Summary The advancements in wireless communication go in leaps and bounds ushering in due attention to spectrum sharing. Spectrum scarcity is one of the major limitations causing hardships in the existing wireless networks. Cognitive Radio Networks (CRNs) emerge as a solution to tide over such humps. It prompts the secondary user (SU) to look out for unused spectrum and utilize them. The CRN helps the SU by permitting it to switch over to unused portions of the spectrum. When a primary user (PU) claims back the spectrum, SU is obliged to perform a spectrum handoff. The SU decides the type of policy to be chosen for the handoff. Such a decision‐making step during the handoff of the spectrum is imperative only if a changing policy is required. In this research work, Artificial Neural Networks (ANNs), Logistic Regression and Support Vector Machine (SVM) are proposed and implemented for a seamless handoff in CRN. From the experimental verifications, it is observed that the training accuracy is 97.9% and 97.6% for ANN and SVM, respectively. But during the actual phase, SVM to a certain extent performed better. This is due to the convergence nature of SVM on global minima.

To meet the needs of innovative sensor network applications, sensor nodes have long evolved from underpowered single microcontroller designs into complex architectures that accommodate multiple processors and Field Programmable Gate Arrays (FPGAs). We address the problem of conceiving and implementing programs for such sensor node architectures. We rely on a universal, layered hardware/software interface that provides seamless interconnection between tasks running on a micro-controller and tasks running on a FPGA. Resource sharing is handled transparently through a shared-bus communication architecture. We demonstrate our methodology, through a heterogeneous sensor node simulator called SUNSHINE [1] for an application running the sensor nodes. We validate SUNSHINE by demonstrating the applications on a multiprocessor sensor node's testbed, which consists of a FPGA, a microcontroller and a radio front-end.

Energy consumption and real-time performance are two important metrics for wireless sensor networks (WSNs). To estimate these metrics, a number of simulation environments have been developed. However, these environments were made specifically for sensor nodes with fixed architectures. The recent generation of sensor nodes often has flexible architectures through the use of programmable hardware components, i.e., Field-programmable gate arrays (FPGAs). So far, no simulators have been developed to evaluate the performance of such flexible nodes in wireless networks. In this paper, we present PowerSUNSHINE, a power- and energy-estimation tool that fills the void. PowerSUNSHINE is the first scalable power/energy estimation tool for WSNs that provides an accurate prediction for both fixed and flexible sensor nodes. In this paper, we first describe requirements and challenges of building PowerSUNSHINE. Then, we present power/energy models for both fixed and flexible sensor nodes. Two testbeds, a MicaZ platform and a flexible node consisting of a microcontroller, a radio and a FPGA based co-processor, are provided to demonstrate the simulation fidelity of PowerSUNSHINE. We also discuss several evaluation results based on simulation and testbeds to show that PowerSUNSHINE is a scalable simulation tool that provides accurate estimation of power/energy consumption for both fixed and flexible sensor nodes.

Most current sensor nodes are composed of a microcontroller and a radio. Their real-time and peak performance would be a bottleneck when executing compute-intensive tasks. Several works demonstrate that adding a hardware co-processor could accelerate the execution speed of the sensor nodes. So far, no simulators can simulate these new sensor nodes in wireless networks. An extension to SUNSHINE [1], a hardware-software emulator is developed to fill the void.

Simulators are important tools for analyzing and evaluating different design options for wireless sensor networks (sensornets) and hence, have been intensively studied in the past decades. However, existing simulators only support evaluations of protocols and software aspects of sensornet design. They cannot accurately capture the significant impacts of various hardware designs on sensornet performance. As a result, the performance/energy benefits of customized hardware designs are difficult to be evaluated in sensornet research. To fill in this technical void, in this paper, we describe the design and implementation of SUNSHINE (Sensor Unified aNalyzer for Software and Hardware in Networked Environments), a scalable hardware-software emulator for sensornet applications. SUNSHINE is the first sensornet simulator that effectively supports joint evaluation and design of sensor hardware and software performance in a networked context. SUNSHINE captures the performance of network protocols, software and hardware up to cycle-level accuracy through its seamless integration of three existing sensornet simulators: a network simulator TOSSIM, an instruction-set simulator SimulAVR and a hardware simulator GEZEL. SUNSHINE solves several sensornet simulation challenges, including data exchanges and time synchronizations across different simulation domains and simulation accuracy levels. SUNSHINE also provides hardware specification scheme for simulating flexible and customized hardware designs. Several experiments are given to illustrate SUNSHINE's simulation capability. Evaluation results are provided to demonstrate that SUNSHINE is an efficient tool for software-hardware co-design in sensornet research.

Most current sensor nodes are composed of a microcontroller and a radio. Their real-time and peak performance would be a bottleneck when executing compute-intensive tasks. Several works demonstrate that adding a hardware co-processor could accelerate the execution speed of the sensor nodes. So far, no simulators can simulate these new sensor nodes in wireless networks. An extension to SUNSHINE [1], a hardware-software emulator is developed to fill the void.