Yuansheng Luo

A multi-criteria network-aware service composition algorithm in wireless environments

Much work has been conducted to design effective and efficient algorithms for Quality of Service (QoS)-aware service algorithms in recent years. With the advent of high-capacity smart mobile devices and ubiquitous presence of wireless communications, service composition in wireless environments starts picking up momentum. While the majority of the know-how from service composition in wired fixed networks is still applicable to service composition in wireless environments, wireless networks also impose their own features that need to be specifically taken into consideration during service engineering process. To this end, this paper proposes a network-aware algorithm for service composition where network features such as network availability and delay are considered during service composing process. These network metrics incorporate with other non-network QoS parameters such as price and reputation, that are usually utilized in service composition in the current literature. Together these metrics specify the QoS constraints that need to be satisfied by a successful service composition, which is formulated as a multi-dimension multi-choice 0-1 knapsack problem (MMKP) in this paper. The evaluation results show the effectiveness of the proposed algorithm and also indicate the impact of the network parameters such as delay on the performance of service composition.

An energy efficient MCDS construction algorithm for wireless sensor networks

In wireless sensor network, a connected dominating set (CDS) can be used as a virtual backbone for efficient routing. Constructing a minimal CDS (MCDS) is good for packet routing and energy efficiency, but is an NP-hard problem. In this article, an efficient approximation MCDS construction algorithm E-MCDS (energy efficient MCDS construction algorithm) is proposed which explicitly takes energy consumption into account. E-MCDS contains two stages: the CDS construction stage and the pruning stage. The constructed CDS is approximately composed of two independent sets (IS). The performance ratio of E-MCDS is analysed in both unit disk graph and disk graphs with bidirectional links, being 9.33opt and 17.33nkopt, respectively. The message complexity of E-MCDS is O(n). The simulation results have shown that E-MCDS performs well both in terms of the size of CDS constructed and the energy efficiency.

A Real-Time Dynamic Pricing Algorithm for Smart Grid With Unstable Energy Providers and Malicious Users

In this paper, we consider a smart power model, where some subscribers share several energy providers and there are some malicious users in this power grid. The energy providers are managed by a power market scheduling center (PMSC), which broadcasts electricity price to subscribers and energy providers. The energy providers and subscribers update their capacities and energy consumption requirements, respectively, according to the electricity prices received. In order to identify the malicious users and the unstable energy providers, the mechanism of identification and processing (MIP) for the malicious users and unstable energy providers is proposed. By integrating the MIP, we proposed a heuristic algorithm called the dynamic pricing algorithm with malicious users and unstable energy providers (DPAMU) to get the optimal electricity price as well as the optimal power requirement and the load capacity. Finally, the simulation results show that the proposed DPAMU has good convergence performance and can shave and clip the peak load effectively.

Device-to-Device Service Selection Framework in Cloud Radio Access Network

Much work has been conducted to design effective and efficient algorithms for Quality of Service (QoS)-aware service selection algorithms in recent years. The wireless mobile computing and cloud computing environments have brought many challenges to QoS-aware service selection. Device-to-Device (D2D) communication and Cloud Radio Accessing Networks (C-RAN) are the new paradigm arising in recent years. This work proposes a D2D service selection framework in C-RAN. The optimal solution is deduced theoretically using queuing theory and convex optimization. The optimal and sub-optimal service selection strategy is proposed at last.

The impact of bitwise operators on hash uniformity in network packet processing

Hash functions are widely applied in network packet processing, and uniformity is one of their determinant attributes. This paper investigates into the impact of bitwise operators on the uniformity of such hash functions. To this end, we first define connection identifier as their input keyword, whose uniformity is quantized by deviation index. Then, we analyze the impact of all bitwise operators on the deviation indices of its operation results. As for binary bitwise operators, we give the conditions where AND, OR and XOR increase bit uniformity, respectively, and conclude that only XOR improves the uniformity of bits in the connection identifier effectively by verifying these conditions with real network traffic. Furthermore, we derive a positive linear relationship of the uniformity of a bit-stream and that of its any bit under simple independence assumption. As for unary bitwise operators, we prove that NOT keeps bit-stream uniformity as the same even combined with XOR. Nevertheless, we find that circular shift with XOR changes bit-stream uniformity with a great probability. In particular, a way is proposed to determine the optimal staggered number of two bit-streams before their XOR operation. Lastly, we provide guidelines to design uniform hash functions with circular shift and XOR. Copyright

A Load Balanced Charging Strategy for Electric Vehicle in Smart Grid

As the number of Electric Vehicle (EV) increases, the uncoordinated charging behaviors may cause the charging demand fluctuations and the charging load unbalanced. Besides, the users’ charging behaviors are affected by many factors. For example, the residual energy of battery decides the travel distance of EV and if an EV has more residual energy, the charging willing is lower. Because EV users don’t have much willing to change their charging time and place just as in the past, the charging habit may also affect the charging decision. In this paper, we propose a smart charging startegy CDF (Charging Decision Function), where three sub-functions related to the residual energy of battery, EV’s charging habit, and the charging efficiency of charging station are all weighted and involved, for improving the balance of charging load and reducing the charging demand fluctuations. The charging decision is resulted from the CDF’s value, and if an EV decides to charge, the charging time as well as charging place is also calculated. Compared with other two related strategies, CDF has the best performance in terms of reducing the charging demand fluctuations. The load balance among different charging stations is also improved.

Data Cost Optimization for Wireless Data Transmission Service Providers in Virtualized Wireless Networks

Cellular communication has played an important role in current data transmission networks. Nevertheless, the price of cellular communication service is much higher than wired communication service. Thus each data transmission service provider has to make optimal decisions on cellular data plan to reduce the cost. This work put forward an optimal data cost scheme for the data transmission service in virtualized wireless networks. A formal cost model is put forward and the optimal algorithms have been used to approach the optimal solution. The simulation results show the efficiency and feasibility of proposed optimal algorithms.

Connected Dominating Set Construction Algorithm for Wireless Networks Based on Connected Subset

—In this paper, a Connected Dominating Set (CDS) construction algorithm CSCDS (Connected Subset based CDS) is proposed, which is based on the connected subset concept. The CSCDS contains two main stages, which are dominating set construction stage and connected dominating set construction stage respectively. In the first stage, the dominators are proposed based on the one hop white neighbor information, and the redundant dominators are reduce to obtain the minimum number of dominators. In the second stage, the CDS is constructed based on the connected subset, which is used as the basis to select the connectors. The message complexity is O(Δ) (O(Δ) is a linear relationship function with Δ, and Δ is the maximum one hop neighbor degree), Simulation results show that CSCDS has smaller size compared with the classical algorithms.

Congestion Probability Balanced Electric Vehicle Charging Strategy in Smart Grid

In this paper, a coordinated charging strategy CCCS (Charging Congestion probability based Charging Strategy) is proposed, which considers the congestion probabilities of the charging stations (CSs), the charging costs of the electric vehicles (EVs), the distance between EV and charging station and EV users’ satisfactions. The coordinated charging issue is formulated as a convex optimization problem, which can be solved to get the distributed charging algorithms, based on which the communication system is further proposed. In order to illustrate the performance, we put forward three benchmarks. In the simulation, we combine the power grid i.e. MATPOWER and the charging module together to build the simulation platform. Simulation results show that CCCS performs well in terms of balancing the congestion probabilities, reducing charging costs, and mitigating the impacts on the power grid voltage.

A Self-Adaptive Regression-Based Multivariate Data Compression Scheme with Error Bound in Wireless Sensor Networks

Wireless sensor networks (WSNs) have limited energy and transmission capacity, so data compression techniques have extensive applications. A sensor node with multiple sensing units is called a multimodal or multivariate node. For multivariate stream on a sensor node, some data streams are elected as the base functions according to the correlation coefficient matrix, and the other streams from the same node can be expressed in relation to one of these base functions using linear regression. By designing an incremental algorithm for computing regression coefficients, a multivariate data compression scheme based on self-adaptive regression with infinite norm error bound for WSNs is proposed. According to error bounds and compression incomes, the self-adaption means that the proposed algorithms make decisions automatically to transmit raw data or regression coefficients, and to select the number of data involved in regression. The algorithms in the scheme can simultaneously explore the temporal and multivariate correlations among the sensory data. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the correlations on the same sensor node and achieve significant reduction in data transmission. Furthermore, the algorithms perform consistently well even when multivariate stream data correlations are less obvious or non-stationary.