Riheng Jia

Asymptotic Analysis on Throughput and Delay in Cognitive Social Networks

In this paper, we study the throughput and delay in wireless cognitive social networks. Specifically, we consider a common scenario for cognitive radio networks (CRNs) where the primary and secondary networks operate at the same time and space and share the spectrum. On this basis, we integrate a social relationship into the CRN where each source node selects its destination upon a rank-based model, which captures the social characteristic well. By applying a cellular time-division multiple-access scheduling scheme, we first characterize the distinct traffic pattern caused by the social relationships between nodes. Then, we derive the achievable throughput and delay for both primary and secondary networks under the new network setting. In addition, we also study the cognitive social networks with infrastructure where I = o1(n) base stations are regularly deployed within the primary network. Given a probabilistic routing strategy, throughput of the proposed network is recalculated. Particularly, due to the social relationships between nodes, we reveal that a larger I is required if we expect a significant capacity gain within the primary network compared with previous works.

Optimal Capacity–Delay Tradeoff in MANETs With Correlation of Node Mobility

In this paper, we analyze the capacity and delay in mobile ad hoc networks (MANETs), considering the correlation of node mobility (correlated mobility). Previous studies on correlated mobility investigated the maximum capacity with the corresponding delay in several subcases; the problem of optimal capacity under various delay constraints (the optimal capacity–delay tradeoff) still remains open. To this end, we deeply explore the characteristics of correlated mobility and figure out the fundamental relationships between the network performance and the scheduling parameters. Based on that, we establish the overall upper bound of the capacity–delay tradeoff in all the subcases of correlated mobility. Then, we try to obtain the achievable lower bound by identifying the optimal scheduling parameters on certain constraints. Results demonstrate the whole picture of how the correlation of node mobility impacts the capacity, the delay, and the corresponding tradeoff between them.

Data Backlog Analysis in Energy Harvesting Communication Systems

Energy harvesting enables the wireless devices to obtain energy for communication from the ambient environment. A general theme in prior works is to investigate the power scheduling policies to increase the utility ratio of the harvested energy, which arrives at random. One key assumption is the infinite data backlog, which means that as long as there is energy, there is data to transmit. However, in real systems, the buffer size is limited, and the arrival of data is also random. When the data backlog fills up the buffer, the subsequent arrival packets will be discarded directly. Therefore, we are motivated to jointly consider the data arrival and energy arrival processes in an energy harvesting communication system (EHCS). Specifically, we first derive the maximum average throughput

Modeling Dynamic Adaptive Streaming Over Information-Centric Networking

\bar {r}

Auction based channel allocation in multi-hop networks

that EHCS can support with a simple online power scheduling scheme. Then, given a data arrival process whose average rate

A hierarchical approach for resource allocation in hybrid cloud environments

\lambda <\bar {r}

Throughput and Delay in Heterogeneous Cognitive Radio Networks with Cooperative Secondary Users

, we characterize the average data backlog for both constant and random data arrivals. Some further analyses are conducted to the variation of data backlog. To achieve a same packet drop rate, the buffer size needed for constant data arrivals is much smaller than that for random data arrivals, which can be seen from both our theoretical and simulation results. The analysis in this paper initiates a first step towards a more dynamic energy harvesting system, where data arrivals are of importance.

Scaling Laws for Heterogeneous Cognitive Radio Networks with Cooperative Secondary Users

The dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-scale deployment of the dynamic adaptive streaming technique. Specifically, we design the network as a two-layer coordinating structure, namely, the control layer and the transmission layer. The control layer employs the statistical data recorders to record the variations of the video popularity, link states, and user demands. On the other side, the network forwards user requests and caches data packets in the transmission layer, based on the statistical data which is obtained in the control layer. In addition, the network executes the real-time monitoring of link conditions in the transmission layer, and adjusts the video bit-rate accordingly. Under the above feedback circumstance, we first develop a distributed algorithm of joint dynamic forwarding and caching to theoretically maximize the total user demand rates within the network stability region. Then, we modify the distributed algorithm with a practical caching strategy to make the system applicable to real scenarios. Simulation results show the superior performance of the modified distributed algorithm in terms of low user delay and high QoE performance.

Dynamic Task Assignment in Crowdsensing with Location Awareness and Location Diversity

With the rapid development of wireless network, demand for the spectrum accessing increases dramatically. However, if selfish users can access the spectrum arbitrarily, it may lead to serious interference problem for the network. In our paper, the spectrum owner in the network regulates the spectrum accessing of radio devices. It uses a new kind of auction called VARYVER to efficiently allocate the channels. We prove that VARYVER auction not only guarantee the truthfulness but also does well in ensuring the fairness among nodes in our multi-hop, multi-channel network. Spectrum owner first determines the winners of the auction, then uses some algorithms to determine the users of the channel based on the information VARYVER collect. Finally, spectrum owner charges the users and redistribute the revenue of the network to both winners and users. Simulation results show that our LP Algorithm does well in both maximizing the throughput of our network and ensuring the fairness among nodes while the Greedy Algorithm may cause data congestion in multi-hop network.

Correction to “Asymptotic Analysis on Throughput and Delay in Cognitive Social Networks”

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.