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LCBPA: twostage task allocation algorithm for highdimension data collecting in mobile crowd sensing network
EURASIP Journal on Wireless Communications and Networking volume 2019, Article number: 281 (2019)
Abstract
Mobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensorequipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting highdimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of highdimensional data collection in mobile crowd sensing network. The lowcost and balanceparticipating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by tradingoff between them. The LCBPA performs in two stages: in the first stage, it divides the highdimensional data into finegrained and smaller dimensional data, that is, dividing an mdimension data collection task into k subtask by Kmeans, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform subtasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.
Introduction
Background
The term mobile crowd sensing (MCS) has been coined by Ganti et al. [1] in 2011, which introduced a new data collection method by leveraging mobile terminals such as smart phones. Compared with traditional data collection technologies, MSC has some unique characteristics. First, the mobile devices have more computing, communication, and storage capability than moteclass sensors. Second, by leveraging the mobility of the mobile terminal users, the deployment cost of specialized sensing infrastructure for largescale data collection applications would be largely reduced. Currently, MSC has been widely used in many applications, including environmental monitoring [2], transportation [3], social behavior analysis [4], healthcare [5], and others [6,7,8,9], which demonstrates that MCS is a useful solution for largescale data collection applications. In general, the MCS process always consists of four steps: assigning sensing tasks to mobile terminals, executing the task on the mobile terminals, collecting, and processing sensed results from the crowd [10,11,12,13,14]. Obviously, assigning sensing tasks to mobile terminals is the primary issue to deal with the following steps, which is also the main issue in this paper.
Related work
Recently, a lot of efforts have been focused on task allocation [15,16,17] and generally can be divided into two categories: rulebased task allocation method [18,19,20] and mapbased task allocation method [21,22,23,24]. The rulebased task allocation mainly allocates task according to each node’s sensing capability, such as position and power of perception. By dividing node characteristics into different task groups, the system assigns the corresponding task to each task group. In Ref [18], a task assignment algorithm dual task assigner (DTA) has been designed. The DTA has leveraged learn weights to evaluate the sensing capability of participations for each task, and the server allocates tasks according to the sensing capability level to maximize the benefit. Angelopoulos et al. [19] have selected appropriate users by selecting the optimal characteristics of nodes (quotation and quality) to allocate tasks, achieving the equalization between cost and task completion ratio. Shibo et al. [20] have considered the number of mobile nodes, the number of tasks and the task completion time, and proposed the optimal scheduling algorithm for each user in the dispatching area to reduce the sensing cost. Secondly, mapbased task allocation method has been used to combine the geographical locations to task types to build a task map, and participations can obtain the content and location of a task by downloading the task map. When participations arrive in a specific task location, they can form a task group by selforganizing, and coordinate with each other to accomplish the sensing task. Dang et al. [21] have proposed the mapbased mobile sensing task allocation framework named Zoom for the first time. Based on Zoom, Huy et al. [22] have studied the assignment of pixel values in the task map, and proposed a scalable reuse method for task map pixel values. In Ref [23], a rastervector mixed task distribution method for mobile crowd sensing system has been proposed, which raster the sensing area first, and encodes the task information to improve the information utilization and reduce the data redundancy. A vector task map has also been proposed in Ref [24], which can substantially reduce the amount of data in the task map by gradually distributing the sensing tasks.
In summary, the above methods for task allocation always believe that the mobile terminals can handle all types of required data. They seldom take into consideration the fact that a sensing node needs to carry out a data sensing task that beyond to its sensing capability. For example, to construct a radio environment map to monitor the wireless resource, the application requires more than twenty types of data: data collection time, GPS information, and the device identification, and some 2G/3G/4G/WiFi network data. Usually, few nodes can collect all types of the data because of their limited sensing capability, and most of the nodes can perform a fraction of types of the sensing data. The problem is that the sensing node with lower sensing capability compared with highdimensional data is difficult, and we defined it as highdimension data sensing problem. In this situation, the first efficient step is to divide the highdimension data sensing task into multiple lower dimensional tasks. The second step is to assign different subtasks to nodes with different sensing capability, and the optimal goal is to minimize the total cost, as well as improving the task completion ratio.
Methods and contributions
This paper proposes an efficient data collection mechanism based on twostage task allocation named lowcost and balanceparticipating algorithm (LCBPA), and the contributions are as follows:
We design a twostage task allocation algorithm LCBPA for highdimensional data collection in MCS network. In the first stage, in order to divide a high mdimension data collection task into kmultiple subtasks with lower dimensional data, we leverage the Kmeans method to make partition based on the similarity of subtasks. In the second stage, we allocate multiple nodes with different sensing capability with one or more subtasks based on certain optimal conditions.
To minimize the total sensing cost and avoid some nodes to be allocated with too many subtasks while some nodes have only few subtasks, we introduce the equality parameter λi to adjust the node participation probability to prevent the inequality problem stated above. We also make our node selection policy by tradingoff between minimizing the total cost and maximizing the equality λi through the weight parameter α.
We also analyze the influence of the variation of subtask number k and tradeoff weight parameter α to the task completion ratio, total sensing cost, and node subtask degree distribution in different scale of networks. Simulation results show that, compared with nontaskdivision methods, our LCBPA can reduce the total cost, and it can also make a more even subtask degree distribution among sensing nodes.
The rest of this paper is organized as follows: System model and task allocation problem are discussed in Section 2. The detailed design of our proposed LCBPA algorithms is discussed in Section 3. The performance of our algorithm is evaluated in Section 4. Section 5 illustrates the conclusions and our future work.
Problem description and challenges
Problem description
As shown in Fig. 1, there is a sensing task which refers to mdimension data collection, and there are N sensing nodes in a minimum sensing unit. It would be difficult for a node with limited sensing capability to finish mdimension data collection in a particular place, for the node can only handle partial types of data sensing. In order to deal with the highdimensional data collection, it needs to divide the highdimensional data collection task into k subtask with lower data dimension to reduce the load of a sensing node. For each subtask, it corresponds to a sensing group with number C sensing nodes. The problem of task allocation is transformed into dividing the mdimension data sensing task into k subtasks, and then for each subtask, choosing enough sensing nodes to form a sensing group to finish the data collection.
For simplicity, we make assumptions as follows: first, the data types contained in each subtask will not overlap and the sum of all subtasks’ dimension is m; second, each subtask requires the same minimum number of sensing nodes as C; third, in each group, the node number C can cover the whole sensing unit, and we will not consider the location of a node. As is shown in Fig. 1, we assume that all the sensing nodes in each group will cover the minimum sensing area.
Challenges in task allocation
We propose a task partition model to solve the problem of the highdimensional data when we collecting data from a single node. Through this method, the data dimension is reduced, and multiple sensing nodes that work together can accomplish highdimensional data collection tasks. Challenges for this highdimensional data collection are as follows:
 (1)
How to improve the task completion ratio. In conventional methods, one node has to sense all types of required data, and this may beyond its sensing capability, which will reduce the task completion ratio. In the MCS, by dividing a highdimensional data into lower dimensions, a node only needs to collect partial types in all of the data types.
 (2)
How to minimize the total sensing cost as much as possible. For a largescale complex sensing task, it requires a significant number of nodes to collect data, and the cost of data collection is enormous for largescale tasks. It needs to select nodes for a suitable subtask to minimize the cost of data collection under the premise of guaranteeing the task completion.
 (3)
How to equalize the participation probability of each node, that is to say, avoiding that some nodes join in a significant number of subtasks while others only join in very few subtasks. The current task allocation methods are always aiming to select the node that can minimize the total cost, which may result in the fact that some nodes may be overloaded to deal with a great number of subtasks, while some nodes only perform small number of subtasks. It is difficult to minimize the whole cost of the system while making subtasks distributed among participants evenly.
The proposed algorithm of LCBPA
Model definition
Assuming that a system requires mobile nodes (mobile terminals) to finish an mdimension data collection task, and for simplicity, one dimension corresponds to one data type. The required data type set is A, which is a matrix with m rows and N columns. Let A = {G_{1}, G_{2}, ⋯, G_{m}} A = {G_{1}, G_{2}, …, G_{m}}, and each G_{i} denotes a data type in the mdimension data task collection. For each vector G_{i}, the element g_{ij} represents that the data type G_{i} can be collected by node j, and j is the node number, (j ∈ (1, N)). If g_{ij} = 1, it means that the ith dimensional data can be collected by the node j, else g_{ij} = 0.
The divided subtask set is S, and S = {s_{1}, s_{2}, …, s_{k}},where k is the number of subtask. For ∀s_{i}, ( 1 ≤ i ≤ k ), we assume that the data types contained in each subtask are not overlapped and the sum of all subtasks’ dimension is m.
Each node corresponds to a triple ψ_{i} = {i, T_{i}, V_{i}}, where i is the node number, and i ∈ (1, N). T_{i} is the task which can be sensed by user i, which is a kdimension vector. For each element t_{ij} in T_{i}, if t_{ij} = 0, it means that the node i does not have the capability to sense the data type required in the subtask s_{j}, while, if t_{ij} = 1, it means that node i can perform the subtask s_{j}. V_{i} is the cost of the sensing task, which is also a k dimensional vector. For each element v_{ij}, the value is the cost of node i for sensing subtask s_{j}.
The total cost for a mobile crowd sensing system to finish the data collection task is
The goal of our system is to find a matrix T^{∗}, which will minimize W, that is
The tradeoff between minimizing the total cost and λi is performed as follows:
Here, α denotes weight factor (0 ≤ α ≤ 1), which can be used to make the tradeoff between cost constraints and node participation on the probability of node selection [25]. Let w_{j} denote the total quoted price that all the nodes can sense G_{j}, then \( {w}_j={\sum}_{i=1}^N{v}_{ij} \). Let r_{j} denote the number of nodes that can perform subtask, and \( {r}_j={\sum}_{i=1}^N{t}_{ij} \).
As stated above, in order to avoid some nodes joining in too many subtasks while others only join in few subtasks, we introduce the adjustment coefficient λi [26]. When a node has already been selected in the previous subtask allocation rounds, this parameter may reduce the probability that a node being selected. When we select a node to participate in a subtask, the system will make a tradeoff between the total cost and the adjustment coefficient λi. We compute λi by Eq. (6):
where t_{ih} represents whether the node i was assigned with the subtask s_{h}, and t_{ih} = 1 means that the node i is assigned to subtask h, else t_{ih} = 0. r_{h} denotes the node number that can perform the subtask s_{h}, which can be computed by Eq. (7):
The twostage task allocation algorithm of LCBPA
The detail steps for the system to select sensing nodes are divided into two stages:
Stage one: Dividing an mdimensional data collection task into k subtasks by Kmeans method. According to the sensing capability attributes (the data type a node can sense) of each node, we need to divided the whole sensing task into k subtasks. Here, we choose the Kmeans algorithm, which is a classical algorithm to solve the clustering problem, and it is simple and fast. When processing large data sets, the kmeans algorithm maintains scalability and efficiency. The general processes are as follows: First, chose k data types from data type set A as the initial clusters center randomly. Second, for the remaining data types in A, assign them to the nearest k initial clusters according to the distance D. When calculating the cluster center of each new cluster, keep repeating the process until the standard measure function D is converged.
The standard measure function D for converging clusters is
where the distance D is computed by the Euclidean distance d_{ij} from G_{i} and G_{j}. The shorter the distance between them, the higher similarity between those two data types, and the higher probability those two data types will be divided into the same subtask.
The correspondent algorithm for the first stage is as follows:
However, dividing the whole sensing task into k subtasks is only a basic step in our proposal, we need further to adjust the mapping between participating nodes and subtasks by other considerations, such as minimizing the total cost and avoiding a node being assigned too many subtasks in the following step.
Stage two: Assigning different nodes with different sensing capability to a suitable subtask by the tradeoff between minimizing the total cost and node participation equality. After the first data type clustering stage, the system will allocate node to each subtask. For each subtask s_{j} ∈ S, it will judge whether a node can perform the subtask and calculate the adjustment factor λi according to formula 6. It will also calculate the probability p_{ij} to decide whether a node can be selected in a subtask by Eq. 5. The system will sort the probabilities p_{ij} of all the nodes from high to low, and select the topC nodes for subtask s_{j}. The correspondent algorithm for the second stage is stated in algorithm 2:
Results and discussion
Simulation setting
In this section, we will evaluate the performance of our proposed LCBPA scheme by simulation. Our simulation environment is Ubuntu 14.04. We compare the performance of our LCBPA with nontaskdivision (NTD) method, which is the method that nodes participate highdimension data collection directly. The simulation parameters are set as follows: the sensing area is 600 × 600 m^{2}, and the sensing radius of each node is 25 m. The network size (sensing node number) varies from 50 to 500, the data dimension is 50, and the subtask number ranges from 10 to 50, and the tradeoff weight parameter α varies from 0.1 to 0.9, the minimum node number required in a minimum sensing unit is 50.
We analyze the performance of the LCBPA algorithm in the following parameters: (1) Task completion ratio η; (2) The total cost of data collection W; (3) The equality of node participation u_{i}. The parameters η and u_{i} are defined as follows:
 (1)
The task completion ratio η is defined as the number of the whole mdimension tasks that can be completed, that is
where
 (2)
μ_{i} denotes the participation equality of node i, which is the percentage of the total number of subtasks assigned to each node:
Simulation results
Comparison of task completion ratio
Figure 2 shows the comparison of the task completion ratio of our LCBPA with NTD method under different network scale. In this scenario, the number of subtask is 20, and α = 0.5. The simulation shows our LCBPA has a higher task completion ratio, and the ratio increases as the network node number increases, which is because the node number for participating each subtask is increased.
Figure 3 shows the comparison of the task completion ratio of our LCBPA with various subtask number k varies from 10 to 50 under different network scale. The result in Fig. 3 shows that the task completion rate is increasing as a highdimensional task is divided into more subtasks. Under the same network scale condition, the task completion ratio is increased when k is increased, which may be because the more detailed the division of the highdimension task, the higher probability a node is assigned to the right subtask, improving task completion ratio.
Figure 4 shows the comparison of the task completion ratio of our LCBPA with the weight parameter α varying from 0.1 to 0.9 under a different network scale. The result in Fig. 4 shows that the five lines are overlapped, which means that the task completion rate remains unchanged with different value of α. It may because that the total number of nodes that need to perform a subtask is more than C, which makes no difference on the task completion ratio.
Comparison of the total cost
Figure 5 shows the comparison of the total cost of data sensing in our LCBPA with NTD method under different network scale. In this scenario, the number of subtask is 20, and α = 0.5. The simulation result shows that our LCBPA has a lower total cost, and the total cost increases as the network scale is extended, which may be because the participating node number for each subtask is increased.
Figure 6 shows the comparison of the total cost of our LCBPA with different subtask number k varying from 10 to 50 under different network scale. The result in Fig. 6 shows that as the size of the network increases, the total cost also increases. Under the same network scale, the larger the number of the subtask k, the lower the total cost of the system, which may be because more subtask k makes different nodes being assigned to more different suitable subtasks, saving some unnecessary cost.
Figure 7 demonstrations the comparison of the total cost of our LCBPA at different network with tradeoff weight parameter α varying from 0.1 to 0.9 and at k = 20. The result in Fig. 7 shows that the total cost is increased as the network scale is higher. Under the same network scale, the larger the number of the subtask, the higher the total cost of the system because a large α value may take less consideration of minimizing the total cost, improving the total cost.
Comparison of the node participation equality under a different network scale
Figure 8 shows the comparison of the node participation equality of our LCBPA with NTD method under the same network scale. In this scenario, the subtask number is 20, and α = 0.5. The simulation shows that in our LCBPA, a larger proportion of nodes are allocated with a smaller proportion of subtasks, and the proportion of nodes to perform more subtasks are significantly reduced.
Figure 9 shows the comparison of the node participation equality of LCBPA with the subtask number k varying from 10 to 50 under same network scale. The result in Fig. 9 shows that the node participation becomes more concentrated when a highdimensional task is divided into more subtasks. It means that the proportion of nodes performing too many tasks will be reduced, and most of the nodes are assigned to an average amount of subtasks.
Figure 10 shows the comparison of the node participation equality of our LCBPA with different tradeoff weight parameter α which varies from 0.1 to 0.9. Figure 10 shows that the participation of nodes become more concentrated as α increased, which means that the proportion of nodes performing too many tasks will be reduced as α is increased, and most of the nodes are assigned to an average amount of subtasks.
Conclusion
This paper proposes a highdimension data collection algorithm LCBPA for mobile crowd sensing network. In particular, we introduce the twostage operation scheme to deal with the problem that a node with lower sensing capability confronted with the higher dimensional collection data. To evaluate our proposed scheme, we formulate the evaluation parameters, and we also calculate the task completion ratio, the total cost of the data, and the node participation equality. As a result, our scheme can effectively work when the network scale varies from 50 to 500, and the subtask number k varies from 10 to 50, with tradeoff weight parameter α varying from 0.1 to 0.9. However, there are also some limitations of our proposed schemes: (1) we only simulated our proposed method by simulations, not in the real sensing activities; (2) Our method can reduce the sensing cost and difficulty, while it is only works based on the assumption that each dimension of the data can be collected independently, which means that there is no correlation between different dimensions of the data, which may be not always the case. (3) When assigning each sensing task to different nodes, we do not consider the location and mobility of a node. (4) In practical, the value of the data will be elapsed as the time passed, which is also not considered in our proposed method. In the future work, we will evaluate hardwarebased experiments, and take the node mobility and location into consideration, and we will also consider the data value as one of the factors in task allocation.
Availability of data and materials
The datasets generated and analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
Abbreviations
 DTA:

Dual task assigner
 LCBPA:

Lowcost and balanceparticipating algorithm
 MCS:

Mobile crowd sensing
 NTD:

Nontaskdivision
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NZ proposed an idea and provided the guidance for deriving these expressions. JZ verified the correctness of the simulations cooperatively. BW improved the presentation of the draft and provided valuable suggestions. JX has great and distinct contribution to the writing and editing on the new revision version of this manuscript, and all the author agree to add him as the forth author. All authors read and approved the final manuscript.
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Zhou, N., Zhang, J., Wang, B. et al. LCBPA: twostage task allocation algorithm for highdimension data collecting in mobile crowd sensing network. J Wireless Com Network 2019, 281 (2019). https://doi.org/10.1186/s1363801916102
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Keywords
 Task allocation
 Mobile crowd sensing
 Highdimensional data collection