Confident Information Coverage Research Articles

Tensor-Based Confident Information Coverage Reliability of Hybrid Internet of Things

The widespread applications of the Hybrid Internet of Things (HIoT) have put forward higher requirements for network reliability. Coverage reliability is one of the important metrics of reliability, and reliable coverage ensures network data perception and transmission to improve the Quality of Service (QoS). In this paper, we define Confident Information Coverage Reliability ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CICR</i> ) based on the Confident Information Coverage Model (CIC), which comprehensively considers sensor multistate, sensor energy, coverage rate, and connectivity robustness to evaluate coverage reliability. Furthermore, a Tensor-based Confident Information Coverage Reliability Algorithm (T-CICR) is proposed based on tensor modeling to evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CICR</i> . The algorithm uses a tensor-based Markov model to predict sensor multistate. Three tensors of coverage rate, sensor multistate, and sensor energy are constructed to provide unified representations. Simulation results show that our proposed algorithm can significantly improve coverage reliability in terms of duty cycle, coverage rate requirement, sensing range, Root Mean Square Error (RMSE) threshold, connectivity robustness requirement, and link reliability.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

A Trust-Based Reliable Confident Information Coverage Model of Wireless Sensor Networks for Intelligent Transportation

Wireless sensor nodes are vulnerable to interference or intrusion, especially in the tunnel scenario of intelligent transportation, which leads to potential risks to network security and reliability. Reliable coverage can ensure data perception and transmission, which helps to strengthen the reliability and quality of service (QoS) of wireless sensor networks (WSNs). Existing coverage models, such as the disc model, do not consider malicious intrusion, which still has a great gap with the realistic application scenarios. Therefore, it is urgent to study a coverage model considering network intrusion and node reliability. In this paper, a novel trust-based reliable confident information coverage (T-RCIC) model is proposed based on the confident information coverage (CIC) model and trust management. Trust management is adopted to determine whether a node is trusted, critical trusted, or malicious. If a node is identified as malicious, all its functions will be blocked. For critical trusted nodes, the trust value is utilized to determine the trust feature distance to reduce its proportion of coverage estimation. Furthermore, the reliable coverage degree (RCD) is defined to evaluate coverage reliability from node trust value and reliable coverage percentage. Simulation results indicate that the proposed model inherits the advantages of the CIC model in the spatial correlation and can effectively identify malicious nodes. The proposed T-RCIC model can significantly enhance coverage reliability.

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

Collaborative Intelligent Confident Information Coverage Node Sleep Scheduling for 6G-Empowered Green IoT

The Internet of Things (IoT) enabled by 6G increases the number of devices and users exponentially. 6G IoT will cover the overall domain of the world. Due to the limited resources in the IoT environment, greener and smarter networks are indispensable for the sustainable development of the 6G IoT. In this paper, the issue of improving resource efficiency and extending network lifetime is studied. We propose a novel Confidence Information Coverage (CIC) node sleep scheduling algorithm based on reinforcement learning (CICRL). In CICRL, collaborative intelligence is achieved through Q-learning to meet the coverage rate with the least active nodes, thus balancing the energy consumption and prolonging the network lifetime. Compared with the Coordination Algorithm based on Reinforcement Learning (COORD), Low-Energy Adaptive Clustering Hierarchy (LEACH) and Sleep Scheduling Approach based on Learning Automata (PCLA), the simulation results demonstrate that the proposed algorithm satisfies coverage with fewer active nodes and improves the network lifetime substantially.

AI-Driven and MEC-Empowered Confident Information Coverage Hole Recovery in 6G-Enabled IoT

With the development of 6G, a series of ultrareliable intelligent IoT applications have been promoted, which puts forward higher requirements for the security and reliability of networks. Coverage Holes (CHs) pose great challenges to the network coverage reliability, and it is essential to repair CHs quickly and effectively. In this paper, we focus on the problem of CHs recovery of 6G IoT based on Mobile Edge Computing (MEC) and Artificial Intelligence (AI) to ensure reliable coverage. A Confident Information Coverage Hole Recovery Algorithm (CICHRA) is proposed based on the fusion model of the disc model and the confident information model, which takes advantage of the spatial correlation. The mobile edge node uses the utility to guide its movement, and the CHs are recovered by repeated games based on Q-learning. Furthermore, an Improved Confident Information Coverage Hole Recovery Algorithm (I-CICHRA) is proposed to enhance recovery performance and reduce time complexity. The I-CICHRA makes edge nodes movement more directional and accurate, thus improving repair efficiency. A series of simulation comparisons with the Distributed Payoff-based Q-learning Algorithm (DPQA) indicate that the proposed algorithms have better performance in overall coverage, iteration times, energy consumption, and energy efficiency.

Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks (WSNs) greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery. The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN. However, existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space. Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN, we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage. The Confident Information Coverage (CIC) model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage (CICMTP) problem to minimize the deployed sensor nodes. As the CICMTP is NP-hard, we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC (LGTA-CIC) and Overall Greedy Search Algorithm based on CIC (OGSA-CIC). The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate. Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP, TPNP and EENP algorithms.

Resilient Deployment of Smart Nodes for Improving Confident Information Coverage in 5G IoT

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.

Reinforcement Learning-Enabled Resampling Particle Swarm Optimization for Sensor Relocation in Reconfigurable WSNs

Aiming to maximize coverage performance and reduce the number of sensors deployed in the reconfigurable wireless sensor networks (RWSNs), in this paper, we first formulate a new cooperative sensing coverage control problem based on the confident information coverage model. Then, inspired by the reinforcement learning and resampling technology, a novel learning automata-based resampling particle swarm optimization (RPSOLA) algorithm is proposed to solve complex multi-peak optimization problem and optimize the cooperative sensing coverage control problem of RWSNs. Experimental results demonstrate that the RPSOLA considerably outperforms other three peer schemes, the RPSO, BASPSO and PSO, in terms of the convergence, coverage rate and sensor redundancy.

Multiplicatively weighted Voronoi-based sensor collaborative redeployment in software-defined wireless sensor networks

Large-scale deployment of mobile wireless sensor networks has been widely used in some dangerous and hostile urban security surveillance scenarios. As a new network architecture, software-defined networks was introduced into wireless sensor networks to form a new software-defined wireless sensor networks to solve the problem of balanced large-scale deployment of sensor networks and simplify the complexity of network management. In this article, we first develop an original confident information coverage–based multiplicatively weighted Voronoi diagram through sensor clustering and sensor collaborative sensing. And then, we propose two sensor collaborative redeployment algorithms based on the novel confident information coverage–based multiplicatively weighted Voronoi diagram and software-defined wireless sensor networks architecture to provide high-confidence coverage and improve the coverage ratio. Finally, we demonstrate the superiority of the confident information coverage–based multiplicatively weighted Voronoi diagram and the effectiveness and efficiency of our proposed algorithms via a series of experiments.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Confident Information Coverage Hole Prediction and Repairing for Healthcare Big Data Collection in Large-Scale Hybrid Wireless Sensor Networks

In the Internet of Things (IoT) for smart healthcare applications, sensors collect a vast amount of healthcare data, while coverage significantly affects the Quality of Service (QoS). In wireless sensor networks (WSNs), the QoS as well as the network lifetime are dramatically degraded with the increment of coverage holes, especially in large-scale hybrid WSNs (LS-HWSNs) where big data are collected by thousands of sensors distributed in a wide monitored area. In a LS-HWSN, two crucial problems, i.e., covering the wide area without coverage holes and designing an energy-efficient manner for dispatching mobile sensors to repair coverage holes, need to be solved. We study the problems from the cutting point of confident information coverage hole repairing (CICHR). To this end, based on the confident information coverage (CIC) model, a CIC hole predicting (CICHP) algorithm, centralized energy-efficient repairing (CEER) algorithm, and distributed energy-efficient repairing (DEER) algorithm are developed. The CICHP algorithm can predict the prior information of CIC holes (CICHs) by using the period-by-period energy consumption information of sensor nodes. Based on the prior information of CICHs, two repairing algorithms: 1) CEER and 2) DEER can schedule mobile sensors to repair CICHs beforehand. Simulation results show that the proposed algorithms can significantly improve the QoS and extend the network lifetime of LS-HWSNs.

Reinforcement-Learning-Enabled Partial Confident Information Coverage for IoT-Based Bridge Structural Health Monitoring

Internet-of-Things (IoT)-based bridge structural health monitoring (BSHM) has recently attracted considerable attention from both academic and industrial communities of civil engineering and computer science. In conjunction with researchers from civil engineering and computer science, this article studied a fundamental problem motivated from practical IoT-based BSHM: how to effectively prolong network lifetime while guaranteeing desired coverage. Integrating a promising reinforcement learning model named learning automata (LA) with confident information coverage (CIC) model, this article presented an energy-efficient sensor scheduling strategy for partial CIC coverage in IoT-based BSHM system to guarantee network coverage and prolong network lifetime. The proposed scheme fully exploits cooperation among deployed nodes and alternatively schedules the wake/sleep status of nodes while satisfying network connectivity and partial coverage ratio. Especially, the proposed scheme takes full advantage of the LA model to adaptively learn the optimal sensor scheduling strategy and significantly extend network lifetime. A series of comparison simulations using real data sets collected by a practical BSHM system strongly verify the effectiveness and energy efficiency of the proposed algorithm. To the best of our knowledge, this is the first study on how to combine the reinforcement learning mechanism with partial coverage for maximizing the network lifetime of the IoT-based BSHM.

Learning-Automata-Based Confident Information Coverage Barriers for Smart Ocean Internet of Things

As an emerging network paradigm, the Internet of Things (IoT) which consists of a significant number of multifunctional and heterogeneous IoT nodes has attracted dramatic attentions from both academia and industry. With the merits of intelligent capacity, desirable scalability, and high reliability, the IoT recently has been applied for smart ocean applications to provide protection for ocean environment monitoring and surveillance. Aiming to provide coverage service for ocean border environmental surveillance, this article studies the barrier coverage problem which investigates how to select a collection of IoT nodes to obtain an IoT node chain and build barrier paths to detect intruders and trespassers crossing the border region of interest. To overcome the disadvantages in the existing works on barrier coverage, we adopt a novel and widely adopted confident information coverage (CIC) model as the fundamental coverage model and formulate the CIC barrier path construction (CICBC) problem with the goals of maximizing the number of barrier paths and minimizing the amount of IoT nodes in each barrier path. We propose a distributed CIC barrier path (CICBP) construction approach based on learning automata (CBLA). The CBLA includes four crucial phases which are initialization phase, learning phase, monitoring phase, and repairing phase. Each IoT node equips a learning automaton. CBLA selects an optimal IoT node to construct the barrier path by learning. The simulation results show that the performance of the CBLA algorithm outperforms two peer algorithms in terms of the number of barrier paths and the average number of nodes in each barrier path.

A Nature-Inspired Node Deployment Strategy for Connected Confident Information Coverage in Industrial Internet of Things

The ever-growing Industrial Internet of Things (IoT) provides a powerful method to sense a series of critical industrial environments. This paper studies how to deploy the fixed number of IoT nodes so that the network lifetime is maximized in a sensing field with obstacles while guaranteeing the requirements of confident information coverage, network connectivity, energy efficiency, fault tolerance, and reliability. An IoT node deployment scheme based on an improved nature-inspired genetic algorithm is proposed to solve the defined constrained optimization problem. In the proposed IoT node deployment scheme, we utilize a population initialization based on the Delaunay triangulation to generate the better initial population, a chromosome modification operation to achieve both connectivity and coverage for each chromosome and a chromosome mirror-crossover operation to produce the better offsprings. Experimental results show that our deployment schema equips better performance in terms of longer network lifetime and comparable coverage ratio compared with the other four peer algorithms.

Multi-sensor fusion based intelligent sensor relocation for health and safety monitoring in BSNs

The body sensor networks (BSNs) have attracted great attention due to their numerous important features and wide applications in health and safety monitoring, especially in some hostile or potentially dangerous workplaces. In this paper, aiming to provide continuous coverage from the initial Region of Interest (ROI) to a New Region of Interest (NROI) to trace the BSNs for human health and safety monitoring, we formulate and define the novel confident information coverage (CIC)-based Intelligent Sensor Relocation for NROI (CIC-ISR-NROI) problem in mobile wireless sensor networks (MWSNs) based on the novel CIC model. For handing the CIC-ISR-NROI problem, we develop two energy-efficient intelligent sensor relocation algorithms based on multi-sensor fusion, the Rapid Relocation Time Low Energy Consumption (RRTLEC) algorithm and optimized RRTLEC algorithm, to relocate redundant mobile sensors from the ROI to cover the NROI in a timely and energy-efficient manner while constructing the communication chain to connect the ROI and the NROI. To verify the effectiveness and efficiency of the proposed schemes, we conduct a series of experiments which correspond to the realistic scenarios in health and safety monitoring in the 272 uranium tailing. Experimental results show that the performance of our approaches is better than the other typical peer approaches by the metrics of the total moving energy consumption and the maximum relocation time.

Offloading-Assisted Energy-Balanced IoT Edge Node Relocation for Confident Information Coverage

The promising Industrial Internet of Things (IIoT) consisting of heterogeneous resource-restricted IoT nodes recently has attracted great attention from both academia and industry communities. However, the battery-powered, computing and communication resource-constrained, and randomly uneven distributed features of the IoT nodes pose several great tough hurdles, including the quality of services of real-time processing, energy efficiency, network lifetime, and coverage holes to the IIoT-based industrial applications. To deal with these challenges, based on the emerging edge computing paradigm and the novel confident information coverage (CIC) model, this paper investigates how to relocate redundant IoT edge nodes to provide timely CIC service in an offloading-assisted energy-balanced manner while extending the network lifetime, which is called as the CIC-based IoT edge node relocation (CICENR) problem. To effectively handle the CICENR problem, we propose an offloading-assisted energy-balanced IoT edge node relocation approach CIC-based offloading-assisted energy-balanced approach (CIC-OAEBA) and the other CIC-based direct replacement approach. Specially, the CIC-OAEBA adopts the Grid-Quorum strategy to quickly detect the redundant IoT edge nodes by offloading the communication-intensive and computing-intensive tasks from grid header nodes to peer IoT edge nodes, and make full use of the cascaded movement strategy to move the nearest redundant IoT edge nodes to the requesting CIC hole locations. Experimental results indicate the proposed approaches remarkably outperform other peer methods in terms of response time, energy efficiency, and especially the network lifetime and coverage performance.

Energy Balanced Dispatch of Mobile Edge Nodes for Confident Information Coverage Hole Repairing in IoT

The promising Internet of Things (IoT) provides a powerful platform for practical smart applications. The limited resources of the IoT nodes as well as the emerged coverage holes pose a great challenge on the quality of service of the IoT. Mobile edge computing (MEC), which can improve the IoT nodes energy consumption efficiency and optimize the utilization effectiveness of the limited resources, provides a novel view for coping with the challenge. Based on the MEC, this paper focuses on how to solve the problem of dispatch of mobile edge nodes for confident information coverage holes repairing (DMEN-CICHR) with the goal of maximizing the network lifetime and guaranteeing the network connectivity. To deal with the DMEN-CICHR problem, we develop an energy-balanced and obstacle-adaptive mobile edge node dispatch algorithm called EBOADMEN-CICHR, which restricts the mobile edge nodes from moving too long distance by setting a bound for each CIC hole and repeatedly updating the bound by a competition mechanism. To guarantee the network connectivity, the EBOADMEN-CICHR recursively performs breadth first search on a constructed undirected graph to find all disconnected subgraphs and then dispatches some mobile edge nodes to connect those disconnected subgraphs until the whole network is connected. A number of experiments emulating the realistic scenarios in radiological pollution monitoring in uranium tailings are executed to verify the effectiveness of the proposed EBOADMEN-CICHR solution. Experimental results show the EBOADMEN-CICHR can perform better than other peer methods in term of higher energy efficiency and longer network lifetime.

Repairing Confident Information Coverage Holes for Big Data Collection in Large-Scale Heterogeneous Wireless Sensor Networks

The quality of service (QoS) and lifetime of wireless sensor networks (WSNs) are severely degraded by coverage holes generated by random deployment or battery exhaustion of sensors. This work firstly introduces a novel confident information coverage CIC) model to dramatically reduce the density of sensor nodes and accurately detect confident information coverage holes (CICHs). Then, the problem of repairing confident information coverage holes (RCICHs) for big data collection in a large-scale heterogeneous WSN (LS-HWSN) which widely spreads over a geographic area with thousands of stationary sensor nodes and mobile sensor nodes is formulated, called as RCICH problem, which is to effectively repair CICHs considering that the transmitted data velocity of sensor nodes is different. Furthermore, we prove it to be NP-completeness. The target of the problem is to find a subset of mobile sensor nodes from all mobile sensor nodes while minimizing the amount of lost throughputs LTs) of all dispatched mobile sensor nodes or maximizing the amount of repairing transmission times (RTTs) for all dispatched mobile sensor nodes, with different objectives. Finally, based on the CIC model and the data-centric perspective, two heuristic schemes including a centralized dispatch scheme and a distributed dispatch scheme are proposed to effectively solve the RCICH problem. Simulation results show that the proposed schemes effectively repair CICHs while increasing the QoS and lifetime of the LS-HWSN with the topology control of a fan-shaped clustering (FSC) protocol.

Healing Multimodal Confident Information Coverage Holes in NB-IoT-Enabled Networks

The Internet of Things (IoT) evolving from the conventional wireless sensor networks (WSNs) with more smart sensors has attracted significant attention. As one of the most crucial metrics for evaluating the quality of service (QoS) of both IoT and WSNs, sensing coverage characterizes the monitoring status of a sensing field of interest. However, the existence of coverage holes will remarkably degrade the QoS of the IoT. Based on the novel confident information coverage (CIC) model, this paper provides an in-depth study on how to energy-efficiently heal the multimodal CIC holes (MCICH) in a narrowband IoT (NB-IoT)-enabled hybrid IoT deployed for radiological pollution monitoring, where both mobile and stationary sensors equip multimodal sensing units for sensing dissimilar multimodal physical attributes and the NB-IoT provides satisfied network connectivity. We pinpoint the MCICH healing (MCICHH) problem with the objective of energy-efficiently dispatching a series of multimodal mobile IoT sensors to the CIC holes such that the MCIC holes can be headed and the CIC performance can be satisfied. After proving the NP-completeness of MCICHH by reducing it to the set partition problem, we develop a family of effective heuristic schemes including the centralized-MCICHH, the distributed-MCICHH and random CIC hole healing, all of which target for efficiently healing the MCIC holes while minimizing the total moving energy consumption of the dispatched multimodal mobile sensors or maximizing the average remaining energy of the multimodal mobile sensors. Extensive experiments verify the effectiveness and practicality of the proposed schemes.