Practical Wireless Sensor Networks Research Articles

Duty Cycle Scheduling in Wireless Sensor Networks Using an Exploratory Strategy-Directed MADDPG Algorithm

This paper presents an in-depth study of the application of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms with an exploratory strategy for duty cycle scheduling (DCS) in the wireless sensor networks (WSNs). The focus is on optimizing the performance of sensor nodes in terms of energy efficiency and event detection rates under varying environmental conditions. Through a series of simulations, we investigate the impact of key parameters such as the sensor specificity constant α and the Poisson rate of events on the learning and operational efficacy of sensor nodes. Our results demonstrate that the MADDPG algorithm with an exploratory strategy outperforms traditional reinforcement learning algorithms, particularly in environments characterized by high event rates and the need for precise energy management. The exploratory strategy enables a more effective balance between exploration and exploitation, leading to improved policy learning and adaptation in dynamic and uncertain environments. Furthermore, we explore the sensitivity of different algorithms to the tuning of the sensor specificity constant α, revealing that lower values generally yield better performance by reducing energy consumption without significantly compromising event detection. The study also examines the algorithms' robustness against the variability introduced by different event Poisson rates, emphasizing the importance of algorithm selection and parameter tuning in practical WSN applications. The insights gained from this research provide valuable guidelines for the deployment of sensor networks in real-world scenarios, where the trade-off between energy consumption and event detection is critical. Our findings suggest that the integration of exploratory strategies in MADDPG algorithms can significantly enhance the performance and reliability of sensor nodes in WSNs.

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

Timestamp-Free Synchronization Under Delays With Arbitrary Distribution in Wireless Sensor Networks

Clock synchronization is a key technology in wireless sensor networks (WSNs). It is significant and challenging to track clock parameters under arbitrary delay distributions. This letter proposes an Adaptive Gaussian mixture extended Kalman particle filter (AGMEKPF), which is robust to arbitrarily distributed delays in practical WSNs and can jointly track clock skew and offset. It is combined with the timestamp-free synchronization, which is a clock synchronization protocol that can effectively reduce energy consumption without exchanging timestamps. Simulation results show that the proposed method outperforms similar methods in terms of accuracy and complexity.

A Timestamp-Free Time Synchronization Scheme Based on Reverse Asymmetric Framework for Practical Resource-Constrained Wireless Sensor Networks

Energy-efficient time synchronizations for wireless sensor networks (WSNs) have been put under the spotlight for years. A promising technique among which is the timestamp-free approach where no timestamps are required to establish the synchronization, thereby sparing the transmissions of the timing messages for conserving significant transmission energy. In this paper, we first investigate the feasibility of adopting timestamp-free time synchronization in practical resource-constrained WSNs; we then identify the issue of inaccuracy in maintaining the pre-defined response interval which affects the foundations of most existing timestamp-free schemes. Based on the investigation and our previously proposed reverse asymmetric time synchronization framework, we further propose an asymmetric timestamp-free time synchronization scheme with two estimation methods tailored for resource-constrained WSNs. We as well introduce the centralized and distributed multi-hop extension methods for the proposed scheme to cover diverse multi-hop scenarios. Experimental results on a real WSN testbed consisting of TelosB motes running TinyOS demonstrate that the proposed scheme achieves high energy efficiency while maintaining microsecond-level time synchronization accuracy compared to three other conventional schemes.

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other state-of-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multi-objective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA .

A family system based evolutionary algorithm for obstacle-evasion minimal exposure path problem in Internet of Things

Barrier coverage in wireless sensor networks (WSNs) is a well-known model for military security applications in IoTs, in which sensors are deployed to detect every movement over the predefined border. The fundamental sub-problem of barrier coverage in WSNs is the minimal exposure path (MEP) problem. The MEP refers to the worst-case coverage path where an intruder can move through the sensing field with the lowest capability to be detected. Knowledge about MEP is useful for network designers to identify the worst coverage in WSNs. Most prior research focused on this problem with the assumption that the WSN has an ideal deployment environment without obstacles, causing existing gaps between theoretical and practical WSNs systems. To overcome this drawback, we investigate a systematic and generic MEP problem under real-world environment networks by presenting obstacles called Obstacle-Evasion-MEP (hereinafter OE-MEP). We propose an algorithm to create several types of arbitrary-shaped obstacles inside the deployment area of WSNs. The OE-MEP problem is an NP-Hard with high dimension, non-differentiation, non-linearity, and constraints. Based upon its characteristics, we then devise an elite algorithm namely Family System based Evolutionary Algorithm (FEA) with our newly-proposed concepts of Family System, tailored to efficiently solve the OE-MEP. We also build an extension to a custom-made simulation environment to integrate a variety of network topologies as well as obstacles. Experimental results on numerous instances indicate that the proposed algorithm is suitable for the converted OE-MEP problem and performs better in solution accuracy than existing approaches.

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Cost-based routing protocols are the main approach used in practical wireless sensor network (WSN) and Internet of Things (IoT) deployments for data collection applications with energy constraints; however, those routing protocols lead to the concentration of most of the data traffic on some specific nodes which provide the best available routes, thus significantly increasing their energy consumption. Consequently, nodes providing the best routes are potentially the first ones to deplete their batteries and stop working. In this paper, we introduce a novel routing strategy for energy efficient and balanced data collection in WSNs/IoT, which can be applied to any cost-based routing solution to exploit suboptimal network routing alternatives based on the parent set concept. While still taking advantage of the stable routing topologies built in cost-based routing protocols, our approach adds a random component into the process of packet forwarding to achieve a better network lifetime in WSNs. We evaluate the implementation of our approach against other state-of-the-art WSN routing protocols through thorough real-world testbed experiments and simulations, and demonstrate that our approach achieves a significant reduction in the energy consumption of the routing layer in the busiest nodes ranging from 11% to 59%, while maintaining over 99% reliability. Furthermore, we conduct the field deployment of our approach in a heterogeneous WSN for environmental monitoring in a forest area, report the experimental results and illustrate the effectiveness of our approach in detail. Our EER based routing protocol CTP+EER is made available as open source to the community for evaluation and adoption.

A many-objective whale optimization algorithm to perform robust distributed clustering in wireless sensor network

The substantial increase in the usage of wireless sensor networks (WSNs) encourages to develop data clustering in event monitoring applications. Many centralized algorithms with single objective optimization are employed to solve this problem. However privacy, security and technical constraints are key issues in traditional centralized approach. Moreover, many WSN applications like condition monitoring and target tracking require more than three objectives for effective partitioning of dataset. This paper proposes many-objective whale optimization algorithm to handle robust distributed clustering in WSN. Initially, a swarm based many-objective whale optimization (MaOWOA) is discussed where reference point based leader selection method is utilized in updating the solutions instead of grid based leader selection as in multi-objective approach. This method gives better convergence and diversity. The simulation result of proposed approach is evaluated on many-objective DTLZ test problems against existing many-objective methods which is faster in terms of simulation time and gives competitive results in terms of generational distance (GD), inverse generational distance (IGD), spacing (SP) and hyper volume difference (HVD). Further, the encouraging results of the proposed MaOWOA are applied to perform robust distributed clustering in WSNs which is termed as distributed many-objective clustering using whale optimization algorithm (DMaOWOA). In this approach, a weight based method is incorporated to detect and remove the outliers and diffusion method of cooperation is used for distributed clustering. The proposed DMaOWOA is tested on one synthetic and three practical WSN datasets. It is observed that DMaOWOA based clustering performs up to 6% and 8% improvement in terms of Silhouette index as compared to particle swarm optimization based many-objective distributed clustering (DMaOPSO) and distributed K-Means (DK-Means) clustering algorithm, respectively.

Cascading Failures Analysis of Wireless Sensor Networks With Varying Routing Schemes

In practical wireless sensor networks (WSNs), routing schemes determine the message-forwarding direction of sensor nodes, which have a significant impact on the cascading process of WSNs. However, the existing cascading failure models fail to consider the impact of routing schemes. Therefore, in this work, we propose an actual cascading failure model for WSNs considering routing factors. In this model, a sink-oriented load metric is proposed to characterize the actual load distribution of WSNs, and four load redistribution mechanisms (i.e., latency-sensitive global routing, congestion-sensitive global routing, capacity-sensitive local routing and latency-sensitive local routing) are proposed to mimic the most commonly used routing schemes. Through extensive simulations, we have obtained some meaningful results: 1) there is a critical threshold for node capacity. When the node capacity is greater than the threshold, the lifting effects of capacity expansion are saturated; 2) there is a cascade interval for the attack range. Cascading failures occur only if the attack range is within this interval; 3) among the four routing schemes, congestion-sensitive global routing has the strongest cascading robustness; 4) flat-structured topology is the most robust, followed by clustering topology, and finally scale-free topology. The obtained results can provide theoretical support for improving the cascading robustness of WSNs.

Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks

In this paper, the secure distributed set-membership filtering problem is investigated for general nonlinear system over wireless sensor networks. For the purpose of getting close to practical wireless sensor networks, both the bias injection attacks and the channel fading of wireless communication are taken into account in the procedure of filter design. By employing linear matrix inequality (LMI) technique and Taylor's expansion formula, the nonlinearity, the channel fading, the bias injection attacks and the non-fragile are handled simultaneously in the set-membership filtering framework and the filter design problem is addressed. For the pre-specified filtering performance, sufficient conditions are obtain to ensure the existence of desired filter, where the filter gains are acquired via solving certain recursive LMI. Furthermore, in order to look for the locally best performance, an optimal algorithm is proposed. Finally, a simulation example is given to demonstrate the effectiveness of our proposed secure filtering algorithm.

Clock Offset and Skew Estimation Using Hybrid One-Way Message Dissemination and Two-Way Timestamp Free Synchronization in Wireless Sensor Networks

In practical wireless sensor networks, time synchronization functions are usually integrated in existing network transmissions to minimize total energy consumption. In this letter, a hybrid synchronization scheme using one-way message dissemination and emerging timestamp-free synchronization is developed for wireless sensor networks without additional communication overhead, which not only estimates the fixed delay efficiently but also provides improved clock offset estimation. Under Gaussian delay model, the maximum likelihood estimator for the joint estimation of clock offset, skew and fixed delay is derived, as well as corresponding Cramer-Rao lower bounds. Simulation results demonstrate significant performance enhancement of the hybrid approach.

SLUA-WSN: Secure and Lightweight Three-Factor-Based User Authentication Protocol for Wireless Sensor Networks.

Wireless sensor networks (WSN) are composed of multiple sensor nodes with limited storage, computation, power, and communication capabilities and are widely used in various fields such as banks, hospitals, institutes to national defense, research, and so on. However, useful services are susceptible to security threats because sensitive data in various fields are exchanged via a public channel. Thus, secure authentication protocols are indispensable to provide various services in WSN. In 2019, Mo and Chen presented a lightweight secure user authentication scheme in WSN. We discover that Mo and Chen’s scheme suffers from various security flaws, such as session key exposure and masquerade attacks, and does not provide anonymity, untraceability, and mutual authentication. To resolve the security weaknesses of Mo and Chen’s scheme, we propose a secure and lightweight three-factor-based user authentication protocol for WSN, called SLUA-WSN. The proposed SLUA-WSN can prevent security threats and ensure anonymity, untraceability, and mutual authentication. We analyze the security of SLUA-WSN through the informal and formal analysis, including Burrows–Abadi–Needham (BAN) logic, Real-or-Random (ROR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Moreover, we compare the performance of SLUA-WSN with some existing schemes. The proposed SLUA-WSN better ensures the security and efficiency than previous proposed scheme and is suitable for practical WSN applications.

WRSNs: Toward an Efficient Scheduling for Mobile Chargers

Wireless Rechargeable Sensor Networks (WRSNs) are emerging systems to build data acquisition systems to monitor critical areas. It has better features than Wireless Sensor Networks (WSNs) since in WRSNs, sensors are rechargeable. Wireless Rechargeable Sensor Networks allow sensors with sensing ability to collect and supply data to the operation center (sink). The Wireless Rechargeable Sensor Network appears as a more advanced solution because it can overcome the challenges and disadvantages such as battery limitation. In practical Wireless Rechargeable Sensor Networks, there is a budgetary constraint for the different expenditure categories, and therefore the number of used mobile chargers needs to be minimized. We investigate the problem of scheduling the minimum number of mobile chargers to replenish energy all sensors in a WRSN such that the network can be operated without limitations, and the total number of used chargers is minimized, referred to as the Periodic Energy Replenishment with Minimum Mobile Charger (PERMMC) problem. We design a new algorithm, namely the Mobile Charger Scheduling Algorithm (MCSA) to address the PERMMC problem. Finally, we conduct extensive simulations and provide analyses to demonstrate the performance of MCSA.

Finite-Time Consensus-Based Clock Synchronization Under Deception Attacks

This paper concentrates on the finite-time clock synchronization problem for wireless sensor networks (WSNs) under deception attacks. Compared with adding additional communication links to a network, we introduce a new mechanism termed as “trusted link” to improve the resilience of the network, and show that with small changes (set a fraction of links as the trusted links) in the network structure the network robustness for deception attacks can be improved significantly. Then, an iterative learning control based consensus control methodology with built-in attack mitigation mechanism is proposed. Not only the security and robustness are guaranteed by the proposed controller, but also the convergence time is fixed, which makes the synchronization algorithm more suitable for practical WSNs. Finally, simulation results are provided to demonstrate the effectiveness of the theoretical results.

Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation.

Wireless sensor networks proliferate more and more in all social scopes and sectors. Such networks are implemented in smart homes, smart cities, security systems, medical resources, agriculture, automotive industry, etc. Communication devices and sensors of such networks are powered with batteries: the enlarging of battery life is a hot research topic. We focus on wireless sensor networks based on ZigBee technology. While sleep standard operation mode is defined for end devices, it is not the case for the rest of devices (routers and Coordinator), which usually always remain in active mode. We designed a formal optimization model for maximizing the enlarging of the battery life of routers and Coordinator, allowing us to delimit practical successful conditions. It was successfully tested with a standard ZigBee datasheet comprising technical data for sensors, routers, and coordinators. It was tested in a practical wireless sensor network assembly with XBee S2C devices. We derived, from the previous model, a novel but simple protocol of communication among routers and coordinators. It was tested in different use cases. We showed that when end devices generate traffic at regular intervals, the enlarging of the battery life of routers and Coordinator was possible only under certain use cases.

Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Network coverage is a measure of efficiency that signifies the extent to which the deployed nodes collectively cover the network area. It is a fundamental and critical quality of service (QoS) parameter for designing wireless sensor networks (WSNs). Various sensing models are reported which can be used to predict the coverage fraction for a given number of nodes in a predetermined network area. However, each of these reported models consider a subset of parameters. In this study, a novel formulation and hence a new model, composite probabilistic sensing model (CPSM) is proposed which combines the cumulative effects of all the possible factors, thus resulting in a realistic study. Further, the model is revisited to estimate the optimal density of randomly deployed nodes required to attain the desired network area coverage. An exhaustive parametric study is carried out and the results obtained are used to empirically derive a formula based on regression analysis using least square polynomial curve fitting technique. The formulation can be readily and accurately used to design any practical WSN system.

Practical wireless safety monitoring system of long-span girders subjected to construction loading a building under construction

The primary goal of this paper is to address the challenges associated with the design and application of a practical wireless measurement system for the continuous safety monitoring of a large-scale structural members under construction. A practical wireless sensor network system (WSNS) was designed and real-time structural health monitoring (SHM) on long–span girders in a building under construction was performed. A WSNS consists of vibrating wire strain gauges (VWSGs) along with a sensor node, a master node, and a monitoring server. The field application of the WSNS using WVWSs was conducted on long-span plate girder and beam members of the K Art Hall during the construction of the building. It was possible to monitor the strain changes of the long-span plate girders and beams in real time as a function of the construction process and temperature changes.

Design of a Practical WSN Based Fingerprint Localization System

Fingerprint positioning technology is among the most promising choices for seamless localization and is anticipated to be the future of seamless-locating services. The convenience of deployment and the high density signal source of wireless sensor networks (WSN) make them an ideal infrastructure for fingerprint positioning. In related researches, most WSN based fingerprint positioning systems are experimental demos that focus on the algorithm effectiveness and ignore the system reliability. This work proposes a practical WSN based fingerprint localization system. The system covers both indoor and outdoor scenarios and fulfills the demand for seamless localization. This paper work presents four measures that improve fault tolerance and system efficiency: a traffic regulation based radiomap (TRRM) establishing method, a full-overlapping clustering strategy, an adaptive feature space (AFS) algorithm, and a praxeological tracking algorithm. The proposed system is verified by hardware experiments on smart phones. Positioning accuracy is within 5 m in pedestrian tests and 10 m in driving tests.

A Grid Cumulative Probability Localization-Based Industrial Risk Monitoring System

With the rapid development of modern industries, more and more risk factors exist in industrial operations, leading to a gradual increase in the frequency of industrial failures. The industrial enterprises have taken a variety of measures to analyze the risk factors in industrial operations, but establishing an effective monitoring and evaluation system remains a technical challenge. To overcome this challenge, a practical industrial risk monitoring system, based on the wireless sensor network (WSN), is established in this paper to improve resilience of the respective industrial operations. In the proposed system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically as the anchor nodes of the localization system, and two grid cumulative probability localization (GCPL) algorithms are proposed based on the GCPL-circle intersection and GCPL-path loss methods, respectively. The GCPL algorithm determines the grid cumulative probability using prior location information of the target node and the received signal strength information between the anchor nodes and target node to locate the target node. Experimental results show that the two GCPL algorithms significantly improve the localization accuracy and stability, and hence can be implemented as a risk monitoring system for real-world applications. Note to Practitioners —In industrial operations, a lot of risk factors exist which result in many frequent operational disruptions and failures. It is hence critical to develop an effective risk monitoring and evaluation system. Motivated by the industrial needs, we develop in this paper a practical wireless sensor network (WSN)-based industrial risk monitoring system. In the system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically. We further propose two grid cumulative probability localization algorithms to establish the system. We show via computational experiments that the proposed risk monitoring system performs well and can be implemented as a real application which helps to improve risk management of industrial operations.

IDEG: Integrated Data and Energy Gathering Framework for Practical Wireless Sensor Networks Using Compressive Sensing

Minimizing the energy consumption through efficient data gathering as well as energy harvesting both are open research problems in the context of practical wireless sensor networks (WSNs). Although the number of approaches has been proposed to cater the above, they are limited in terms of computational complexity, energy efficiency, and the ease of implementation. In this paper, we propose an integrated data and energy gathering framework, named hereby as “iDEG,” for practical WSNs. The proposed work is based on compressive sensing and utilizes partial canonical identity (PCI) matrix that samples/picks fewer sensor nodes randomly at any time point instead of picking up a linear combination of data from all sensor nodes at all time points as used in the conventionally popular Gaussian or Bernoulli matrices. This reduces the computation cost, implementation complexity, and energy losses while improving the data recovery. Furthermore, the fewer sensors at any time point selected by PCI contribute for recovering data, while the rest of the nodes harvest energy during those time points. Performance of iDEG has been tested on a real WSN dataset from Intel Lab. Comparative results of iDEG with the conventional approaches highlight its efficacy.