Throughput analysis of full-duplex communication cognitive radio network

In this paper we deal with the throughput of full-duplex cognitive communication radio which exploits unused band of primary user (PU) network. Classical cognitive radio uses half-duplex communication spectrum sensing to perform spectrum sensing and data transmission at different time intervals. It’s well-established fact that in half-duplex communication cognitive radio spectrum sensing time increases at low SNR which gives rise to lesser data transmission time for secondary user (SU) and hence results in less throughput for SU. It’s useful idea to do spectrum sensing and data transmission at the same time with two different antennas co-located on the SU transceiver. This shall not only guarantee high probability of detection of PU but also increased data transmission which means more throughput for SU. However, simultaneous sensing and data transmission has inherent problem of self-interference. One of the possible solution is to use polarisation discrimination in which sensing and data transmission antennas must use different polarisation. This is feasible if there is prior information about the polarisation of the signals emitted by the PUs. It shall be of special interest to assess throughput using analytical expressions for probability of detection PD, probability of false alarm PFA at various values of SNR for time-slotted cognitive radio which uses half-duplex spectrum sensing and non-time-slotted cognitive radio which uses full-duplex communication cognitive radio

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid