Bing-Hong Liu

The critical-square-grid coverage problem in wireless sensor networks is NP-Complete

Wireless sensor networks are formed by connected sensors, each of which has the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. These characteristics allow wireless sensor networks to be used in a wide range of applications. In many applications, such as smart home, fire fighting system, nuclear, biological, and chemical (NBC) attack detection, and so on, critical areas and common areas must be distinguished adequately. It is more practical and efficient to monitor critical areas rather than common areas if the sensor field is large, or the available budget cannot provide enough sensors to fully cover the entire field. In most available state-of-the-art sensor deployment algorithms, the sensor field is divided into square grid cells and sensors are deployed on constrained locations such as grid points. To study the computational complexity of sensor deployment, the problem of deploying the minimum number of sensors on grid points to construct a wireless sensor network fully covering critical square grid cells, termed critical-square-grid coverage, is introduced and shown to be NP-Complete in this paper.

Efficient Algorithm for Constructing Minimum Size Wireless Sensor Networks to Fully Cover Critical Square Grids

Wireless sensor networks are formed by connected sensors that each have the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. These characteristics allow wireless sensor networks to be used in a wide range of applications. In many applications, such as environmental monitoring, battlefield surveillance, nuclear, biological, and chemical (NBC) attack detection, and so on, critical areas and common areas must be distinguished adequately, and it is more practical and efficient to monitor critical areas rather than common areas if the sensor field is large, or the available budget cannot provide enough sensors to fully cover the entire sensor field. This provides the motivation for the problem of deploying the minimum sensors on grid points to construct a connected wireless sensor network able to fully cover critical square grids, termed CRITICAL-SQUARE-GRID COVERAGE. In this paper, we propose an approximation algorithm for CRITICAL-SQUARE-GRID COVERAGE. Simulations show that the proposed algorithm provides a good solution for CRITICAL-SQUARE-GRID COVERAGE.

On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees

Data gathering is a basic requirement in many applications of wireless sensor networks (WSNs). Because the energy of sensors is limited, the data-gathering mechanism must be carefully designed to save the energy consumption of sensors to prolong the network lifetime. Recently, many researchers have studied gathering data efficiently in WSNs to minimize the total energy consumption when a fixed number of data are allowed to be aggregated into one packet. However, when the total energy consumption is minimized, the energy consumption of sensors for data gathering cannot be guaranteed to be balanced, and thus, the network lifetime cannot be guaranteed to be maximized. This motivates us to study the problem of scheduling virtual data aggregation trees to maximize the network lifetime when a fixed number of data are allowed to be aggregated into one packet, termed the Maximum Lifetime Data Aggregation Tree Scheduling (MLDATS) problem. The MLDATS problem is shown to be NP-complete in the paper. In addition, a local-tree-reconstruction-based scheduling algorithm (LTRBSA) is proposed for the MLDATS problem. We use simulations to evaluate and demonstrate the performance of the LTRBSA when the sink has 2-hop, 3-hop, and all information in the networks. Simulation results show that the LTRBSA of using sinks 3-hop information provides comparable performances to that of using all information in the networks, and outperforms other methods proposed in the simulation.

A Maximum-Weight-Independent-Set-Based Algorithm for Reader-Coverage Collision Avoidance Arrangement in RFID Networks

Radio frequency identification (RFID) systems have been widely developed and applied in identification applications. In RFID systems, a tag can be read by a reader when the tag is within the readers interrogation range. Reader deployment has received a great deal of attention for providing a certain service quality. Many studies have addressed deploying/activating readers such that all the tags in a field can be read. However, in a practical environment, tags cannot be read due to collisions. In addition, the number of tags read by a reader is often limited due to the constraints of processing time and link layer protocols. This motivated us to study the problem of activating readers and adjusting their interrogation ranges to cover maximum tags without collisions subject to the limited number of tags read by a reader, termed the reader-coverage collision avoidance arrangement (RCCAA) problem. In this paper, the RCCAA problem is shown to be NP-complete. In addition, an approximation algorithm, termed the maximum-weight-independent-set-based algorithm (MWISBA), is proposed for the RCCAA problem. The simulation results show that the MWISBA provides good performance for the RCCAA problem.

Efficient distributed data scheduling algorithm for data aggregation in wireless sensor networks

Abstract With the rapid development of applications for wireless sensor networks, efficient data aggregation methods are becoming increasingly emphasized. Many researchers have studied the problem of reporting data with minimum energy cost when data is allowed to be aggregated many times. However, some aggregation functions used to aggregate multiple data into one packet are unrepeatable; that is, every data is aggregated only at most once. This problem motivated us to study reporting data with minimum energy cost subject to that a fixed number of data are allowed to be aggregated into one packet and every data is aggregated at most once. In this paper, we propose novel data aggregation and routing structures for reporting generated data. With the structures, we study the problem of scheduling data to nodes in the networks for data aggregation such that the energy cost of reporting data is minimized, termed MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING. In addition, we show that MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING is NP-complete. Furthermore, a distributed data scheduling algorithm is proposed accordingly. Simulations show that the proposed algorithm provides a good solution for MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING.

Message-Efficient Location Prediction for Mobile Objects in Wireless Sensor Networks Using a Maximum Likelihood Technique

In the tracking system, a better prediction model can significantly reduce power consumption in a wireless sensor network because fewer redundant sensors will be activated to keep monitoring the object. The Gauss-Markov mobility model is one of the best mobility models to describe object trajectory because it can capture the correlation of object velocity in time. Traditionally, the Gauss-Markov parameters are estimated using an autocorrelation technique or a recursive least-squares estimation technique; either of these techniques, however, requires a large amount of historical movement information of the mobile object, which is not suitable for tracking objects in a wireless sensor network because they demand a considerable amount of message communication overhead between wireless sensors which are usually battery powered. In this paper, we develop a Gauss-Markov parameter estimator for wireless sensor networks (GMPE_MLH) using a maximum likelihood technique. The GMPE_MLH model estimates the Gauss-Markov parameters with few requirements in terms of message communication overhead. Simulations demonstrate that the GMPE_MLH model generates negligible differences between the actual and estimated values of the Gauss-Markov parameters and provides comparable prediction of the mobile objects location to the Gauss-Markov parameter estimators using an autocorrelation technique or a recursive least-squares estimation.

Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks

Recently, many studies have investigated scheduling mobile devices to recharge and collect data from sensors in wireless rechargeable sensor networks (WRSNs) such that the network lifetime is prolonged. In reality, because mobile devices are more powerful and expensive than sensors, the cost of the mobile devices often consumes a high portion of the budget. Due to a limited budget, the number of mobile devices is often limited. Some sensors in WRSNs may not be operated without time limits due to the limited number of mobile devices. Therefore, sensors in a sensing field must be weighted by their importance, that is, the more important an area covered by a sensor, the higher the weight of the sensor. Therefore, in this paper, the problem of scheduling limited mobile devices for energy replenishment and data collection in WRSNs with multiple sinks such that the total weight of the recharged sensors that can be operated without time limits is maximized, termed the periodic energy replenishment and data collection with limited mobile devices (PERDCLMD) problem, is studied. In addition, the greedy scheduling algorithm (GSA) is proposed for the PERDCLMD problem accordingly. Simulation results show that the GSA provides a good performance in terms of the total weight of the recharged sensors.

An efficient algorithm of constructing virtual backbone scheduling for maximizing the lifetime of dual-radio wireless sensor networks

Wireless sensor networks have often been used to monitor environmental conditions, such as temperature, sound, and pressure. Because the sensors are expected to work on batteries for a long time without charging their batteries, the major challenge in the design of wireless sensor networks is to enhance the network lifetime. Recently, many researchers have studied the problem of constructing virtual backbones, which are backbones used for different time periods, to prolong the network lifetime. In this paper, we study the problem of constructing virtual backbones in dual-radio wireless sensor networks to maximize the network lifetime, called the Maximum Lifetime Backbone Scheduling for Dual-Radio Wireless Sensor Network problem, where each sensor is equipped with two radio interfaces. The problem is shown to be NP-complete here. In addition, rather than proposing a centralized algorithm, a distributed algorithm, called a Dominating-Set-Based Algorithm (DSBA), is proposed for a wide range of wireless sensor networks to find a backbone when a new one is required. Simulation results show that the proposed algorithm outperforms some existing algorithms.

Constrained node-weighted Steiner tree based algorithms for constructing a wireless sensor network to cover maximum weighted critical square grids

Deploying minimum sensors to construct a wireless sensor network such that critical areas in a sensing field can be fully covered has received much attention recently. In previous studies, a sensing field is divided into square grids, and the sensors can be deployed only in the center of the grids. However, in reality, it is more practical to deploy sensors in any position in a sensing field. Moreover, the number of sensors may be limited due to a limited budget. This motivates us to study the problem of using limited sensors to construct a wireless sensor network such that the total weight of the covered critical square grids is maximized, termed the weighted-critical-square-grid coverage problem, where the critical grids are weighted by their importance. A reduction, which transforms our problem into a graph problem, termed the constrained node-weighted Steiner tree problem, is proposed and used to solve our problem. In addition, three heuristics, including the greedy algorithm (GA), the group-based algorithm (GBA), and the profit-based algorithm (PBA), are proposed for the constrained node-weighted Steiner tree problem. Simulation results show that the proposed reduction with the PBA provides better performance than the others.

A dynamic-range-based algorithm for reader-tag collision avoidance deployment in RFID networks

Recently, the problem of scheduling readers to read maximum tags such that readers and tags can be avoided collision within a given multiple interrogation ranges of reader has received a great deal of attention in Radio frequency identification (RFID) systems. Many methods have been proposed for the RFID systems such that readers can read maximum tags. However, most of previous works often require lots of readers or exist reader-tag collisions. In this paper, we propose a new algorithm, called the Dynamic-Range-based algorithm (DRBA), to maximize the number of read tags and avoid collisions within the multiple interrogation ranges of readers. Simulation results show that the DRBA has better performance than other existing methods.