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Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

In recent years, the wireless traffic has grown heavily and this case leads to crowd wireless spectrum. According to the current policy that wireless spectrum assignment is fixed, only licensed users can utilize the licensed wireless spectrum. Even though the wireless spectrum is idle, unlicensed users cannot use the idle spectrum. Therefore, the current policy of spectrum assignment leads to low ratio of wireless spectrum utilization. To solve this problem, cognitive radio has recently emerged to improve wireless spectrum utilization [

With the development of mobile terminals such as smartphones and pads, a new paradigm called mobile crowd sensing and computing (MCSC) appears [

Inspired by MCSC, mobile terminals configured with sensors are leveraged to accomplish spectrum sensing task. In the same spirit, with the recent Federal Communications Commission (FCC) ruling that a geolocation database could be used by Secondary TV spectrum users to obtain the spectrum availability, it is assumed that there is a crowdsourcing-based fusion center (FC). FC assigns sensing task to mobile users and receives the sensing data from them. To incentivize mobile users to carry out sensing tasks, FC needs to provide monetary benefits. This way is called crowdsourcing.

In this paper, we propose the crowdsourcing paradigm to assign the spectrum sensing task to many mobile users. It is assumed that there is a crowdsourcing-based fusion center (FC). FC assigns the sensing task to mobile users. During the assignment process, we have considered some factors. At first, the remaining energy is very important to mobile users. Only when a mobile user has enough energy can the wireless spectrum be sensed. Then mobile users should be given incentives to carry out spectrum sensing. With a limited budget, FC may choose a subset of whole mobile users to carry out spectrum sensing. At last, the positions of mobile users also influence the sensing results. Considering these factors, we propose precise sensing effect function for the crowdsourcing-based sensing task assignment. And the objective function considers a local constraint. Then we prove that the sensing task assignment is NP-hard. Therefore, we design an optimal algorithm based on particle swarm optimization (PSO) to solve the problem. Simulation results show our proposed algorithm achieves higher performance than other algorithms.

In this paper, we study the problem of sensing task assignment. The main contributions of this paper are summarized below.

Considering the remaining energy of mobile users, budget constraint, and mobile users’ positions, we propose precise objective function with a local constraint. We define the local constraint which means the sensing effect of a channel in a location is not less than a threshold. Compared to other literatures, we aim to not only maximize global sensing effect but also satisfy the local sensing constraint. And we prove the sensing task assignment is NP-hard.

Since the sensing task assignment is NP-hard, we design an optimal algorithm based on particle swarm optimization (PSO) to solve the problem. To the best of our knowledge, there is no related work designing the PSO-based algorithm to solve sensing task assignment in cognitive radio networks.

Simulation results show our proposed algorithm achieves higher performance than other algorithms.

The rest of the paper is organized as follows. In Section

In cognitive radio networks, licensed users activity will decide whether the spectrum is idle or not [

There have been some related literatures about cooperative spectrum sensing. In wideband wireless system, users exchange their compressed sensing results. According to the sensing results, they estimate the spectrum states cooperatively [

The aforementioned literatures use centralized algorithms. There are some distributed methods about spectrum sensing. In [

It is assumed that there is a crowdsourcing-based fusion center (FC). FC assigns the sensing task to mobile users. Remaining energy and positions of mobile users, as well as limited budget, may influence the assignment process. Considering these constraints, we propose precise sensing effect function with a local constraint. Then we prove the sensing task assignment is NP-hard.

We assume that there are many locations needed to be sensed. In each location, there are many channels that needed sensing. By crowdsensing task assignment, we aim to maximize the sensing effect with a local constraint.

Let

To obtain optimized sensing effect, we aim to maximize the sensing effect function in (

There are some factors which should be considered as follows.

For the mobile users, the remaining energy should be considered at first. Only when one mobile user’s remaining energy is higher than the threshold could the mobile user carry out the task of spectrum sensing. Let

Let

Additionally, the incentive scheme allows FC to pay for the mobile users that try to sense channels. However, the cost of crowdsensing must be in the acceptable range. Let

The optimal object of crowdsensing task assignment can be described as

Figure

An example of crowdsensing task assignment.

The problem of crowdsensing task assignment is difficult to solve since we prove this problem is NP-hard. The reason is that the problem of crowdsensing task assignment is as hard as maximum coverage problem which is NP-hard [

The maximum coverage problem is described as follows: given a number

The problem of crowdsensing task assignment is NP-hard.

By showing a special case of crowdsensing task assignment is as hard as maximum coverage problem, we prove that the problem of crowdsensing task assignment is NP-hard.

The special case is described as follows: each mobile user has enough energy to carry out spectrum sensing, the local threshold

Let

The problem of crowdsensing task assignment is no easier than the special case. Therefore, the problem of crowdsensing task assignment is NP-hard.

Since the crowdsensing task assignment problem is NP-hard, we design the optimal algorithm based on particle swarm optimization (PSO) to solve this problem in this section. The PSO algorithm is good at NP-hard problem optimization [

In the PSO algorithm [

We design an optimal algorithm based on PSO to solve crowdsensing task assignment. According to PSO algorithm, each particle’s position represents a solution to the crowdsensing task assignment problem. It can be denoted by a matrix as follows.

When there are

We optimize the crowdsensing task assignment based on PSO algorithm (PSO-CTA). The optimized algorithm is described as follows. Initialize

For a mobile user, its remaining energy should be considered at first. If its remaining energy is higher than the threshold, the mobile user could carry out the task of spectrum sensing. Then it chooses a channel to sense randomly in its corresponding locations. All mobile users with enough energy choose channels like this. If the local constraint of sensing effect in (

Initialize

Based on the current matrix, the crowdsensing effect function of the particle is obtained following (

According to a particle’s historical best position _{1} denote the current matrix of a particle and_{2} and_{3} denote historical best solution of the particle and the global best solution, respectively. The merging matrix can be described as the combination of_{1},_{2}, and_{3}. Then we optimized the merging matrix as follows.

In the merging matrix, if a channel in a sublocation is sensed by multiple users, only one user with higher energy is reserved and other users are given up. That means only an element is set to one in the column vector of the merging matrix after optimization. If a user chooses different channels to sense in_{1},_{2}, and_{3}, there are more than elements set to one in the row vector of the merging matrix. Considering the global property of PSO, we optimize the row vectors of the merging matrix with specific probability decided by the parameters in (_{1} based on the probability _{2} based on the probability _{3} based on the probability

The proposed algorithm for crowdsensing task assignment problem is described in Algorithms

Objective function according to formula (

A local constraint

The number of mobile users

The number of locations

The number of channels

The number of sub-locations

The maximum cost

The maximal generation

Randomly generate each particle;

(

(

(

(

(

(

(

_{1} is derived;

_{id} (matrix _{2}) based on crowdsensing effect function;

_{3}) with the optimal

_{1}, _{2}, _{3};

The complexity of proposed PSO-CTA algorithm is computed as follows. The computation complexity is

In Line

When particles update their velocities and positions, the computation complexity is

The proposed PSO-CTA algorithm is evaluated by simulations. The average solution is obtained by running the algorithm 100 times. We compare our PSO-CTA algorithm with the algorithm in [

Figure

Crowdsensing effect function with 50 mobile users.

Figure

Crowdsensing effect function with 20 locations.

Figure

Average remaining energy with the number of spectrum sensing instances.

It is assumed that there are four channels and three locations which can be divided into three sublocations. The nonnegative weight is not identical for each channel. We set the weights equal to 0.3, 0.3, 0.3, and 0.1 for these four channels. Under the aforementioned conditions, the local sensing effect for the fourth channel (weight equaling 0.1) is shown in Figure

Local sensing effect with weight equaling 0.1 for three locations.

When there are 40 users, Figure

Local sensing effect with weight equaling 0.1 for 40 users.

For a large-scale region, this paper proposes the crowdsourcing method to assign the spectrum sensing task to many mobile users such as smartphones and pads. Considering some constraints such as remaining energy, locations, and costs of mobile users, we propose a sensing effect function with a local constraint and aim to maximize the sensing effect function. Since the problem of sensing task assignment is proved to be NP-hard, we design an optimal algorithm based on PSO to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This study is supported in part by National Natural Science Foundation of China (no. 61402270), Natural Science Foundation of Shandong Province, China (nos. BS2015DX003, ZR2014FQ009), Key Research and Development Program of Shandong Province, China (no. 2017GGX10142), and China Postdoctoral Science Foundation (no. 2014M561930).