Large-scale Sensor Networks Research Articles

Combining Heterogeneous Vehicles to Build a Low-Cost and Real-Time Wireless Charging Sensor Network

A wireless charging system that supports a large sensor network not only needs to provide real-time charging services but also needs to consider the cost of construction in order to meet the actual applications and considerations. The energy transfer between mobile devices is extremely difficult, especially at large distances, while at close distances a wireless, fast-charging, automatic, and high-precision landing system would be required. However, previous studies that used wireless charging cars, wireless charging unmanned aerial vehicles (UAVs) alone, or wireless charging pads to build a large-scale wireless charging system could not meet the above two conditions at the same time. If we can make good use of the existing high-speed backbone transport, such as the vehicular ad hoc network (VANET), it can not only consider the cost of construction and the speed of movement but also support the real-time charging service required by large-scale sensor networks. In this work, we propose an integration of heterogeneous mobile and charging vehicles, including VANET and UAV net, to provide a wireless rechargeable sensor network with a low-cost and high-efficiency charging system. Moreover, reward mechanisms are also designed to encourage private cars to provide transportation and charging capabilities to UAVs in the designed system.

Fast and Low-Overhead Time Synchronization for Industrial Wireless Sensor Networks with Mesh-Star Architecture.

Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh-star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS.

A Biologically Inspired Self-Organizing Underwater Sensor Network

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol.

Enabling Semantic-Functional Communications for Multiuser Event Transmissions via Wireless Power Transfer.

A central concern for large-scale sensor networks and the Internet of Things (IoT) has been battery capacity and how to recharge it. Recent advances have pointed to a technique capable of collecting energy from radio frequency (RF) waves called radio frequency-based energy harvesting (RF-EH) as a solution for low-power networks where cables or even changing the battery is unfeasible. The technical literature addresses energy harvesting techniques as an isolated block by dealing with energy harvesting apart from the other aspects inherent to the transmitter and receiver. Thus, the energy spent on data transmission cannot be used together to charge the battery and decode information. As an extension to them, we propose here a method that enables the information to be recovered from the battery charge by designing a sensor network operating with a semanticfunctional communication framework. Moreover, we propose an event-driven sensor network in which batteries are recharged by applying the technique RF-EH. In order to evaluate system performance, we investigated event signaling, event detection, empty battery, and signaling success rates, as well as the Age of Information (AoI). We discuss how the main parameters are related to the system behavior based on a representative case study, also discussing the battery charge behavior. Numerical results corroborate the effectiveness of the proposed system.

Adapting to Evolving Cyber Threat Landscapes with Dynamic Security Protocol Management in Large-Scale IoT Sensor Networks

The Adaptive Security Protocol Framework (ASPF) is introduced as a sophisticated algorithm designed for dynamic security protocol adaptation in large-scale IoT sensor networks. Comprising five integral algorithms, namely ASPF, MLTD, DKMS, BAP, and CTIS, the framework ensures a comprehensive and adaptive defense mechanism against evolving cyber threats. ASPF initiates with data collection, preprocessing, and feature extraction, employing supervised learning for model training. Anomaly detection triggers alerts and responses, guiding continuous learning and security protocol adaptation. MLTD enhances real-time threat detection through dynamic model training and threat intelligence integration. DKMS focuses on secure key management for data transmissions, calculating device thresholds and ensuring adaptive key exchanges. BAP leverages historical data for behavioral profiling, enabling real-time anomaly detection and adaptive profile updates. CTIS assesses and aggregates threat levels, fostering continuous collaboration and collective defense. The ablation study emphasizes the indispensable role of each algorithm, showcasing their synergistic contributions to the overall system's adaptability and robustness. Evaluation through comprehensive tables and visual representations highlights the proposed method's superiority over existing security protocols. The ablation study underscores the holistic nature of ASPF, solidifying its efficacy in addressing the dynamic challenges of cybersecurity in large-scale IoT sensor networks.

Information-weighted Consensus Filtering under Limited Communication

A distributed sensor network refers to a set of stationary or mobile agents equipped with sensors, each of which observes and estimates the state of some system, while utilizing the communication channels of the sensor network to improve the overall state estimation performance of the network. In particular, consensus-based distributed state estimation algorithms can compute accurate state estimates of the system at each agent under relaxed observability conditions, using a low communication bandwidth. A drawback of many existing consensus-based approaches is that they are developed under the presumption that a large number of consensus subiterations are used, which may not be possible in large-scale sensor networks, in the presence of communication delays and/or energy constraints. In this paper, we develop an extension of the Information-weighted Consensus Filter (ICF) for the case where only a limited number of consensus subiterations are possible. Through mathematical analysis and numerical simulations, it is shown that the proposed algorithm improves upon the existing algorithms under identical computation and communication requirements.

A PDE Model Approach to Formation Control of Large-Scale Mobile Sensor Networks with Boundary Uncertainties

This paper investigates the formation problem of an array of large-scale mobile sensor networks. A new framework for the dynamic of mobile sensors as a continuum described by the parabolic system with boundary disturbance is proposed. The communication topology of agents is a chain graph and fixed. Leader feedback laws which are designed in a manner to the boundary control of large-scale mobile sensor networks allow the mobile sensors to achieve the formation steadily. By referring to the Lyapunov functional method and employing a boundary control approach, a new protocol is established to deal with a formation problem for the large-scale mobile sensor networks. Finally, numerical examples are given to illustrate the usefulness of the results.

DMCP: A Distributed Mobile Charging Protocol in Wireless Rechargeable Sensor Networks

On-demand charging of sensor nodes (SNs) in wireless rechargeable sensor networks has garnered immense attention. Existing works with multiple mobile chargers (MCs) overlooked the benefits of partial charging and distributed control in large-scale wireless rechargeable sensor networks. In addition, most of them have not considered the complete heterogeneity of SNs’ energy profiles and idleness of the MCs. In this article, we address the aforesaid issues and present a game theory based distributed mobile charging protocol, called DMCP. We formulate the mobile charging problem as a series of repeated games played among the MCs and represent a game as a 0-1 integer linear programming to maximize the total profit of the MCs. In each game, DMCP dynamically determines the factor of charging of the energy-deficit SNs based on their relative criticality and selects the next-to-be-recharged SNs using an efficient bi-objective payoff function. In addition, it utilizes an innovative strategy and a self-enforcing agreement to promote cooperation among the MCs. Through extensive simulations and hypothesis testing, we show that DMCP reduces the charging delay up to 44.04% and enhances the charging coverage and survival rate up to 45.12% and 49.97%, respectively.

Transmission schedule for jointly optimizing remote state estimation and wireless sensor network lifetime

This paper studies scheduling multiple sensors for jointly optimizing remote state estimation and wireless sensor network lifetime. Each sensor in the network sends its packet to the remote center for further estimation over wireless communication channels. Sensors in the network will inevitably run out of battery power due to transmission consumption and then reach the end of sensor network lifetime. This article focuses on the problem how to schedule multiple sensors minimizing the total remote estimation errors and simultaneously prolonging the sensor network lifetime. The problem is formulated as a special class of Markov decision process (MDP), stochastic shortest path (SSP) problem. Under this framework, we first derive a set of conditions to guarantee the optimality of structural policies in SSP. Subsequently, threshold structure of the optimal scheduling scheme is obtained by verifying the conditions. Moreover, two Value-of-Information-based multi-sensor scheduling algorithms and a linear-architecture-based learning algorithm are designed to tackle the curse of dimensionality and unknown channel statistics in large-scale sensor networks. Numerical simulations are given to validate effectiveness of our results.

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch.

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa-1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

Reducing Operation Cost of LPWAN Roadside Sensors Using Cross Technology Communication

Low-Power Wide-Area Network (LPWAN) is an emerging communication standard for Internet of Things (IoT) that has strong potential to support connectivity of a large number of roadside sensors with an extremely long communication range. However, the high operation cost to manage such a large-scale roadside sensor network remains as a significant challenge. In this article, we propose Low Operation-Cost LPWAN (LOC-LPWAN), a novel optimization framework that is designed to reduce the operation cost using the cross-technology communication (CTC). LOC-LPWAN allows roadside sensors to offload sensor data to passing vehicles that in turn forward the data to a LPWAN server using CTC aiming to reduce the data subscription cost. LOC-LPWAN finds the optimal communication schedule between sensors and vehicles to maximize the throughput given an available budget. Furthermore, LOC-LPWAN optimizes the fairness among sensors by preventing certain sensors from dominating the channel for data transmission. LOC-LPWAN can also be configured to ensure that data packets are received within a specific time bound. Extensive numerical analysis performed with real-world taxi data consisting of 40 vehicles with 24-hour trajectories demonstrate that LOC-LPWAN reduces the cost by 50% compared with the baseline approach where no vehicle is used to relay packets. The results also show that LOC-LPWAN improves the throughput by 72.6%, enhances the fairness by 65.7%, and reduces the delay by 28.8% compared with a greedy algorithm given the same amount of budget.

Streaming Data Preprocessing via Online Tensor Recovery for Large Environmental Sensor Networks

Measuring the built and natural environment at a fine-grained scale is now possible with low-cost urban environmental sensor networks. However, fine-grained city-scale data analysis is complicated by tedious data cleaning including removing outliers and imputing missing data. While many methods exist to automatically correct anomalies and impute missing entries, challenges still exist on data with large spatial-temporal scales and shifting patterns. To address these challenges, we propose an online robust tensor recovery (OLRTR) method to preprocess streaming high-dimensional urban environmental datasets. A small-sized dictionary that captures the underlying patterns of the data is computed and constantly updated with new data. OLRTR enables online recovery for large-scale sensor networks that provide continuous data streams, with a lower computational memory usage compared to offline batch counterparts. In addition, we formulate the objective function so that OLRTR can detect structured outliers, such as faulty readings over a long period of time. We validate OLRTR on a synthetically degraded National Oceanic and Atmospheric Administration temperature dataset, and apply it to the Array of Things city-scale sensor network in Chicago, IL, showing superior results compared with several established online and batch-based low-rank decomposition methods.

Physical Layer Security in Large-Scale Random Multiple Access Wireless Sensor Networks: A Stochastic Geometry Approach

This paper investigates physical layer security for a large-scale WSN with random multiple access, where each fusion center in the network randomly schedules a number of sensors to upload their sensed data subject to the overhearing of randomly distributed eavesdroppers. We propose an uncoordinated random jamming scheme in which those unscheduled sensors send jamming signals with a certain probability to defeat the eavesdroppers. With the aid of stochastic geometry theory and order statistics, we derive analytical expressions for the connection outage probability and secrecy outage probability to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we formulate an optimization problem for maximizing the sum secrecy throughput subject to both reliability and secrecy constraints, considering a joint design of the wiretap code rates for each scheduled sensor and the jamming probability for the unscheduled sensors. We provide both optimal and low-complexity sub-optimal algorithms to tackle the above problem, and further reveal various properties on the optimal parameters which are useful to guide practical designs. In particular, we demonstrate that the proposed random jamming scheme is beneficial for improving the sum secrecy throughput, and the optimal jamming probability is the result of trade-off between secrecy and throughput. We also show that the throughput performance of the sub-optimal scheme approaches that of the optimal one when facing a stringent reliability constraint or a loose secrecy constraint.

Toward Democratizing Access to Facilities Data: A Framework for Intelligent Data Discovery and Delivery

Data collected by large-scale instruments, observatories, and sensor networks (i.e., science facilities) are key enablers of scientific discoveries in many disciplines. However, ensuring that these data can be accessed, integrated, and analyzed in a democratized and timely manner remains a challenge. In this article, we explore how state-of-the-art techniques for data discovery and access can be adapted to facilitate data and develop a conceptual framework for intelligent data access and discovery.

MMEDD: Multithreading Model for an Efficient Data Delivery in wireless sensor networks

Nowadays, the use of Wireless Sensor Networks (WSNs) is increasingly growing as they allow a large number of applications. In a large scale sensor network, communication among sensors is achieved by using a multihop communication. However, since the sensor is limited by its resources, sensors' operating systems are developed in order to optimize the management of these resources, especially the power consumption. Therefore, the hybrid operating system Contiki uses a low consumption layer called Rime which allows sensors to perform multihop sending with a low energy cost. This is favored by the implementation of lightweight processes called protothreads. These processes have a good efficiency/consumption ratio for monolithic tasks, but the management of several tasks remains a problem. In order to enable multitasking, Contiki provides to users a preemptive multithreading module that allows the management of multiple threads. However, it usually causes greater energy wastage. To improve multithreading in sensor networks, a Multithreading Model for an Efficient Data Delivery (MMEDD) using protothreads is proposed in this paper. Intensive experiments have been conducted on COOJA simulator that is integrated in Contiki. The results show that MMEDD provides better ratio message reception rate/energy consumption than other architectures.

RETRACTED ARTICLE: Multiple Fixed Target Location Algorithms in Large-Scale MIMO Wireless Sensor Networks for IIoT

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Ting Yang (2022) Multiple Fixed Target Location Algorithms in Large-Scale MIMO Wireless Sensor Networks for IIoT, IETE Journal of Research, DOI: 10.1080/03772063.2022.2061611 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

High-Spatial-Resolution High-Temperature Sensor Based on Ultra-Short Fiber Bragg Gratings With Dual-Wavelength Differential Detection

Type II fiber Bragg gratings (FBGs) inscribed with femtosecond ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> s) laser possess significant potential for high temperature sensing. In this work, we propose and demonstrate a method for fabricating a parallel-integrated ultra-short type II FBG (PI-US-FBG) by using an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> s laser point-by-point technology. The PI-US-FBG, featuring by an ultra-short grating length of 80 µm, consists of six identical FBGs parallel-inscribed into a fiber core at different radial positions in the same cross section. The fabricated PI-US-FBG exhibits a broadband Gaussian-shape spectrum with a low reflectivity of ∼10%, an ultra-low out-of-band insertion loss of 0.01 dB, and a large full width at half maximum bandwidth of 9.4 nm. Moreover, this PI-US-FBG could be used as a high temperature sensor with a wide measurement range from 25 to 1000 °C, and an excellent linearity was achieved with a dual-wavelength differential detection. The temperature sensitivity could be increased from 0.00316 to 0.00945 (dB/ °C) by enlarging the wavelength spacing of the tunable laser. In addition, two cascaded PI-US-FBGs were used to precisely measure the temperature field distribution in a CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> laser spot with a very high spatial resolution of 100 µm. Hence, the proposed PI-US-FBGs could be used for a large-scale fiber sensor network, especially in future distributed temperature measurement with a high spatial resolution.

Novel method for optimizing performance in resource constrained distributed data streams

The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy.

A security enhancement model based on switching edge strategy in interdependent heterogeneous cyber-physical systems

With the advent of cross-domain interconnection, large-scale sensor network systems such as smart grids, smart homes, and intelligent transportation have emerged. These complex network systems often have a CPS (Cyber-Physical System) architecture and are usually composed of multiple interdependent systems. Minimal faults between interdependent networks may cause serious cascading failures between the entire system. Therefore, in this paper, we will explore the robustness detection schemes for interdependent networks. Firstly, by calculating the largest giant connected component in the entire system, the security of interdependent network systems under different attack models is analyzed. Secondly, a comparative analysis of the cascade failure mechanism between interdependent networks under the edge enhancement strategy is carried out. Finally, the simulation results verify the impact of system reliability under different handover edge strategies and show how to choose a better handover strategy to enhance its robustness. The further research work in this paper can also help design how to reduce the interdependence between systems, thereby further optimizing the interdependent network system's structure to provide practical support for reducing the cascading failures. In the later work, we hope to explore our proposed strategies in the network model of real-world or close to real networks.

Semantic Interaction Strategy of Multiagent System in Large-Scale Intelligent Sensor Network Environment

In a multiagent system, the semantic interaction between agents is an important aspect affecting multi-intelligence. The purpose of interaction is to reasonably arrange task objectives and behaviors through information sharing and communication interaction, so as to maximize the overall performance of multiagent system. This paper analyzes the communication and interaction process between agents from the perspective of semantic layer and introduces the BDI (belief, desire, intention) model of agent’s thinking state into the communication and interaction process. Furthermore, we propose a multiagent semantic interaction strategy model based on a large-scale intelligent sensor network, which supports various types of negotiation and interaction on the basis of basic interaction behavior to solve the problem of information operational conflicts. In addition, this paper limits the scale of historical information through the definition of equivalence and the merging theorem of history, and it uses reinforcement learning algorithm to detect possible conflicts and delay communication and makes rational use of limited resources to improve system revenue and coordination efficiency. The experimental results show that compared with the previous methods such as debate and negotiation, the strategy model can realize the flexible interaction based on scene and is more practical. At the same time, the existence of reinforcement learning improves the efficiency analysis and the convergence performance of semantic interaction strategy.