Chenkai Yang

Primary Secrecy Is Achievable: Optimal Secrecy Rate in Overlay CRNs with an Energy Harvesting Secondary Transmitter

To tackle the challenging secrecy communication problem in energy harvesting cognitive radio networks, this paper considers an overlay system with one energy harvesting secondary user (SU) to assist primary transmission under the assumption that the primary channel at primary receiver is worse than the eavesdropper. Under such scenario, we optimize the secrecy rate of the PU transmitter by jointly investigating energy harvesting slot, cooperative transmission slot and so on. Given the transmission rate requirement between SUs, the optimization problem is formulated as a mixed integer non-linear (MINLP) program. Due to the special features, we design a polynomial time algorithm SRMA to optimally solve this problem. The algorithm computes the lower bound and upper bound of the transmission power in a secondary transmitter, which are relative with the QoS requirement and energy harvesting parameters. Then SRMA determines its optimal transmission power by iteratively searching between two bounds. Numerical results demonstrate that the primary secrecy rate grows with the increasing energy save ratio and optimal energy save ratio is inversely proportional to the energy harvesting rate.

Joint channel and sink assignment for data collection in cognitive wireless sensor networks

Summary Data collection is a fundamental operation in cognitive wireless sensor networks (CWSNs). However, previous works on data collection assume that a node can transmit data to any node within its transmission range, which is not reasonable in CWSNs. To ensure that the previous works can be applied in CWSNs, this paper focuses on the joint channel and sink assignment problem, which is a critical preparatory work for data collection in CWSNs. Because the capacity performance of a CWSN is usually considered as a key problem in fundamental understanding, we are interested in finding a joint channel and sink assignment for each sensor node, such that the minimum capacity of all sensor nodes is maximized. We formulate our problem as a mixed integer linear program by some elaborate mathematical skills. Then, two algorithms based on greedy strategy and linear relaxation technique are proposed. Extensive simulations results show that our algorithms are efficient and able to achieve near-optimal performance. Copyright

Optimizing virtual machine placement in distributed clouds with M/M/1 servers

As more and more applications migrate into clouds, the placement of virtual machines for these applications has a significant impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement in distributed clouds with stochastic requests from the tenants. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, based on the analyses of distributed cloud resources, the VMs with their data nodes are modeled as simple M/M/1 queueing systems. Then, we propose the problems of VM placement with two distinct optimization objectives. For each objective, we present the formal definition and prove its NP-hardness. To deal with them, we propose some algorithms and the performances of them are analysed in each section. For applying to the situation of lacking of resource, we propose two extended algorithms. We conduct abundant simulation experiments in distributed cloud environment to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms can significantly improve the performance of their corresponding objectives.

(Invited) popularity-based content replication scheme for wireless mesh network

Wireless Mesh Network (WMN) is now being extensively used as a cost-effective means for coverage extension and backhaul relaying. One of the key challenges it suffers from is data access efficiency arising from resource constraints of wireless communications. Therefore, many content replication schemes based on the popularity of objects have been proposed for WMNs. However, most of the existing works on object replication schemes in WMNs ignore the restricted service rate of the replica servers. In this paper, we investigate the techniques of popularity-based content replication in WMNs to optimize the overall performance of average latency. We define the popularity of objects and model the replica server as simple M/M/1 queuing systems. Then, a formal definition and formalization of the popularity-based replication problem will be given. We show this problem is NP-complete and propose a distributed scheme composed of two-phase. We conduct abundant simulation experiments to evaluate the performance of our proposed scheme. The simulation results show that the proposed scheme gains much lower average latency without increasing the network load.

Achieving primary secrecy in energy harvesting CRNs using stackelberg game

Physical layer security is a critical issue in cognitive radio networks (CRNs). This paper thus focuses on the joint primary user (PU) secrecy rate maximization and secondary user (SU) transmission capacity optimization problem in overlay CRNs. The PU leases its partial transmission spectrum to the SU for cooperative relaying opportunity by the SU. Different from previous works, the SU transmitter is energy constrained and can harvest energy from the environment. Both the PU and SU are selfish which means they only concentrate on their own performance. Given energy harvesting rate, we study the communication between PU and SU using Stackelberg game. A unique Nash Equilibrium point is achieved through mathematical analysis. Then we derive a distributed algorithm to jointly schedule the PU and SU behaviors in the network system. Both analytical result and simulation result demonstrate that both of PU network and SU network can achieve their optimal utilities.

Replica placement in content delivery networks with stochastic demands and M/M/1 servers

Content Delivery Network (CDN) is proposed for replicating data objects at multiple locations in the network and encounters vast potential for future development, as a result of which, a number of replica placement techniques have been proposed over the last decade. However, most of the existing works on replica placement (RP) ignore the statistical property of the demands and the restricted service rate of the servers. In this paper, we investigate the techniques of replica placement in CDNs with stochastic demands and M/M/1 servers to optimize the overall performance in the network. We first model the demands and the servers as independent Poisson streams and simple M/M/1 queueing systems, respectively. Then, a formal definition and formalization of RP problem will be given. We show that RP problem is NP-complete and propose two heuristic algorithms: Greedy Dropping (GD) and Tabu Search (TS). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. According to our simulation results, both of the two algorithms are efficient in finding a feasible solution with high probability. Especially, the TS decreases the average delay of the demands about 50% on average.

A self-adaptive reconfiguration scheme for throughput maximization in municipal WMNs

Wireless mesh networks (WMNs) are being used and deployed widely all around the world for various reasons such as public safety, environmental monitoring and city-wide wireless Internet services [4], [12]. Although faced with some difficulties, wireless mesh network is thought to be a preferred municipal Internet service provider [5] with huge potential due to its automatic connection, ease of installation, dynamic route discovery, flexibility and other advantages. Since plenty of cities such as Singapore and the city of Cambridge have put providing ubiquitous Internet access on the agenda [3], the research on providing QoS-guaranteed municipal WMNs is in urgent need.

A scale-independent way for differential estimation in dynamic radio frequency identification systems

Cardinality estimation in radio frequency identification systems has been applied to estimate the population of tags in many applications. However, it is more meaningful to estimate the number of tags moved in and out in a dynamic radio frequency identification system, which is called differential estimation problem. Zero differential estimator is a newly proposed algorithm to solve this problem. However, the time slots consumed by zero differential estimator are relevant to the system scale under the accuracy constraint. This will result in low time efficiency when the system scale is very large. In this paper, we thus propose a scale-independent algorithm for differential estimation called zero-one differential estimator. The numbers of tags moved in and out are estimated from the idle slots in two consecutive frames. We can prove that the time slots consumed in our proposed algorithm are not relevant to the system scale under the accuracy constraint. Moreover, we conduct abundant simulations to evaluate the performance of the proposed approach. The simulation results show that the estimation error grows little as the system scale grows. It indicates that our proposed algorithm is indeed scale-independent. Copyright

Minimizing response latency via efficient virtual machine placement in cloud systems

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.