High market demand for wireless networking services and devices has resulted in increased complexity of an infrastructure of networking testbeds because of the scale, diversity and exploitation levels of the involved resources. In this paper, we address the problem of managing the infrastructure of large wireless networking testbed by adopting a semantics-based approach to knowledge-driven system management. A novel data transformation language, fully based on the semantic web standards, is introduced. Using the introduced language, an XML description of testbed services can be transformed to semantically enriched data. Workflow of the transformation is explained on the example of semantic lifting of the ORBIT testbed infrastructure. The example provides experimental evidence for successful application of the proposed approach.

Spectrum sensing is one of the core functionalities of a true cognitive radio (CR) that supports operation over a broad range of frequencies and can autonomously adapt transmission parameters to the operating environment. There are several types of hardware ranging from sophisticated (i.e. Nutaq Radio420X FPGA mezzanine card) to low cost (i.e. WiSpy) that can be used to experiment with spectrum sensing. This hardware is available for use in several testbeds across the world (i.e. ORBIT, w-iLab.t, TWIST and LOG-a-TEC). Each testbed provides a specific mechanism to define, deploy and execute experiments making it difficult for an individual researcher to use more than one testbed. In this work we propose an information model for describing spectrum sensing functionality with the ultimate goal of developing and promoting a Common CR language that can describe the resources in existing GENI and FIRE testbeds.

Constrained coding is used in recording systems to translate an arbitrary sequence of input data to a channel sequence with special properties required by the physics of the medium. Very often, more than one constraint is imposed on a recorded sequence; typically, a run-length constraint is combined with a spectral-null constraint. We introduce a low-complexity encoder structure for composite constraints, based on loose multimode codes. The first channel constraint is imposed strictly, and the second constraint is imposed in a probabilistic fashion. Relaxing the second constraint is beneficial because it enables higher code rates and simplifies the encoder. To control the second constraint a multimode encoder is used. We represent a set of multimode coded sequences by a weighted trellis and propose using a limited trellis search to select optimal output. Using this method, we modify the EFM+ code used in digital versatile disk (DVD). We combine EFM+'s run-length constraint with the first- and second-order spectral-null constraints. The resulting EFM++ code gives more than 10-dB improvement in suppression of low-frequency spectral content in the servo bandwidth over the original EFM+ code with the same complexity.

The multiprocessor scheduling problem can be stated as finding a schedule for a task graph to be executed on a multiprocessor architecture so that the execution time can be minimized. Since this problem is known to be NP-hard, in all but a few very restricted cases, the main research efforts in this area are focused on heuristic methods for obtaining near-optimal solutions in a reasonable amount of time. A new compile-time single-pass multiprocessor scheduling technique, called chaining, has been developed and is presented in this paper. Chaining can be used to schedule task graphs onto multiprocessor architectures that contain an arbitrary number of processors connected in an irregular fashion, taking into account the expected execution and communication requirements of the task graph on the given multiprocess architecture. This technique can be viewed as a generalization of the list scheduling technique, that does not impose any preconditions about the ordering according to which tasks are selected for scheduling. Varying the selection criteria, implemented in this technique, we have generated a new class of scheduling algorithms. An evaluation of this class was made on 360 randomly generated examples, and the estimated performances were compared with two list scheduling algorithms, the dynamic level scheduler proposed by Sih and Lee, and the earliest task first algorithm proposed by Hwang et al.