Zhenchun Wei

The dynamic routing algorithm for renewable wireless sensor networks with wireless power transfer

Wireless power transfer is recently considered as a potential approach to remove the lifetime performance bottleneck for wireless sensor networks. By using a wireless charging vehicle (WCV) to periodically recharge each sensor nodes battery, a wireless sensor network may remain operational forever. In this paper, we aim to jointly optimize a dynamic multi-hop data routing, a traveling path (for the WCV to visit all the sensor nodes in a cycle), and a charging schedule (charging time for each sensor node) such that the ratio of the WCVs vacation time over the cycle time can be maximized. The key challenge of this problem (caused by time-varying data routing) is the integration and differentiation terms in problem formulation, which yields a very challenging non-polynomial program. To remove these non-polynomial terms, we introduce the concept of ( N + 1 ) -phase solution, which adopt a special dynamic routing scheme. We prove that an optimal ( N + 1 ) -phase solution can achieve the same objective value as that by an optimal time-varying solution. We further prove that the optimal traveling path must follow the shortest Hamiltonian cycle. Finally, we linearize the problem for data routing and charging schedule and thus obtain an optimal solution in polynomial-time.

An efficient interference management framework for multi-hop wireless networks

Interference management is an important problem in wireless networks. In this paper, we focus on the successive interference cancellation (SIC) technique, and aim to design an efficient cross-layer solution to increase throughput for multi-hop wireless networks with SIC. We realize that the challenge of this problem is its mixed integer linear programming formulation, which has bunches of integer variables. In order to solve this problem efficiently, we propose an iterative framework to improve the solution for integer variables and use a linear programming to solve the problem for other variables. Our analysis indicates that the proposed algorithm is with polynomialtime complexity. Simulation results show that SIC can increase throughput of a multi-hop wireless network by around 300%.

Optimal Base Station Placement for Wireless Sensor Networks with Successive Interference Cancellation

We consider the base station placement problem for wireless sensor networks with successive interference cancellation (SIC) to improve throughput. We build a mathematical model for SIC. Although this model cannot be solved directly, it enables us to identify a necessary condition for SIC on distances from sensor nodes to the base station. Based on this relationship, we propose to divide the feasible region of the base station into small pieces and choose a point within each piece for base station placement. The point with the largest throughput is identified as the solution. The complexity of this algorithm is polynomial. Simulation results show that this algorithm can achieve about 25% improvement compared with the case that the base station is placed at the center of the network coverage area when using SIC.

Cost Minimization Algorithms for Data Center Management

Due to the increasing usage of cloud computing applications, it is important to minimize energy cost consumed by a data center, and simultaneously, to improve quality of service via data center management. One promising approach is to switch some servers in a data center to the idle mode for saving energy while to keep a suitable number of servers in the active mode for providing timely service. In this paper, we design both online and offline algorithms for this problem. For the offline algorithm, we formulate data center management as a cost minimization problem by considering energy cost, delay cost (to measure service quality), and switching cost (to change serverss active/idle mode). Then, we analyze certain properties of an optimal solution which lead to a dynamic programming based algorithm. Moreover, by revising the solution procedure, we successfully eliminate the recursive procedure and achieve an optimal offline algorithm with a polynomial complexity. For the online algorithm, We design it by considering the worst case scenario for future workload. In simulation, we show this online algorithm can always provide near-optimal solutions.

The Power Control Strategy for Mine Locomotive Wireless Network Based on Successive Interference Cancellation

In a mine locomotive wireless network, multiple locomotives move along a tunnel and communicate with access points (APs) on the side of this tunnel. The underground working environment is not safe and thus it is important to maintain high quality communication links. We consider throughput maximization for a mine locomotive wireless network with successive interference cancellation (SIC) and power control. We define time segments such that within each segment, the set of locomotives that can communicate with an AP is fixed and the distance from each locomotive to this AP can be approximated as a constant. To maximize throughput for each segment, we first prove the existence of optimal solutions that satisfy certain features on SIC decoding order and SINR under SIC. Then we can formulate a linear programming problem to obtain optimal solutions. We further propose a concept of the maximum SIC set to reduce problem size and obtain a polynomial complexity algorithm. Simulation results show that our algorithm can increase throughput significantly by comparing with the algorithm using SIC only (no power control) and comparing with the algorithm without using SIC and power control.

Reinforcement Learning for a Novel Mobile Charging Strategy in Wireless Rechargeable Sensor Networks

The charging strategy for the mobile charger (MC) has been a hot research topic in wireless rechargeable sensor networks. We focus on the charging path for the MC, since the MC stops at each sensor node until the sensor node is fully charged. Most of the existing reports have designed optimization methods to obtain the charging path, with the target like minimizing the charging cost. However, the autonomous charging path planning for the MC in a changeable network is not taken into consideration. In this paper, Reinforcement Learning (RL) is introduced into the charging path planning for the MC in WRSNs. Considering the influences of the energy variation and the locations of the sensor nodes, a novel Charging Strategy in WRSNs based on RL (CSRL) is proposed so that the autonomy of the MC is improved. Simulation experiments show that CSRL can effectively prolong the lifetime of the network and improve the driving efficiency of the MC.

A Multi-objective Algorithm for Joint Energy Replenishment and Data Collection in Wireless Rechargeable Sensor Networks

In the existing researches on the Wireless Rechargeable Sensor Networks (WRSNs), the charging path is scheduled firstly, and then the method of data collection is decided based on the path, which fails to ensure the high charging service quality and the performance of data collection. To solve this problem, a multi-objective path planning optimization model is proposed with the objectives of maximizing the remaining lifespan of sensor nodes and the amount of data collection. To deal with it, a Multi-Objective Discrete Fireworks Algorithm (MODFA) based on grid is proposed in this paper. Simulation results show that the algorithm proposed has better performance than NSGA-II, SPEA-II and MOEA/D in term of the diversity and convergence of Pareto front.

The mine locomotive wireless network strategy based on successive interference cancellation with dynamic power control

Mine locomotives are widely used in mining industry for transporting. Usually, these locomotives need to move along the tunnels and communicate with access points which are equipped on the side of these tunnels. It is important to maintain high-quality communication services due to the unsafe underground working environment. In this article, we design the mine locomotive wireless network strategy based on successive interference cancellation with dynamical power control. We first divide the whole schedule time into time segments and build the problem model in each time segment. To maximize throughput for each time segment, we formulate a linear programming problem based on certain features of successive interference cancellation decoding order. However, this problem has lots of constraints which makes it hard to solve in polynomial time. Then, we propose a concept of the maximum successive interference cancellation set to reduce the problem size. Based on this concept, we propose a polynomial complexity algorithm named max-SIC-set algorithm. Simulation results show that our algorithm can increase throughput significantly compared with the algorithm using successive interference cancellation only (no power control) and with the algorithm without using successive interference cancellation and power control.

A task scheduling algorithm based on Q-learning and shared value function for WSNs

Abstract In dynamic Wireless Sensor Networks (WSNs), each sensor node should be allowed to schedule tasks by itself based on current environmental changes. Task scheduling on each sensor node should be done online towards balancing the tradeoff between resources utilization and application performance. In order to solve the problem of frequent exchange of cooperative information in existing cooperative learning algorithms, a task scheduling algorithm based on Q-learning and shared value function for WSNs, QS is proposed. Specifically, the task model for target monitoring applications and the cooperative Q-learning model are both established, and some basic elements of reinforcement learning including the delayed rewards and the state space are also defined. Moreover, according to the characteristic of the value of the function change, QS designs the sending constraint and the expired constraint of state value to reduce the switching frequency of cooperative information while guaranteeing the cooperative learning effect. Experimental results on NS3 show that QS can perform task scheduling dynamically according to current environmental changes; compared with other cooperative learning algorithms, QS achieves better application performance with achievable energy consumption and also makes each sensor node complete its functionality job normally.

A Theoretical Study on the Orientation Problem in Linear Wireless Sensor Networks

A theoretical approach of acquiring arrival angles of signals sensed by sensor nodes in linear wireless sensor networks is introduced. The arrival angles of signals can be obtained by the estimation of signal covariance matrices. In this article, firstly, the existence of the solution to the estimation problem is studied intensively. Later on, the solution to this problem of estimating real-valued covariance matrices is discussed by the approach of maximum-likelihood estimation. Finally, this approach is expanded to the realm of complex-valued covariance matrices.