Energy Efficient Data Communication Research Articles

Energy efficient data communication for WSN based resource constrained IoT devices

In the Internet of Things (IoTs) and wireless sensor networks (WSNs), improving security and energy efficiency are key concerns. Clustering, which involves managing cluster heads, plays a pivotal role in extending network lifetime. The selection of a cluster head, responsible for data transfer between nodes, is a key aspect of network management. This paper proposes two variants of a novel algorithm designed for energy efficient communication in a resource constrained IoT environments. One variant considers remaining energy, distance, and node degree for cluster head selection, while the other focuses on remaining energy and distance only. Including node degree ensures cluster heads do not waste energy by remaining idle or performing unnecessary tasks such as the cluster head selection process in every round. The authors tested these variants against several well known algorithms using MATLAB simulation environment, evaluating factors such as operating nodes, number of clusters, transmission energy, and remaining energy. The proposed algorithm extends network lifetime by maintaining more operating nodes for longer, not changing clusters or cluster heads frequently, minimizing energy consumption for transmission, and conserving more remaining energy. Consequently, the proposed algorithm outperforms existing approaches by addressing issues like zero cluster head selection, compulsory cluster head selection in every round, avoiding cluster heads that connect to no nodes, and preventing network destabilization due to unnecessary re-elections.

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications.

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"-a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Mobile Sink Node with Discerning Motility Approach for Energy Efficient Delay Sensitive Data Communication over Wireless Sensor Body Area Networks

The sensors nearby the static sink drains their energy resources rapidly, since they continuously involve to build routes in Wireless sensor networks, which are between data sources and static sink. Hence, the sensors nearby the sink having limited lifespan, which axing the network lifetime.The mobile-sink strategy that allows the sink to move around the network area to distribute the transmission overhead to multiple sensor nodes. However, the mobile-sink strategy is often tall ordered practice due to the continuous need of establishing routes between source nodes and the mobile sink (MS) at new position occurred due to its random mobility. In regard to above stated argument, this manuscript proposed a novel energy data transmission strategy which is effective for WSN with mobile sink. Unlike the traditional contributions, which relies on mobile sink with random mobility strategies, the proposal defines a discerning path for mobile sink routing between sectioned clusters of the WSN. The proposal of the manuscript titled “Mobile Sink Node with Discerning Motility Approach (MSDMA) for Energy Efficient Data Communication over WBAN”. The method defined in proposed model sections the target network in to multiple geographical clusters and prioritize these clusters by the delay sensitivity of the data transmitted by the sensor nodes of the corresponding clusters. Further, discriminating these clusters by their delay sensitive priority to define mobile sink route. For estimation of the delay sensitive priority of the clusters, set of metrics are proposed. The experimental study carried on simulation to assess the significance of the suggested method. The performance improvement of the suggested method is ascended through comparative analysis performed against benchmark model under divergent metrics.

Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications

Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.

A Unified Clock-Gated Error Correction Scheme with Three-Phase Latch-Based Pipeline for Energy-Efficient Wide Supply Voltage Range Router

Network-on-chip (NoC) has played a vital role in enabling high-speed and energy-efficient data communication among different cores in a multi-or many-core System-on-Chip (SoC). To mitigate the pessimistic timing margin reversed for severe process, voltage, and temperature (PVT) variations, we presented a wide supply voltage range router with a novel error detection and correction (EDAC) technique adopted in its latch-based pipeline for high energy efficiency. Especially, a unified clock-gated error correction scheme is presented to address dynamic timing errors under PVT variations and functional pipeline stalls when occurring allocation failure (packet fails to obtain an output port) or flow control (the next router stalls the current packet transmitting) in one cycle. Moreover, a latch-based pipeline structure with three non-overlapped clocks is adopted to further reduce the short-path padding overhead, which is significantly increased in the ultra-low voltage (ULV) regime. Consequently, we prototyped a 2 × 2 2D mesh NoC test chip with 28-nm CMOS technology. Compared with the margined baseline with a 10% VDD drop, measurement results show that the proposed router enables 1.4×, 32.4%, and 7.5% improvements in frequency, energy, and area, respectively. Furthermore, the proposed techniques can reduce packet loss by 200× and increase throughput by 19.9% for the NoC at 0.4 V, 25∘C.

Quadratic ensemble weighted emphasis boosting based energy and bandwidth efficient routing in Underwater Sensor Network

Underwater Sensor Network (UWSN) is a network that comprises a large number of independent underwater sensor nodes to perform monitoring tasks over a given area. UWSN minimized propagation delay, bandwidth, and packet loss. However, the implementation of efficient communication is a significant problem at UWSN. Therefore, Energy and Bandwidth-aware Quadratic Ensemble Weighted Emphasis Boosting Classification (EB-QEWEBC) method for performing energy-efficient routing in UWSN is proposed. Initially, different numbers of underwater sensor nodes are considered as input. Next, the bandwidth and energy consumption of every underwater sensor node is measured. After that, classification between underwater sensor nodes is made by considering energy and bandwidth as factors using Regularized Quadratic Classifier (i.e., weak classifier) for performing routing with minimum delay. Followed by, Weighted Emphasis Boosting is utilized to ensemble weak learners to form strong learners for improving data routing performance results with the biconvex combination. Finally, after classifying the node, data packets are sent to higher energy and bandwidth-efficient underwater sensor nodes. The classification process is carried out at every underwater sensor node for transmitting data packets to the sink node with minimum delay. This method determines the energy-efficient data communication through classification and boosting to reduce the misclassification rate. Experimental results EB-QEWEBC shows a minimization of 14%, 21%, 26%, and 54% in terms of bandwidth, energy consumption, end-to-end delay, and misclassification rate as compared to state-of-art-methods respectively.

Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Wireless Sensor Networks (WSNs) is the innovative technology that covers wide range of application that possesses high potential merits such as long-term operation, unmonitored network access, data transmission, and low implementation cost. In this context, Internet of Things (IoT) have evolved as an exciting paradigm with the rapid advancement of cellular mobile networks, near field communications and cloud computing. WSNs potentially interacts with the IoT devices based on the sensing features of web devices and communication technologies in sensors. At this juncture, IoT need to facilitate huge amount of data aggregation with security and disseminate it to the reliable path to make it reach the required base station. In this paper, Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication Mechanism (UAIPCA-BSO) is proposed for Securing data and estimate the optimal path through which it can be forwarded in the IoT environment. In specific, Unity Attractors Inspired Programmable Cellular Automata is adopted for guaranteeing security during the data transmission process. It also aids in determining the optimal path of data transmission based on the merits of Barnacles Swarm Optimization Algorithm (BSOA), such that data is made to reach the base station at the required destination in time. The simulation results of UAIPCA-BSO confirmed minimized end-to-end delay , accuracy and time taken for malicious node detection, compared to the baseline approaches used for comparison.

A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks

Ensuring the reliable and energy efficient data routing in Wireless Sensor Networks (WSNs) is still remains one of the challenging and demanding tasks due to its dynamic architecture. For this purpose, the different types of routing methodologies and security schemes have been developed in the conventional works. However, it faced the problems related to increased network overhead, high cost consumption, reduced Quality of Service (QoS), and inefficient bandwidth utilization. The main contribution of this work is to implement an optimization based secured routing methodology for establishing an energy efficient data communication in WSNs. For clustering the nodes, the parameters such as residual energy, trust score, and mobility have been considered, which also helps to simplify the networking operations. Moreover, the outliers in the network are detected with the help of Spatial Temporal Fuzzy Inference System technique, which generates the set of inference rules based on the distance, energy, and mobility measures. Also, the Crow-Whale Optimized Secured Robin Routing (CWOSRR) is used to enable the data transmission in the network through the optimized path, which avoids the loss of information. During the simulation analysis, the results of the proposed technique is validated and compared by using various performance measures.

Hybrid Power Divider and Combiner for Passive RFID Tag Wireless Energy Harvesting

This paper presents three- and five-ports radio frequency (RF) hybrid power divider combiner (HPDC) designs with multiband characteristics operating at 2.4 GHz (ISM, IEEE 802.11b,g); 5.8 GHz (IEEE 802.11n, a and 802.11ac); and 6 GHz (IEEE 802.11ax) wireless standards for energy-efficient 5G-enabled passive Internet of Things (IoT) sensors; energy harvesting (E.H); passive radio frequency identification (RFID) tags; multiple-input multiple-output (MIMO) antenna beamforming; and data communication applications spanning DC to the 6-GHz frequency range. The presented HPDC designs operate at a centre-design frequency of 3 GHz on a Rogers RO4350 substrate. The designed novel HPDC demonstrates a good match between the ports, high isolation between the output ports, and equal power distribution between the output ports. Furthermore, the obtained return and isolation losses are less than −10 dB for the Wi-Fi 6E standards. The reported findings hold an excellent promise for RF energy harvesting and utilisation, adaptive intelligent energy-efficient data communication, and seamless ubiquitous satellite-cellular convergence connectivity applications.

Brief Performance Analysis of Routing Protocols

Abstract: Routing in Wireless Sensor Networks (WSNs) plays an important role in areas such as environment-oriented surveillance and traffic monitoring. Here we have explored the large contribution of routing to WSNs. This paper is primarily intended to categorize routing issues and investigate optimization issues related to routing. The following describes various features related to routing energy, security, speed, and reliability issues. The documentation is then analyzed for simulated environments and test configurations, knowledge of quality of service (QoS) and implementations in various applications.. Keywords: Wireless Sensor Networks; reliability, energy-efficient data communication; energy management; energy enviorment

DEDC: Sustainable data communication for cognitive radio sensors in the Internet of Things

The distributed tree-based data communication protocol is a widely accepted efficient method in wireless sensor network-enabled Internet of Things (IoT). Due to increasing demand for IoT applications, cognitive radio sensors play a vital role in spectrum utilization. This short communication proposes a delay aware, energy-efficient data communication protocol (DEDC) for cognitive radio sensor networks (CRSN). This research proposes a first communication protocol that integrates the solution for delay and energy issues in CRSNs specific to IoT application. The respective simulation results and comparative studies with the existing models on other parameters prove the novel contribution of this research in terms of delay and energy utilization.

Quality-Based and Energy-Efficient Data Communication for the Internet of Things Networks

Large volumes of real-world observation and measurement data are collected from sensory devices in the Internet of Things (IoT) networks. IoT data is often generated in highly distributed and dynamic environments. Continuous transmission of large volumes of data collected between sensor and head/sink nodes induces a high communication cost for individual nodes. This results in a significant increase in the overall energy cost for IoT applications such as environmental monitoring. Decreasing data transmission between nodes can effectively reduce energy consumption and prolong the network lifetime, especially in battery-powered nodes/networks. In this article, we describe an adaptive method for data reduction (AM-DR), a data reduction approach for reducing the overall data transmission and communication between sensor nodes in IoT networks such that fine-grained sensor readings can be used to reconstruct the original data within a user-defined accuracy boundary. Evaluation with real-world data shows that AM-DR achieves a communication reduction in some scenarios up to 95% while retaining a high prediction accuracy. To fully achieve the energy savings enabled by AM-DR, we provide a communication cost model. The proposed model is also integrated into the LEACH protocol to demonstrate how our proposed approach reduces energy consumption and effectively prolongs the network lifetime.

Learning Frameworks for Cooperative Spectrum Sensing and Energy-Efficient Data Protection in Cognitive Radio Networks

This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs) harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU) on the channel to a fusion center (FC). We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor–critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy); then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes.

Energy Efficient Clustering Scheme (EECS) for Wireless Sensor Network with Mobile Sink

The participants in the Wireless Sensor Network (WSN) are highly resource constraint in nature. The clustering approach in the WSN supports a large-scale monitoring with ease to the user. The node near the sink depletes the energy, forming energy holes in the network. The mobility of the sink creates a major challenge in reliable and energy efficient data communication towards the sink. Hence, a new energy efficient routing protocol is needed to serve the use of networks with a mobile sink. The primary objective of the proposed work is to enhance the lifetime of the network and to increase the packet delivered to mobile sink in the network. The residual energy of the node, distance, and the data overhead are taken into account for selection of cluster head in this proposed Energy Efficient Clustering Scheme (EECS). The waiting time of the mobile sink is estimated. Based on the mobility model, the role of the sensor node is realized as finite state machine and the state transition is realized through Markov model. The proposed EECS algorithm is also been compared with Modified-Low Energy Adaptive Clustering Hierarchy (MOD-LEACH) and Gateway-based Energy-Aware multi-hop Routing protocol algorithms (M-GEAR). The proposed EECS algorithm outperforms the MOD-LEACH algorithm by 1.78 times in terms of lifetime and 1.103 times in terms of throughput. The EECS algorithm promotes unequal clustering by avoiding the energy hole and the HOT SPOT issues.

Improved Compressed Network Coding Scheme for Energy-Efficient Data Communication in Wireless Sensor Networks

An improved energy-efficient compressed network coding method is proposed for the data communication in the wireless sensor networks (WSNs). In the method, the compressed sensing and network coding are jointly used to improve the energy efficiency, and the two-hop neighbor information is employed to choose the next hop to further reduce the number of the transmissions. Moreover, a new packet format is designed to facilitate the intermediate node selection. To theoretically verify the efficiency of the proposed method, the expressions for the number of the transmissions and receptions are derived. Simulation results show that, the proposed method has higher energy efficiency compared with the available schemes, and it only requires a few packets to reconstruct measurements with reasonable quality.

Fuzzy based Hierachical Unequal Clustering in Wireless Sensor Networks

Objectives: Sensor nodes in Wireless Sensor Networks are battery powered and non rechargeable, and hence in most of the real time applications energy consumption is a major problem. The lifetime of the network also depends upon the energy consumption model which may affect the entire performance of the networks. The main aim of the proposed Hierarchical Unequal Clustering Fuzzy Algorithm (HUCFA) is to reduce the energy consumption of over all networks. Methods/Statistical Analysis: Clustering is an important technique used for energy efficient data communication in WSN. Fuzzy logic is applied in the proposed algorithm to choose the cluster head, which enhances the energy efficiency. To choose a better cluster head, the characteristics of the nodes are taken as input for fuzzy inference system. Based on the fuzzy rules, best nodes are selected as cluster heads. Fuzzy Logic Toolbox in Matlab R2010a is utilized for the simulation. Findings: Total Energy Consumption of the network (TEC) in HUCFA is 7.05% less when compared to LEACH. Standard deviation of energy distribution among all the clusters in the HUCFA is 10.46% less than that of LEACH protocol and 0.79% less than Hierarchical Unequal Clustering Algorithm. Application/Improvements: The proposed HUCFA using Fuzzy Logic is found to be better and more energy efficient for the applications involving low powered sensor nodes which is proved through simulation results.

Smartphone-assisted energy efficient data communication for wearable devices

In dynamically changing network environments, no single data communication approaches for wearable devices are guaranteed to yield the best performance-cost ratio. To illustrate how different approaches perform in different environments, we conduct a theoretical analysis to four basic approaches that rely on either Wi-Fi, or smartphone-tethered cellular network, or both, to transmit data on wearable devices. In order to achieve energy efficient data communication on wearable devices (and associated smartphones), we propose a Lyapunov based on-line approach designation mechanism that dynamically chooses an appropriate data communication approach based on data transmission queue, estimated network conditions and the device moving speed. Due to the property of Lyapunov optimization framework, the proposed mechanism is able to minimize the power consumption of data communication for wearable devices (and associated smartphones) while meeting the delay time constraint. Moreover, it requires no prior knowledge of future network conditions and data request arrivals. Our trace-driven simulations demonstrate that our on-line designation mechanism delivers very close performance to the mechanism that can foresee the future, leaving very little space for further improvement.

RPRDC: Reliable Proliferation Routing with low Duty-cycle in Wireless Sensor Networks

Ensuring reliable energy efficient data communication in resource constrained Wireless Sensor Networks (WSNs) is of primary concern. Traditionally, two types of re-transmission have been proposed for the data-loss, namely, End-to-End loss recovery (E2E) and per hop. In these mechanisms, lost packets are re-transmitted from a source node or an intermediate node with a low success rate. The proliferation routing 1 for QoS provisioning in WSNs low End-to-End reliability, not energy efficient and works only for transmissions from sensors to sink. This paper proposes a Reliable Proliferation Routing with low Duty Cycle [RPRDC] in WSNs that integrates three core concepts namely, (i) reliable path finder, (ii) a randomized dispersity, and (iii) forwarding. Simulation results demonstrates that packet successful delivery rate can be maintained upto 93% in RPRDC and outperform Proliferation Routing 1 .

Energy-efficient communication in multihop wireless sensor networks using ternary encoding and silent symbols

The ternary-with-silent-symbol TSS energy-efficient data communication technique was earlier proposed in Ghosh et al. 2008 which coupled ternary encoding of data stream with the use of silent periods for transmitting the ternary digits. The performance analysis of TSS using bit-error rate BER, however, is inadequate to capture the transitive error-propagation in the transmitted binary data stream. In this paper, we use the concept of frame-error rate FER to successfully capture the effects of the error-propagation and the message length reduction. Our FER-based analysis shows 23% average saving in transmitter energy compared to the non-coherent BFSK for AWGN channels, rather than 20% saving as in BER-based analysis. For coherent detection-based receiver we show that the average transmitter energy savings are about 30% and 17% relative to the non-coherent and coherent BFSK respectively. Simultaneously, there is energy saving of about 37% in the receiver relative to BFSK, which makes TSS a very suitable scheme for multihop communication in wireless sensor networks.

An Energy-Optimal Algorithm for Neighbor Discovery in Wireless Sensor Networks

We consider sensor networks in which individual nodes with on-board sensing and low-power transmitters and receivers establish connections with neighboring nodes. The overall objective is to enable energy-efficient data communication, relayed between arbitrary nodes on the network. We develop a distributed algorithm which minimizes the power required for neighbor discovery.Initially nodes do not have deterministic knowledge of the location of their neighbors, and we model the distribution of the nodes as a two-dimensional Poisson process with known intensity. This corresponds to a situation in which a large number of nodes are randomly distributed over a given area. The process of neighbor discovery is modeled as a Markov decision process, and the resulting control policy is a finite automaton, driven by the underlying probability distribution, that minimizes the average power consumed. This policy can be computed offline and stored in each node with very low requirements for online memory and processor capability.