Wireless Sensor Network Nodes Research Articles

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

SummaryA wireless sensor network (WSN) is a network of autonomous, small sensors that can detect, collect, and send data about their surrounding environment. In the Internet of Things (IoT) infrastructure, WSNs are the smart devices that provide the platform with resource input. Security breaches and insider attacks are possible due to the WSN's resource‐constrained design. However, the IoT platform's intelligence may be extended to WSN nodes for managing device and data‐level security. This paper proposes Monitored Access Constraint Security (MACS) to ensure the privacy of data collected via the ubiquitous processing enabled by the Internet of Things. The IoT platform performs frequent checks on the quality of the interactions between the various nodes to ensure that they are functioning properly and that the sensor aggregation instances are accountable. Node liability is considered while adjusting the aggregate level and the continuity. The method guarantees secure information from the environment and the data sources. The quality of the data gathered in the suggested technique is evaluated based on node liability and information extraction feature. Accordingly, security measures are implemented at data gathering and filtering levels and then assessed using a recurrent learning process. Since there are fewer security breaches overall, the rate of aggregation increases. Aggregation loss, delay time, false rate, throughput, and verification time are used to evaluate the performance.

Quadruple parameter adaptation growth optimizer with integrated distribution, confrontation, and balance features for optimization

Growth optimizer is a novel metaheuristic algorithm that has powerful numerical optimization capabilities. However, its parameters and search operators become crucial factors that significantly impact its optimization capability for engineering problems and benchmarks. Therefore, this paper proposes a quadruple parameter adaptation growth optimizer (QAGO) integrated with distribution, confrontation, and balance features. In QAGO, the quadruple parameter adaptation mechanism aims to reduce the algorithmic sensitivity for parameter setting and enhance the algorithmic adaptability. By employing parameter sampling that adheres to specific probability distributions, the parameter adaptation mechanism achieves dynamic tuning of the algorithm hyperparameters. Moreover, one-dimensional mapping and fitness difference methods are designed in the triple parameter self-adaptation mechanism based on the contradictory relationship to adjust the operator’s parameters. After that, ”spear” and ”shield” are balanced based on the Jensen–Shannon divergence in information theory. Furthermore, the topological structure of the operators is redesigned, and by combining the parameter adaptation mechanism, operator refinement is achieved. Refined operators can effectively utilize different evolutionary information to improve the quality of the solution. The experiment evaluates the performance of QAGO on distinct optimization problems on the CEC 2017 and CEC 2022 test suites. To demonstrate the capability of QAGO in solving real-world applications, it was applied to tackle two specific problems: multilevel threshold image segmentation and wireless sensor network node deployment. The results demonstrated that QAGO delivers highly promising optimization results compared to seventy-one high-performance competing algorithms, including the five IEEE CEC competition winners. The source code of the QAGO algorithm is publicly available at https://github.com/tsingke/QAGO.

A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

Today, wireless sensor networks (WSNs) are growing rapidly and provide a lot of comfort to human life. Due to the use of WSNs in various areas, like health care and battlefield, security is an important concern in the data transfer procedure to prevent data manipulation. Trust management is an affective scheme to solve these problems by building trust relationships between sensor nodes. In this paper, a cluster-based trusted routing technique using fire hawk optimizer called CTRF is presented to improve network security by considering the limited energy of nodes in WSNs. It includes a weighted trust mechanism (WTM) designed based on interactive behavior between sensor nodes. The main feature of this trust mechanism is to consider the exponential coefficients for the trust parameters, namely weighted reception rate, weighted redundancy rate, and energy state so that the trust level of sensor nodes is exponentially reduced or increased based on their hostile or friendly behaviors. Moreover, the proposed approach creates a fire hawk optimizer-based clustering mechanism to select cluster heads from a candidate set, which includes sensor nodes whose remaining energy and trust levels are greater than the average remaining energy and the average trust level of all network nodes, respectively. In this clustering method, a new cost function is proposed based on four objectives, including cluster head location, cluster head energy, distance from the cluster head to the base station, and cluster size. Finally, CTRF decides on inter-cluster routing paths through a trusted routing algorithm and uses these routes to transmit data from cluster heads to the base station. In the route construction process, CTRF regards various parameters such as energy of the route, quality of the route, reliability of the route, and number of hops. CTRF runs on the network simulator version 2 (NS2), and its performance is compared with other secure routing approaches with regard to energy, throughput, packet loss rate, latency, detection ratio, and accuracy. This evaluation proves the superior and successful performance of CTRF compared to other methods.

Performance analysis of a stochastic wireless sensor node based on Markovian queuing theory

ABSTRACT In this article, we present stochastic nature of a Wireless Sensor Network (WSN) node and perform extensive analysis based on the Markovian queuing theory for First In First Out (FIFO) and priority queue disciplines, considering a single server. The results demonstrate that, in addition to distribution of events, number of missed events also depends on average waiting time of event in the queue. The results also make it evident that selection of queue type in WSN node is critical for enhancing energy-efficiency performance of the node, and hence, there exists a need to assign priority to events before buffering stage.

High-performance hybrid nanogenerator for self-powered wireless multi-sensing microsystems

Wireless sensor network nodes are widely used in wearable devices, consumer electronics, and industrial electronics and are a crucial component of the Internet of Things (IoT). Recently, advanced power technology with sustainable energy supply and pollution-free characteristics has become a popular research focus. Herein, to realize an unattended and reliable power supply unit suitable for distributed IoT systems, we develop a high-performance triboelectric-electromagnetic hybrid nanogenerator (TEHNG) to harvest mechanical energy. The TEHNG achieves a high load power of 21.8 mW by implementing improvements of material optimization, configuration optimization and pyramid microstructure design. To realize a self-powered integrated microsystem, a power management module, energy storage module, sensing signal processing module, and microcontroller unit are integrated into the TEHNG. Furthermore, an all-in-one wireless multisensing microsystem comprising the TEHNG, the abovementioned integrated functional circuit and three sensors (temperature, pressure, and ultraviolet) is built. The milliwatt microsystem operates continuously with the TEHNG as the only power supply, achieving self-powered operations of sensing environmental variables and transmitting wireless data to a terminal in real time. This shows tremendous application potential in the IoT field.

Application of Improved Immune Particle Swarm Optimization in 3D Node Localization

When 2D localization algorithm is extended to 3D space, it achieves node localization with better localization accuracy. An improved immune particle swarm optimization (IIPSO) localization algorithm is proposed to estimate the localization of nodes in wireless sensor networks (WSN). The results show that IIPSO algorithm outperforms the traditional two-dimensional localization algorithm based on iterative estimation of the center of mass by 3%-6%. The proposed algorithm has better resistance to RSSI measurement errors and can achieve more than 99% node localization coverage through multiple iterations, which is effective for WSN.

Hybrid Marine Predators and Border Collie Optimization algorithm for multipath routing in IoT

SummaryInternet of Things (IoT) is a network of smart things that connects people and objects for gathering and exchanging information by means of embedded sensor. IoT‐based wireless sensor network (WSN) nodes are monitored as a resource parameter at the variety of methods, including resource calculation, energy resource, and storage resource, with multipath routing protocols necessary to enable extended network lifespan and achieve high energy utilization. The existing routing protocols did not provide optimal path from the multipath route. Hence, in this manuscript, a Hybrid Marine Predators and Border Collie Optimization algorithm is proposed for multipath routing in IoTs. The main aim of this endeavor is to “increase network lifetime.” IoT is simulated initially and multipath routing initiates in internet of things network. Multipath routing is achieved by proposed Hyb‐MP‐BCOA; this is the combination of Hybrid Marine Predators Algorithm and Border Collie Optimization algorithm. The proposed Hyb‐MP‐BCOA algorithm chooses the optimum path from the multiple path obtainable for routing, depending upon fitness parameter, like context awareness, network lifetime, residual energy, trust, and delay. Based on the fitness parameter, the optimal path is selected by using Hyb‐MP‐BCOA algorithm. Finally, the proposed Hyb‐MP‐BCOA‐MR‐IoT model is applied in MATLAB, and efficacy of proposed technique is estimated by using various performance metrics, like drop, normalized network energy, lifetime of network, delay, throughput, energy consumed, packet delivery ratio, and number of sensor nodes versus aggregation level. The proposed method attains lower delay of 14.285%, 50%, 53.846%, and 57.142%, lower packet drop of 14.285%, 50%, 53.846%, and 57.142%, lower energy consumption compared with existing methods such as multiobjective sun flower optimization with gray wolf optimizer (GWO) for multipath routing in IoT (SFO‐GWO‐MR‐IoT), multiobjective Whale Lion Fireworks optimization algorithm and fitness function for multipath routing in IoT (WLFA‐MR‐IoT), multipath routing in IoT using shuffled frog leaping algorithm (SFLA‐MR‐IoT), and multipath routing in IoT using hybrid Whale Optimization Algorithm‐Moth Flame Optimization (MFO)(hyb‐WOA‐MFO‐MR‐IoT). Finally, the safe communication is attained in the IoT network.

Energy-Efficient Long-Range Sectored Antenna for Directional Sensor Network Applications

The popularity of Directional Sensor Network (DSN) is increasing due to their improved transmission range, spectral reusability, interference mitigation, and energy efficiency. In this paper, the radio module of the DSN is implemented using an eight-sector antenna array. Two types of sectored antennas, namely the Rectangular Patch Sectored Antenna (RPSA) and the Triangular Patch Sectored Antenna (TPSA), are proposed to operate at frequency of 2.4 GHz ISM band. The RPSA has a half-power beamwidth (HPBW) of 45° and a peak gain of 5.2 dBi, while the TPSA has an HPBW of 48° and a peak gain of 4.16 dBi. The design and performance evaluation of RPSA and TPSA in terms of gain, reflection characteristics (|S11|), and HPBW are conducted using Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA). To demonstrate the concept, the fabricated sectored antennas are connected to MicaZ Wireless Sensor Network (WSN) nodes using an indigenously designed Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard. The performance of the DSNs based on RPSA and TPSA is evaluated using the Cooja simulator and a testbed consisting of MicaZ nodes. The results show that RPSA outperforms TPSA and omnidirectional-based WSNs in terms of power consumption, received signal strength, and packet delivery ratio.

A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Joint Clustering and Routing Optimisation for Low-power Wireless Sensor Networks

Wireless sensor networks (WSNs) have been one of the fields that have attracted a lot of attentions from many scientific researchers in recent years. The sensor nodes of the network are fixed or moved to detect the environment and impart the data accumulated from the remote monitored regions via wireless connections. It is indicated in complex environments such as forests, deep seas, urban areas, etc., the sensor nodes in WSNs are usually tiny and battery-driven devices. Thus, energy-effective data accumulation methods required to improve the network’s lifetime are very necessary. In this paper, we propose a joint technique of fuzzy clustering and heuristic ant routing (FCHAR) to save the energy for low-power WSNs. Simulation results are shown to demonstrate the benefits of the proposed FCHAR compared to other conventional ones.

An Efficient Anti-Intrusion Detection Algorithm Based on MEP for Directional Sensor Networks

In wireless sensor networks(WSNs), the minimum exposure path (MEP) represents the worst coverage of the whole network, so the exploration of MEP is of great significance to prevent unauthorized objects from passing through the monitoring area. The traditional research on finding MEP only focuses on how to avoid actual obstacles, without considering how to avoid high coverage areas. MEP-based coverage optimization studies the scheme of directly adding nodes, which does not conform to the characteristics that WSNs nodes are difficult to add or replace once deployed. In order to effectively improve the monitoring ability of coverage area, this paper proposes an efficient anti-intrusion detection algorithm based on MEP for directional sensor networks. Firstly, in the stage of finding MEP, the relationship between ant colony path planning algorithm and MEP is constructed by using dynamic threshold strategy. Secondly, in order to find MEP more accurately, the exposure factor is added to the state transition probability formula of the original ant colony algorithm. Finally, in the coverage enhancement stage, this paper innovatively proposes a barrier coverage enhancement strategy, which constructs a barrier by adjusting the position of redundant nodes. The simulation results show that the MEP found in this paper accurately avoids the high coverage area, and the improved ant colony algorithm is better than the MEP found by the original ant colony algorithm. Compared with the baseline algorithm, the barrier coverage enhancement strategy has strong applicability and low cost, which significantly improves the network monitoring ability.

Laser-Induced Graphene Formation on Chitosan Derivatives toward Ecofriendly Electronics

Laser-induced graphene (LIG) has aroused a wide range of research interests ranging from micro-nano energy devices to the Internet of Things (IoT). Nevertheless, the non-degradability of most-used synthetic polymer carbon sources poses a serious threat to the environment. In this work, ecofriendly chitosan-based derivatives, including carboxymethyl chitosan (CMCS), chitosan oligosaccharide, and chitosan hydrochloride, are successfully converted into LIGs for the first time via a convenient one-step CO2 laser engraving at ambient air. The obtained LIGs are characterized by a three-dimensional hierarchical porous structure and exhibit good sheet conductivity. The consecutive carbonization and graphitization mechanism of target precursors induced by laser heat accumulation is also deeply discussed. Besides, based on a mechanically reliable LIG/CMCS composite film and tribo-negative acrylic/polyimide anti-layers, two contact-separation mode triboelectric nanogenerators are built and their power densities range from 1.44 to 2.48 mW cm–2. These devices with long cycle life can be used for low-frequency mechanical energy harvesting and commercial capacitance charging, which could be potentially applied in the wireless sensor network nodes. Such a family of chitosan derivatives paves a new route for LIG synthesis and provides new ideas for ecofriendly LIG electronics.

Guest Editorial: Polymer electrets and ferroelectrets

Guest Editorial: Polymer electrets and ferroelectrets

WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQ) of each sensor recharged. Therefore, this paper proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time and energy usage efficiency.

Mobile chargers scheduling algorithm for maximum data flow in wireless sensor networks

Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.

Research on node coverage in agricultural wireless sensor networks based on deterministic coverage

The application of wireless sensor network in agriculture has always been a concern. In the research of wireless sensor network extension, coverage problem is one of the hot issues in wireless sensor network. Research on the node coverage of agricultural wireless sensor networks based on deterministic deployment is of great significance to improve the coverage quality of agricultural wireless sensor networks and reduce energy consumption. According to the feature of deterministic coverage of wireless sensor network nodes in the agricultural field, this paper summarizes and analyzes the existing deterministic coverage schemes, and proposes to determine the minimum number of nodes according to the size of the repeated coverage area, which provides ideas for the problem of node coverage with deterministic area characteristics in the agricultural field.

Energy redistribution assisted charging of WRSNS with multiple mobile chargers having multiple base stations

The utilization of mobile chargers (MCs) for charging the nodes in wireless rechargeable sensor networks (WRSNs) has been a significant research subject. However, existing works mainly focused on planning the moving paths of MCs whereas neglecting opportunities to enhance charging efficiency through the exploitation of energy redistribution (ERD) processes among nodes and the broadcast nature of radio signals. To improve charging efficiency by exploiting these opportunities, we investigate the underlying energy redistribution assisted charge scheduling (ERACS) problem. This problem aims to identify a charging schedule that meets the nodes’ energy demands while minimizing energy loss and time span in WRSNs with multiple MCs and multiple base stations. After demonstrating the NP-hardness of the ERACS problem, we propose an energy loss minimization charge scheduling (ELMCS) algorithm to solve the problem. The ELMCS algorithm first constructs and solves a linear programming problem to obtain a time-length list that leads to minimum transmission energy loss. It then constructs the moving path set of MCs using a heuristic MCs moving path planning strategy that we propose in this paper to minimize moving energy loss. Additionally, after generating an MC energy transmission list that describes the MCs’ energy transmission time intervals, the ELMCS algorithm minimizes the time span by capitalizing on concurrent energy transmission opportunities while ensuring optimal energy loss. Finally, we compare our ELMCS algorithm with typical algorithms, and the simulation results demonstrate that our algorithm is markedly superior.

Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks

Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an essential approach for extending the lifetime of wireless sensor networks (WSNs), which makes sensor nodes adapt to changing environments by calculating optimal decision solutions in real time. However, as WSN grows, error propagation cannot keep up with topological changes in the networks, which prevents the sensor nodes’ routing decisions from being timely optimized. In this paper, we propose the DNN-HR algorithm, which optimizes the reinforcement learning process of WSN in three ways. First, this paper constructs a two-level learning structure. Discrete nodes perform reinforcement learning during the interaction with their neighboring nodes. Simultaneously, the neural networks that fit the V values guide the underlying nodes’ learning. Second, the algorithm divides the sensor networks into two-level subspaces according to their geographic locations, and cluster heads are elected in each subspace to form weight incremental propagation trees. Third, each sensor node sends the weight increments it learned to its first-level cluster head. At the first-level cluster head, the weight increments sent by multiple nodes are received and accumulated, and then the results are sent to the second-level cluster head. The base station acquires the weight increments of all the second-level cluster heads and then broadcasts the updated neural network weights to all the WSN nodes. Experiments determine the appropriate heuristic parameters. Compared to other algorithms, the experiments show that our proposed scheme significantly improves the packet delivery rate, node survival rate, and energy standard deviation of nodes.

An epidemic model for the investigation of multi‐malware attack in wireless sensor network

AbstractThe protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

Energy-aware multipath routing in WSN using improved invasive weed elephant herd optimization

PurposeIn a wireless sensor network (WSN), the sensor nodes are distributed in the network, and in general, they are linked through wireless intermediate to assemble physical data. The nodes drop their energy after a specific duration because they are battery-powered, which also reduces network lifetime. In addition, the routing process and cluster head (CH) selection process is the most significant one in WSN. Enhancing network lifetime through balancing path reliability is more challenging in WSN. This paper aims to devise a multihop routing technique with developed IIWEHO technique.Design/methodology/approachIn this method, WSN nodes are simulated originally, and it is fed to the clustering process. Meanwhile, the CH is selected with low energy-based adaptive clustering model with hierarchy (LEACH) model. After CH selection, multipath routing is performed by developed improved invasive weed-based elephant herd optimization (IIWEHO) algorithm. In addition, the multipath routing is selected based on certain fitness functions like delay, energy, link quality and distance. However, the developed IIWEHO technique is the combination of IIWO method and EHO algorithm.FindingsThe performance of developed optimization method is estimated with different metrics, like distance, energy, delay and throughput and achieved improved performance for the proposed method.Originality/valueThis paper presents an effectual multihop routing method, named IIWEHO technique in WSN. The developed IIWEHO algorithm is newly devised by incorporating EHO and IIWO approaches. The fitness measures, which include intra- and inter-distance, delay, link quality, delay and consumption of energy, are considered in this model. The proposed model simulates the WSN nodes, and CH selection is done by the LEACH protocol. The suitable CH is chosen for transmitting data through base station from the source to destination. Here, the routing system is devised by a developed optimization technique. The selection of multipath routing is carried out using the developed IIWEHO technique. The developed optimization approach selects the multipath depending on various multi-objective functions.