Energy conversation is one of the primary objectives of duty cycle MAC protocols. Different synchronous and asynchronous duty cycle MAC protocols have been proposed in recent years. These protocols perform well under low traffic loads, but efficiency of these protocols degrade under high traffic loads. We present an asynchronous hybrid duty cycle MAC protocol called Hybrid MAC (H-MAC), which uses both sender and receiver initiated mechanisms to combat the packet delivery latency. In H-MAC each node schedules its sleep and wake up time based on cross layer routing information on the receiver initiated part and on the sender initiated part each sender chooses its wake up time based on the receiver's wake up information. We have evaluated H-MAC in diverse network under dynamic traffic loads. Experiments reveal that, H-MAC significantly reduces packet delivery latency and energy consumption compared to RI-MAC.

Phased array antennas enable the use of real-time beam-forming and null-steering to further increase control of signal strength and interference in wireless networks. Understanding the potential of this platform for both mesh and single-hop networks is becoming more important as smart antennas begin to appear in emerging networking standards. Prior attempts to test non-standard antenna platforms have typically focused around simulations, fixed (non-steerable) directional antenna testbeds, and small scale temporary setups utilizing 1 or 2 phased array antenna nodes over the span of a few hundred meters. This paper presents the challenges encountered – and solutions developed – in building WART, a permanent, campus-wide testbed for wireless networking with beam-forming antennas.

The common practice in wireless networking optimization is to address a problem domain, such as channel assignment or transmit power control, and test it across various environments, topologies and traffic rates. This approach, however, provides only a limited context for researchers and administrators, who must decide not only the best combination, but also the best ordering of these independently derived and tested solutions. This work presents a system architecture and methodology for determining the best features of a configuration of optimizations. These techniques are used to evaluate the joint application of several optimization strategies from five optimization domains, including: channel assignment, association control, transmit power control, bit-rate adaptation and beam form selection. Results from the simulation and field deployment of a 45x45 meter outdoor WLAN show that for the tested scenario: (1) channel assignment should precede association control, (2) minimizing transmit power is ineffective, (3) greedy channel assignment outperforms the Hsum algorithm, (4) load balancing across APs is not significant and (5) null steering is ineffective.

Wireless networking optimizations are typically designed and evaluated independently of one another under the assumption that they can later be applied jointly. These works, however, do not provide sufficient information for future researchers and network administrators to determine which algorithms to combine, what order to combine them in, and how these joint optimizations will perform with one another. In this paper we describe the optimization algorithms and testing system used to address these challenges. The algorithms fall into five categories: channel assignment, association control, beam form selection, transmit power control and rate adaptation. These algorithms have been specifically designed and chosen to make use of the observed inputs from the network. While none of these algorithms has been individually proven to be an optimal or efficient solution, they are representative of many categories of solutions. For example, some algorithms stress load balancing, while other algorithms stress increasing RSS values. Thus, these algorithms should be sufficient for revealing significant themes in the interactions among different channel assignment, association control, beam form selection, transmit power control and rate adaptation algorithms.

Increasingly, directional antennas are being used in wireless networks. Such antennas can improve the quality of individual links and decrease overall interference. However, the interaction of environmental effects with signal directionality is not well understood. We observe that state of the art simulators make simplifying assumptions which are often unrealistic and can give a misleading picture of application layer performance. Because simulators are often used for prototyping and validating new ideas, their realism and accuracy are of primary importance. In this paper, we apply a new empirical simulation method for directional antennas and study how well this models reality. We show that not only is our model easy to implement, but is also more accurate and thus better able to predict the performance of propagation-sensitive applications.

One of the most important components of any mobile system is the antenna; antenna design can overcome or cause a number of problems that then must be addressed at other technology layers. Modern mobile platforms are beginning to include novel antenna technology such as MIMO and beam steering; these technologies increase the complexity of evaluating the effectiveness of topology formation algorithms, routing and overall performance due to the large number of configuration states the system can contain. Directional antennas allow for significant improvements in link quality and spatial reuse in wireless communication. Traditional antennas with fixed direction are effective but unable to respond to station mobility or a dynamic environment including such factors as wind and foliage growth. There is a growing body of work on using steerable and sectored antenna systems to harness the efficiency of directional antennas while retaining the flexibility of ad-hoc networks; however, there has been very little work on implementation and measurement of such networks. We examined the physical-layer properties of directional links in two real RF environments, and have evaluated higher-layer strategies for utilizing these antennas. Our results indicate the topology formation process must be a network operation, and that simple link-by-link topology optimization is likely to lead to poor overall performance. These observations drive the formation of the testing and evaluation tools we have developed. This paper describes the tools, methodology and metrics we are using in the evaluation of topology formation algorithms using a dynamically steerable phase array system.

In wireless communication systems, advances in computer architecture and processor technology have made it possible for functionality previously implemented in hardware to become tunable via software. These software-defined radios (SDRs) will allow new radio devices to sense, reason, and adapt to changes in the RF environment and/or application requirements making them cognitive radios (CRs). Fully exploiting the flexibility of cognitive radios, however, requires an understanding of how different permutations of radio parameters impact application-specific performance metrics. For example, a CR that is not meeting its bit loss goals could change its operating frequency to reduce the impact of interference. However, the added overhead from changing frequencies could result in an application failing its latency requirements. This paper describes one such method for configuring a cognitive radio and demonstrates the efficacy of the technique on both a simulation based analysis and an in situ evaluation on a software radio platform. Our reconfiguration system quantifies the influence of radio parameters such as frequency agility, bit rate, and transmit power for adapting communication at the application, medium access control, and physical layers. The method calls for exhaustively evaluating a set of CR configurations against a variety of performance metrics and applying statistical processes to determine which settings will have the most significant impact on performance. Once this is done, the experimental results are then used to inform the design of an algorithm that is able to reconfigure to meet performance goals.

Much of the sensor networking research over the last six years depicts a similar picture of deployment. Specifically, a sensor network is deployed (either randomly or placed in a specific location), sits statically for several months collecting data, and adapts itself through various protocols. Yet this research often overlooks potential optimizations gained by adding motes to the network on-demand and within seconds. This paper introduces a shift in the traditional outdoor, static sensor network paradigm by considering the possibilities and limitations of a rapid, just-in-time (JIT) deployment. We look at what sensor networks would be like if we could add new nodes in real-time and how existing protocols would change. We also observe that the technology to utilize this paradigm for outdoor deployments exists as an extension of a low-cost, commercially accessible solution; i.e., a ball launcher or a cannon that tosses sensor nodes.

In this paper we present X-MAC, a low power MAC protocol for wireless sensor networks (WSNs). Standard MAC protocols developed for duty-cycled WSNs such as BMAC, which is the default MAC protocol for TinyOS, employ an extended preamble and preamble sampling. While this "low power listening" approach is simple, asynchronous, and energy-efficient, the long preamble introduces excess latency at each hop, is suboptimal in terms of energy consumption, and suffers from excess energy consumption at nontarget receivers. X-MAC proposes solutions to each of these problems by employing a shortened preamble approach that retains the advantages of low power listening, namely low power communication, simplicity and a decoupling of transmitter and receiver sleep schedules. We demonstrate through implementation and evaluation in a wireless sensor testbed that X-MAC's shortened preamble approach significantly reduces energy usage at both the transmitter and receiver, reduces per-hop latency, and offers additional advantages such as flexible adaptation to both bursty and periodic sensor data sources.