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Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications.

The offset quadrature amplitude modulation (OQAM) with filter bank multicarrier (FBMC) has turn on considerable interest in last decades over orthogonal frequency division multiplexing (OFDM) because of their a significant number of advantages. However, largest peak to average power ratio (PAPR) minimization techniques developed for OFDM systems may not be applicable directly for OQAM-FBMC systems, due to the super-imposed formation of OQAM-FBMC signals. In this article, the issue of power ratio reduction technique is address theoretically and complementary cumulative distribution function for OQAM-FBMC systems using various companding transforms are analyzed. Theoretical analysis and simulation results shows that the $$\mu$$ -law companding method is efficiently reduced PAPR among all other companding techniques.

Wireless sensor network (WSN) comprises of numerous sensors deployed either directly or randomly in the region of interest. Due to the limited power of the sensors, these networks are energy-constrained and thus need efficient power utilisation. Efficient clustering and cluster head (CH) selection ensures balanced energy distribution to the sensors within the WSN and hence prolong the network lifetime. This study proposes the method for evaluating the threshold for the CH selection after each round, which increases the network lifetime and throughput significantly. The threshold for CH selection is modified considering the normalised first-order and second-order statistical parameters, such as mean average low-energy adaptive clustering hierarchy (AvgLEACH) and variance (VarLEACH) of the overall network energy. These methods have been formulated after studying the effect of the number of working nodes in each round on the threshold value selection. Apart from including energy parameter to the threshold equation, the methods of VarLEACH and AvgLEACH are augmented with a residual energy parameter that is local to the nodes and named as VarRLEACH and AvgRLEACH. The simulation results comparing all the methods suggest that the proposed method AvgRLEACH outperforms LEACH by a factor of 1.5 in delivering data to the base station and outlives the network driven by LEACH protocol by 30–40%.

The advent of Industrial Internet of Things (IIoT) has determined the proliferation of smart devices connected to the Internet and injected a vast amount of data into it, which may undergo many computational stages at several clusters. On the one hand, the benefits brought by these technologies are well known; however, in the envisaged scenario, the exposure of data, services and infrastructures to malicious attacks has definitely grown. Even a single breach on any of the links of the data–service–infrastructure chain may seriously compromise the security of the end-user application. Therefore, the logical and smart clustering while satisfying security and reliability is a key issue for IIoT networks. A novel clustering method proposed based on power demand assures security of data information in IIoT-based applications. First, security capacity of the system is calculated from mutual information of primary channel and eavesdropping channel. Then, under the maximum transmit power constraint, an optimal transmit power is found based on deep learning technique, which maximizes security capacity of the system. Finally, the network is clustered according to the calculated power demand. Experimental results accredit the proposed method has higher security and reliability, as well as lower network time overhead and power consumption.

Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed.

In this paper, two new companders are designed to reduce the ratio of peak to average power (PAPR) experienced by filter bank multicarrier (FBMC) signals. Specifically, the compander basic model is generalized, which alter the distributed FBMC signal amplitude peak. The proposed companders design approach provides better performance in terms of reducing the PAPR, Bit Error Rate (BER) and phase error degradation over the previously existing compander schemes. Many PAPR reduction approaches, such as the Mu-law companding technique, are also available. It results in the formation of spectrum side lobes, although the proposed techniques result in a spectrum with fewer side lobes. The theoretical analysis of linear compander and expander transform for a few specific parameters are derived and analyzed. The suggested linear companding technique is analytically analysed using simulations to show that it efficiently decreases the high peaks in the FBMC system. Keywords- BER, CCDF, Compander, FBMC, OQAM, PAPR, PSD., Comment: 15 pages , 7 figures and 1 table

Wireless sensor networks (WSNs) have a wide range of applications in various fields. One of the most recent emerging applications are in the world of Internet of Things (IoT), which allows inter-connection of different objects or devices through the Internet. However, limited battery power is the major concern of WSNs as compared to mobile ad-hoc network, which affects the longevity of the network. Hence, a lot of research has been focused on to minimise the energy consumption of the WSNs. Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimise the energy of the WSNs. In this study, the existing low-energy adaptive clustering hierarchy (LEACH) clustering protocol is modified by introducing a threshold limit for cluster head selection with simultaneously switching the power level between the nodes. The proposed modified LEACH protocol outperforms as compared to the existing LEACH protocol with 67% rise in throughput and extending the number of alive nodes to 1750 rounds which can be used to enhance the WSN lifetime. When compared with other energy efficient protocols, it is found that the proposed algorithm performs better in terms of stability period and network lifetime in different scenarios of area, energy and node density.

In order to increase the spectral efficiency of any communication systems, spectrum sensing techniques may be used for proficient utilization of inadequate spectrum resources. It identifies the unused spectrum holes, which is originally assigned to the primary users (PU). These spectrum holes are then assigned to the secondary or cognitive users with avoiding interference to the primary users. In this paper, a spectrum assignment technique based on energy detection technique is proposed. This enhanced energy detection technique works well at low signal-to-noise ratio (SNR), which makes the communication system more power efficient and can be for low power applications. Further, the performance of the proposed spectrum sensing method is examined for cognitive radio (CR) network. The performance of the proposed method is also examined by calculating the probability of detection, probability of false alarm and error probability in presence of additive Gaussian noise and the effect of different sensing parameters on the probability of error in detecting primary users are also evaluated.

Wireless Sensor Networks (WSNs) experiences several technical challenges such as limited energy, short transmission range, limited storage capacities, and limited computational capabilities. Moreover, the sensor nodes are deployed randomly and massively over an inaccessible or hostile region. Hence, WSNs are vulnerable to adversaries and are usually operated in a dynamic and unreliable environment. Animal tracking using wireless sensors is one such application of WSN where power management plays a vital role. In this paper, an energy-efficient hybrid routing method is proposed that divides the whole network into smaller regions based on sensor location and chooses the routing scheme accordingly. The sensor network consists of a base station (BS) located at a distant place outside the network, and a relay node is placed inside the network for direct communications from nodes nearer to it. The nodes are further divided into two categories based on the supplied energy; such that the ones located far away from BS and relay have higher energy than the nodes nearer to them. The network performance of the proposed method is compared with protocols like LEACH, SEP, and SNRP, considering parameters like stability period, throughput and energy consumption. Simulation result shows that the proposed method outperforms other methods with better network performance.

Future wireless communication systems should have the ability to accommodate large number of mobile users increasing day by day. Moreover, various high-speed mobile applications such as online video streaming and online gaming, interconnection of different wireless devices for Internet of Things (IoT) are also some of the key requirements must be fulfilled. Hence, next-generation 5G wireless communication systems and smart grid (SG) communication systems requires high data rate, low latency, high spectral efficiency, low out-of-band (OOB) radiation, low power consumption, secure connectivity and ability to accommodate more number of users as well as diversified wireless devices distributed in large geographical regions for different mobile applications with maintaining uninterrupted connectivity at high speed. More sophisticated signaling schemes are required to overcome the limitations of orthogonal frequency-division multiplexing (OFDM), which is widely accepted by many researchers as one of the potential modulation schemes for 3.5G and 4G wireless standards. Even some of the multicarrier modulation schemes suitable for 5G applications, which work well at the frequency range of around 28 GHz, may not be suitable for millimeter wave (mmWave) frequency range of 60–90 GHz. Hence, in this chapter a comprehensive analysis of various types of potential waveforms such as generalized frequency-division multiplexing (GFDM), filter bank multicarrier (FBMC), universal filtered multicarrier (UFMC), and some of the extended version of OFDM, which can be used in 5G wireless communication systems. Some single-carrier modulation schemes suitable for mmWave are studied and discussed. Various issues for implementing these new waveforms and their advantages and disadvantages are also discussed. Smart grid (SG) communication technologies can be more reliable, secure and faster by using some of the modern signaling schemes. Finally, some of the standards of SG and its applications are discussed.

In this paper data aided (DA) timing synchronization scheme for orthogonal frequency division multiplexing (OFDM) system is proposed. It works in time domain and the timing metric is independent of the specific structure of the preamble. Quadrature phase shift keying (QPSK) signals, independent Rayleigh fading multi path channel in presence of additive white Gaussian noise (AWGN) are considered. The proposed algorithm estimates the starting point of OFDM frames by using matched filter (MF) at the receiver end. The performance is evaluated and compared in terms of probability of erasure (PE) or probability of not detecting a frame and mean squared error (MSE) with the existing timing synchronization methods for OFDM systems. The simulation results indicate that the proposed method improves the performance of the system significantly. Also the computational time for timing synchronization is less as compared to previously existing methods.