Fast Approximation Algorithm for Maximum Lifetime Aggregation Trees in Wireless Sensor Networks

Tree-based routing structures are widely used to gather data in wireless sensor networks. Along with tree structures, in-network aggregation is adopted to reduce transmissions, to save energy and to prolong the network lifetime. Most existing works that focus on the lifetime optimization for data aggregation do not take the link quality into consideration. In this paper, we study the problem of Maximizing Reliability of Lifetime Constrained data aggregation trees (MRLC) in WSNs. Considering the NP-completeness of the MRLC problem, we propose an algorithm, namely Iterative Relaxation Algorithm (IRA), to iteratively relax the optimization program and to find the aggregation tree subject to the lifetime bound with a sub-optimal cost. To adapt to the distributed nature of the WSNs in practice, we further propose a Prufer code based distributed updating protocol. Through extensive simulations, we demonstrate that IRA outperforms the best known related work in term of reliability.

We consider two related problems, the Minimum Bounded Degree Matroid Basis problem and the Minimum Bounded Degree Submodular Flow problem. The first problem is a generalization of the Minimum Bounded Degree Spanning Tree problem: we are given a matroid and a hypergraph on its ground set with lower and upper bounds f(e) ≤ g(e) for each hyperedge e. The task is to find a minimum cost basis which contains at least f(e) and at most g(e) elements from each hyperedge e. In the second problem we have a submodular flow problem, a lower bound f(v) and an upper bound g(v) for each node v, and the task is to find a minimum cost 0-1 submodular flow with the additional constraint that the sum of the incoming and outgoing flow at each node v is between f(v) and g(v). Both of these problems are NP-hard (even the feasibility problems are NP-complete), but we show that they can be approximated in the following sense. Let opt be the value of the optimal solution. For the first problem we give an algorithm that finds a basis B of cost no more than opt such that f(e) − 2Δ + 1 ≤ |B ∩ e| ≤ g(e) + 2Δ− 1 for every hyperedge e, where Δ is the maximum degree of the hypergraph. If there are only upper bounds (or only lower bounds), then the violation can be decreased to Δ− 1. For the second problem we can find a 0-1 submodular flow of cost at most opt where the sum of the incoming and outgoing flow at each node v is between f(v) − 1 and g(v) + 1. These results can be applied to obtain approximation algorithms for different combinatorial optimization problems with degree constraints, including the Minimum Crossing Spanning Tree problem, the Minimum Bounded Degree Spanning Tree Union problem, the Minimum Bounded Degree Directed Cut Cover problem, and the Minimum Bounded Degree Graph Orientation problem.KeywordsPolynomial Time AlgorithmFeasibility ProblemSpan Tree ProblemSubmodular FunctionDegree ConstraintThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of data transmissions required in CS method is still large. It is observed that there are many zero elements in the measurement matrix. In each round of data transmission in CS method, the sensor nodes corresponding to the zero elements in the measurement matrix do not have their own data to transmit. To further reduce the number of data transmissions in the network, we aim to compute a data gathering tree by taking advantages of these zero elements in the measurement matrix, such that the total number of data transmissions is minimized. We formulate the problem as linear programming with boolean variables. The problem is NP-hard. We propose heuristic algorithm to compute the Minimum Transmission Tree (MTT) for data gathering in CS methods. The MTT algorithm constructs a spanning tree by iteratively including the edge whose average incremental transmission cost is minimum. The simulation results demonstrate that our algorithm can reduce the number of transmissions significantly, compared with the methods using minimum spanning tree (MST), shortest path tree and the CS method with nonzero measurement coefficient using MST.

Data gathering is a fundamental operation for various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward data to a sink node via multihop wireless communications. Typically, data in a WSN is relayed over a tree topology to the sink for effective data gathering. A number of tree-based data gathering schemes have been proposed in the literature, most of which aim at maximizing network lifetime. However, the timeliness and reliability of gathered data are also of great importance to many applications in WSNs. To achieve low-latency, high-reliability data gathering in WSNs, in this paper, we construct a data gathering tree based on a reliability model, schedule data transmissions for the links on the tree and assign transmitting power to each link accordingly. Since the reliability of a link is highly related to its signal to interference plus noise ratio (SINR), the SINR of all the currently used links on the data gathering tree should be greater than a threshold to guarantee high reliability. We formulate the joint problem of tree construction, link scheduling and power assignment for data gathering into an optimization problem, with the objective of minimizing data gathering latency. We show the problem is NP-hard and divide the problem into two subproblems: Construction of a low-latency data gathering tree; Jointly link scheduling and power assignment for the data gathering tree. We then propose a polynomial heuristic algorithm for each subproblem and conduct extensive simulations to verify the effectiveness of the proposed algorithms. Our simulation results show that the proposed algorithms achieve much lower data gathering latency than existing data gathering strategies while guaranteeing high reliability.

Data gathering is a fundamental operation in various applications of wireless sensor networks (WSNs), where sensor nodes sense information and forward data to a sink node via multi-hop wireless communications. Typically, data in a WSN is relayed over a tree topology to the sink for effective data gathering. A number of tree-based data gathering schemes have been proposed in the literature, most of which aim at maximizing network lifetime. However, the timeliness and reliability of gathered data are also of great importance to many applications in WSNs. To achieve low-latency, high-reliability data gathering in WSNs, in this paper, we construct a data gathering tree based on a reliability model, schedule data transmissions for the links on the tree and assign transmitting power to each link accordingly. Since the reliability of a link is highly related to its signal to interference plus noise ratio (SINR), the SINR of all the currently used links on the data gathering tree should be greater than a threshold to guarantee high reliability. We formulate the joint problem of tree construction, link scheduling and power assignment for data gathering into an optimization problem, with the objective of minimizing data gathering latency. We show the problem is NP-hard and divide the problem into two subproblems: Constructing a low-latency data gathering tree; Jointly link scheduling and power assignment for the data gathering tree. We then propose a polynomial heuristic algorithm for each subproblem and conduct extensive simulations to verify the effectiveness of the proposed algorithms. Our simulation results show that the proposed algorithms achieve much lower data gathering latency than existing data gathering strategies while guaranteeing high reliability. Moreover, the algorithms also have a comparable energy efficiency and network lifetime to other algorithms.

Scheduling of data aggregation trees using Local Heuristics to enhance network lifetime in sensor networks

Sensor play an important role in the growth of big data. It is a basic operation for nodes to periodically transmit reports to a sink in many applications in a Wireless Sensor Network (WSN). Because of the limited energy of the nodes, it is important to process data efficiently. Under such applications, a tree is generally adopted the path structure for maintaining the routing tables of sensors. In this paper, considering the difference in the amount of data collected and the speed of data acquisition, we assign different acquisition frequency to the nodes. In addition, we propose a new Minimum Spanning Tree (MST) algorithm which is devoted to construct a data aggregation tree with the lowest energy consumption so that the network lifetime is maximized. Simulation results illustrate that the proposed algorithm outperforms traditional MST in terms of energy consumption.

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each for a group of sensor nodes, to collect measurements at a single sink node. The goal is to construct such trees which gather measurements in the most energy efficient manner and minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus appropriate link scheduling is required to overcome interference. We refer to this problem as Forwarding Tree Construction and Scheduling (FTCS). Each tree may be constructed independently and then its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Further, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of best forwarding trees which, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of our work is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances where no known exact solutions can be found.

Data gathering is a typical operation in wireless sensor networks where data flow through a data gathering tree towards a sink node. Plenty of work had been done on constructing energy-efficient data gathering trees at the routing layer. However, data generation in wireless sensor networks could be bursty as it is driven by the events of interest. Without any sleep scheme, the sensor nodes will stay idle for most of the time. Therefore energy-efficient data gathering trees alone are not enough for energy saving. We propose a routing layer data gathering sleep scheme (DGSS) which could be incorporated into existing data gathering tree formation algorithms to save unnecessary energy consumption due to idle listening and meanwhile adapts to bursty traffic condition. We compare DGSS with DMAC, a popular energy efficient MAC protocol for data gathering, through simulations and show that DGSS can perform better than DMAC for bursty traffic at relatively high data rates.

A wireless sensor network (WSN) is consisting of a set of sensor nodes with a limited energy stored in their batteries. Generally, replacing or charging the battery is hard and inefficient. Further, the main critical aspect of applications based on wireless sensor networks is their lifetime. Therefore, judicious power management with optimized routing protocols can effectively optimize the energy consumption of sensor nodes and thus extend the network lifetime. In this paper, an optimized routing protocol for wireless sensor nodes is proposed. We are interested in constructing an efficient routing spanning tree that minimizes the energy consumption among all nodes in the network and fit for WSN with reduced energy for achieving a longer lifetime. The main idea of this algorithm comes from the Minimum Spanning Tree (MST) graph theory. This approach focuses on the minimal hop count of each node to reach the destination (sink node) within an optimal path.

In this paper we present the first distributed algorithm on general graphs for the minimum degree spanning tree problem. The problem is NP-hard in sequential. Our algorithm gives a spanning tree of a degree at most 1 from the optimal. The resulting distributed algorithm is asynchronous, it works for named asynchronous arbitrary networks and achieves O(|V|) time complexity and O(|V| |E|) message complexity.

Data aggregation in wireless sensor networks using ant colony algorithm

Echo-Aided Bat Algorithm is proved a good evolutionary computing to solve continuous problems in previous investigate. In this study, EABA is applied to solve the discrete problem that construct a network topology with spanning tree in wireless sensor networks (WSNs). In this application, the presentation of bat and design of fitness function perhaps affect the evolutionary results. For the demonstration of simulated results, EABA still presents a satisfied performance in some scenarios designed in this study. In addition, the constructed network topology of spanning tree based on global optimum solution found by EABA is used to estimate the network lifetime. Overall, this framework including network topology constructed by EABD and network lifetime estimated by transmission simulation is created in this study to aid the plan for duration of WSN deployment.

Data aggregation is an essential operation in many applications of Wireless Sensor Networks (WSNs). For energy constrained and delay sensitive WSNs, how to prolong the lifetime and reduce the latency of the network are two critical problems. We deal with these two problems together in data aggregation by proposing a Load Balanced and Delay Efficient Algorithm (LBDEA), which consists of two phases. First, construct a load-balanced data aggregation tree based on Semi-Matching algorithm in the graph theory. Second, seek a collision-free scheduling based on greedy strategy for the transmission links in the aggregation tree. Through balancing the load of the sensor nodes, the lifetime of the network is optimized. By avoiding collisions and retransmissions of data delivery, the total latency is decreased. Simulations show that LBDEA not only improves the lifetime but also cuts down the delay for data aggregation in WSNs, compared with other related algorithms.

In this paper we consider a problem of building a forwarding tree for multicast and convergecast traffic in short-range wireless sensor networks. Interference awareness and energy efficiency are the major design objectives for WSN protocols in order to maximize the network lifetime. The existing multicast algorithms aim at constructing low-energy cost trees. Adding interference-awareness, however, leads to increased throughput and further reduces the energy consumption by avoiding unnecessary retransmissions due to interference-induced packet losses. We propose a Localized Area-Spanning Tree (LAST) protocol for wireless short-range sensor networks. Unlike previous similar protocols, the LAST protocol reaches all the nodes in a given geographical area, rather than only specific individual nodes. When creating the tree, the protocol jointly optimizes the energy cost and the interference imposed by the structure.