With the emergence of cloud computing, users are increasingly using storage services of cloud providers to satisfy their storage needs. However, these cloud providers are not 100% reliable, resulting in periodic service outages. End users require a solution that minimizes the impact of cloud service outages and optimizes the chances of successfully retrieving stored objects in cases of provider failures. In this paper, we propose to build a federated cloud agent, enabling end users to simultaneously deploy storage objects to multiple cloud service providers, based on their unit storage costs and failure probabilities. We study two service provider failure models: the first case assumes that at most one service provider fails at any given time, and the second case assumes that cloud service providers may fail independently with certain probabilities. We cast both scenarios as constrained optimization problems to minimize the overall storage cost while enhancing the chances of recovering all the storage objects in case of service outages. In the experiments, we evaluated the proposed optimal storage schemes against a fixed server deployment strategy and find our schemes can achieve good recovery ratios, while incurring minimal storage costs.

Efficient transmission control (e.g., power/rate control) in the physical layer, link scheduling in the link layer and routing in the network layer are critical design issues of wireless ad hoc networks. By considering both the quality-of-service and the utilization of resources under the temporally correlated uncertainties of the network conditions, a sequential decision framework is proposed for protocol design and layering. By observing that there are usually different performance concerns in the network layer versus lower layers, two correlated sequential decision models for the operations at different layers are proposed. With the decision model in the link and physical layers, scheduling and transmission control are jointly optimized. By adding a decision model in the network layer, the benefit of cross-layer information is quantified and the optimal routing/forwarding strategy is characterized. Practical algorithms based on the proposed decision models are also developed to achieve optimal/near-optimal performance.

We consider the location service in a mobile ad-hoc network (MANET), where each node needs to maintain its location information by 1) frequently updating its location information within its neighboring region, which is called neighborhood update (NU), and 2) occasionally updating its location information to certain distributed location server in the network, which is called location server update (LSU). The trade off between the operation costs in location updates and the performance losses of the target application due to location inaccuracies (i.e., application costs) imposes a crucial question for nodes to decide the optimal strategy to update their location information, where the optimality is in the sense of minimizing the overall costs. In this paper, we develop a stochastic sequential decision framework to analyze this problem. Under a Markovian mobility model, the location update decision problem is modeled as a Markov Decision Process (MDP). We first investigate the monotonicity properties of optimal NU and LSU operations with respect to location inaccuracies under a general cost setting. Then, given a separable cost structure, we show that the location update decisions of NU and LSU can be independently carried out without loss of optimality, i.e., a separation property. From the discovered separation property of the problem structure and the monotonicity properties of optimal actions, we find that 1) there always exists a simple optimal threshold-based update rule for LSU operations; 2) for NU operations, an optimal threshold-based update rule exists in a low-mobility scenario. In the case that no a priori knowledge of the MDP model is available, we also introduce a practical model-free learning approach to find a near-optimal solution for the problem.

We consider the location service in a mobile ad-hoc network (MANET), where each node needs to maintain its location information in the network by (i) frequently updating its location information within its neighboring region, which is called neighborhood update (NU), and (ii) occasionally updating its location information to a certain (fixed) distributed location server in the network, which is called location server update (LU). A trade-off exists between the costs in location update operations, on one hand, and the additional incurred costs in (position-based) routing due to location errors, on the other hand. In this paper, we develop a stochastic sequential decision framework to analyze this trade-off and provide design guidelines on selecting good location update strategies in practice. Under a Markovian mobility model, the location update decision problem is modeled as a Markovian decision process (MDP). Based on the separable cost structure of the proposed MDP model, we first show that the location update decisions on NU and LU can be independently carried out without loss of optimality. Then we investigate the optimality of simple threshold-based updating rules in NU and LU operations. We finally introduce a model-free learning approach which is practically useful to find a near-optimal solution for the problem.

In this paper, we investigate the low-complexity synchronization design for multi-band orthogonal-frequency-division-multiplexing (MB-OFDM) ultra-wideband (UWB) systems. We propose a unified synchronizer design based on auto-correlation-function. The key component in the proposed synchronizer is a parallel signal detector structure in which multiple auto-correlation units are instantiated and their outputs are shared by other functional units in the synchronizer, including time-frequency pattern detection, symbol timing, carrier frequency offset estimation and correction and frame synchronization. We show that, via analysis and simulations, such a design achieves not only a low computation cost which makes it attractive in implementation, but also equal or better performance compared to the cross-correlation based designs.

We develop an analytical framework to study the throughput and routing overhead for proactive and reactive routing strategies in random access mobile ad hoc networks. To characterize the coexistence of the routing control traffic and data traffic, we model the interaction as a multi-class queue model at each node, where the aggregate control traffic and data traffic are two different classes of customers of the queue. We investigate the scaling property of the throughput, maximum mobility degree supported by the network and mobility-induced throughput deficiencies, under both classes of routing strategies. The proposed analytical model can be extended to incorporate various optimization techniques in routing. We present one exemplary technique and discuss its impacts on the scaling properties of throughput and routing overhead. The connection between the derived throughput result and some well-known network throughput capacity results in the literature is also addressed.

In order to adapt to time-varying wireless channels, various channel-adaptive schemes have been proposed to exploit inherent spatial diversity in mobile/wireless ad hoc networks where there are usually alternate next-hop relays available at a given forwarding node. However, current schemes along this line are designed based on heuristics, implying room for performance enhancement. To seek a theoretical foundation for improving spatial diversity gain, we formulate the selection of the next-hop as a sequential decision problem and propose a general "optimal stopping relaying (OSR)" framework for designing such next-hop diversity schemes. As a particular example, assuming Rayleigh fading channels, we implement an OSR strategy to optimize information efficiency (IE) in a protocol stack consisting of greedy perimeter stateless routing (GPSR) and IEEE 802.11 MAC protocols. We present mathematical analysis of the proposed OSR together with other strategies in literature for a single forwarding node. In addition, we perform extensive simulations (using QualNet) to evaluate the end-to-end performance of these relaying strategies in a multi-hop network. Both the mathematical and simulation results demonstrate the superiority of OSR over other existing schemes.

We consider the scenario of distributed data aggregation in wireless sensor networks, where each sensor can obtain and estimate the information of the whole sensing field through local data exchange and aggregation. The intrinsic trade-off between energy and delay in aggregation operations imposes a crucial question on nodes to decide optimal instants for forwarding their samples. The samples could be composed of the information from their own sensor readings or an aggregation of information with other samples forwarded from neighboring nodes. By considering the randomness of the sample arrival instants and the uncertainty of the availability of the multiaccess communication channel due to the asynchronous nature of information exchange among neighboring nodes, we propose a decision process model to analyze this problem and determine the optimal decision policies at nodes with local information. We show that, once the statistics of the sample arrival and the availability of the channel satisfy certain conditions, there exist optimal control-limit type policies which are easy to implement in practice. In the case that the required conditions are not satisfied, we provide two learning algorithms to solve a finite-state approximation model of the decision problem. Simulations on a practical distributed data aggregation scenario demonstrate the effectiveness of the developed policies, which can also achieve a desired energy-delay tradeoff.

In order to adapt to the time-varying nature of wireless channels, various channel-adaptive schemes have been proposed to exploit inherent spatial diversity in wireless ad hoc networks where there are usually alternate forwarding nodes available at a given forwarding node. However, existing schemes along this line are designed based on heuristics, implying room for performance enhancement. Thereby, to seek a theoretical foundation for improving spatial diversity gain, we formulate the selection of the next-hop relay as a sequential decision problem and derive a general "optimal stopping relaying (OSR)" framework for designing such spatial-diversity schemes. As a particular example, assuming Rayleigh fading channels, we implement an OSR strategy to optimize information efficiency (IE) in a protocol stack consisting of greedy perimeter stateless routing (GPSR) and IEEE 802.11 MAC protocols. We present an analysis of the algorithm for a single node. In addition, we perform extensive simulations (using QualNet) to evaluate the end-to-end performance of the proposed forwarding strategy. The results demonstrate the superiority of OSR over other existing schemes

In mobile ad hoc networks, relaying is one of the most fundamental functions. In this paper, we analyze the stability of several relaying strategies. By using the notion of node lifetime instead of the commonly used link lifetime, our analysis offers a complementary and unique finding not available in the literature previously. Our results show that a route of parallel relays have a significantly longer lifetime than a route of serial relays although the latter of the two has previously predominated the networking research.

Ultra-wideband (UWB) technology is one of the promising solutions for short-range indoor wireless communication applications. UWB systems spread the transmitted signal power over an extremely large frequency band, and the power spectral density of the signal is very low. Due to the wide bandwidth of the transmitted signal, UWB signal energy will spread over the frequency bands allocated to other radio systems. In order to evaluate the effects of UWB system to narrowband systems (such as IEEE802.11a wireless LAN) in its overlay band, it is important to understand and quantify the power spectral density (PSD) of UWB signal. In this paper, a detailed analysis on the PSD of direct sequence coded UWB (DS-UWB) signal is presented. The theoretical and simulation results reveal that the interfering DS-UWB power can be reduced by regulating UWB chip rate, pulse shape, pulse width and scrambling codes in the frequencies of victim narrowband systems.