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Wireless sensor networks (WSNs) are predominantly used for monitoring applications. The sensor nodes are resource-constrained devices, and hence efficient energy utilization of these nodes is one of the major challenges. The communication distances directly impact on the energy consumption of the sensor nodes. Clustering methods are popularly used to reduce communication distances and prolong the network lifetime. Multi-sink deployment is another method to reduce communication distances. It also resolves congestion and hotspot issues. In multi-sink WSNs, the number of sinks to be considered is a challenging task as it affects the network topology, lifetime and deployment cost. In this research work, multi-sink deployment and clustering scheme with sink selection algorithm are jointly proposed to maximize the network lifetime and minimize the deployment cost. An iterative filtering model is proposed to estimate optimal number of sinks, while sink positions are determined based on Fuzzy logic inference system (FLIS). Fuzzy-c-means algorithm is used to form balanced clusters in the network. Cluster representative and sink selection processes are based on FLIS. The proposed optimal multi-sink deployment scheme reduces the deployment cost and the propagation delay of the system, while enhancing the network lifetime. The proposed scheme is also energy efficient in the case of higher node density. Hence, the proposed scheme can be suitably implemented for large-scale monitoring applications of WSNs. [ABSTRACT FROM AUTHOR]

Spatial modulation (SM) is a spectrum and energy-efficient simple digital modulation scheme, which uses the transmitter antenna indices to convey the additional bits. The conventional SM variants have high complex receivers and offer poor performance under spatially correlated and line-of-sight (LOS) channel conditions. In this work, a new variant, modified spatial modulation (MSM) for 8 × 8 multiple input multiple output (MIMO) configuration is proposed, which uses one or two transmitter antennas based on the incoming bit patterns. The adaptive mapping used at the transmitter side for antenna selection and modified maximum likelihood (ML) detection used at the receiver side reduces the computational complexity. Every possible bit per channel use (bpcu) is mapped to a unique transmit symbol vector, which minimizes the receiver complexity, while combating the effect of spatial correlation. Through simulation results, it is observed that the average bit error rate (ABER) performance of the MSM-ML scheme is much better than that of the conventional SM-ML, generalized spatial modulation (GSM)-ML and redesigned spatial modulation (ReSM)-ML schemes under spatially correlated and LOS channel conditions. The promising results prove that the proposed scheme can be a better alternative to spatial multiplexing (SMX) in battery-driven compact handheld devices, which can be used for international mobile telecommunication-2020 (IMT-2020). [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is expected to play a significant role in the fifth-generation (5G) networks. To support seamless mobility and service continuity, the device(s) undergoing D2D communication should be handed over to the best access network among all the available wireless access networks. A seamless vertical handover (VHO) across the heterogeneous wireless networks is the key enabling solution for achieving the seamless service continuity and mobility. The VHO algorithm should be intelligent, and the decisions should also consider the quality requirements other than the conventional received signal strength (RSS). In this work, a two-stage fuzzy-logic-based VHO decision algorithm is developed to select suitable access network based on the quality-of-service requirements. The quality parameters like data rate and latency are given as the fuzzy inputs along with RSS. The resource availability check is also carried out for the target network, which makes the decision more intelligent. The simulation results show that the proposed scheme offers better performance than the traditional multi-attribute decision-making schemes. [ABSTRACT FROM AUTHOR]