Energy-constrained Wireless Sensor Networks Research Articles

Optimizing [formula omitted]-coverage in energy-saving wireless sensor networks based on the Elite Global Growth Optimizer

For energy-constrained wireless sensor networks, enhancing network fault tolerance and extending lifetime are of paramount importance. In this study, we focus on addressing the Minimum Power k-Coverage (MinPKC) problem. The objective of MinPKC is to minimize and balance the transmission (Tx) power by sensor nodes (SNs), thus extending network lifetime. Furthermore, MinPKC requires satisfying the constraints of k-coverage for the targets to enhance network fault tolerance. The problem is non-convex, multimodal, and NP-hard. To address this problem, we propose a novel metaheuristic algorithm named Elite Global Growth Optimizer (EGGO), which incorporates efficient global search and elite evolution strategies into the GO and utilizes time-varying multi-strategies for population updates. This study extensively compares EGGO with ten popular algorithms on 20 classic benchmark test functions using the Friedman and Wilcoxon rank-sum tests. The results indicate that EGGO significantly outperforms all the compared algorithms. Ablative experiments confirm the crucial role of the proposed mechanisms in EGGO for improving algorithm performance. Meanwhile, the study implements ten MinPKC methods based on comparative algorithms and compares them with the EGGO-based MinPKC method in terms of total Tx power, network lifetime, and simulation time. The experimental results indicate that the EGGO-based MinPKC method significantly increases the network lifetime by approximately 14.2%, 38.2%, and 1.9% compared to the second-best algorithm, and by 57%, 451%, and 84.8% compared to the GO, while balancing the Tx power among SNs and reducing the total Tx power by approximately 28%, 12.7%, and 0.5% compared to the second-best algorithm, and by 47.1%, 72%, and 30% compared to the GO, respectively, when k=1,2,3. Additionally, a detailed analysis is conducted on the impact of the number of SNs, the maximum target coverage capacity of each SN, as well as the effects of obstacles and their distribution on signal strength attenuation in reducing Tx power. The source code is available at https://github.com/iNet-WZU/EGGO.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

APAP: An adaptive packet-reproduction and active packet-loss data collection protocol for WSNs

In lossy, large-scale wireless sensor networks (WSNs), data collection protocols face the challenge of balancing expected data transmission reliability and limited resources of sensors. Packet redundancy strategies have been widely employed to guarantee transmission reliability. However, these schemes often result in energy waste, especially in sink-neared hotspot districts. We propose the APAP to balance the two competing interests in energy-constrained WSNs. APAP mainly consists of two parts: adaptive packet-reproduction routing and active packet-loss mechanism. In the first part, we design a compensation function to compensate for packet losses and employ it in the packet-reproduction routing. Through available network parameters, such a function offers an optimized number of redundant packets. In the second part, we devise a novel distributed packet-loss mechanism to detect and intercept redundant packets in the hotspot area. Hotspot nodes record the simple traffic information in their vicinity on a local micro-table, these nodes transform their mode based on the records to reach a basic consensus with one-hop neighbors, they then collaboratively intercept excess packets from outside, thus the injection traffic can be mitigated. Such an interception area has the potential to be expanded and prolonged as the redundancy becomes more severe. Our mechanism imposes low-complexity requirements on general devices. Moreover, thorough mathematical analyses are offered for the performance of APAP. Simulation results indicate that with high reliability, the maximum node energy consumption is reduced by 44.7% to 66.3% compared to conventional protocols in one round. Besides, the network lifetime is prolonged by 91.1% to 200%.

Trust-based clustering and best route selection strategy for energy efficient wireless sensor networks

Energy efficiency is one of the most researchable topics of the energy constrained Wireless Sensor Networks (WSN). Due to the widespread usage of WSN in real-time applications, network lifetime is the serious concern and numerous research solutions are presented in the literature for handling this issue. Several techniques are employed to handle energy consumption and the most promising techniques are clustering and routing other than data compression. Hence, this paper presents a clustering and routing-based solution for energy efficient WSN with trust notion as its base. The nodes are clustered with the help of Dempster-Shafer Theory (DST), while the best route is selected by employing Lion Optimization Algorithm (LOA). The work efficiency of the proposed work is proven with reasonable outcomes with respect to different performance measures. This work proves an average packet delivery rate of 94.24%, average latency of 13.43 s and 191 alive nodes at the end of 500th second better network lifetime when compared to the existing works.

An Energy-Efficient Scheduling Algorithm With SIC and Power Control in WSNs

In large-scale wireless sensor networks (WSNs), spectrum efficiency and energy consumption are two challengeable problems to be solved due to the increasing traffic requirement and its limited energy. Successive interference cancellation (SIC) technology, which supports multiple parallel transmissions, gives an opportunity to improve the efficiency of data collection. However, those classical schemes suffer from high energy consumption, which presents a severe challenge to energy-constrained WSNs. To improve the transmission and energy efficiency of WSNs, we study an energy-efficient scheduling algorithm joint with SIC and power control in this paper. We first develop a non-uniform M-Level scheduling model to identify the beneficial opportunities and guarantee the transmission quality of SIC concurrency. Then, we formulate an energy efficiency maximization scheduling (EEMS) utility and further transform it into a combinatorial optimization problem. To reduce the computational complexity and obtain meaningful insights, two low-complexity optimization algorithms are proposed from the perspective of SIC scheduling and power control, respectively. Finally, extensive results demonstrate that the SIC scheduling design enables our proposed algorithm to achieve 51% higher throughput than the algorithm without SIC. Moreover, the energy efficiency of our proposed algorithm is improved by 37% compared with the existing algorithms, which indicates that our algorithm can search for the optimal configuration of SIC-concurrent transmissions and better manage the concurrent transmission power.

Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.

Framework for Implementing Load Balancing Within Cluster in Underground Mining Environments

Abstract: Underground mines are more hazardous in nature and always contain unsafe for their workers. Underground mining causes huge losses due to accidents and disaster. There is the need to develop an effective, secure, reliable and low-cost surveillance and safety system in underground mine environments. Wireless Sensor Networks (WSNs) are increasingly being used for such application. Since wireless sensor nodes are highly energy constrained devices and they have limited battery life due to various constraints of sensor nodes. Therefore, optimal usage of node energy is the major challenge in wireless sensor networks. Clustering of sensor nodes is an effective method to use the node energy optimally and prolong lifetime of such energy constrained wireless sensor networks. Here we propose a framework for load balancing within clusters in the underground mine environment. The proposed clustering algorithm ensures balanced size cluster formation and it has been proposed to achieve balanced energy consumption among nodes. This project aims to give a secure and efficient communication for the underground are using these respective sensors and here a concept of cluster and their rotation is implemented so that the loss of signals can be restricted and helps in a secure communication

Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network

The usefulness of wireless sensor networks has fascinated the world’s attention. Usage of low-power microcontrollers and wireless sensors to handle real-world problems such as environmental, medicinal, and structural monitoring has exploded. Wireless sensor nodes are extremely tiny and are designed for low-duty applications such as recording physical characteristics. Wireless sensor network operations such as sensing, calculations, and communication take extensively more energy than these low-powered sensor nodes. They are used both in attainable and inaccessible areas and are usually powered by batteries. Since the sensor is powered by batteries, replacing and charging the battery after its depletion are challenging. Manual battery replacement is hampered by geographical restrictions, which results in significant reduction of wireless sensor network performance and longevity. As a result, this study addresses the energy-constrained wireless sensor networks by creating a technological model to a heterogeneous clustered wireless sensor network in an outdoor application using solar-enabled energy harvesting photovoltaic cells. Due to lack of energy, this effort was employed to overcome the challenges of relatively restricted processing performance and limited radio frequency transmission bandwidth. The program was created to efficiently utilize the energy produced from solar panels and charge the batteries in a variety of ways. The algorithm also controls the discharging process. According to the results of extensive investigation and experimentation, the sensor is continuously operated even when there is no solar light. The batteries are charged on bright day and discharged at night and on overcast days. The algorithm is used to govern the energy supplement to rechargeable lithium-ion polymer batteries as well as a load (sensor). In the worst scenario (no solar light/cloudy, and sensors reporting data every 30 minutes), the present cluster head sensor with 6 ordinary nodes in the cluster lasts just 16.6667 days. But the life duration of the newly designed model algorithm has been raised to 54.16667 days. The normal sensor node’s life duration has also been enhanced from 50 to 91.66667 days.

A Novel RF Energy Harvester for Event-Based Environmental Monitoring in Wireless Sensor Networks

Perpetual energy source is the major research issue in the research community for energy-constrained wireless sensor network (WSN) and IoT applications. Radio-frequency (RF) energy harvesting has become a technology available round the clock due to increased communication activities. However, efficient RF energy harvesting for low-power regions [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PRF \leq (0 {\mathrm{ dBm}})$ </tex-math></inline-formula> ] is still a challenge. This article proposes a novel RF energy harvesting technique along with an intelligent dynamic energy flow control algorithm. The algorithm works with dedicated energy flow control hardware. It is also able to provide perpetual life time to the ultralow power sensor nodes. A close approximation of the size of the RF energy harvester array is proposed for the perpetual lifetime of the WSN node deployed with wake-up radio for event-based monitoring applications. The model is verified by mathematical analysis, simulation, and fabricated prototype results for the operability in the given system. It is found that the circuit provides improved dc output voltage over the previous benchmark at low power in the range of −20 to 0 dBm. The system can charge a 2.7 V, 350 F supercapacitor with the rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.16~\mu \text{V}$ </tex-math></inline-formula> /s at 0 dBm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.125~\mu \text{V}$ </tex-math></inline-formula> /s at −6 dBm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.08~\mu \text{V}$ </tex-math></inline-formula> /s at −20 dBm. To prove the utility of the proposed idea, an RF field survey is also conducted and found that the proposed circuit can harvest energy from high power region (≥ −6 dBm) for LTE850 and LTE 900 bands.

Energy-Efficient Cooperative Caching for Information-Centric Wireless Sensor Networking

Although information-centric networking (ICN) can boost the performance of sensor data delivery in wireless sensor networks (WSNs), its energy efficiency may be significantly impaired due to the receiver-driven request-response transmission model. ICN sensors have to keep long-time active mode to wait for the requests that may arrive at any time, resulting nontrivial energy waste for energy-constrained WSNs. An intuitive method to conserve energy is to schedule partial nodes to fall asleep, but at the cost of extra data fetching delay. In this work, we investigate multihop cooperative caching for green WSN over receiver-driven ICNs. By leveraging regional caches, not only can the energy be saved, but also the data fetching delay can be reduced. We formulate those two cooperation gains and capture the tradeoff between energy saving and delay reduction. Based on the optimal tradeoff, we present a solution of scoped multihop cooperative caching and elaborate a green cooperation policy for each sensor to determine its operation scope and conduct a proper cooperation decision. Our simulation results show that our proposed multihop cooperative caching can save more than 91.0% energy and achieve 28.9% delay reduction than the basic ICWSN, and also reduce 60.2% energy consumption than the existing cooperative caching schemes.

Lifetime-enhancement Schemes for Power-constrained Routing in Wireless Sensor Networks: A Survey

Notwithstanding the broad applicability of wireless sensor network (WSN), the successful development and deployment in various real-world applications is currently challenging. The node-devices are powered by constrained-batteries that are typically impossible to substitute after deployments. Therefore, WSNs are energy-constrained and liable to early failure while expected to run for long-terms without battery replenishments.Diverse researchers have recently presented specific lifetime enhancement routing solutions based on data aggregation technology targeted at reducing energy-consumption,and improving the communication reliability of various WSNs.This paper presents a brief survey of the research field of data aggregation-ware routingin energy-constrained WSN. The paper overviews and classifies various existing routing solutions based on their delivery models and also identifies someof their majordrawbacks.

Multi-lead ECG Compression Based on Compressive Sensing

Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-lead Electrocardiogram (MECG) signals. This paper develops the compressed sensing theory for sparse modeling and effective multi-channel ECG compression. A basis matrix with Gaussian kernels is proposed to obtain the sparse representation of each channel, which showed the closest similarity to the ECG signals. Thereafter, the greedy orthogonal matching pursuit (OMP) method is used to obtain the sparse representation of the signals. After obtaining the sparse representation of each ECG signal, the compressed sensing theory could be used to compress the signals as much as possible. Following the compression, the compressed signal is reconstructed utilizing the greedy orthogonal matching pursuit (OMP) optimization technique to demonstrate the accuracy and reliability of the algorithm. Moreover, as the wavelet basis matrix is another sparsifying basis to sparse representations of ECG signals, the compressed sensing is applied to the ECG signals using the wavelet basis matrix. The simulation results indicated that the proposed algorithm with Gaussian basis matrix reduces the reconstruction error and increases the compression ratio.

Compressive sensing based the multi-channel ECG reconstruction in wireless body sensor networks

Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-channel Electrocardiogram (MECG) signals. In this paper, we have used the Kronecker sparsifying bases to exploit the spatio-temporal correlations of the MECG signals for improving the compression of the signals transmitted by the sensors. Furthermore, a compressed sensing-based method with low-rank constraint is proposed for effective data acquisition and signal reconstruction in the energy-constrained wireless body sensor networks. More specifically, in the proposed algorithm, an optimization formula consisting of two constraints is defined. The sparsity constraint is presented through the minimization of the l1 norm and the low-rank constraint is specified through the minimization of the nuclear norm. Afterward, a robust and efficient alternating direction method of multipliers (ADMM) based method is developed for the reconstruction of the MECG signals that solves the resulting optimization problem more effectively. Numerical experiments verify that the proposed algorithm achieves greater reconstruction accuracy with the smaller number of required transmissions, lower computational complexity, and smaller reconstruction errors, as compared to the latest CS-based recovery methods.

An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor networks

In energy-constrained wireless sensor networks (WSNs), geographic routing (GR) also known as location-aware routing has been developed because it uses neighbourhood locality data as an alternative of overall topology information for routing. But, this protocol frequently suffers from the energy holes in the data transmission resulting in path failure. To avoid this problem, energy-aware dual-path GR (EDGR) protocol has been suggested to improve the routing path from energy holes. However, it is unable to heal the energy holes since the node mobility can generate new energy holes. Also, higher mobility distance can cause high energy consumption (EC) and node failure. Hence in this article, an energy-efficient distributed collaboration mechanism is proposed with EDGR for $$k$$ -coverage energy holes detection and healing in WSN that minimizes the delivery delay (DD). In this protocol, the distributed Voronoi-based collaboration (DVOC) method is applied in which the nodes can cooperate in energy holes detection and recovery. The nodes are enabled to monitor each other node’s critical locations around themselves by constructing the local Voronoi diagrams (LVDs). Moreover, an optimized DVOC (ODVOC) is proposed with EDGR in which intelligent water drop (IWD) algorithm is used to find the globally optimized routes to minimize the DD. Finally, the simulation outcomes demonstrate the ODVOC-EDGR accomplishes higher effectiveness than the EDGR and DVOC-EDGR in terms of different performance metrics.

Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks

In energy-constrained wireless sensor networks, low energy utilization and unbalanced energy distribution are seriously affecting the operation of the network. Therefore, efficient and reasonable routing algorithms are needed to achieve higher Quality of Service (QoS). For the Dempster–Shafer (DS) evidence theory, it can fuse multiple attributes of sensor nodes with reasonable theoretical deduction and has low demand for prior knowledge. Based on the above, we propose an energy efficient and reliable routing algorithm based on DS evidence theory (DS-EERA). First, DS-EERA establishes three attribute indexes as the evidence under considering the neighboring nodes’ residual energy, traffic, the closeness of its path to the shortest path, etc. Then we adopt the entropy weight method to objectively determine the weight of three indexes. After establishing the basic probability assignment (BPA) function, the fusion rule of DS evidence theory is applied to fuse the BPA function of each index value to select the next hop. Finally, each node in the network transmits data through this routing strategy. Theoretical analysis and simulation results show that DS-EERA is promising, which can effectively prolong the network lifetime. Meanwhile, it can also reach a lower packet loss rate and improve the reliability of data transmission.

A Hybrid Genetic Algorithm With Bidirectional Mutation for Maximizing Lifetime of Heterogeneous Wireless Sensor Networks

Sleep scheduling is an effective mechanism to extend the lifetime of energy-constrained Wireless Sensor Networks(WSNs). It is often that the sensors are divided into sets with some constraints after plentiful sensors are deployed randomly, and then the sensors are scheduled to be activated successively according to the numbering of the sets. Many approaches divide the sensors into disjoint sets, which are not suitable for heterogeneous WSNs because of the waste of energy. In this paper, we propose a hybrid genetic algorithm which adopts greedy initialization and bidirectional mutation operations, termed BMHGA, to find a number of non-disjoint cover sets to prolong lifetime of heterogeneous WSNs, while subject to ensuring the full coverage of the monitoring area during the network lifetime. BMHGA adopts two-level structured chromosome to indicate the sensors and energy assignment to each set. A novel greedy method only uses little time to initialize the population that avoids time waste of random initialization. A new bidirectional mutation is proposed to keep multiplicity and global search. Through simulations, we show that the proposed algorithm outperforms the other existing approaches, finding the cover sets with longer lifetime by consuming less running time, especially in the large-scale networks. The experiment study also verifies the effectiveness of the proposed genetic operations and reveals the proper parameter settings for BMHGA.

Joint node selection, flow routing, and cell coverage optimisation for network sum‐rate maximisation in wireless sensor networks

In this study, the problems of joint node selection, flow routing, and cell coverage optimisation in energy-constrained wireless sensor networks (WSNs) are considered. Due to the energy constraints on network nodes, maximising network sum-rate under target network lifetime, flow routing, cell coverage, and minimum rate constraints is of paramount importance in WSNs. To this end, a mixed-integer non-linear programming problem is formulated, where the aim is to optimally select which network nodes to act as sensors or relays while ensuring connectivity to the fusion centre optimised network flows, and full network coverage. The formulated problem happens to be NP-hard (i.e. computationally prohibitive). In turn, a solution procedure based on the branch and bound with the reformulation-linearisation technique (BB-RLT) is devised to provide a ( 1 − ϵ ) -optimal solution to the formulated problem. Simulation results are presented to validate the efficacy of the devised BB-RLT solution procedure. This work provides significant theoretical results on network sum-rate maximisation for WSNs under a variety of practical constraints.

Energy‐efficient circle zones stable election protocol with helper nodes for heterogeneous WSNs

The design of an energy-efficient routing protocol is crucial for extending the life-time of limited energy-constrained wireless sensor networks (WSNs). To achieve this goal, the authors propose three proactive routing protocols; the first protocol is a circle zone stable election protocol (CZSEP). This protocol is a two-level heterogeneous energy protocol with a circle zone located at the centre of the network field and used to deploy the normal nodes and with advanced nodes that are randomly distributed around the circle zone. The performance of the proposed CZSEP was compared to that of the conventional ZSEP considering the same total initial energy. To further improve the performance of CZSEP, they propose the second protocol named as, CZSEP with helper nodes (CZSEP-HN) which is a three-level heterogeneous energy protocol. In CZSEP-HN, they add helper nodes in certain places in the network field to minimise the energy consumption in the considered network. Both CZSEP and CZSEP-HN can overcome the problem of large transmission distance between normal nodes and the base station (BS) and between cluster-heads and BS exists in conventional ZSEP. Finally, they propose the third protocol, which is a modified CZSEP-HN (MCZSEP-HN) to overcome the problem of energy consumption in CZSEP-HN. MCZSEP-HN is a four-level heterogeneous energy protocol with two circle zones one for normal nodes and the other for intermediate nodes. The obtained results showed that the MCZSEP-HN outperforms the other protocols in terms of life-time, throughput, stability and instability period and can reduce the energy consumption in WSNs.

Location Aware Relay Node Placement for Energy Constrained Wireless Sensor Networks

Location Aware Relay Node Placement for Energy Constrained Wireless Sensor Networks

A novel bio-inspired routing algorithm based on ACO for WSNs

The methods to achieve efficient routing in energy constrained wireless sensor networks (WSNs) is a fundamental issue in networking research. A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. The issue of path selection to reach the nodes and vital correspondence parameters, for example, the versatility of nodes, their constrained vitality, the node residual energy and route length are considered since the communications parameters and imperatives must be taken into account by the imperative systems that mediate in the correspondence procedure, and the focal points of the subterranean insect framework have been utilized furthermore. Utilizing the novel technique and considering both the node mobility and the existing energy of the nodes, an optimal route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified using MATLAB and the simulation results demonstrate that new ACO based algorithm achieved improved performance, about 30% improvement compared with the traditional ACO algorithm, and faster convergence to determine the best cost route, and recorded an improvement in the energy consumption of the nodes per transmission.