Baohua Zhao

ESWC: Efficient Scheduling for the Mobile Sink in Wireless Sensor Networks with Delay Constraint

This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks where the information delay caused by moving the sink should be bounded. Due to the combinational complexity of this problem, most previous proposals focus on heuristics and provable optimal algorithms remain unknown. In this paper, we build a unified framework for analyzing this joint sink mobility, routing, delay, and so on. We discuss the induced subproblems and present efficient solutions for them. Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin problem. In simulations, we show the benefits of involving a mobile sink and the impact of network parameters (e.g., the number of sensors, the delay bound, etc.) on the network lifetime. Furthermore, we study the effects of different trajectories of the sink and provide important insights for designing mobility schemes in real-world mobile WNNs.

Scalable video multicast with adaptive modulation and coding in broadband wireless data systems

Future mobile broadband networks are characterized with high data rate and improved coverage, which will enable real-time video multicast and broadcast services. Scalable video coding (SVC), combined with adaptive modulation and coding schemes (MCS) and wireless multicast, provides an excellent solution for streaming video to heterogeneous wireless devices. By choosing different MCSs for different video layers, SVC can provide good video quality to users in good channel conditions while maintaining basic video quality for users in bad channel conditions. A key issue to apply SVC to wireless multicast streaming is to choose appropriate MCS for each video layer and to determine the optimal resource allocation among multiple video sessions. We formulate this problem as total utility maximization, subject to the constraint of available radio resources. We prove that the formulated problem is NP-hard and propose an optimal, two-step dynamic programming solution with pseudo-polynomial time complexity. Simulation results show that our algorithm offers significant improvement on the video quality over a naive algorithm and an adapted greedy algorithm, especially in the scenarios with multiple real video sequences and limited radio resources.

Joint Resource Allocation and User Association for SVC Multicast Over Heterogeneous Cellular Networks

Scalable video coding (SVC) is attractive technology for multicasting video to users with different available transmission capacities. In this paper, we investigate the joint optimization of resource allocation and user association problems for SVC multicast over heterogeous cellular networks (HetNet) employing the schemes of cell range expansion (RE) and almost blank subframe (ABS). We solve the joint optimization problem by decoupling it into two problems, namely, resource allocation (RA) subproblem and user association (UA) master problem. For the RA subproblem, we propose a dynamic programming based algorithm to optimally set the transmission profile. For the UA master problem, we propose a similarity-based negotiation protocol (SBNP) based algorithm to obtain the Pareto-optimal range expansion bias. The simulation results demonstrate the efficiency of these algorithms.

Maximize Lifetime of Heterogeneous Wireless Sensor Networks with Joint Coverage and Connectivity Requirement

In this paper, we address the problem of lifetime maximization under coverage and connectivity requirements for heterogeneous wireless sensor networks where different targets need to be covered (sensed ) by different types of wireless sensors running at possibly different sampling rates as well as different initial energy reserve. The problem is particularly challenging since we need to consider both connectivity requirement and so-called target

Wireless data multicasting with switched beamforming antennas

\mathcal{Q}

Target coverage problem in wireless sensor networks: A column generation based approach

-coverage requirement, i.e., different targets may require different sensing quality in terms of the number of transducers, sampling rate, sensing data rate, etc. In this paper, we formulate a lifetime maximization problem, which is general and allows unprecedented diversity in coverage requirements, sampling rates, transmission energy consumption models, communication ranges, and target sensing ranges. Our approach is based on column generation, where a column corresponding to a feasible solution; our idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the maximum lifetime solution. Through extensive simulations, we systematically study the effect of sampling rates, transmission energy consumption models, communication ranges, and target sensing ranges on the lifetime.

Towards an Optimal Sink Placement in Wireless Sensor Networks

Using beamforming antennas to improve wireless multicast transmissions has received considerable attention recently. The work in [7] proposes to partition all single-lobe beams into groups and to form composite multi-lobe beam patterns to transmit multicast traffic. The key challenge left is, how to partition the beams into multiple groups in order to minimize the total multicast delay. This work makes significant progress in both algorithmic and analytic aspects of the problem. We prove that, under the asymmetric power split (ASP) model, it is NP-hard to have ( 3 over 2 − ε)-approximation algorithm for any ε > 0. We then develop an APTAS, an asymptotic ( 3 over 2 + β)-approximation solution (where β ≥ 0 depends on the wireless technology), and an asymptotic 2-approximation solution to the problem by relating the problem to a generalized version of the bin-packing problem. Extensive trace-driven simulations based on real-world channel measurements corroborate our analytical results by showing significant improvement compared to state of the art algorithms.

A Global-Energy-Balancing Real-Time Routing in Wireless Sensor Networks

Target coverage problem in wireless sensor networks remains a challenge. Due to nonlinear nature, previous work has mainly focused on heuristic algorithms, which remain difficult to characterize and have no performance guarantee. To solve the problem, this paper offers two important contributions.

EMS: Efficient mobile sink scheduling in wireless sensor networks

Recently, sink deployment, in the form of deploying the sink among different sites so as to leverage traffic burden, is shown to be a promising scheme to save energy and prolong network lifetime in wireless sensor networks. For this paradigm, the choice of sink sites plays a critical role in the overall system performance. In this paper, we address the optimal deployment problem for the sink in wireless sensor networks, where routing issues are naturally involved. The major contribution of this paper is the development of an efficient grid-based algorithm to solve this problem. By dividing the continuous search space into a limited number of so-called communication intersections, computational complexity has been significantly reduced. A formal proof of optimality for this algorithm is given and several interesting properties have been revealed by theoretic analysis as well as experimental results.

A fast, simple and near-optimal content placement scheme for a large-scale VoD system

Many applications in wireless sensor networks like video surveillance have the requirement of timely data delivery. Real-time routing is needed in these applications. Because of the limitation of node energy, energy efficiency is also an important concern in routing protocol design in order to increase the network lifetime. In this paper, we propose a global-energy-balancing routing scheme (GEBR) for real-time traffic based on directed diffusion (DD), which balances node energy utilization to increase the network lifetime. GEBR can find an optimal path in sensor networks for data transfer considering global energy balance and limited delivery delay. Simulation results show that GEBR significantly outperforms DD in uniform energy utilization. GEBR achieves better global energy balance and longer network lifetime. The time for real-time service of the sensor networks using GEBR is prolonged by about 4.37% and the network lifetime is prolonged by 44.6%.