Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for network routing in multi-radio multi-channel wireless mesh networks under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By analyzing the traces collected at wireless access points, the traffic estimation component predicts future traffic demand based on its historical value using time-series analysis, and represents the prediction result in two forms - mean value and statistical distribution. The optimal mesh network routing strategies then take these two forms of traffic demand estimations as inputs. In particular, two routing algorithms are proposed based on linear programming which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate traffic dynamics in mesh network routing, where both algorithms outperform the shortest path algorithm in about 80% of the test cases.

Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution in practice, one must take into account the dynamic and uncertain nature of wireless traffic demand. There are two basic approaches to address the traffic uncertainty in network routing: (1) predictive routing which infers the traffic demand with maximum possibility based in its history and optimizes the routing strategy based on the predicted traffic demand and (2) oblivious routing which considers all the possible traffic demands and selects the routing strategy where the worst-case network performance could be optimized. This paper conducts a systematic comparison study of these two approaches based on the extensive simulation study over a variety of network and traffic scenarios. It identifies the key factors of the network topology and traffic profile that affect the performance of each routing strategy and provides guidelines towards the strategy selection in mesh network routing under uncertain traffic demands.

High-throughput P2P streaming relies on peer selection, the strategy a peer uses to select other peer(s) as its parent(s) of streaming. Although this problem has been thoroughly investigated in the classical optimization framework, it still remains unaddressed as how to sustain throughput competitive to the optimum under highly dynamic peer churning. We propose an online distributed peer selection algorithm based on "shortest- path" routing where the overlay edge length is an asymptotic function of its traffic load. This basic algorithm is further extended under practical settings such as multi-parent streaming, admission control, delay constraint, and simplified topology. In all above settings, we prove approximation bound of our algorithm to the optimal throughput. Through evaluation under different topological setups and peer churning sequences, we show our solution to consistently deliver competitive throughput, which greatly outperform its theoretical bound.

Layered streaming is an effective solution to address the receiver heterogeneity in peer-to-peer (P2P) multimedia distribution. This paper targets a fundamental challenge in this application, i.e., how to find optimal routing structure maximizing receiver throughput and achieving intra-layer and inter-layer fairness. We formulate the problem using the multicommodity flow theory, and propose a series of routing algorithms that fully explore the tradeoff between theoretical optimality and practicability. For each algorithm, we have proved its optimality to achieve maximum throughput under fairness constraint, or approximation bound to the optimal rate. Experimental results confirm our algorithms to greatly outperform their theoretical bounds.

P2P (peer-to-peer) technology has proved itself an efficient and cost-effective solution to support large-scale multimedia streaming. Different from traditional P2P applications, the quality of P2P streaming is strictly determined by performance metrics such as streaming delay. To meet these requirements, previous studies resorted to intuitions and heuristics to construct peer selection solutions incorporating topology and proximity concerns. However, the impact of proximity-aware methodology and delay tolerance of peers on the scalability of P2P system remains an unanswered question. In this paper, we study this problem via an analytical approach. To address the challenge of incorporating Internet topology into P2P streaming analysis, we construct a H-sphere network model which maps the network topology from the space of discrete graph to the continuous geometric domain, meanwhile capturing the the power-law property of Internet. Based on this model, we analyze a series of peer selection methods by evaluating their performance via key scalability metrics. Our analytical observations are further verified via simulation on Internet topologies.

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. Network routing plays a critical role in determining the performance of a wireless mesh network. To study the best mesh network routing strategy which can maximize the network throughput while satisfying the fairness constraints, existing research proposes to formulate the mesh network routing problem as an optimization problem. These works usually make ideal assumptions such as known static traffic input. Whether they could be applied for practical use under the highly dynamic and uncertain traffic in wireless mesh network is still an open issue. The main objective of this paper is to understand the effects of traffic uncertainty in wireless mesh networks and consider these effects in throughput maximization routing. It identifies the appropriate optimization framework that could integrate the statistical user traffic demand model into a tractable throughput maximization problem. The trace-driven simulation study demonstrates that our algorithm can effectively incorporate the traffic demand uncertainty in routing optimization, and outperform the throughput maximization routing which only considers static traffic demand.

Video sensor network is evolving from an isolated system to an integral component of the global information infrastructure. In this paper, we argue that when video sensor network becomes a public information source on the Internet, DRM (digital rights management) must be enforced, due to the sensitivity and the privacy natures of sensor content. Moreover, existing DRM solutions do not suffice because the explicit one-to-one mapping between content producer and consumer does not apply in the sensor network domain. We propose a DRM-enabled content service architecture for video sensor network. Within this architecture, we propose a binary-tree-based hierarchical key generation scheme for data encryption, and adopt a label-guided watermarking strategy to address the unique challenges of video sensor content. We present the evaluation results of our solution based on a preliminary video sensor testbed system