Shouling Ji

Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. First, we study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects data of a snapshot to the base station in a distributed manner without any time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Finally, extensive simulation results indicate that ADDC can effectively finish a data collection task and significantly reduce data collection delay.

Capacity of dual-radio multi-channel wireless sensor networks for continuous data collection

Data collection is an important operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. Most existing works focus on the capacity of unicast, multicast or snapshot data collection in single-radio single-channel wireless networks, and no dedicated works consider the continuous data collection capacity for WSNs in detail under the protocol interference model. In this paper, we first propose a multi-path scheduling algorithm for the snapshot data collection in single-radio multi-channel WSNs and prove that its achievable network capacity is at least equation, which is a tighter lower bound compared with the previously best result in [5] which is W over 8ρ2, where W is the bandwidth over a channel, H is the number of the available orthogonal channels, ρ is the ratio of the interference radius over the transmission radius of a sensor and o(ρ) is a linear equation of ρ. For the continuous data collection problem, although the authors in [5] claim that data collection can be pipelined with existing works, we find that such an idea cannot actually improve network capacity. We explain the reason for this and propose a novel continuous data collection method for dual-radio multi-channel WSNs. This method significantly speeds up the data collection process, and achieves a capacity of equation when Δe ≤ 12, or equation when Δe > 12, where n is the number of sensors, M is a constant value and usually M ≪ n, and Δe is the maximum number of leaf nodes having a same parent node in the routing tree (i.e. data collection tree). The simulation results also indicate that the proposed algorithms significantly improve network capacity compared with the existing works.

Approximation algorithms for load-balanced virtual backbone construction in wireless sensor networks

Inspired by the backbone concept in wired networks, a Virtual Backbone (VB) is expected to benefit routing in Wireless Sensor Networks (WSNs). A Connected Dominating Set (CDS) based VB is a competitive approach among the existing methods used to establish VBs in WSNs. Most existing works focus on constructing a Minimum-sized CDS (MCDS), a k-connected m-dominating CDS, a minimum routing cost CDS or a bounded-diameter CDS. However, few works consider the load-balance factor. In this work, the size and the load-balance factors are both taken into account when constructing a VB in WSNs. Specifically, three NP-hard problems are investigated in the paper, namely, the MinMax Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual Backbone (LBVB) problem, and the MinMax Valid-degree non-Backbone node Allocation (MVBA) problem. Furthermore, their approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented. Finally, our theoretical analysis and the simulation results indicate that the proposed algorithms outperform the existing state-of-the-art approaches.

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.

Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay.

Distributed data collection in large-scale asynchronous wireless sensor networks under the generalized physical interference model

Wireless sensor networks (WSNs) are more likely to be d-pistributed asynchronous systems. In this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. Our main contributions include five aspects. First, to avoid data transmission interference, we derive an ℜ0-proper carrier-sensing range (ℜ0-PCR) under the generalized physical interference model, where ℜ0 is the satisfied threshold of data receiving rate. Taking ℜ0-PCR as its carrier-sensing range, any sensor node can initiate a data transmission with a guaranteed data receiving rate. Second, based on ℜ0-PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration. Theoretical analysis of DDC surprisingly shows that its achievable network capacity is order-optimal and independent of network size. Thus, DDC is scalable. Third, we discuss how to apply ℜ0-PCR to the distributed data aggregation problem and propose a Distributed Data Aggregation (DDA) algorithm. The delay performance of DDA is also analyzed. Fourth, to be more general, we study the delay and capacity of DDC and DDA under the Poisson node distribution model. The analysis demonstrates that DDC is also scalable and order-optimal under the Poisson distribution model. Finally, we conduct extensive simulations to validate the performance of DDC and DDA.

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 Ω(1/ 5ω ln n·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, ω 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√n/√(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.

Continuous Data Collection Capacity of Wireless Sensor Networks under Physical Interference Model

Data collection is a common operation of Wireless Sensor Networks (WSNs). The performance of data collection can be measured by its achievable \emph{network capacity}. However, most existing works focus on the network capacity of \emph{unicast}, \emph{multicast} or/and \emph{broadcast}, which are different communication modes from data collection, especially continuous data collection. In this paper, we study the \emph{Snapshot/Continuous Data Collection} (SDC/CDC) problem under the Physical Interference Model (PhIM) for randomly deployed dense WSNs. For SDC, we propose a \emph{Cell-Based Path Scheduling} (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is

Minimum-Latency Broadcast Scheduling for Cognitive Radio Networks

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Structural Data De-anonymization: Quantification, Practice, and Implications

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