for performance analysis.

Fig. 7. Outage probability verses transmitting SNR of SU in dB.

1000) taken over a specified time instants are reproduced in Table 1. The table shows the relays selected to reach its destination over different instants of time along with the associated C parameter (delay in microseconds) and outage probability computed using Eq. (18). From the table, it is observed that the proposed Optimal Multi-hop Relay Selection (OMRS) method and the MID method select path having total delay almost same (T 1 ;T 3 and T 5 ). However, it may be noted that the path selected by both the methods are totally different and in all time instants the OMRS method provides a lower probability of failed transmission as well

as lower delay. At time instants T2 and T 4 , the C of the relay selected by the OMRS method are 200.26 ls and 198.46 ls respectively, while the MID method fails to reach the packet to the destination as it is experiencing a low SNR intermediate link resulting in the abortion of the transmission mid-way. The corresponding outage is obviously 1. The outage value calculated for the OMRS method, on the other hand, is approximately 0.13. In a large-scale practical realization, it is expected that the proposed OMRS method will enable the choice of a reliable path having significantly lower delay. Averaging over multiple runs for a given cSUS the overall outage performance is as depicted in Fig. 7. In other words, the better outage performance of OMRS method over MID method is mainly due to the fact that under certain situations of poor links in the next hop, the MID method is forced to terminate the transmission, whereas in the OMRS method, the next best optimal path is computed to enable the completion of the transmission. The computation of complex and high energy consuming tasks such as spectrum sensing and path selection requires dedicated hardware to run the program. Therefore, shifting of such sophisticated tasks from mobile nodes (SUs) to independent entities (cloud or SAP) is practically feasible rather than computing them over the mobile nodes. In the proposed network architecture, the SAP and the cloud perform the computational tasks of spectrum sensing and path selection, and the results are communicated to the specific entity as required. This increases the lifetime of the network at the cost of communication overhead as the amount of current spectrum state obtained while scanning a wide range of frequency band is huge. However, from the perspective of saving the energy, the proposed network architecture is highly efficient as demonstrated in Section 4.

Table 1 Selected path and corresponding outage probability as SU S and SU D moves around the deployed area. Time

SU S connected to

SU D connected to

SAP combines to form an optimal relay

T1 T2 T3 T4 T5

SAP 1 SAP 4 SAP 7 SAP 8 SAP 9

SAP9 SAP6 SAP3 SAP2 SAP1

SAP 9 SAP 4 SAP 7 SAP 8 SAP 9

OMRS  SAP8  SAP5  SAP4  SAP5  SAP8

MID  SAP 7 SAP4  SAP1  SAP 6  SAP 1 SAP2  SAP3  SAP 2  SAP 7 SAP4  SAP1

SAP1 SAP4 SAP7 SAP8 SAP9

 SAP2  SAP7  SAP8  SAP7  SAP6

 SAP3 SAP6  SAP 9  SAP6  SAP5 SAP 6  SAP3  SAP2  SAP5 SAP 4  SAP1

C (delay in micro-second) of the selected relay

Outage probability

OMRS

MID

OMRS

MID

392.520 200.260 391.544 198.460 392.520

393.549 Fails to reach the destination 396.181 Fails to reach the destination 394.104

0.2982 0.1312 0.2967 0.1312 0.2983

0.2986 1 0.2986 1 0.2989

A.K. Varma, D. Mitra / Int. J. Electron. Commun. (AEÜ) 106 (2019) 32–39

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