WAN bandwidth remains a constrained resource that is economically infeasible to substantially overprovision. Hence, it is important to allocate capacity according to service priority and based on the incremental value of additional allocation. For example, it may be the highest priority for one service to receive 10Gb/s of bandwidth but upon reaching such an allocation, incremental priority may drop sharply favoring allocation to other services. Motivated by the observation that individual flows with fixed priority may not be the ideal basis for bandwidth allocation, we present the design and implementation of Bandwidth Enforcer (BwE), a global, hierarchical bandwidth allocation infrastructure. BwE supports: i) service-level bandwidth allocation following prioritized bandwidth functions where a service can represent an arbitrary collection of flows, ii)independent allocation and delegation policies according to user-defined hierarchy, all accounting for a global view of bandwidth and failure conditions, iii) multi-path forwarding common in traffic-engineered networks, and iv) a central administrative point to override (perhaps faulty) policy during exceptional conditions. BwE has delivered more service efficient bandwidth utilization and simpler management in production for multiple years.

We introduce a self-organizing stochastic-communications scheme, called BSTeR, for wireless ad-hoc/sensor networks (mobile or stationary). The introduced scheme involves three ideas essentially: 1) Each node i determines its transmit power dynamically according to a random variable Pi; 2) Node i computes the Probability Mass Function (PMF) of Pi, based on the topology of the network and the PMFs of other nodes in the network dynamically; and 3) Each time node i needs to transmit a data packet, it generates a random number piiaccording to Pi, and transmits the data with power level pii. Compared to the widely employed fixed-power communication schemes, the introduced scheme provides significant performance gains over many performance aspects concurrently, including network connectivity, end-to-end power/energy consumption, end-to-end delay and throughput, energy-delay relation, system longevity, and coverage.

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

We study a novel “coverage by directional sensors” problem with tunable orientations on a set of discrete targets. We propose a Maximum Coverage with Minimum Sensors (MCMS) problem in which coverage in terms of the number of targets to be covered is maximized whereas the number of sensors to be activated is minimized. We present its exact Integer Linear Programming (ILP) formulation and it is used as a baseline for comparison. Then we provide a distributed greedy algorithm (DGA) solution. By incorporating a measure of the sensors’ residual energy into DGA, we further develop a Sensing Neighborhood Cooperative Sleeping (SNCS) protocol which performs adaptive scheduling on a larger time scale. Finally, we evaluate the properties of the proposed solutions and protocol in terms of providing coverage and maximizing network lifetime through extensive simulations.

We have developed adaptive coordinated medium access control (AC-MAC), a contention-based medium access control protocol for wireless sensor networks. To handle the load variations in some real-time sensor applications, ACMAC introduces the adaptive duty cycle scheme within the framework of sensor-MAC (S-MAC). The novelty of our protocol is that it improves latency and throughput under a wide range of traffic loads while remaining as energy-efficient as S-MAC. We illustrate such optimized trade-offs of AC-MAC via extensive simulations performed over wireless sensor networks. Our simulation results show that AC-MAC is as energy-efficient as S-MAC while its latency and throughput are always trying to follow the classic IEEE 802.11 MAC (no duty cycle), which outperform the S-MAC (fixed duty cycle), specially under the heavy load.