An Energy Balance Cluster Network Framework Based on Simultaneous Wireless Information and Power Transfer Juan Xua Ruofan Wanga Yan Zhanga Hongmin Huanga_2

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An Energy Balance Cluster Network Framework Based on

Simultaneous Wireless Information and Power Transfer

Juan Xua,∗, Ruofan Wanga, Yan Zhanga, Hongmin Huanga

aCollege of Electronic and Information Engineering, Tongji University, 201804, Shanghai, China

Abstract

Wireless NanoSensor Network (WNSN) is a brand-new type of sensor network with

broad application prospects. In view of the limited energy of nano-nodes and unstable

links in WNSNs, we propose an energy balance cluster network framework (EBCNF)

based on Simultaneous Wireless Information and Power Transfer (SWIPT). The EBCNF

framework extends the network lifetime of nanonodes and uses a clustering algorithm

called EBACC (an energy balance algorithm for intra-cluster and inter-cluster nodes)

to make the energy consumption of nodes more uniform. Simulation shows that the

EBCNF framework can make the network energy consumption more uniform, reduce

the error rate of data transmission and the average network throughput, and can be used

as an eﬀective routing framework for WNSNs.

Keywords: cooperative communication, routing protocol, SWIPT, WNSNs.

1. Introduction

Due to the development of nanotechnology and the emergence of new materials like

controlling materials from one nanometer to several hundred nanometers , the realization

and application of Wireless Nano Sensor Networks (WNSNs) is feasible[1]. Due to the

extremely limited storage capacity of nano batteries, the communication performance of

WNSNs is limited [2, 3]. Therefore, energy harvesting has always been a research focus

of WNSNs. Obtaining energy from the surrounding environment provides a promis-

ing method for improving the network lifetime and performance of energy-constrained

WNSN [3]. Since electromagnetic signals not only carry information but also energy,

?This work was supported by the National Natural Science Foundation of China under Grant 61202384

and 61971314.

∗corresponding author

Email addresses: jxujuan@tongji.edu.cn (Juan Xu ), wrfbwcx@163.com (Ruofan Wang),

1830732@tongji.edu.cn (Yan Zhang), freeastime@163.com (Hongmin Huang)

Preprint submitted to Nano Commun.Netw. October 7, 2022

arXiv:2210.02927v1 [cs.NI] 6 Oct 2022

a method for processing environmental electromagnetic signal information while col-

lecting energy is proposed [4]. For WNSN, SWIPT(Simultaneous Wireless Information

and Power Transfer) is diﬀerent from piezoelectric energy harvesting systems. It can

provide stable energy for nano-nodes and is a promising charging method. In traditional

electromagnetic communication, Varshney ﬁrst proposed the idea of transmitting power

and information at the same time [4]. Grover et al. analyzed SWIPT based on frequency

selective channel which provides ideas for simultaneous transmission of power and in-

formation on the THz band [5]. Taking into account the non-linearity of the rectiﬁer,

Bruno proposed a non-linear rectiﬁer model for SWIPT technology [6]. Considering

that SWIPT can overcome the energy bottleneck of nano sensor networks, Rong et al.

designed a nano particle energy harvesting model [7]. Although there have been some

researches on SWIPT waveform design, segmentation coeﬃcient optimization, SWIPT

mechanism design, etc., there are few researches on SWIPT technology as an energy

harvesting method for terahertz nano sensor networks.

Considering the limited energy of nano-nodes, we propose a SWIPT-based energy

balance cluster network framework for WNSNs (EBCNF). The EBCNF network frame-

work uses SWIPT technology to extend the network lifetime. As for the coeﬃcient

optimization problem in SWIPT, we transform the problem into a maximum-minimum

problem for processing optimization. In addition, distance and energy are considered in

the clustering process: the closer the nano-node is to the nano control node, the higher

the probability of becoming a CH(cluster head node). The lower the energy of the CH

and the closer the distance to the nanocontrol node, the smaller the cluster formed[8].

This allows the CHs close to the nano control node to allocate a portion of energy to

process data from other CHs.

In Section 2, the network model, channel model and energy model of EBCNF are

introduced. Next, the communication mechanism of EBCNF, cluster formation and up-

date, and coeﬃcient optimization are introduced in Section 3. Then, in Section 4, we

demonstrate and analyze the superiority of EBCNF in terms of network survival time,

data transmission success rate, throughput, and so on. Finally, in Section 5, we summa-

rize the advantages of EBCNF and the directions that can be improved.

2. System Model and Problem Formulation

In WNSNs, clustering is generally used to divide and manage the network to reduce

the pressure caused by increased network scale. For WNSNs, the ultimate goal is to

transfer the data collected in the network to the macro network. Therefore, in addition

to ordinary nano sensor nodes, there are also nano control nodes that connect macro net-

works and WNSNs. The role of the nano sensor node is mainly to collect data, package

the data and send it to the nano control node. Data is transmitted from the nano sensor

nodes to the CH, and then many CHs forward the information to the nano control node

(NC).

Nano Control Node (NC)

Cluster Head Node

Nano Sensor Node

WPT

WIT

SWIPT

Figure 1: Schematic diagram of EBCNF framework.

2.1. Network Model

We use G=(V,E)represent the network topology, where the set of nano-nodes

is represented byV={v1,v2,...,vn}, and n=|V|is the toal number of nano-nodes.

Figure 1 is a schematic diagram of the EBCNF we proposed. In the ﬁgure, there is a

nano control node and multiple nano sensor nodes. NC can wirelessly charge all sensor

nodes, and it can also be used as a sink node to collect information about each cluster.

It should be noted that only NC can provide stable energy. NC regularly broadcasts

terahertz waves, and all nano-nodes obtain energy from the terahertz waves. Several

CHs are selected from the nano-nodes, a CH and surrounding nodes form a cluster, and

the cluster size is determined according to the energy of the CH and the distance from

the NC[8].

The establishment of the network model is based on the following assumptions:

•All nano-nodes in the network are in ﬁxed positions, and the energy of NC is

always suﬃcient.

•All nano-nodes sense volatile organic compounds, temperature and other informa-

tion, and the environmental information sensed by each sensor node is transmitted

through data packets of the same size.

•Nano-nodes can get the location information, remaining energy and channel qual-

ity of neighbor nodes through Hello messages, and all nano-nodes are identiﬁed

by a unique ID.

•Nano-node processing data does not consume energy, while sending and receiving

data consumes energy.

•Nano-node processing data does not consume energy, while sending and receiving

data consumes energy.

•Node transmit power can be adjusted according to the speciﬁc transmission time

slot length.

•The nano-node can perceive its own remaining energy value. Through WPT(Wireless

Power Transfer) and SWIPT, the nanonode can collect energy from the environ-

ment through electromagnetic waves.

2.2. Terahertz Channel Model

The path loss in the terahertz band can be expressed as the product of propagation

loss and molecular absorption loss[9]:

PL (f,d)=PLspr (f,d)×PLabs (f,d)(1)

where PLspr (f,d)is the propagation loss, and PLabs (f,d)is the molecular absorption

loss. fis the transmission frequency and dis the propagation distance[10]. The propa-

gation loss can be expressed as:

PLspr (f,d)= 4πf d

c!2

(2)

where crepresents the speed of light in vacuum. Under normal circumstances, when

electromagnetic waves propagate in the medium, molecules will absorb part of the elec-

tromagnetic energy, causing molecular absorption loss. The magnitude of the absorption

loss is related to the type of molecules present in the medium and the frequency of elec-

tromagnetic waves. The molecular absorption loss can be expressed as[10]:

PLabs (f,d)=ek(f)d(3)

where k(f)is the molecular absorption factor, which can be expressed as[11]:

k(f)=X

i,g

ki,g(f)(4)

where ki,g(f)represents the absorption factor of the gas molecule iin the medium g.

The channel capacity of a terahertz channel is equal to the sum of the capacities of the

subchannels that make up the channel:

C(d)=X

∆flog2 1+S(fi)

PL (fi,d)N(fi,d)!(5)

where fiis the center frequency, ∆fis the bandwidth of the subchannels, S(fi)is

PSD(power spectral density) of the transmitted signal, and N(fi,d)is PSD of the noise

in the channel[12]. Molecular absorption noise dominates THz channel noise sources,

so N(fi,d)can be expressed as PSD of molecular absorption noise:

N(f,d)=KBT01−e−k(f)d(6)

where KBis Boltzmann’s constant, and T0is the reference temperature.

2.3. Energy Consumption Model

The energy consumption model can be expressed as the energy used by nano-nodes

to transmit and receive data:

Etot−con =Etr−con +Ere−con (7)

where Etr−con represents the energy used to transmit data and Ere−con represents the

energy used to receive data. According to[13], the energy required by the nanonode to

transmit data can be expressed as:

Etr−con =k∆f S (f)Tbit (8)

where krepresents the number of bits of a data packet, ∆frepresents the bandwidth of

the current node’s transmission signal, S(f)represents PSD of the transmission signal,

and Tbit represents the time required to transmit 1 bit of data.

Ere−con represents the energy used for receiving data packets. In Section 2.1, it

has been assumed that the environmental information sensed by each sensor node is

transmitted through data packets of the same size. so we assume Ere−con to be a constant

φ. Then the total energy consumption of the nanonode can be expressed as:

Etot−con =k∆f S (f)Tbit +φ(9)

2.4. Energy Harvesting Model

The energy harvesting model is an actual non-linear energy harvesting model based

on the logistic function proposed in[13–15]. Compared with the linear model, this model

can capture the nonlinear behavior of the energy harvesting process. In the nonlinear

model, the energy collected in the kth symbol interval can be expressed as:

Ehar =T Psψ(ρk)−γ

1−γ(10)

where γis a constant to ensure zero input and zero output response, ψ(ρk)is a lo-

gistic function, Psis the maximum power at which the energy harvesting circuit is

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AnEnergyBalanceClusterNetworkFrameworkBasedonSimultaneousWirelessInformationandPowerTransferJuanXua,,RuofanWanga,YanZhanga,HongminHuangaaCollegeofElectronicandInformationEngineering,TongjiUniversity,201804,Shanghai,ChinaAbstractWirelessNanoSensorNetwork(WNSN)isabrand-newtypeofsensornetworkwithbroad...

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An Energy Balance Cluster Network Framework Based on Simultaneous Wireless Information and Power Transfer Juan Xua Ruofan Wanga Yan Zhanga Hongmin Huanga_2

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