To perform spectrum handoff, cognitive radio (CR) nodes communicating with each other need to exchange licensed user detection information, i.e., perform spectrum coordination, over a common control channel. The spectrum coordination can be fulfilled either via existing cognitive radio interface with time division or via a separate dedicated radio, i.e., a common control interface (CCI), continuously. CR nodes with CCI can instantly exchange licensed user detection information and cease frame transmission, while spectrum coordination can only be performed after the frame transmission period without CCI. Nevertheless, the impact of CCI incorporation into CR nodes in terms of common performance metrics must be thoroughly assessed to evaluate the worthiness of additional radio cost. In this paper, an analytical framework is presented to assess the impact of CCI incorporation into CR nodes for spectrum handoff. The developed framework enables analyzing potential benefits and disadvantages of employing CCI for spectrum handoff, in terms of achievable delay, energy consumption, spectrum utilization and event estimation performance. Extensive performance evaluations are presented to illustrate the impact of CCI utilization on efficiency of spectrum handoff. The network and communication regimes that would yield having CCI favorable are characterized in terms of spectrum conditions and CR parameters.
Sensor nodes, one of the most crucial elements of Internet of Things (IoT), sense the environment and send their observations to a remote Access Point (AP). One drawback of sensor nodes in an IoT setting is their limited battery supply. Hereby, energy harvesting (EH) stands as a promising solution to reduce or even completely eliminate lifetime constraints of sensors with exploitation of available resources. In this paper, we propose an electric-field EH (EFEH) method to enable batteryless execution of sensor-based IoT services for Smart Grid (SG) context. For this purpose, for the first time in the literature, harvestable energy through EFEH method is investigated with a transformer room experimental setup. Our experiments reveal that 40 mJ of energy can be harvested in a period of 900 sec with the proposed EFEH method. Building on this energy profile, we define a throughput objective function θ for a "harvest-then-transmit" type system model, to shed light on the harvesting-throughput trade-off specific to IoT-assisted SG applications. Numerical results disclose non-trivial relationships between optimal harvesting period TH , optimal transmission period TT and critical network parameters such as node-AP hop distance, path loss exponent and minimum reporting frequency requirement.
Abstract-Molecular communication is a bio-inspired paradigm, proposed to communicate nanomachines via diffusion of molecules through an aqueous medium. The type and structure of the molecules to be propagated bear great importance since they directly affect the modulation structure of molecular communication. We propose a messenger-based molecular communication model where information is encoded on the atoms of polyethylene molecules in the form of CH 3(CH X)nCH2F , where X is either an H or F atom, representing 0 and 1 bits, respectively. The encoded polyethylene molecules are released from the transmitter nanomachine, and their propagation towards the receiver is modelled as a Brownian Motion. Using an erasure channel model, our analysis focuses on calculating the capacity of this channel and revealing the parameters affecting it such as molecule size and number of redundant molecules for one transmission.
Sensing coverage of a field of interest and connectivity are two very important performance measures in Wireless Sensor Networks (WSNs). Existing design methodologies and protocols for enhanced field sensing coverage and connectivity in WSNs are not directly applicable to Cognitive Radio Sensor Networks (CRSNs) due to their cognitive nature. In this chapter, the authors first review sensing coverage and connectivity models for traditional WSNs. Then, they propose novel approaches for sensing coverage and connectivity establishment in CRSN, benefiting from useful existing models from WSN and Cognitive Radio Ad Hoc Networks (CRAHNs). Proposed approaches span a wide variety of CRSN requirements and also point out open research problems in the field to guarantee sufficient sensing coverage quality and connectivity in CRSN.
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