CN110061862B - Fairness-based distributed multi-task crowd sensing method in dense network - Google Patents

Abstract

The invention provides a distributed multi-task crowd-sourcing perception method based on fairness in a dense network, which describes the relationship between the reward obtained by each user and the cost by introducing a reward rate RoR, adopts a reward distribution algorithm CRA based on consistency and applicable to a density network with topological limitation, distributes the reward of each task according to the corresponding cost of each user, leads all the reward rates RoR of the users to be consistent, and each user can update the cost of the user to the task and the resource distribution obtained from a server by utilizing the information of neighbor users participating in the task, leads each user to obtain the corresponding reward according to the contribution of the user, thereby ensuring the fairness and effectiveness of the system. The method of the invention can ensure the fairness and effectiveness of the income obtained by each user and the rapid convergence of the system, and realize the fairness of the reward distribution in the distributed multi-task system.

Description

Fairness-based distributed multi-task crowd sensing method in dense network

Technical Field

The invention relates to the field of wireless communication and mobile sensing, in particular to a distributed multi-task crowd sensing method based on fairness in a dense network.

Background

In recent years, with the continuous development of the internet of things technology, especially the popularization of personal mobile terminals such as smart phones, tablet computers, vehicle-mounted sensing devices and the like, the development of large-scale and complex social sensing tasks which are difficult to realize only by individuals is promoted. Crowd Sensing (Crowd Sensing) is to use the mobile device of a common user as a basic Sensing unit by combining crowdsourcing and mobile Sensing, and to form a Crowd Sensing network through network communication, thereby realizing Sensing task distribution and Sensing data collection and completing large-scale and complex social Sensing tasks. Therefore, the mobile crowd sensing has wide application prospect and can be applied to the fields of environment monitoring, traffic detection, city management, social networks and the like.

Compared with the traditional data collection and perception method, the crowd sensing method has the advantages that no additional hardware equipment is needed, and data collection can be carried out only by a mobile terminal of a common user, so that the method has the advantages of low network deployment cost, easiness in maintenance, good system expandability and the like. However, while the user sends the collected data, the user also consumes its own resources, such as power, bandwidth, computing and storing resources, and risks exposing its own location and other private information. Therefore, it is necessary to provide the user with a reasonable reward or compensation to make up for the loss of the user in sending the collected data. How to design a reasonable reward mechanism is a first problem to be faced when the crowd sensing system operates.

Currently, most of the excitation mechanisms are mainly classified into the following three categories: entertainment, services, and money.

Some systems convert crowd sensing tasks into sensing games, which, by designing interesting games, allow users to participate and contribute to their mobile computing or sensing capabilities. For example, Barkhuus et al have devised a mobile phone treasure hunt game to map WiFi coverage in a given area. Players participate in the game by carrying mobile devices with GPS or WiFi. They need to pick up virtual coins scattered in different areas in the game and then upload the coins to the server to obtain corresponding game points. The player is encouraged to find areas with stronger WiFi coverage because better network connections will have a greater probability of successfully uploading the collected gold. To address the same problem, Bell et al devised a location-based game named Feeding Yoshi, which requires players to search for various open and closed WiFi hotspots. Wherein the virtual fruit represents an open hot spot and the virtual pet, called Yoshi, represents a closed hot spot. To win more points, players need to search as much fruit as possible and go to Yoshi for feeding. The process of finding the hot spot clearly draws the WiFi coverage in the designated area and uploads the WiFi coverage to the server.

The second type of service incentive mechanism is based on the principle of mutual benefit and reciprocity. The consumer of the service is also the provider of the service. In other words, if a user wants to benefit from the services provided by the system, he must also contribute to the system. Traffic supervision is a typical example. When a user drives a vehicle to run on a road, if real-time traffic data is uploaded to a cloud server through a network, the user is a provider at the moment. The server processes the information provided by all the providers and generates a real-time traffic state, thereby better serving each user (provider).

The third, also currently the most common, is a money-based incentive mechanism. The system stimulates the user to upload more and better information by providing the information uploader with a monetary reward. Rivest and Shamir attempt to obtain usage information for the web page content by giving the user some amount of money to encourage the user to visit the web site. With the popularity of online music and online applications, reward mechanisms are also applied to these areas. Amazon turkish robotics also applies a monetary incentive mechanism to task fulfillment. For humans, issuing a request is an easy task to accomplish, but for computers is difficult, and the worker undertaking the task is paid an additional amount.

At present, the relationship between the server and the user is mainly processed by a method of game theory, and particularly, a Stackelberg model is adopted. Most of the work is based on a centralized strategy. Since the server must collect all the user's information, it is easy to create computational/communication load problems and may result in a single point of failure. Therefore, a distributed strategy is urgently needed to deal with the crowd sensing problem. Furthermore, the fairness property, i.e. the reward allocated to each participant matches its cost, making it feel a fair treatment, is another key factor that motivates users to join the cluster-aware task.

Disclosure of Invention

Based on the problems in the prior art, the invention provides a distributed multi-task crowd-sourcing perception method based on fairness in a dense network, which aims to realize fairness of user reward distribution under the multi-task condition and ensure that each user can realize the goal that reward is in direct proportion to cost only based on information of neighbor users by utilizing the principle of multi-agent consistency. The strategy provides a reward distribution scheme based on consistency, and is mainly applied to density networks with topological limitation.

In order to achieve the purpose of the invention, the invention adopts the following technical scheme:

the invention provides a distributed multi-task crowd-sourcing perception method based on fairness in a dense network, which has the main idea that the concept of fairness is provided, the relationship between the reward obtained by each user and the cost is described by introducing the reward Rate (Return of Rate), which is called RoR for short, and an incentive mechanism capable of working in a distributed multi-task system is provided. The invention provides a Reward distribution algorithm (Consensus based rewarded Allocation) which is applicable to a density network with topological limitation, and is abbreviated as CRA, and the Reward of each task is distributed according to the corresponding cost of each user, so that all Reward rates RoR of the users tend to be consistent.

The method comprises the following steps:

s1: establishing a mobile crowdsourcing system model:

the server distributes the task set Γ to n users, each user may participate in and execute multiple tasks simultaneously to obtain a reward, and each user i has a cost C due to perception_i，i＝1，…，n；

The task set Γ has m tasks, each task l having a total reward R^l,l＝1,…,m；

Definition of

Representing the rate of remuneration RoR gained by user i to complete task l at time k,

wherein

And

respectively represent the corresponding remuneration and cost when

When the user is in the first task, the ith user can not execute the ith task, and a corresponding RoR value does not exist;

s2: interaction between the server and the user:

s21, the server sends the task set gamma to all user nodes, and obtains the number N of users corresponding to each task^l；

S22, the server returns R^l/N^lL ═ 1, …, m is sent to all user nodes;

s23, each user exchanges information through local communication;

s24, after the local communication is completed, all users upload the collected data to the server;

s3: interaction among users is as follows:

s31, adopting CRA algorithm to iterate, each time, one user obtains the RoR information of its neighbor users;

s32, each user updates the reward value obtained by the user participating in the task according to the adjacent user and the own RoR information thereof so as to enable the RoR value of each task to be equal;

s33, all users redistribute their own cost to all tasks they participate in, and the RoR value of each task they participate in is equal;

s34, repeating the steps S31 to S33 to make the RoR value of each task participated by all users consistent.

Preferably, the neighbor user of the user i is a user capable of obtaining the information of the user i and sharing the same task l with the user i; the same user i has a neighbor set with the number equal to that of tasks participated in, and a connection path exists between any two users i and a user j.

Preferably, the connection relationship between the user nodes satisfies the following condition:

1) for a given task, when the in-degree and the out-degree are equal, a user reaches balance on the task, and when all users on the task achieve the fairness, the topology reaches balance;

2) when all users perform the CRA algorithm in different tasks and the topology of each task is balanced, their ROR values are asymptotically synchronized.

Compared with the prior art, the invention has the following beneficial effects:

the invention provides a concept of reward rate (RoR) to describe the relationship between the reward obtained by each user and the cost, thus realizing the fairness of reward distribution in the distributed multi-task system; meanwhile, the incentive mechanism provided by the invention can work in a distributed multi-task system, and a crowd sensing network is constructed by utilizing the mobile equipment of the user to complete a large-scale and complex social sensing task; on the basis of the network topology, the proposed resource allocation scheme may be applicable to dense networks with topological constraints.

Drawings

Fig. 1 shows a system model structure according to the present invention.

FIG. 2 is a detailed flowchart of the CRA algorithm of the present invention;

fig. 3 is a simulation diagram of a CRA algorithm execution result in an embodiment of the present invention.

Fig. 4 is a graph of average convergence times obtained by using the CRA algorithm when the number of users and tasks changes in the embodiment of the present invention.

Detailed Description

The following describes embodiments of the present invention in detail with reference to the accompanying drawings.

The distributed multi-task crowd-sourcing perception method based on fairness in the dense network firstly establishes the following crowd-sourcing system model according to the characteristics of a distributed multi-task system, as shown in fig. 1:

the mobile crowdsourcing system consists of a server and a user. The server functions to aggregate environmental monitoring data, network coverage data, etc. requirements and collect data from the user's handset. Assume that task set Γ has m tasks, each with a total reward R^lAnd l is 1 … m. The server distributes the set of tasks to n users, each of which may participate and perform multiple tasks simultaneously to obtain a reward. And each user has a cost C due to perception_i. For the sake of analysis, it is assumed that there is sufficient reward,so that all users are willing to undertake the data collection task. In order to describe the network topology of users and tasks, the neighbors of user i are defined as users who can obtain the information of user i and share the same task l with user i. It should be noted that for a user i, it has a set of neighbors equal to the number of tasks it participates in. The paths between two users i and j represent different connection sequences, and for simplicity of analysis, the present invention assumes that for any two users i and j, there is a connection path between each other.

In a TDMA MAC protocol, time is divided into a number of equal-length time slots, each time slot being long enough to ensure that information can be successfully transmitted. Each user i has a given cost C_iI-1, …, n, may participate in several tasks in a task set. According to the general principles of fairness, the more contributions, the greater the reward. Therefore, we propose

Indicating the rate of remuneration RoR gained by user i to complete task l at time k, i.e.

Wherein

And

representing the corresponding reward and cost, respectively. In special cases

I.e. the ith user cannot perform the ith task and there is no corresponding RoR value.

Secondly, the server and the user interact with each other, and the interaction process is as follows:

s21, the server sends the task set gamma to all user nodes, and obtains the number N of users corresponding to each task I^l。

S22, the server returns R^l/N^l,l＝1…, m is sent to all user nodes.

S23, each user exchanges information by local communication.

S24, after completing the local communication, all users upload the collected data to the server.

Thirdly, the users interact with each other, and for a density network with topological limitation, the invention provides a reward distribution scheme CRA algorithm based on consistency, and the specific steps are as shown in FIG. 2, and the method comprises the following steps:

and S31, each user can only acquire the RoR information of the neighbor users at each updating.

And S32, each user updates the reward value obtained by the user participating in the task according to the adjacent user and the own RoR information so as to enable the RoR value of each task to be equal.

S33, all users redistribute their own costs to all tasks they participate in, so that the RoR values for each task they participate in are all equal.

S34, repeating S31-S33 for multiple times to reach the goal that RoR values of all users are consistent.

In order to ensure the fairness of all users, it is necessary to ensure that the connection relationship between user nodes meets certain conditions:

1) for a given task, a user is balanced on the task if and only if its in-degree and out-degree are equal. The topology is called balanced only if all users on the task achieve fairness.

2) When all users perform the CRA algorithm in different tasks and the topology of each task is balanced, their ROR values are asymptotically synchronized.

In order to evaluate the proposed algorithm, the present invention verifies the convergence and convergence time of the theoretical algorithm through simulation experiments.

Assume that there are a total of 10 users n and 4 tasks m. The total reward of each task is R¹＝1000,R²＝500,R³＝700,R⁴The costs for 10 users, 400, are 12, 5, 6, 15, 6, 11, 4,10, 8 and 4 (for ease of analysis we omit units). In this simulation, the relationship between tasks and users is shown in table 1, where the symbol indicates the corresponding task in which each user participates.

Table 1 simulation setup (#representsparticipation)

	Task 1	Task 2	Task 3	Task 4
					User 1	*	*
User 2	*	*	*
					User 3		*		*
User 4	*	*	*	*
					User 5	*		*
User 6	*		*	*
					User 7		*		*
User 8	*	*		*
					User 9		*	*	*
User 10	*			*

The results of the experiment were evaluated as follows:

1. convergence property

In the case of dense networks with topological constraints, the simulation results using the CRA algorithm are shown in fig. 3. According to the results, in the case of a dense network with topology limitation, the RoR values in the allocation scheme are synchronized, and fairness of reward allocation in the distributed multitask system is realized. When the CRA algorithm is applied, the convergence speed is faster and the convergence time is shorter. This is because in the CRA algorithm, all nodes are updated at the same time each time and converge toward the direction of reaching consistency, all parameters are updated at the same time, convergence can be reached more quickly, and all RoR values in each simulation converge consistently step by step, so that fairness in the system is achieved.

2. Time of convergence

From the simulation results, it can be seen that when the CRA algorithm is applied, the convergence speed is fast and the convergence time is short. This is because in the CRA algorithm, all nodes are updated at the same time each time and converge in the direction of reaching consistency, and all parameters are updated at the same time, so that convergence is fast.

Meanwhile, in order to explore the relationship between the convergence time and the number of tasks and the number of users, the invention discusses the convergence time change condition of the CRA algorithm when the number of tasks is from 2 to 5 and the number of participating users is from 2 to 15. The total reward of each task is assumed to be a random value of 1-1000, the cost value of each user is a random value of 2-12, and a connection path exists between each user and all tasks. For each case of the number of users and the number of tasks, 1000 iterations are performed, respectively, and the average time required to achieve convergence is shown in fig. 4. According to the results, the CRA algorithm is adopted to achieve fast convergence, the convergence can be achieved by about 5 to 6 times on average, and the more the number of users and the number of tasks are, the faster the RoR value achieves the convergence speed, so that the requirements of a larger task allocation system can be met.

While the preferred embodiments and principles of this invention have been described in detail, it will be apparent to those skilled in the art that variations may be made in the embodiments based on the teachings of the invention and such variations are considered to be within the scope of the invention.

Claims (2)

1. A distributed multi-task crowd-sourcing perception method based on fairness in a dense network is characterized in that: the method comprises the following steps:

s1: establishing a mobile crowdsourcing system model:

the server distributes the task set Γ to n users, each user may participate in and execute multiple tasks simultaneously to obtain a reward, and each user i has a cost C due to perception_i，i＝1，…，n；

The task set Γ has m tasks, each task l having a total reward R^l,l＝1,…,m；

Definition of

Representing the rate of remuneration gained by user i to complete task l at time k,

wherein

And

respectively represent the corresponding remuneration and cost when

When the user is in the first task, the ith user can not execute the ith task, and a corresponding reward rate value does not exist;

s2: interaction between the server and the user:

s21, the server sends the task set gamma to all user nodes, and obtains the number N of users corresponding to each task^l；

S22, the server returns R^l/N^lL ═ 1, …, m is sent to all user nodes;

s23, each user exchanges information through local communication;

s24, after the local communication is completed, all users upload the collected data to the server;

s3: interaction among users is as follows: iterating by adopting a reward distribution scheme algorithm based on consistency; the reward distribution scheme algorithm based on consistency is specifically as follows:

s31, each time of iteration, one user obtains the reward rate information of the neighbor user; the neighbor users of the users are users which can obtain the information of the users and share the same task with the users; the neighbor users of the user i are users which can obtain the information of the user i and share the same task l with the user i; the same user i has a neighbor set with the number equal to the number of tasks participated in, and a connection path exists between any two users i and a user j; s32, each user updates the reward value obtained by the user participating in the task according to the reward rate information of the neighbor user and the user;

s33, all users redistribute their own cost to all tasks they participate in;

and S34, judging whether the reward values of all users are consistent, if not, returning to the step S31, and if so, ensuring that the reward values of all the tasks participated by all the users are consistent, thereby realizing the fairness of the system.

2. The method of claim 1, wherein:

the connection relation between the user nodes meets the following conditions:

1) for a given task, when the in-degree and the out-degree are equal, a user reaches balance on the task, and when all users on the task achieve the fairness, the topology reaches balance;

2) when all users execute the consistency-based reward distribution scheme algorithm in different tasks and the topology of each task reaches equilibrium, their reward rate values are asymptotically synchronized.

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