OSCAR: A Collaborative Bandwidth Aggregation System

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface enabled devices have enabled researchers to develop solutions for those challenges. Such solutions aim to exploit available interfaces on such devices in both solitary and collaborative forms. These solutions, however, have faced a steep deployment barrier. In this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system. We present the OSCAR architecture that does not introduce any intermediate hardware nor require changes to current applications or legacy servers. The OSCAR architecture is designed to automatically estimate the system's context, dynamically schedule various connections and/or packets to different interfaces, be backwards compatible with the current Internet architecture, and provide the user with incentives for collaboration. We also formulate the OSCAR scheduler as a multi-objective, multi-modal scheduler that maximizes system throughput while minimizing energy consumption or financial cost. We evaluate OSCAR via implementation on Linux, as well as via simulation, and compare our results to the current optimal achievable throughput, cost, and energy consumption. Our evaluation shows that, in the throughput maximization mode, we provide up to 150% enhancement in throughput compared to current operating systems, without any changes to legacy servers. Moreover, this performance gain further increases with the availability of connection resume-supporting, or OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput

Revenue Maximization in an Optical Router Node Using Multiple Wavelengths

In this paper, an optical router node with multiple wavelengths is considered. We introduce revenue for successful transmission and study the ensuing revenue maximization problem. We present an efficient and accurate heuristic procedure for solving the NP-hard revenue maximization problem and investigate the advantage offered by having multiple wavelengths

Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ

Optimal pricing in a free market wireless network

We consider an ad-hoc wireless network operating within a free market economic model. Users send data over a choice of paths, and scheduling and routing decisions are updated dynamically based on time varying channel conditions, user mobility, and current network prices charged by intermediate nodes. Each node sets its own price for relaying services, with the goal of earning revenue that exceeds its time average reception and transmission expenses. We first develop a greedy pricing strategy that maximizes social welfare while ensuring all participants make non-negative profit. We then construct a (non-greedy) policy that balances profits more evenly by optimizing a profit fairness metric. Both algorithms operate in a distributed manner and do not require knowledge of traffic rates or channel statistics. This work demonstrates that individuals can benefit from carrying wireless devices even if they are not interested in their own personal communication