Probabilistic Wireless Sensor Networks Research Articles

Constructing Reliable Virtual Backbones in Probabilistic Wireless Sensor Networks

Most existing algorithms used for constructing virtual backbones are based on the ideal deterministic network model (DNM) in which any pair of nodes is either fully connected or completely disconnected. Different from DNM, the probabilistic network model (PNM), which presumes that there is a probability to connect and communicate between any pair of nodes, is more suitable to the practice in many real applications. In this paper, we propose a new algorithm to construct reliable virtual backbone in probabilistic wireless sensor networks. In the algorithm, we firstly introduce Effective Degree of Delivery Probability (EDDP) to indicate the reliable degree of nodes to transfer data successfully, and then exclude those nodes with zero EDDP from the candidate dominator set to construct a reliable connected dominating set (CDS). Moreover, each dominatee selects the neighbor dominator with the maximum delivery probability to transfer data. Through simulations, we demonstrate that our proposed algorithm can remarkably prolong the network lifetime compared with existing typical algorithms.

Reliable and energy efficient topology control in probabilistic Wireless Sensor Networks via multi-objective optimization

In Wireless Sensor Networks (WSNs) instead of using the possible network connectivity to its maximum extent, a deliberate choice must be made to restrict the topology of the network. Constructing a virtual backbone network using Connected Dominating Sets (CDS) is a promising choice for topology control. Currently, almost all existing studies employ heuristic and/or meta-heuristic optimizations for formulating minimum-sized CDS under the deterministic network model. In this paper, we address the problem of constructing energy efficient CDS in WSNs while improving network reliability. The problem is modelled as a multi-objective optimization that simultaneously maximizes two contradictory parameters: reliability and energy efficiency. Unlike most of the existing studies, the reliability parameter is expressed as a probabilistic inference using probabilistic network model due to uncertainty in connections among sensor nodes. Extensive simulation results indicate that the proposed approach in this paper achieves more reliability, longer stability period and more energy efficient CDS compared to other approaches in the literature.

Constructing Load-Balanced Data Aggregation Trees in Probabilistic Wireless Sensor Networks

Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs). Currently, most of the existing works focus on constructing DATs according to different user requirements under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a DAT under the realistic Probabilistic Network Model (PNM). Moreover, the load-balance factor is neglected when constructing DATs in current literatures. Therefore, in this paper, we focus on constructing a Load-Balanced Data Aggregation Tree (LBDAT) under the PNM. More specifically, three problems are investigated, namely, the Load-Balanced Maximal Independent Set (LBMIS) problem, the Connected Maximal Independent Set (CMIS) problem, and the LBDAT construction problem. LBMIS and CMIS are well-known NP-hard problems and LBDAT is an NP-complete problem. Consequently, approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the paper. Finally, our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

Research on Reliability-Oriented Data Fusaggregation Algorithm in Large-Scale Probabilistic Wireless Sensor Networks

A lot of facts show that many researches just place emphasis on data aggregation or data fusion, which is not beneficial to analyze the sensed data thoroughly and will lead to the aggregation results' not being used fully; worse yet, the actual networks are always existed with lossy links; many now available aggregation algorithms are based on ideal network models and not any further analysis and fusion about aggregation results are done. Thus, we propose the concept of data fusaggregation so as to support processing sensed data while transmitting in large-scale probabilistic wireless sensor networks and propose a reliability-oriented data fusaggregation algorithm (RODFA) to assist users to get the monitoring information from the monitored geographic environment and measure the reliability of the information they get. RODFA also facilitates network administrator to improve the system sensing performance for large-scale probabilistic WSNs. In RODFA, the parameter η, which could reflect the reliability of aggregation result intuitively, is defined and calculated and it plays an important part in helping users to process aggregation result further. In our experiment, the validity of RODFA is verified by our simulation results, and the influence of network sizes and network performances on data fusaggregation is analyzed.

Snapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networks

Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the performance can be measured by its achievable network capacity. Most existing works studying the network capacity issue are based on the unpractical model called deterministic network model. In this paper, a more reasonable model, probabilistic network model, is considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm that achieves capacity of $(\Omega ({1/ 5\omega \ln n} \cdot W))$ in the sense of the worst case and order-optimal capacity in the sense of expectation, where $(n)$ is the number of sensor nodes, $(\omega)$ is a constant, and $(W)$ is the data transmitting rate. For continuous data collection, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the continuous data collection process by forming a data transmission pipeline, and achieves a capacity gain of $(N \sqrt{n}/ \sqrt{\log n} \ln n)$ or $(n/ \log n \ln n)$ times better than the optimal capacity of the snapshot data collection scenario in order in the sense of the worst case, where $(N)$ is the number of snapshots in a continuous data collection task. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works.

Constructing load‐balanced virtual backbones in probabilistic wireless sensor networks via multi‐objective genetic algorithm

AbstractA connected dominating set (CDS) is used as a virtual backbone (VB) for efficient routing and broadcasting in wireless sensor networks (WSNs). Currently, almost all existing works focus on constructing minimum‐sized CDS under the deterministic network model. However, because of the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a VB under the realistic probabilistic network model (PNM). Moreover, load‐balance factor cannot be neglected when constructing a VB to prolong network lifetime. Hence, in this paper, we propose a multi‐objective genetic algorithm to construct a load‐balanced VB under PNM. Through simulations, we demonstrate that our proposed methods extend network lifetime by 69% on average compared with the existing state‐of‐the‐art approaches. Copyright © 2014 John Wiley & Sons, Ltd.

Continuous data aggregation and capacity in probabilistic wireless sensor networks

Due to the existence of many probabilistic lossy links in Wireless Sensor Networks (WSNs) (Liu et al., 2010) [25], it is not practical to study the network capacity issue under the Deterministic Network Model (DNM). A more realistic one is actually the Probabilistic Network Model (PNM). Therefore, we study the Snapshot Data Aggregation (SDA) problem, the Continuous Data Aggregation (CDA) problem, and their achievable capacities for probabilistic WSNs under both the independent and identically distributed (i.i.d.) node distribution model and the Poisson point distribution model in this paper. First, we partition a network into cells and use two vectors to further partition these cells into equivalent color classes. Subsequently, based on the partitioned cells and equivalent color classes, we propose a Cell-based Aggregation Scheduling (CAS) algorithm for the SDA problem in probabilistic WSNs. Theoretical analysis of CAS and the upper bound capacity of the SDA problem show that the achievable capacities of CAS are all order optimal in the worst case, the average case, and the best case. For the CDA problem in probabilistic WSNs, we propose a Level-based Aggregation Scheduling (LAS) algorithm. LAS gathers the aggregation values of continuous snapshots by forming a data aggregation/transmission pipeline on the segments and scheduling all the cell-levels in a cell-level class concurrently. By theoretical analysis of LAS and the upper bound capacity of the CDA problem, we prove that LAS also successfully achieves order optimal capacities in all the cases. The extensive simulation results further validate the effectiveness of CAS and LAS.