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Pengyuan Cao, Xiaojun Zhu, "Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores", Wireless Communications and Mobile Computing, vol. 2018, Article ID 3830285, 12 pages, 2018. https://doi.org/10.1155/2018/3830285
Speeding Up Exact Algorithms for Maximizing Lifetime of WSNs Using Multiple Cores
Maximizing the lifetime of wireless sensor networks is NP-hard, and existing exact algorithms run in exponential time. These algorithms implicitly use only one CPU core. In this work, we propose to use multiple CPU cores to speed up the computation. The key is to decompose the problem into independent subproblems and then solve them on different cores simultaneously. We propose three decomposition approaches. Two of them are based on the notion that a tree does not contain cycles, and the third is based on the notion that, in any tree, a node has at most one parent. Simulations on an 8-core desktop computer show that our approach can speed up existing algorithms significantly.
In wireless sensor networks, each sensor node has only a limited amount of energy. When a node sends or receives messages, it consumes the corresponding amount of energy. Thus, the amount of traffic of a node influences how long the node can work, which in turn determines the lifetime of the network. To this end, finding a routing tree to get longer lifetime is a key issue, which is known to be NP-hard . Recall that algorithms that can guarantee finding the optimal routing tree are called exact algorithms. It is clear that, unless P=NP, all exact algorithms for the lifetime maximization problem are not polynomial time algorithms.
In fact, all existing exact algorithms run in exponential time [1–3]. A straightforward approach is to perform exhaustive search over the solution space (e.g., ). This process can be improved by dynamically eliminating suboptimal solutions in the search process , or integrating fast integer linear programming solvers . However, these algorithms implicitly use only one CPU core and do not use the full potential of current multicore CPUs. Indeed, most computers, even smartphones, are equipped with multiple cores.
In this work, instead of designing a new algorithm, we consider speeding up existing exact algorithms by using multicore CPUs to their full potential. The basic idea is to decompose the problem into independent subproblems and then solve them on different cores using existing exact algorithms. The challenge is how to decompose the problem. We propose three decomposition methods for different exact algorithms. The first is based on the fact that a tree does not contain (undirected) cycles, so we can break the network into subnetworks whenever we encounter an undirected cycle. This approach applies to all algorithms that consider the network as either an undirected graph or a directed graph. The second is based on directed cycle, and the network is divided whenever we find a directed cycle. The third is based on the fact that every node has only one parent node, so the network is divided according to different parent choices of a given node. The second and the third approaches apply to algorithms that consider the network as a directed graph.
Our contributions can be enumerated as follows:(1)We consider using the multicore of current computers to speed up existing algorithms. The proposed approaches are applicable to all exact algorithms based on one CPU core.(2)We propose three problem decomposition approaches. These approaches can decompose the problem into subproblems, which can be solved on different cores using any exact algorithm. We also propose a mechanism to expose information of solved subproblems to help solve other subproblems.(3)We implement our approach on an 8-core desktop computer and perform numerical simulations. The results suggest that, in general, the proposed approaches can reduce the empirical time of existing exact algorithms, especially when the problem size is large.
The rest of the paper is organized as follows. Section 2 reviews related work. Section 3 reviews the definition of the problem and proposes a solution framework. Section 4 proposes three decomposition approaches. Section 5 discusses several problems. Section 6 presents numerical simulation results. Finally, Section 7 concludes the paper.
2. Related Works
Finding routing paths of messages to maximize lifetime is a critical problem in wireless sensor networks (e.g., [1–3, 5–7]). Unfortunately, it is NP-hard in most scenarios when nodes can or cannot perform data aggregation. Researchers resort to polynomial-time approximation algorithms by sacrificing accuracy (e.g., ), or exponential-time exact algorithms by sacrificing running time (e.g., [1, 3]). While both algorithms have important applications, we focus on exact algorithms in this paper.
A simple method is to enumerate all spanning trees , which has a very poor running time. To improve the efficiency,  decomposes the underlying network graph into biconnected subgraphs to reduce problem size. A limitation is that the technique does not work when the graph is already biconnected. Reference  proposes to incorporate graph decomposition with integer linear programming. The basic idea is to decompose the graph into biconnected subgraphs and formulate the problem on each subgraph as an integer linear programming problem, which is solved by an integer linear programming solver. Besides routing, energy efficiency is also considered in other contexts such as compressive sensing-based encryption  and rechargeable sensor networks .
Contrary to these works, our work in this paper focuses on how to use the multiple cores in current computers to their full potential. The proposed approaches can be incorporated with existing exact algorithms. Though the idea of using multicores in wireless sensor networks is not new, existing works do not focus on our problem. For example,  uses the cores within a GPU to speed up lifetime simulation for sensor nodes, and  designs multicore sensor node hardware.
3. A Framework to Use Multicores
We first review the problem and then introduce the solution framework. A sensor network contains sensors nodes , and a sink node . Each sensor node senses the environment periodically, generating a data packet in each period. It needs to send the data packet to the sink node. The network can be represented as an undirected graph , where is the set of nodes and is the set of communication links. Sensor node has initial energy and the sink node has infinite initial energy; i.e., . The energy consumed for receiving a message is and that for transmitting a message is . For any tree rooted at the sink, in each time period, the energy consumed by node is , where is the number of descendants of node in tree . The lifetime of node in tree is the number of rounds it can support until it runs out of energy: The lifetime of tree is the smallest node lifetime; i.e., Lifetime maximization problem is to find a tree that has the maximum lifetime. It has been proven to be NP-hard .
In this work, we assume that an operating system does not perform automatic multicore optimization; i.e., a single thread program can use at most one CPU core. To this end, we perform a simple experiment as follows. We run a dead loop program on two computers, one of which has 4 cores and the other has 8 cores. Both computers are equipped with the Windows operating system. The CPU utilization ratio is roughly 25% on the 4-core computer and is about 13% on the 8-core computer, which is consistent with our assumption. Note that if there are multiple threads, then the operating system will distribute the threads on different cores automatically.
3.1. Problem Decomposition Overview
We refer to the set of feasible solutions of a lifetime maximization problem as its solution space, i.e., the set of directed trees pointing to the sink. A subproblem is a lifetime maximization problem with smaller solution space. The basic idea is to find a set of subproblems whose solution space contains at least one optimal solution. A decomposition method is feasible if three conditions are satisfied.(i)Each subproblem is feasible; i.e., each subproblem contains at least one feasible solution.(ii)At least two subproblems are returned, unless the original problem has only one feasible solution; i.e., the original network graph is itself a tree.(iii)The union of the solution spaces of all subproblems contains at least one optimal solution to the original problem.
Figure 1 illustrates the basic idea. Given a problem, we apply a feasible decomposition method to get a set of subproblems. Then we solve these subproblems concurrently, compare the optimal solutions to subproblems, and select the best one, which is the optimal solution to the original problem.
3.2. Generating a Sufficient Number of Subproblems
A challenge for the above framework is that a feasible decomposition method might not generate a sufficient number of subproblems. For example, a decomposition method may only give two subproblems. To this end, we observe that sequentially combining several feasible decomposition methods results in a feasible decomposition method.
Proposition 1. Suppose and are two feasible decomposition methods. Apply on a problem , and let the set of subproblems be . If we apply on a subproblem and get subproblem set , then the union of solution spaces of subproblem set contains at least one optimal solution to problem .
Proof. Simply note that an optimal solution to subproblem is contained in the solution space of , so it can be replaced by .
Therefore, we can repeatedly apply a feasible decomposition method until the number of subproblems is sufficient. Algorithm 1 presents the detailed procedure.
In Algorithm 1, after initializing a variable to store the final subproblem set in line (1), we apply the feasible decomposition method and get subproblem set in line (2). We will ensure that and are disjoint throughout the algorithm, and contains at least one optimal solution to the original problem. If the number of subproblems is insufficient, i.e., , we will remove one subproblem from in line (4) and apply to to get another subproblem set in line (5). However, may contain only one subproblem, e.g., when is already a directed tree. In this case, we will insert to in line (7). Otherwise, we include to . The above process is repeated until either the number of subproblems is sufficient or is empty. Finally, we insert all elements remaining in to .
Theorem 2. Algorithm 1 is a feasible decomposition method. It will call for at most times where is the desired number of subproblems. At termination, either or the solution space of each subproblem in is simply a directed tree pointing to the sink.
Proof. To see that Algorithm 1 is a feasible decomposition method, observe that all subproblems are obtained by sequentially applying . The result follows from Proposition 1.
For the number of calls to , consider the potential function . It is easy to see that prior to the while loop, and at the last iteration. We claim that is increased by 1 in each iteration, indicating that the number of calls to is at most .
To prove this claim, observe that each iteration of the while loop either increases by at least one or increases by one. On the one hand, if line (9) is executed, then is at least 2, so is increased by at least one and is unchanged. On the other hand, if line (7) is executed, then is increased by one, so is unchanged. Consequently, is increased by one. The claim is proved.
For the last part, if , then we are done. Otherwise, the while loop terminates with . In this case, all elements in are included in line (7), so these subproblems cannot be further divided, meaning that the solution space contains only one directed tree pointing to the sink.
This theorem suggests that when is a constant, Algorithm 1 has the same asymptotic running time as the given method . In Section 4, we will propose several feasible decomposition methods. These methods are used as subroutines of Algorithm 1 to generate a sufficient number of subproblems.
3.3. Solving Subproblems Concurrently on Multiple Cores
The straightforward method is to create threads whose number is equal to the number of subproblems. Each thread invokes an exact algorithm on a subproblem. Then the operating system will schedule the threads on available CPU cores automatically. Unfortunately, there are several drawbacks in this approach.
First, if the number of subproblems is greater than the number of cores, then there exists a core on which several threads are running. These threads compete in the core unnecessarily, wasting precious CPU time. Second, if the number of subproblems is required to be less than or equal to the number of cores, then some cores are wasted if their threads terminate early. Third, subproblems are solved independently, so that solving one subproblem cannot help solving the other problems. For example, if a solved subproblem has a solution with lifetime 100, then for the other unsolved subproblems, we should not waste time finding solutions with less lifetime.
To address these limitations, we create a thread for a core, so that threads are created on a computer with CPU cores. Each thread repeatedly performs the following three operations until all subproblems are solved.(i)Retrieve an unsolved problem and the best solution up to now.(ii)Invoke an exact algorithm on the unsolved subproblem with the best solution up to now as a lower bound.(iii)Mark the subproblem as solved, and update the best solution up to now.
Figure 2 shows the change to the framework, where the solved subproblems provide feedback to unsolved subproblems. Instead of solving subproblems independently, we maintain the current best solution to reduce unnecessary recomputation in unsolved subproblems.
We can see that this approach does not have the above limitations. First, since the number of threads is equal to the number of cores, no two threads compete in the same core. Second, CPU cores are fully used, since they will keep running until all subproblems are solved. Third, when a thread attempts to solve a subproblem, it will retrieve the status of solved subproblems, e.g., the lifetime of the current best solution, which will help reduce unnecessary computation.
4. Three Feasible Decomposition Methods
We propose three decomposition methods based on different observations. First, a tree does not contain undirected cycles. Second, a tree does not contain directed cycles. Third, a node has only one parent in a directed tree.
4.1. Decomposition by Breaking Undirected Cycle
This approach applies to undirected graphs. Observe that a feasible solution to the lifetime maximization problem is a tree, so any feasible solution cannot contain a cycle. The basic idea of our approach is to find an undirected cycle and create subproblems by breaking the cycle, i.e., removing one edge at a time. Each decomposed subproblem contains one less edge than the original problem. Figure 3 gives an example. By breaking cycle , we get three subproblems , , and . Note that applying this method to directed graphs needs slight modifications.
One design issue is to decide which cycle to break. We propose to choose the cycle containing the minimum number of edges. The motivation is to generate a small number of subproblems at each time, so that the total number of subproblems can be controlled more easily when calling Algorithm 1. We will discuss this motivation in Section 5.
Algorithm 2 presents our approach. We first use the algorithm in  to find a minimum cycle in line (1). Then, in lines (2)-(5), we create subproblems whose number is equal to the number of edges in the cycle. Each subproblem is obtained by deleting one edge from the cycle.
Theorem 3. Algorithm 2 is a feasible decomposition method. It runs in time, where is the number of edges and is the number of vertices.
Proof. It is easy to verify that the first two conditions of a feasible decomposition method are satisfied, since a cycle contains at least three edges. For the third condition, let the original problem be and the constructed subproblems be with corresponding removed edges . Let be the solution space of problem . We claim a stronger result thatTo prove this, consider an arbitrary feasible solution to problem , i.e., . Because is a tree, it cannot contain all edges in the cycle. Suppose it does not contain . Then, . The claim follows immediately.
For the running time, note that the algorithm in  runs in time. The found cycle contains at most edges, so the for loop has at most iterations. Since constructing a subproblem can be finished in time, the for loop runs in time. The overall running time is .
4.2. Decomposition by Breaking Directed Cycle
When the network graph is directed, we can see that no solution contains a directed cycle. Thus, we first find a directed cycle and create a subproblem by removing one edge from the cycle. We choose the minimum cycle to create subproblems, so that the total number of subproblems can be better controlled. Figure 4 gives an example. In the problem, we can break cycle ABCA to get three subproblems.
One problem for this approach is that there may exist a subproblem that does not contain any feasible solution to the original problem. For example, in Figure 4, if we consider the cycle BCDB, then the subproblem by removing edge DB does not contain a feasible solution since no path connects D to the sink. To solve this problem, we check the feasibility of each subproblem and remove infeasible ones. If there is only one feasible subproblem, then we find directed cycles from the subproblem. Since one edge has been removed, the subproblem contains fewer edges than the original problem; the process will terminate.
Algorithm 3 presents the decomposition method. We first check whether the graph itself is a tree. If it is a tree, we return the graph immediately in line (1). Otherwise, we find a directed cycle with the minimum number of edges. If no directed cycle exists, then there exists at least one vertex with out degree larger than 1. We identify such a vertex and insert all its out edges into in line (4). Then, we construct subproblems by removing one edge from in lines (6)-(11). Different from Algorithm 2, we need to verify whether the constructed subproblem is feasible. This is done by reversing the directions of the edges and performing a breadth-first search from the sink in line (8). The subproblem is feasible if and only if all vertices are visited. If the subproblem is feasible, we store it in line (11). Finally, if we get only one feasible subproblem, then we recursively call Algorithm 3 to get subproblems in lines (13) and (14). Otherwise, we return in line (16).
Theorem 4. Algorithm 3 is a feasible decomposition method. It runs in time.
Proof. Consider the recursion tree of Algorithm 3. If the last call to Algorithm 3 (i.e., the leaf node in the recursion tree) returns in line (1), then the original problem contains exactly one feasible solution. If it returns in line (16), then at least two feasible subproblems are returned. Similar to the proof of Theorem 3, the union of solution spaces of these subproblems contains at least one optimal solution to the original problem. So the algorithm is a feasible decomposition method.
For the running time, line (2) in Algorithm 3 runs in time. Observe that lines (3)-(11) run in time. So, except for the recursive call in line (14), the rest of the algorithm runs in time. Consider the recursion tree of the algorithm. Since each call to the algorithm will remove at least one edge from the input graph in line (7) and there are edges, the depth of the recursion tree is . Therefore, the overall running time is .
4.3. Decomposition by Fixing the Parent Node
Observe the fact that a node except for the sink has one parent in a directed tree. Thus, given a node, we can create subproblems by keeping one out edge to fix its parent and deleting other out edges. Figure 5 gives an example. Vertex has three out edges pointing to nodes , , and , so we can construct three subproblems , , and , where the parent of is fixed to , , and , respectively.
Two issues need to be solved. First, a subproblem may not be feasible. This is similar to Section 4.2, and we can also introduce the verification step to remove infeasible subproblems. For the example in Figure 5, if is the sink, then we cannot remove edge , since the resulting subproblem will be infeasible. Second, we need to consider which node to choose. We propose to choose the node with the minimum initial energy. This is based on the intuition that nodes with less energy are usually the bottleneck for the network’s lifetime.
Algorithm 4 presents the method. We sort nodes in ascending order by their initial energy in line (2). We will consider nodes in this order one by one (lines (3)-(4)). For each node, we visit its out edges and construct a subproblem by keeping one out edge and deleting others in line (6), which essentially fixes the node’s parent in the routing tree. To check whether the resulting subproblem is feasible, we reverse the directions of the edges and perform a breadth-first search from the sink in line (7). The subproblem is feasible if and only if all vertices are visited. If the subproblem is feasible, we include it to subproblem set in line (1). In either case, we continue to consider the next node until either is empty or contains at least two subproblems.
Theorem 5. Algorithm 4 is a feasible decomposition method, and it runs in time.
Proof. Algorithm 4 terminates if either is empty or contains at least two feasible subproblems. In the first case, the original problem contains exactly one feasible solution. In the second case, at least two feasible subproblems are returned and it is easy to prove that the union of solution spaces of these subproblems contains at least one optimal solution to the original problem. So the algorithm is a feasible decomposition method.
For the running time, sorting nodes in line (2) runs in time. Observe that, in the worst case, each edge in will be examined once in line (5), and lines (6)-(10) run in times, so the while loop runs in time. The overall running time is .
In this section, we analyze the overall running time of algorithms and discuss several related issues.
Lemma 6. Suppose there are subproblems and cores. Then, there exists a core that solves at most subproblems.
Proof. It follows from the pigeonhole principle.
Incorporating Algorithm 1 with the three decomposition methods, i.e., decomposition by breaking undirected cycle, decomposition by breaking directed cycle, and decomposition by fixing the parent, gives three algorithms, which are denoted by UnCycle, DCycle, and FixP.
Theorem 7. Let be the worst-case running time of an exact algorithm to solve a problem containing directed edges and vertices, be the number of subproblems, and be the number of CPU cores. One has the following results.(i)UnCycle runs in time.(ii)DCycle runs in time.(iii)FixP runs in time.
Proof. Observe that the running time of each algorithm consists of two parts, one of which is for dividing the problem into subproblems and the other is for solving the subproblems. The first part is a single thread program, and, following Theorem 2, its running time is times the running time of the corresponding decomposition method. Due to Theorems 3, 4, and 5, the first part running time for UnCycle is , that for DCycle is , and that for FixP is .
The second part uses cores to solve the subproblems. By Lemma 6, there exists one core that solves at most subproblems. Consider this core. We can see that when this core finishes computing subproblems, there are no subproblems left. (Otherwise, this core should pick up another subproblem to solve.) This suggests that the other cores either already terminate or are computing the last subproblem. Thus, the running time is at most the time for computing subproblems. Further note that each subproblem in UnCycle contains edges, each subproblem in DCycle contains edges, and each subproblem in FixP contains at most edges. The theorem follows immediately.
Note that this theorem studies the worst-case running time. Though FixP seems to have the same complexity with DCycle, the edges of each subproblem in FixP are usually less than .
Another concern for our approach is that the same tree may be produced by different subproblems, so that computations are wasted. This is indeed true for UnCycle and DCycle. However, this happens only if the two subproblems are being solved on different cores at the same time, because if they are solved sequentially, then the solved subproblem provides feedback to the unsolved subproblem, eliminating redundant trees. This mechanism is shown in Figure 2. Since subproblems are smaller than the original problem, we find that using multiple cores does not increase the running time. In addition, the redundant computation problem does not exist for FixP. In different subproblems of FixP, at least one node has a different parent due to the decomposition method. Thus, it is not possible for two subproblems to produce the same tree.
In this paper, we consider constructing a single tree for the network. If multiple trees are allowed, i.e., the network uses a different routing tree after some time, then the overall lifetime can be further extended. The drawback of this approach is that sensor nodes need to perform complex operations, e.g., either to record multiple routing paths in memory to change parents periodically or to receive commands from the network periodically. We plan to extend our result to this scenario in the future.
Finally, we discuss the motivation for finding the minimum cycle in UnCycle and the minimum directed cycle in DCycle. There are several reasons. Ideally, we should find a cycle with length so that we can break the problem into subproblems, where is provided by the user. However, this problem is NP-hard to solve. To see this, simply note that this problem contains the Hamiltonian path problem as a special case (when t=n). We cannot afford another exponential time algorithm to get the desired cycle. On the contrary, finding a minimum cycle or a cycle with arbitrary length can be done in polynomial time. To this end, we need to use Algorithm 1 to get the desired number of subproblems. If we decompose the graph by finding a cycle with arbitrary length (e.g., by performing a DFS search to get an arbitrary cycle), then it is probable that the number of subproblems may be much more than . Instead, by finding a cycle with minimum length, we get small granularity in that each time we add a few subproblems to the set of subproblems. An additional benefit is that a resulting subproblem may be obtained by calling the decomposition method several times, so it has fewer edges.
We compare our approach with previous single-thread approaches on randomly generated sensor networks. Sensors are uniformly and randomly distributed in a square field, and the sink node is located at the center. Two nodes can receive messages from each other if and only if their distance is not greater than 20 meters. Thus, the graph is essentially a unit disk graph. Figure 6 shows one such network with 41 nodes. Each node has its initial energy uniformly drawn from . The energy consumption for receiving and transmitting a message is and , respectively. These settings are consistent with those in [1, 3]. We use Java language to program all algorithms and run them on a desktop computer with configuration listed in Table 1.
We implement the proposed decomposition methods to generate subproblems including decomposition by breaking undirected cycle (UnCycle), decomposition by breaking directed cycle (DCycle), and decomposition by fixing the parent (FixP). We consider networks with nodes, and for each node number, we generate 20 network instances, so that there are 120 problem instances. We vary the number of subproblems from 8 to 20 with increments of 4. Note that if the number of subproblems is fixed to 1, then no decomposition is performed. We set the maximum allowed running time to 10 minutes, after which we terminate an algorithm and mark the network as fail for the algorithm. We implement two previous algorithms: ILP-B that uses integer linear programming with binary search  and ILP-BD that improves ILP-B by adding a procedure to decompose the network graph into biconnected subgraphs .
6.1. Performance Improvement on ILP-B
We study the improvement of our approach on ILP-B in terms of average running time. Figure 7 shows the results. When the number of subproblems is 1, our approach is not applied and the result corresponds to the original algorithm. Note that we do not take into account problem instances that no algorithm can solve within ten minutes. Other than these networks, we approximate the running time of an algorithm on a problem instance to ten minutes if it fails to get the optimal solution. We will also study the effect of this approximation.
We have two observations from the figure. First, our approach can significantly reduce the average running time. This is in line with intuition since all CPU cores are used. Second, when the number of subproblems is either small (8) or large (20), the average running time is not the smallest. We get smaller running time when the number of subproblems is 12 or 16. When the number of subproblems is small, most subproblems are still very similar to the original problem. But when we get too many subproblems, even though most subproblems are simpler, it is more likely that we encounter a difficult subproblem. Indeed, in lifetime maximization problem, small problem size is not a guarantee of less running time. Thus, we recommend to set the number of subproblems to within twice the number of CPU cores.
We show that the approximation of the running time of unsolved instance as ten minutes is reasonable in that the relationship of running time of different algorithms remains the same under this approximation. To this end, we vary the maximum allowed running time from 2 minutes to 10 minutes with increments of 2 minutes. Figure 9 shows the resulting average running time for networks containing 46 nodes. We can see that, with the increase of the maximum allowed running time, the average running time is increased since unsolved problem instances contribute more running time due to the approximation. However, the relationship between different algorithms remains the same; i.e., FixP has the smallest average running time in all cases, DCycle has the second smallest average running time, and so forth. Therefore, we believe the approximation is reasonable.
6.2. Performance Improvement on ILP-BD
We study the improvement on ILP-BD with the same problem instances in Section 6.1. We do not take a problem instance into account if all configurations cannot solve it. Figure 8 shows the results. The results are similar.
We can see again that our approach greatly reduces the average running time. The improvement is more significant when the number of nodes becomes larger. Setting the number of subproblems to 12 or 16 gives smaller running time than 8 or 20. Note that it is not fair to compare ILP-BD with ILP-B using Figures 7 and 8, because the problem instances used in average running time computation are different for the two approaches. In fact, ILP-BD can solve more problem instances in general, so some solved difficult problem instances increase the average running time.
6.3. Performance Improvements in the Number of Failed Networks
Besides average running time, we show that the number of failed networks is smaller for our approach. Figures 10 and 11 show the number of failed networks of each decomposition method on algorithms ILP-B and ILP-BD. Note that we omit the result for networks with 30 and 35 nodes, since all approaches can solve all problem instances within ten minutes. We can see that our methods can solve more networks than previous single-thread method. The improvement is more obvious when the network contains more nodes. In addition, ILP-BD can solve more problem instances than ILP-B, and our approach can further improve ILP-BD significantly.
6.4. Performance on a Real Network
We test the algorithms on a real network reported in . The network consists of 49 sensor nodes deployed on a grid. The distance between adjacent columns is roughly 5 meters. Two nodes are connected by an edge if the received signal strength is at least -74db, giving a network topology in Figure 12. We run our algorithms with ILB-BD on the network and show the running time of each method in Table 2. We can see that using multiple cores can indeed reduce the running time. While the original single-thread program needs about 3 minutes, FixP terminates in less than 1 second.
In this paper, we proposed to use multiple cores to speed up existing exact algorithms for finding optimal routing tree of wireless sensor networks. The basic idea is to decompose the original problem into multiple subproblems and run them on different CPU cores. We propose three decomposition methods and prove their correctness. Numerical results show that the three methods can speed up the calculation significantly in terms of average running time and the number of solved problems.
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
This work was supported by the National Natural Science Foundation of China (61502232) and China Postdoctoral Science Foundation (2015M570445, 2016T90457).
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Copyright © 2018 Pengyuan Cao and Xiaojun Zhu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.