Frontiers in Computing and Intelligent Systems 
ISSN: 2832-6024 | Vol. 1, No. 2, 2022 

 

44 

Efficient incentive mechanism for video distribution on 
the home Internet of Things devices 

Rongrong Zhu, Xiaofang Zhang* 

School of Intelligent Engineering, Shaoguan University, Shaoguan 512000, China. 

* Corresponding author: Xiaofang Zhang (Email: findlabzrr@163.com). 

 

Abstract: Providing users with nearby video distribution services based on Edge Computing (EC) reduces backbone traffic 

and improves users' viewing experience. However, the behavior of edge networks is difficult to manage and cannot guarantee 

the rationality of video resource distribution, which has become a significant challenge for online video services. This paper 

takes the idle resources from home Internet of Things (IoT) devices for video distribution service as the research point. It proposes 

an incentive mechanism based on behavior management and balance of resources (BMBR). By paying attention to the rationality 

of resource distribution and establishing reasonable reward and punishment measures for edge devices, the incentive mechanism 

makes the initiative of edge devices and the relationship between resource supply and demand reach a stable equilibrium. 

Simulation results and analysis show that the incentive mechanism can effectively improve the enthusiasm for resource sharing 

in the edge network, ensure the rationality of resource distribution to reduce the resource price fluctuations, and maintain the 

balance of resource supply and demand. 

Keywords: Internet of Things; Edge Computing; Idle resources; Incentive; Resource balance. 

 

1. Introduction 

With the rapid development of online video, live broadcasts, 

and other services, consumers have put forward higher re-

quirements for video content and a good viewing experience 

[1]. In the current global network, data traffic is growing 

explosively, and video has become the primary carrier of 

network traffic [2]. Cisco Visual Networking Index [3] shows 

that 75% of global data traffic in 2017 was generated by video, 

and this figure will reach 86% in 2024. In terms of IoT devices, 

by 2022, there are more than 60 billion home IoT devices, but 

effective utilization of resources is only 28% [4]. Home IoT 

device brings convenience for the users and generates 

significant idle resources. To reduce the investment of video 

service providers in bandwidth, storage, and other network 

resources, it has become reliable solution to build content 

distribution services by mining the idle resources of home IoT 

devices [5]. 

The content distribution service of the edge network refers 

to the deployment of content data on edge devices close to 

users or user terminal devices to provide lower content 

service delay [6]. Video content distribution based on EC has 

become a hot spot in the current academic and industrial 

circles. Sun et al. [7] proposed a framework for video content 

distribution through the network, storage, and computing 

capabilities of EC. They gave the optimization strategy design 

of video distribution such as edge caching and replacement, 

edge content prefetching, edge content collection, and EC 

migration under this framework. Markakis et al. [8-9] 

proposed unified computing, caching, and communication 

(3C) solution for 5g, which allows service, content, and 

function providers to deploy their services, content, and func-

tions near end users. The solution also will enable end-users 

to share and cooperate to form a virtual edge resource pool. 

End-users will be able to become 3C resource providers of the 

edge network ecosystem. 

Some network systems and peer-to-peer (P2P) systems use 

end-user devices to share content resources, such as the file 

sharing system platforms, BitTorrent, boinc [10], and seti@ 

home [11]. These volunteer platforms apply to file-sharing 

systems. Researchers at the University of Minnesota have 

studied the possibility of using users' resources to provide 

content services near edge networks and proposed a 

distributed edge cloud system. Nebula [12] supports dis-

tributed data-intensive applications through close interaction 

between resources, supports field data-intensive application 

deployment through location-aware data placement, replica-

tion, and recovery, and utilizes edge resources. 

Li et al. [13] designed network middleware for developing 

countries, such as low network bandwidth and unstable 

service quality. They realized a better offline download 

experience by combining the download methods based on 

cloud hosts and intelligent routers, that is, the middleware. 

The software downloads the content requested by the user in 

advance; after downloading, users can get the content at high 

speed. Hu [14] et al. proposed a new architecture for video 

content distribution in edge networks. Under this new 

architecture, intelligent routers deployed in users' homes play 

the role of small content distribution nodes, serving neigh-

boring user requests by caching content and improving the 

user experience. Chen et al. [15] developed a crowdsourcing-

based content distribution model by utilizing idle bandwidth 

resources and storage resources of intelligent routers scattered 

in users' homes and measured the performance of the system 

through practical deployment. Chang et al. [16] The proposed 

fog computing system, Indie Fog, which considers consumer 

resource sharing and suggests that network infrastructure 

providers or cloud service providers could leverage user 

equipment to provide edge-based services. Given some 

incentives, consumers will be willing to share their devices 

with providers to provide services. 

In addition to utilizing the idle resources of consumers, it 

is also possible to self-deploy data centers to provide content 

services, such as CloudPath proposed by Mortazavi et al. [17], 

which deploys a multilayer cloud structure within the 

geographic scope of the network, along with IoT devices and 



 

45 

a cloud path to a data center, deploying a chain of data centers 

that grow in size to provide storage resources. CloudPath 

supports the dynamic installation of lightweight, stateless 

functions and a distributed uniform storage system. 

CloudPath can also automatically migrate application data 

across data centers to minimize latency and bandwidth usage. 

Kamiyama et al. [18] proposed that the response time of 

modern websites can be reduced by generating dynamic web 

content on edge nodes close to ending users. In addition, the 

author measured the response time of about 1000 popular web 

pages from 12 locations around the world to assess whether 

the edge network is suitable for providing dynamic web 

content. According their experiments, edge networks can 

reduce Web response time around the world. 

Mehta et al. [19] focused on solving the following 

problems: where the edge data center must be placed and how 

much computing power needs to be allocated to each data 

center to achieve high-cost performance while meeting the 

bandwidth and performance requirements of applications. 

The author believes that adding an edge layer data center can 

save many costs for network-intensive applications, while 

adding an intermediate data center near the data center 

benefits low and medium-demand computing-intensive 

applications. Norris et al. [20] provide a new capacity 

planning solution for hierarchical edge clouds. The scheme 

considers the response delay and the Quality of Service (QoS) 

requirements for CPU, GPU, and network resources. It im-

proves edge utilization by meeting the QoS requirements and 

combining supplementary resources. 

El Kafhali et al. [21] propose a mathematical analysis 

model based on queuing theory to determine the number of 

edge nodes required to meet QoS metrics given IoT workload. 

From this model, the authors derive formulas for key 

performance indicators, including system response time, 

system packet loss, system throughput, CPU utilization, and 

the average number of message requests, and cross-validate 

them using discrete-event simulator simulations. 

The above literature has positively contributed to edge 

network architecture and content distribution strategy 

research. The solutions mentioned in the literature, such as the 

deployment of data centers, the investment of computing 

resources, and the optimization of distribution strategies, have 

made in-depth and practical research in improving edge 

network computing power and response speed. However, 

there are few studies on using home IoT devices for edge 

computing services in the academic field. As the primary 

source of idle resources, home IoT devices have natural 

location advantages, sufficient computing resources, and low-

cost access and maintenance. It is well combined for edge 

computing, but the behavior is difficult to manage, and the 

reasonable allocation of resources cannot be guaranteed, 

which has become an unstable factor for it as an edge 

computing node. 

This paper takes home IoT devices as the research object 

and, based on distributed EC services, proposes an incentive 

mechanism based on behavior management and balance of re-

sources to manage edge devices, and improve their 

enthusiasm for resource sharing and utilization of idle 

resources through the incentive mechanism. Section 2 

introduces the rules, definitions, and calculation methods of 

some critical data of BMBR. Section 3 analyzes how to 

improve the enthusiasm for resource sharing of edge devices 

and the formation process of resource balance through game 

theory. The simulation results are given and discussed in Sec-

tion 4. Section 5 provides the conclusion of this paper and the 

follow-up work arrangement. 

2. BMBR Model  

The model is mainly composed of three parts, including the 

reputation mechanism, competition mechanism, and punish-

ment mechanism. Through the cooperation of the three, an 

incentive mechanism with reputation value as the core is 

formed to manage edge devices’ behavior while ensuring the 

rationality of resource distribution. We call home IoT devices 

that provide edge computing as edge nodes. 

2.1. Reputation mechanism 

The model is mainly composed of three parts, including the 

reputation mechanism, competition mechanism, and punish-

ment mechanism. Through the cooperation of the three, an 

incentive mechanism with reputation value as the core is 

formed to manage edge devices’ behavior while ensuring the 

rationality of resource distribution. We call home IoT devices 

that provide edge computing as edge nodes. 

2.1.1. Reputation mechanism 

The reputation mechanism describes the definition and 

calculation method of reputation-related parameters in the 

service process of edge nodes. Reputation represents the 

situation of the node's request and contributing resources 

before time 𝑡 . Income refers to the reward given by the 

system after devices share idle resources. The reputation 

mechanism is described as follows:  

Definition 1. The initial reputation value of the edge node 

and the minimum reputation threshold are both 0. When 𝑅𝑖 <
0, there would be no income when providing services, but the 

reputation value would increase. 

Definition 2. The system prices resources based on the 

existing quantity of each resource and the number of times it 

has been requested in the most recent period. Define the 

degree of scarcity𝑆(𝑚, 𝑡) =
𝑁𝐹(𝑚,𝑡)

𝑁𝐶(𝑚,𝑡)
 , and the resource price 

can be calculated as follows: 

𝑃(𝑚, 𝑡) = 𝑆(𝑚, 𝑡)𝐾𝑏 =
𝑁𝐹(𝑚, 𝑡)

𝑁𝐶(𝑚, 𝑡)
𝐾𝑏 , 𝐾𝑏 ≠ 0 (1) 

𝐾𝑏  is the base price of the resource, 𝑁𝐶(𝑚, 𝑡) is the quantity 

of the existing resource 𝑚  at time 𝑡 , and  𝑁𝐹(𝑚, 𝑡)  is the 

number of requests for the resource m in the most recent 𝑇0 

period. The price of resources will change with the degree of 

scarcity of resources in time 𝑇0 , which has good real-time 

performance. Resources with high lack are usually in short 

supply, and the node can obtain higher benefits by sharing 

resources with high scarcity. 

Definition 3. Let the reputation value consumed by the 

edge node provide resources each time 𝐺(𝑖), which is related 

to the bandwidth and storage overhead of the current node.  

Definition 4. Let the number of resources contributed by 

the edge node currently be  𝑐𝑖  , and define the contribution 

coefficient 𝑔(𝑐𝑖) , then the contribution coefficient of the 

node can be calculated as follows:   

𝑔(𝑐𝑖) = (
𝑐𝑖

1 + 𝑐𝑖

) , 𝛼 > 0 (2) 

It can be seen that the contribution coefficient increases 

gradually with the increase of the number of resources 

contributed by the edge nodes. 

Definition 5. The increment of the resource reputation each 

time the node contributes is 𝑅+(𝑐𝑖 , 𝑚, 𝑡) = 𝑔(𝑐𝑖)𝑃(𝑚, 𝑡). The 

more resources contributed by edge nodes, the greater the 



 

46 

contribution coefficient, so the more reputation value 

increments obtained by nodes contributing resources. 

2.2. Competitive mechanism 

The platform offers a competition mechanism to select 

edge nodes with higher reliability to provide video transmis-

sion services. The selected nodes are called active nodes, and 

the reliability of nodes is related to bandwidth, storage, and 

reputation value. 

Definition 1. 𝐵(𝑖, 𝑡)  is set as the remaining bandwidth 

percentage of the node at time 𝑡, which can be calculated as 

follows:  

𝐵(𝑖, 𝑡) =
𝐵𝑡_𝑖

𝐵𝑚𝑎𝑥_𝑖

(3) 

𝐵𝑡_𝑖 represents the remaining bandwidth value of the node 

at the current time 𝑡 , and 𝐵𝑚𝑎𝑥_𝑖  represents the initial 

bandwidth value of the node. Under the same conditions, the 

larger the remaining bandwidth percentage of the node, the 

easier it is to be selected as a candidate node. 

Definition 2. 𝐻(𝑖, 𝑡)  is set as the remaining cache 

percentage of the node at time t, which can be calculated as 

follows: 

𝐻(𝑖, 𝑡) =
𝐻𝑡_𝑖

𝐻𝑚𝑎𝑥_𝑖

(4) 

𝐻(𝑖, 𝑡) represents the remaining cache value of the device 

at time 𝑡, and 𝐻𝑚𝑎𝑥_𝑖 represents the maximum cache value 

of the node. Under the same conditions, the larger the 

remaining cache percentage of the node, the easier it is to be 

selected as a candidate node. 

Definition 3. 𝑊(𝑖, 𝑡) is set as the resource abundance of 

the node at time t, it can be calculated as follows: 
𝑊(𝑖, 𝑡) = 𝜀𝐵(𝑖, 𝑡) + 𝜔𝐻(𝑖, 𝑡) (5) 

𝜀 and 𝜔 are the weight values of 𝐵(𝑖, 𝑡) and 𝐻(𝑖, 𝑡), and 

𝜀 + 𝜔 = 1. The greater the resource abundance of the node, 

the more abundant the remaining bandwidth and storage, and 

the stronger the ability to provide services. 

Definition 4. The minimum threshold of resource 

abundance is 𝑊(𝑖, 𝑡) . When 𝑊(𝑖, 𝑡) < 𝑊𝐿 , the edge node 

does not have enough idle resources and will not be chosen 

first and will only be considered when the number of nodes is 

insufficient. 

Definition 5. 𝐷(𝑖, 𝑡) is set as the node reliability at time t, 

which can be calculated as follows: 
𝑊(𝑖, 𝑡) = 𝐷(𝑖, 𝑡)𝑅(𝑖, 𝑡) (6) 

   𝐷(𝑖, 𝑡) is a decisive factor for whether an edge node can 

be selected? It is reasonable to determine whether an edge 

node is reliable based on resource abundance and device 

reputation value. The reputation value of the node is lower 

when 𝑅(𝑖, 𝑡) < 0. However, it is still possible to transform 

into a node with a higher reputation value through resource 

sharing, so it has the opportunity to be selected as a candidate 

node. 

2.3. Punishment mechanism 

With sufficient idle resources, edge nodes may still provide 

poor quality services, this kind of device is called a negative 

node, and the system introduces a punishment mechanism to 

restrict its behavior. 

Rule 1. At time 𝑡, the user initiates a resource request, and 

the system selects a candidate node that can provide resources 

according to the actual demand. If a candidate node has prob-

lems in the service process, the node will be marked as a 

negative node; then, the node will be punished by time 𝑇𝑃, 

which can be calculated as follows:  

𝑇𝑝 = 𝑇0

𝑎𝑛

𝑅(𝑖, 𝑡)
, 𝑎 > 1 (7) 

𝑛 is the number of times the node is punished, and 𝑇0 is 

the basic penalty time. It can be seen that the more times a 

node is punished, the longer it is punished. 

Rule 2. During the penalty period, the negative node will 

increase the reputation value of resource sharing but will not 

be able to gain benefits. In addition, negative nodes need to 

download the scarce resources specified by the system to 

maintain the resource balance. The system asks once per unit 

time, and the negative node can obtain 𝑎  time reduction 

reward at time 𝑇0 by downloading the highly scarce resources. 

That is, the penalty time becomes 𝑇𝑃 − 𝑇0. In this way, the 

negative nodes that download the scarce resources can be 

encouraged to provide services to users after the punishment 

ends. At the same time, the imbalance between the supply and 

demand of scarce resources can be alleviated. 

3. Node selection and income analysis 

3.1. Node selection strategy 

Assuming the number of edge nodes required to complete 

a video, transmission is 𝑊(𝑖, 𝑡), the number of nodes that can 

provide services in this area is 𝑁𝑆 . It is necessary to select 

nodes with better comprehensive conditions for service to 

ensure the QoS. We discuss the selection method of nodes in 

the following two cases: 

𝑵𝑺 ≥ 𝑵𝑹, the steps are as follows: 

Calculate the resource abundance 𝑊(𝑖, 𝑡) of each node. 

Count the number of nodes 𝑁𝐶   that satisfy 𝑊(𝑖, 𝑡) ≥
𝑊𝐿(𝑖, 𝑡). 

If 𝑁𝐶 ≥ 𝑁𝑅, calculate the reliability 𝐷(𝑖, 𝑡) of 𝑁𝐶  nodes. 

Select 𝑁𝑅 nodes with larger 𝐷(𝑖, 𝑡) as candidate nodes. 

If 𝑁𝐶 < 𝑁𝑅, all 𝑁𝐶  nodes are as candidate nodes. 

Calculate the reliability 𝐷(𝑖, 𝑡) of the remaining 𝑁𝑆 − 𝑁𝐶 

nodes. 

Select 𝑁𝑅 − 𝑁𝐶  nodes with larger 𝐷(𝑖, 𝑡)  as candidate 

nodes. 

𝑵𝑺 < 𝑵𝑹, the steps are as follows: 

Calculate the resource abundance 𝑊(𝑖, 𝑡) of each node. 

Count the number of nodes 𝑁𝐶   that satisfy 𝑊(𝑖, 𝑡) ≥
𝑊𝐿(𝑖, 𝑡). 

Take 𝑁𝑐 nodes that meet the conditions as candidate nodes. 

The remaining traffic is compensated by the central server. 

3.2. Income calculation 

The income after the node provides a service is determined 

by reliability, resource tightness, service quality, and other 

conditions. The income will be reduced if there is a problem 

with the service quality in the video transmission process. 

Definition 1. The reduced income due to packet loss or 

offline service quality degradation is defined as 𝐼𝐿 , which can 

be calculated as follows:  

𝐼𝐿 = 𝐾𝑖𝑅𝑙𝑜𝑠𝑠

𝑃(𝑚, 𝑡)

𝑅𝑙𝑜𝑠𝑠

=
𝑁𝐹(𝑚, 𝑡)𝐾𝑖𝐾𝑏𝑅𝑙𝑜𝑠𝑠

𝑁𝐶(𝑚, 𝑡)
(8) 

𝐾𝑖   is the reverse yield coefficient, 𝑅𝑙𝑜𝑠𝑠 is the packet loss 

rate, and 𝑁𝑅  is the number of nodes providing services? 

Definition 2. The income obtained by a node by providing 

services is 𝐼𝑇 , which can be calculated as follows: 

𝐼𝑇 = 𝐷(𝑖, 𝑡)
𝑃(𝑚, 𝑡)

𝑁𝑅

𝐾𝑑 (9) 

𝐾𝑑 is the positive return coefficient? Therefore, the node 

should be paid as follows: 

𝐼 = 𝐼𝑇 − 𝐼𝐿 



 

47 

𝐼 =
𝑁𝐹(𝑚, 𝑡)𝐾𝑏

𝑁𝐶(𝑚, 𝑡)𝑁𝑅
((𝜀𝐵(𝑖, 𝑡) + 𝜔𝐻(𝑖, 𝑡))𝑅(𝑖, 𝑡)𝐾𝑑 − 𝐾𝑖𝑅𝑙𝑜𝑠𝑠) (10) 

4. BMBR game analysis 

According to the above incentive mechanism, edge node 

𝑖 is assumed to be selected as a candidate node. The reputation 

function for node 𝑖 to choose cooperation in a game can be 

calculated as follows: 

𝑅𝐶 = 𝑅(𝑖, 𝑡) + 𝑅+(𝑐𝑖 , 𝑚, 𝑡) − 𝐺(𝑖) 
= 𝑅(𝑖, 𝑡) + 𝑔(𝑐𝑖)𝑃(𝑚, 𝑡) − 𝑔 (11) 

In the process of resource interaction, the system is 

responsible for selecting the node that provides the resource, 

so the node cannot predict whether it will participate in the 

next game. Assuming that the probability that a node is 

required to contribute resources in a unit of time is  𝛬 , the 

mathematical expectation of the penalty time 𝑇𝐵  is 𝐸(𝑇𝐵) . 

Assuming that the probability that node 𝑖  chooses to 

download the scarce resource during the penalty period is 𝑝, 

the final penalty time is 𝐸(𝑇𝐵) − 𝑝𝑇0 . During the penalty 

time, the node will have 𝛬(𝐸(𝑇𝐵) − 𝑝𝑇0) opportunities to 

provide resources. Then the reputation function of node 𝑖 
refusing to cooperate in a game can be calculated as follows:  

( )0

( )
( ) [ ]

( )

F
P B

C

N m,t
R R i,t E T pT K

N m,t
= − −     (12) 

To eliminate the noncooperation motive of node 𝑖, first of 

all, it must be ensured that the reputation benefit of choosing 

cooperation is greater than the reputation benefit of choosing 

noncooperation, that is, 𝑅𝐶 > 𝑅𝑃, which can be obtained: 

0

( ) ( )
( ) ( ) ( [ ] )

1 ( ) ( )

i F F
B

i C C

r N m,t N m,t
R i,t K g R i,t E T pT K

r N m,t N m,t





 
+ −  − − 

+ 
 

( )0

( )
[ ]

1 ( )

i F
B

i C

r N m,t
E T pT K g

r N m,t





  
+ −    +  

   (13) 

Second, it is necessary to ensure that the price return of the 

resource when it chooses to cooperate is greater than 0, that 

is, 𝐼 > 0: 

( )( ) ( ) ( ) 0d i lossB i,t H i,t R i,t K K R + −   

( )( ) ( ) ( ) i loss

d

K R
B i,t H i,t R i,t

K
 +      (14) 

By setting reasonable 𝑇0, K, g, and 𝛼 values, according 

to the actual situation of system resource interaction, 

Equation (13) can be held constant, that is, 𝑅𝐶 > 𝑅𝑃 can be 

held constant. By setting a reasonable 𝜀, 𝜔, 𝐾𝑖, 𝐾𝑑, and the 

packet loss rate are within a reasonable range, Equation (14) 

can be established constantly, that is, 𝐼> 0 can be established 

constantly. The cooperation strategy that satisfies both the 

increase of reputation of edge nodes and the increase of 

income is the best strategy for node selection, and nodes will 

tend to choose cooperation. 

5. Experiment analysis 

5.1. Experiment environment 

Use Python to build an environment for simulation 

experiments. Initially, set 100 video resources and 400 nodes, 

including 200 positive nodes (The probability of choosing 

cooperation is 80%) and 200 negative nodes (The probability 

of choosing cooperation is 20%). Initialization node 

bandwidth and storage occupancy are randomly distributed 

between 0-50%. Finally, the entire simulation time is 1500s, 

totaling 1500 resource interactions. 

5.2. Experimental results 

The experiment is divided into the following three parts: 

BMBR distinguishes between positive nodes and    

negative nodes 

Figures 1(a) and 1(b) show the reputation value distribution 

of positive nodes and negative nodes in the distributed edge 

computing system without incentive mechanism and BMBR, 

respectively. Without an incentive mechanism, the system 

does not treat the two differently. Figure 1(a) shows that the 

reputation value distribution of positive and negative nodes is 

not much different. Comparing Figure 1(b) with figure 1(a), 

we can see that the reputation value of positive nodes can be 

concentrated in the range of [12, 17] by using BMBR. In 

contrast, the reputation value of negative nodes can be 

concentrated in the range of [4, 10]. The experimental results 

show that using the incentive mechanism and the system can 

effectively distinguish between positive and negative nodes. 

 
(a)           (b) 

Figure 1. Comparison of the distribution of positive nodes and 
negative nodes under no incentive and BMBR 

The influence of BMBR on the reliability and income of 

edge nodes 

Figure 2(a) shows the change in node reliability with 

increased service time in the distributed edge computing 

system without incentive mechanism and BMBR. It can be 

seen that without an incentive mechanism, the reliability of 

the node does not change significantly with the increase in 

service time, and the reliability of the node has been clearly 

distinguished under the action of BMBR. Figure 2(b) shows 

that, in the absence of an incentive mechanism, the income 

from the service provided by the node is not affected by the 

reputation value, which is concentrated in the range of [40, 

60], and the node with a larger reputation value cannot obtain 

higher income. Under the action of BMBR, the income from 

node service has been significantly improved with the 

increased reputation value, and the enthusiasm for node 

resource contribution has increased. 

 
(a)           (b) 

Figure 2. Comparison of changes in node reliability and income 
under BMBR and no incentive mechanism 

(3) The influence of BMBR on the fluctuation of resource 

price.  

The BMBR proposed in this paper adds the concept of 

resource price, which is used to measure the scarcity of 

resources. At the same time, nodes in the penalty period can 

shorten the penalty time by downloading resources in short 



 

48 

supply, increasing the number of resources in short supply, 

balancing the price of resources, and ensuring the rational 

distribution of resources. In addition, the resource price set in 

this paper also has the characteristics of real-time and 

dynamic self-adjustment, which changes with the tightness of 

resources in different periods. Figure 3 shows the price 

fluctuation variance of each resource within 1500s without 

the incentive mechanism and BMBR. It can be seen from the 

figure that after adding BMBR, the price fluctuation of each 

resource is significantly reduced. The smaller the price 

fluctuation of resources, the higher the balance between 

supply and demand of resources. Therefore, the incentive 

mechanism can effectively control the balance of the number 

of resources according to the demand situation. 

 

Figure 3. Comparison of variance of resource price fluctuation 

6. Conclusions 

To promote home IoT devices to actively and reliably share 

idle resources and ensure the reasonable distribution of video 

resources, the author proposes an incentive mechanism based 

on behavior management and balance of resources to manage 

edge devices. The experimental results show that using 

BMBR is significantly better than no incentive mechanism. 

BMBR improves the enthusiasm for edge network resources 

sharing by distinguishing between positive devices and 

negative devices and establishes reasonable reward and 

punishment measures while maintaining the balance between 

the supply and demand of system resources. 

The BMBR model proposed in this paper needs to run on a 

centralized platform. Home IoT devices can contribute idle 

resources after accessing the platform by deploying the 

relevant environment. All requests and processing need to go 

through platform, which the following three problems will 

accompany: 

(1) The platform must process the selection of transmission 

nodes, information recording, behavior management, and 

income distribution, and the centralized platform is under too 

much pressure. 

(2) In the process of video distribution, the information of 

IoT devices is easy to leak, and security cannot be guaranteed. 

(3) The centralized platform will charge a certain service 

fee and cannot maximize the interests of users and resource 

providers. 

The decentralization of blockchain and the programmabil-

ity of smart contracts provide ideas for solving the above 

problems. Blockchain provides a decentralized network 

through which home IoT devices can directly establish 

connections with video requesting users. Smart contracts 

automatically perform node selection, reputation calculation, 

and income distribution. Therefore, the focus of the following 

research is how to design smart contracts according to the 

requirements based on this paper. 

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