Jiazhen Zhou

Scalable Distributed Communication Architectures to Support Advanced Metering Infrastructure in Smart Grid

In this paper, we investigate the scalability of three communication architectures for advanced metering infrastructure (AMI) in smart grid. AMI in smart grid is a typical cyber-physical system (CPS) example, in which large amount of data from hundreds of thousands of smart meters are collected and processed through an AMI communication infrastructure. Scalability is one of the most important issues for the AMI deployment in smart grid. In this study, we introduce a new performance metric, accumulated bandwidthdistance product (ABDP), to represent the total communication resource usages. For each distributed communication architecture, we formulate an optimization problem and obtain the solutions for minimizing the total cost of the system that considers both the ABDP and the deployment cost of the meter data management system (MDMS). The simulation results indicate the significant benefits of the distributed communication architectures over the traditional centralized one. More importantly, we analyze the scalability of the total cost of the communication system (including MDMS) with regard to the traffic load on the smart meters for both the centralized and the distributed communication architectures. Through the closed form expressions obtained in our analysis, we demonstrate that the total cost for the centralized architecture scales linearly as O(λN), with N being the number of smart meters, and λ being the average traffic rate on a smart meter. In contrast, the total cost for the fully distributed communication architecture is O(λ2/3 N2/3), which is significantly lower.

A Scalable Vehicular Network Architecture for Traffic Information Sharing

In this paper we investigate the scalability of communication architectures to provide traffic information services in a vehicular network based on the peer-to-peer (P2P) network technology. We study a general scenario in a metropolitan area where there are a huge number of data sources disseminating traffic data accessible to the vehicles through a multitude of roadside units. The large quantities of both data sources and customers post paramount challenges to the network scalability design. The existing work in P2P multicast, including the clustering based architecture, can only solve the scalability problem for applications with a limited number of data sources. We study the scenario that has a large number of data sources and prove that the clustering based architecture does not scale well with the traffic load at each node. Therefore, we propose a proxy based scalable architecture, in which data download traffic on each node is proved to keep constant even when the network size grows. We further verify the accuracy of the analysis using simulations.

Network coding-aware channel allocation and routing in cognitive radio networks

In this paper, we investigate a network coding-aware channel allocation and routing scheme for multi-hop cognitive radio networks. We consider network coding and channel availability in cognitive radio networks and maximize the throughput by allocating the channel and link rate appropriately. First, we model the activities of the primary users and the interference among the secondary users in a cognitive radio network and show how to implement network coding in the multi-hop cognitive radio network. Second, we formulate an optimization problem to maximize the throughput of the network. It takes advantage of the network coding opportunities and considers the channel availability constraint. By solving this optimization problem, we can determine how to allocate channels and rates of links in different channel availability scenarios. Furthermore, we compare the performance of our scheme with the coding oblivious routing for different scenarios of channel availability and maximum number of channels in a random wireless network. Our work brings insights on the benefits of network coding-aware routing in multi-hop cognitive radio networks.

Quasi-Optimal Dual-Phase Scheduling for Pigeon Networks

In situations where communication infrastructures are destroyed or do not exist at all, implementing a disruption/delay-tolerant network (DTN) may be the only choice. A special type of DTN, which is known as a pigeon network, utilizes controllable special-purpose vehicles called pigeons to convey messages among segregated nodes and gateways. This paper studies a basic scenario in a pigeon network, where the segregated nodes are in an area that is far from the gateway. Our focus is on the optimal scheduling problem that determines how long a pigeon should stay at each node so that the average delay of messages is minimized. Although this problem is related to the server vacation model, existing literature in this area mainly focuses on the performance evaluation of different scheduling schemes, whereas the study on the optimal scheduling schemes is rarely seen, particularly for bursty traffic. In search of the optimal scheduling scheme, we first introduce a peak-valley message generation model and investigate the optimal scheduling schemes for the peak phase and the valley phase, respectively. Based on the analytical result, we then propose a quasi-optimal dual-phase (QODP) scheme. We further design a robust phase identification scheme such that a pigeon is able to apply the locally optimal scheduling scheme at the corresponding phase. Simulation studies have demonstrated QODPs adaptability in facing different types of traffic and its significant performance improvement over the existing scheduling schemes.

Traffic scheduling for smart grid in rural areas with cognitive radios

In this paper, we study the communication architecture of smart grid for rural areas that employ cognitive radio technique. Since the communication in a cognitive radio network is normally unreliable, it is a great challenge to support applications which have stringent delay requirements, for instance, the transmission line monitoring application. To this end, we propose to reroute the data traffic with stringent delay requirement through the neighboring cells that work properly. This leads to a challenging scheduling problem, with the goal of maximizing the total throughput of the network while preserving the priority of real-time traffic in the local cell. To solve this problem, we present a scheduling algorithm that outperforms the earliest deadline scheduling and priority queue scheduling. The simulation results verify the effectiveness of our algorithm in achieving the design goals.

Constructing backbone of a multi-hop cognitive radio network with channel bonding

In this paper, we propose a backbone construction scheme for multi-hop wireless cognitive radio networks (MWCRN) with a channel bonding technique. In our proposed scheme, the backbone is established purely in licensed bands using cognitive radio (CR) technology. We introduce a channel bonding technique to get higher performance of the network. To get higher reliability of MWCRN, we use backup channels in our proposed scheme. The proposed scheme includes two major steps. The first step is to formulate a backbone network; and the second step is to assign operating channels and backup channels to each backbone link. Simulation results show that the proposed scheme achieves higher network performance of multi-hop wireless cognitive radio networks.

Minimizing the Average Delay of Messages in Pigeon Networks

A type of disruption/delay tolerant networks, known as pigeon networks, utilizes controllable special purpose vehicles called pigeons to convey messages among segregated areas. This paper aims to provide optimization methods for the challenging problem that how a pigeon should design its route to minimize the average delay of messages. For small scale networks, we construct an exact optimization algorithm that shows significant reduction in delay over the existing heuristic algorithms. For large scale networks, we present both the lower bound and upper bound of the average delay, and devise partitioning-based optimization algorithms that perform close to the lower bound. Both theoretical analysis and numerical results show that the delay could be reduced by as much as 50% compared to the existing algorithm.

Scaling of On-Demand Broadcast Scheduling in Stressed Networks

The United States is deploying the broadband wireless network for public safety and emergency response. While this will provide a foundational network when disasters or other emergencies appear, the network capacity could still be insufficient when large scale emergency events, such as earthquakes or flooding, happen. On-demand broadcast is a possible solution to address this situation, but its effectiveness remains unknown. In this paper, we aim to uncover the effectiveness of on-demand broadcast at different situations. Specifically, we study the scaling of average response time on the information requests with regard to the indicators of the content diversity - the number of distinct content files and the content popularity distribution. To achieve this goal, we first derive the lower bound of the average response time. We further investigate broadcast for streaming videos and uncover that parallel broadcast could have the same lower bound as the sequential broadcast under certain conditions. Based on the lower bound we derived, we evaluate the scaling of response time with regard to the number of files under different distributions. We further provide numerical results to demonstrate the accuracy of the approximation and the value of the lower bound on evaluating the optimality of a basic heuristic on-demand broadcast scheme.

Message scheduling and delivery with vehicular communication network infrastructure

Wide deployment of communication devices on vehicles is on the horizon due to the development of intelligent transportation systems. Although these communication devices are originally designed for highway efficiency and safety applications, the mobile communication ability on vehicles also enables a lot of other emerging applications. In this paper we study an application scenario on utilizing vehicles to carry messages for a set of sensor collectors, which are deployed at places without other communication infrastructure. A core problem that we study is on how to schedule messages so that the message delay is minimized. To achieve this goal, we propose three different strategies: the first feasible vehicle strategy, the optimal bound strategy, and the multiple attempt strategy. We further provide mathematical analysis on these strategies that are also verified by simulations, and compare the performance of these three strategies in simulations.We draw the conclusion that the optimal bound strategy is an effective strategy as it achieves low delay of the messages. At the same time it avoids the disadvantages such as high energy consumption and large storage size as in the multiple attempt strategy.

Application-aware routing for multi-hop cognitive radio networks with channel bonding

Due to the unequal spectrum resources in different areas, multi-hop wireless cognitive radio network (MWCRN) faces the challenge of a relatively poor performance. In this paper, we propose an application-aware routing scheme with channel bonding technique to help improve the efficiency and performance of a MWCRN network. More specifically, we formulate an optimization problem aiming to meet the application request, as well as operating at low transmission cost. To solve this problem, we first study the greatest lower bound of transmission cost named as Utopian cost, as well as an upper bound of transmission cost. Moreover, we propose two distributed algorithms for practical applications. Simulation results show that the proposed application-aware scheme achieves low transmission cost in MWCRN while satisfying demands of application requests.