Sensor Network Nodes Research Articles

Supporting Ultralow-Power Nodes in 6TiSCH Industrial Wireless Sensor Networks

Supporting Ultralow-Power Nodes in 6TiSCH Industrial Wireless Sensor Networks

Transit search algorithm based on oscillation exploitation factor and Roche limit for wireless sensor network deployment optimization

To optimize the deployment of nodes in Wireless Sensor Networks (WSN) and effectively control network node energy consumption, thereby improving the quality of perception services, a Transit search algorithm based on oscillation exploitation factor and Roche limit is proposed. The Roche limit-inspired approach enhances the stellar phase of the algorithm, accelerating the convergence rate in the mid-to-late stages of iteration while ensuring adequate exploration of the solution space. Subsequently, five weakening oscillation development factors are introduced to refine the algorithm’s exploitation phase and improve its fine-tuning accuracy. To validate the effectiveness of these strategies, various approaches are applied to optimize the coverage, waste rate and energy consumption in two models of WSN deployment, with connectivity recorded. The comparison reveals the optimal improved algorithm, SEROTS, which enhances coverage by 1.34% in the obstacle-free model compared to the original TS algorithm, with waste and energy consumption rates reduced by 2.05% and 0.00016%, respectively. In the obstacle model, coverage increases by 1.49%, while waste and energy consumption rates decrease by 6.96% and 0.0004%, respectively. To demonstrate the efficiency of the improved algorithm in optimizing WSN deployment, SEROTS is compared with four optimization algorithms: Egret Swarm Optimization Algorithm (ESOA), Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA) and Differential Evolution (DE). Two models are selected, integrating the three objectives into a single objective function. Simulation results indicate that SEROTS performs best in both models, with an improvement of 0.53% and 0.79% over the second-best algorithm, respectively. Furthermore, the proposed strategies are compared with simulation results from five other studies, achieving higher coverage rates by 1.57%, 3.33%, 0.87%, 3.81% and 0.21%, respectively. Finally, experiments discuss the application in large-scale scenarios, verifying the feasibility and efficiency of the SEROTS algorithm in WSN deployment optimization.

Evolutionary game and LGPSO for attack-defense confrontation analysis in WSN from macro perspective

Wireless sensor network node security requires effective security mechanisms to protect the security and reliability, so as to ensure that sensor nodes can operate normally and provide reliable data in various environments. The security state change of wireless sensor network nodes is an important research content in the field of wireless sensor security. In this paper, an evolutionary game model is proposed to analyze the security state changes of sensor network nodes from a macro perspective. The existing methods define the parameters subjectively, which leads to the lack of rationality. In this paper, a Levy Flight Global Learning Particle Swarm Optimization (LGPSO) is proposed to calculate the Nash equilibrium of the game and quantify the parameters of the evolutionary game. The method proposed in this paper determines the parameters of the evolutionary game by solving the Nash equilibrium, which makes the model more realistic. The LGPSO has better optimization performance than other algorithms in solving Nash equilibrium problems. The CEC2013 dataset is used to test the performance of LGPSO. The experimental results show that LGPSO is superior to all other algorithms on 68% of the problems. This paper also discusses the influence of the game difficulty coefficient on the evolution process, and experiments show that a larger coefficient is more beneficial to the defender. The macro analysis in this paper provides the method of active defense for the attack-defense confrontation in wireless sensor networks.

A secure and lightweight trust evaluation model for enhancing decision-making in resource-constrained industrial WSNs

The dependability of nodes in an Industrial Wireless Sensor Network (IWSN) is vital for precise decision-making and the overall functioning of the network. Unreliable nodes have the potential to result in incorrect data, which can undermine the reliability of monitoring and control systems. Inaccurate decision-making in IWSNs can result in operational failures, safety risks, inadequate resource utilization, elevated energy consumption, and susceptibility to security vulnerabilities. Trust models tackle these challenges by recognizing and reducing the impact of unreliable nodes, improving the precision of data, and strengthening the security of the IWSNs. To overcome these challenges, this research work introduces a “Novel approach for Trust Utilization and Reliability Enhancement” (NATURE) in IWSNs. The unique approach employed in proposed NATURE model is distinguished by its multi-level clustered model, which improves attack mitigation, reduces communication overhead, and enhances overall network efficiency. This model is particularly effective in industrial environments where the network structure and the nature of data traffic are highly dynamic. The use of a multifactor trust estimation framework allows NATURE to assess the trustworthiness of SNs based on a comprehensive set of criteria, including behavior patterns, energy consumption, and communication reliability. By operating on multiple levels, NATURE can dynamically adjust trust functions to accurately distinguish between trustworthy and faulty SNs, thereby improving network reliability. Moreover, NATURE employs the temporal decay factor to prioritize recent node behaviors, ensuring that obsolete actions have minimal impact. Additionally, it uses the dynamic adjustment factor to balance the influence of negative interactions, encouraging reliable and responsive trust evaluations. Additionally, NATURE integrates a dynamically adjustable logical time window to enhance monitoring precision and adaptability, outperforming fixed-length windows in anomaly detection. NATURE integrates an Optimal Lead Node Election Algorithm (OLNEA) to improve cluster leader selection process. OLNEA considers network density, link quality, and Lead Node (LN), battery life, ensuring competent data aggregation and load balancing. By periodically selecting robust LNs and seamlessly switching to alternatives, NATURE promotes reliability and mitigates the impact of low LN battery levels. Additionally, NATURE employs a trust-based attacks detection algorithm to fortify IWSN security. This algorithm employs keen methods to verify data integrity, monitor energy levels, and ensure message authenticity, effectively safeguarding the IWSN from malevolent attacks and ensuring the safe transmission of data. Experimental results highlight NATURE’s exceptional performance across significant metrics when compared to existing trust schemes. In a WSN of 500 SNs with 30% being malicious, NATURE detects malicious behavior with a 97% accuracy, outperforming other models. Even with 50% malicious SNs, NATURE maintains a high detection accuracy of 91%, again surpassing alternative approaches. Additionally, NATURE significantly reduces energy consumption while achieving efficient throughput rates, underscoring its effectiveness in challenging network conditions.

Self-powered wireless sensor networks based on the radioisotope thermoelectric generator for aquatic temperature monitoring

This study focuses on the development of ultra-long-life wireless sensor network (WSN) node based on the thermoelectric effect for use in areas such as aquatic temperature monitoring. A long-lasting radioisotope thermoelectric generator (RTG) was used as the energy source for the WSN node to address issues such as short runtime and pollution associated with traditional chemical batteries and solar cells. A compact RTG was fabricated by developing thermoelectric modules with high adaptability to cylindrical heat sources. The electrical output performance at the ocean surface and in the underwater environment was analyzed, with the maximum output power reaching 2326.6 μW. An integrated circuit module comprising direct current to direct current (DC-DC) boost converters, control switch circuits, and supercapacitors was employed to power the WSN node from the RTG directly. Powered by the RTG, a WSN node for monitoring ocean temperature was also constructed. Its performance was evaluated under different environmental conditions by investigating the effects of RTG’s output, sensor data variations, distance, and other factors on WSN node stability. Results demonstrate that the designed self-powered WSN node can operate stably and continuously at the ocean surface and in underwater environments, thereby showing its promising prospects.

Performing Magnetic Boundary Modulation to Broaden the Operational Wind Speed Range of a Piezoelectric Cantilever-Type Wind Energy Harvester.

Small piezoelectric wind-induced vibration energy harvesting systems have been widely studied to provide long-term sustainable green energy for a large number of wireless sensor network nodes. Piezoelectric materials are commonly utilized as transducers because of their ability to produce high output power density and their simple structure, but they are prone to material fracture under large deformation conditions. This paper proposes a magnetic boundary modulated stepped beam wind energy harvesting system. On the one hand, the design incorporates a composite stepped beam with both high- and low-stiffness components, allowing for efficient vibration and electrical energy output at low wind speeds. On the other hand, a magnetic boundary constraint mechanism is constructed to prevent the piezoelectric sheet from breaking due to excessive deformation. Experiments have confirmed that the effective operational wind speed range of the harvester with magnetic boundary constraints is doubled compared to that of the harvester without magnetic boundary constraints. Furthermore, by adjusting the magnetic pole spacing of the boundary, the harvesting system can generate sufficiently high output power under high-wind-speed conditions without damaging the piezoelectric sheet.

Algorithm for Target Coverage Problem Based on Deep Learning in Wireless Sensor Networks

Aiming at the uncertain mechanism of node activation strategies and redundancy of feasible solution sets in the process of solving target coverage problem in wireless sensor networks, we proposed a deep learning based target coverage algorithm to learn the scheduling strategies of nodes in wireless sensor networks. Firstly , the algorithm abstracted the construction of feasible solution sets into Markov decision process, and intelligently selected activated sensor nodes as discrete actions according to the network environment. Secondly, a reward function evaluated the performance of the intelligent agent in selecting actions based on the coverage capacity and its residual energy of the active node. The simulation experiment result shows that the algorithm is effective in different network environments, and the network lifecycle is superior to the three greedy algorithms, the maximum lifetime coverage algorithm and the adaptive learning automaton algorithm.

Green Communication in IoT for Enabling Next-Generation Wireless Systems

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput.

Energy-efficient and fault-tolerant routing mechanism for WSN using optimizer based deep learning model

Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant; it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.

Simulation of marine wireless sensor network coverage based on improved grey wolf optimization algorithm

Abstract A new improved algorithm (IGWO) is proposed based on the Grey Wolf Optimization (GWO) algorithm to solve the issue of low overall coverage easily caused by the random deployment of nodes in wireless sensor networks. The IGWO algorithm improves its searchability by adjusting the convergence factor a and changing the updating rules of individual gray wolf positions. IGWO improves the overall search capabilities of the algorithm by converging and adjusting the trend of convergence factors and dynamically adjusts the gray wolf position update strategy by taking the Euclidean distance ratio values of the omega wolf to the alpha wolf, beta wolf, and delta wolf as parameters, further expanding the search capability of the algorithm. Using MATLAB for simulation, we select 20, 30, and 40 sensor nodes respectively, and the coverage of IGWO compared to GWO’s WSN increases by 0.09%, 2.09%, and 1.17%, respectively, proving that the IGWO algorithm can effectively improve the coverage and performance of WSN.

Self-Supplying Photovoltaic-Hygroelectric Coupling System Powering Internet of Things Sensors.

The rapid advancement in the Internet of Things (IoT) has prompted a proliferation of sensor applications with increasing focus on harnessing environmental energy sources for powering these sensors. However, owing to the variability inherent in climatic and geographic conditions, a singular approach to environmental energy harvesting often fails to deliver sustainable and reliable power. Hygroelectric technology, leveraging the electrical coupling between nanostructured materials and water to convert moisture-derived energy into electricity, complements the advantages of light energy collection technology. In this study, an ion diode moisture-based power generation array was fabricated to yield an open-circuit voltage of 8 V and a short-circuit current of 3 mA under an 86.9% relative humidity (RH). Subsequently, integration of a photovoltaic module with the hygroelectric generator assembly via an energy management circuit augmented the system's overall power generation, manifesting a remarkable 1150% increase over the original moisture-based power generation configuration. Furthermore, the incorporation of such a hybridized system bolstered the operational efficiency, extending the duration of continuous power supply cycles by up to 78.2% in comparison with the control group with no moisture-derived energy input. This pioneering endeavor unveils a novel light-moisture coupling energy-harvesting paradigm tailored for sustaining uninterrupted power provision in wireless sensor network nodes, and the harvesting of one energy source is not affected by the other energy source at all. With its inherent adaptability, this approach holds promise for deployment in outdoor environments characterized by low illumination levels and high humidity, presenting a versatile solution to address sensor powering challenges.

FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU

Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

Delay sensitive and energy adaptive clustering hierarchy protocol using enhanced agglomerative hierarchy algorithm with time‐controlled jellyfish optimization

AbstractWireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time‐controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy‐aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering‐based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.

Buffer with N Policy and Active Management

The N policy is a buffer and transmission management scheme proposed for nodes in wireless sensor networks to save energy. It exploits the concept that the output radio of a node is initially switched off until a critical queue of packets is built up. Then, the output transmission begins and continues until the buffer is completely flushed. The cycle then repeats. In this study, we analyze a buffer with the N policy, equipped additionally with active queue management, which allows for dropping some packets depending on the current buffer occupancy. This extension enables controlling the performance of the node to a much greater extent than in the original N policy. The main contribution is the formulae for the key performance characteristics of the extended policy: the queue size distribution, throughput, and energy efficiency. These formulae are proven for a model with a general distribution of service time and general parameterizations of active management during the energy-saving and transmission phases. Theoretical results are followed by sample numerical calculations, demonstrating how the system’s performance can be controlled using active management in the transmission phase, the energy-saving phase, or both combined. The influence of the threshold value in an actively managed buffer is then shown and compared with its passive counterpart. Finally, solutions to some optimization problems, with the cost function based on the trade-off between the queue length and throughput, are presented.

Critical infrastructure monitoring in CBRNe scenarios: a reliable and robust communication network for distributed multimodal sensors

The malicious misuse of CBRNe agents can inflict extensive damage to critical infrastructure and terrorize public society. The effects of such attacks can range from financial or structural drawbacks to significant casualties. Recent terror attacks in Cucuta (2021) or Kabul (2021) demonstrate the need to protect infrastructures such as airports, power plants, and transportation infrastructure. In the face of these modern-day threats, the need for a reliable and effective monitoring system for critical infrastructure has become increasingly important. In a first contribution to the long-term development, we present an initial version of a concept that requires the implementation paradigm of diverse redundancy and secure communication using MQTTS relying on the included cipher suite. An initial set of node types is defined and assigned requirements for implementation. The software tools architecture we propose based on these requirements is designed to support network operators and developers by providing standard features for network management and sensor node implementation. This includes the definition of a secure semi-automatic onboarding process for new sensor nodes, which is presented in detail. We strive for independency from specific hardware platforms, software frameworks, and network technologies to enable an open standard for communication within a critical infrastructure sensor network and also between such networks in the future.

Selective Features Based Machine Learned Intrusion Detection Framework for Wireless Sensor Networks: A Need for Cryptographic Approach

Intruder attacks are a curse to wireless sensor network (WSN) nodes that affect their regular performance differently. The attacker cheats the cooperative characteristics of sensing elements pretending as a trustworthy node. Such intruders drastically degrade the WSN performance by affecting the sensing, collecting, processing, and transmitting capabilities of ideal nodes in the network. Due to their pretending behaviour, detecting such harmful nodes in the network is difficult since they are sometimes foes and other times friends. This paper introduces a selective feature-based machine-learning (SF-BML) framework that can detect an intruder node with a higher accuracy. The WSN-DS dataset constructed for four different attacks (Flooding, Blackhole, Grayhole, and Scheduling) is experimented with different feature attributes. The optimum features are found and tested for detection accuracy relating to all five categories. The experimental results using six significant features (SF) and a combination of eight other semi-significant features (SSF) showed that the normal-attacker node's detection accuracy increases with increasing features up to fourteen. The maximum training and test accuracy using fourteen features for the support vector machine (SVM) was 99.17% and 99.00% respectively.

Energy-efficient routing protocol for WSN based on relay node selection and A-star algorithm

Due to miniaturization constraints and manufacturing costs, wireless sensor networks (WSN) sensor nodes are generally outfitted with restricted resources concerning computing capability, energy, data storage capacity, and data delivery rate. These constraints encourage a significant portion of the research issues in WSNs, particularly the energy constraint, which is a fundamental issue. This paper suggests an innovative routing protocol that optimizes energy use called RNS_A-star, which combines a routing algorithm using a relay node and an improved heuristic for the A-star algorithm. The RNS_A-star protocol first applies a data routing algorithm using a relay node, which allows either using a relay node or simply sending the intra-cluster data to the CH. Then, the A-star algorithm is used to construct a multi-hop routing route between the CHs and the base station (BS) in order to find the best path for reducing energy consumption during data transmission. Simulation results show that the suggested RNS_A-star protocol performs better than ECO-BAT, LEACH-IACA, and LEACH protocols regarding energy conservation and extending network lifetime.

Lure: A simulator for networks of batteryless intermittent nodes

The emerging paradigm of batteryless intermittent sensor networks (BISNs) presents new challenges for researchers of low-power wireless systems and protocols. The nature of these challenges exacerbates the difficulty of evaluating networks of physical sensor nodes, making simulation an even more important component in evaluating performance metrics, such as communication throughput and delay, for BISN designs. To our knowledge, existing simulators and analytical models do not meet the unique needs of BISN research; therefore, we have created a new open-source BISN simulator named Lure. Lure is designed from the ground-up for simulation of batteryless intermittent systems and networks. Written in Python, Lure is powerful, flexible, highly configurable, and supports rapid prototyping of new protocols, systems, and applications, with a low learning curve. In this paper, we present Lure and validate it with experimental data to show that Lure can accurately reflect the reality of BISNs. We then demonstrate the process of applying Lure to research questions in select case studies.

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

PPSO and Bayesian game for intrusion detection in WSN from a macro perspective

The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.