Large-scale Wireless Sensor Networks Research Articles

Optimizing Quality of Service of Clustering Protocols in Large-Scale Wireless Sensor Networks with Mobile Data Collector and Machine Learning

The rise of large-scale wireless sensor networks (LSWSNs), containing thousands of sensor nodes (SNs) that spread over large geographic areas, necessitates new Quality of Service (QoS) efficient data collection techniques. Data collection and transmission in LSWSNs are considered the most challenging issues. This study presents a new hybrid protocol called MDC-K that is a combination of the K-means machine learning clustering algorithm and mobile data collector (MDC) to improve the QoS criteria of clustering protocols for LSWSNs. It is based on a new routing model using the clustering approach for LSWSNs. These protocols have the capability to adopt methods that are appropriate for clustering and routing with the best value of QoS criteria. Specifically, the proposed protocol called MDC-K uses machine learning K-means clustering algorithm to reduce energy consumption in cluster head (CH) election phase and to improve the election of CH. In addition, a mobile data collector (MDC) is used as an intermediate between the CH and the base station (BS) to further enhance the QoS criteria of WSN, to minimize time delays during data collection, and to improve the transmission phase of clustering protocol. The obtained simulation results demonstrate that MDC-K improves the energy consumption and QoS metrics compared to LEACH, LEACH-K, MDC maximum residual energy leach, and TEEN protocols.

BOND

In a large-scale wireless sensor network, hundreds and thousands of sensors sample and forward data back to the sink periodically. In two real outdoor deployments GreenOrbs and CitySee, we observe that some bottleneck nodes strongly impact other nodes’ data collection and thus degrade the whole network performance. To figure out the importance of a node in the process of data collection, system manager is required to understand interactive behaviors among the parent and child nodes. So we present a management tool BOND (BOttleneck Node Detector), which explains the concept of Node Dependence to characterize how much a node relies on each of its parent nodes, and also models the routing process as a Hidden Markov Model and then uses a machine learning approach to learn the state transition probabilities in this model. Moreover, BOND can predict the network dataflow if some nodes are added or removed to avoid data loss and flow congestion in network redeployment. We implement BOND on real hardware and deploy it in an outdoor network system. The extensive experiments show that Node Dependence indeed help to explore the hidden bottleneck nodes in the network, and BOND infers the Node Dependence with an average accuracy of more than 85%.

Towards improved clustering and routing protocol for wireless sensor networks

Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multi-hop communication and a shorter transmission range. Clustering and routing are treated separately in different solutions and, therefore, efficient solutions in terms of energy consumption and network lifetime could not be provided. This work focuses data collection from IoT-nodes distributed in an area and connected through WSN. We address two interlinked issues, clustering and routing, for large-scale IoT-based WSN and propose an improved clustering and routing protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. During process of clustering, cluster-heads are selected in such a way that provide fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop-count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

Hierarchical clustering and routing protocol to ensure scalability and reliability in large-scale wireless sensor networks

Cluster-based routing protocols have been proven efficient in prolonging the life cycle of wireless sensor networks (WSNs). Periodic and multi-hop clustering are the most popular techniques which provide the required energy-efficient communication and scalability in large-scale WSNs. In clustering, WSN is divided into number of clusters, and cluster head is selected in each cluster. However, in the existing clustering protocols, CH’s near base station undergoes large number of receiving, aggregating and transmitting operations in comparison with far away CHs. This imbalance of load on CHs and lack of structured multi-level clustering framework leads to early death of WSNs. Moreover, resolving the issues of scalability and data reliability along with load balancing is a very tedious task. In this paper, a hierarchical clustering and routing (HCR) protocol is proposed to formulate a load-balanced approach for clustering while taking care of energy efficiency, reliability and scalability. Firstly, a hierarchical layered framework is created to split the WSN into virtual circular layers for efficient transmission of data in hierarchical fashion. Subsequently, an ant lion optimizer is employed for the selection of CHs to ensure reliable, energy balanced and scalable cluster formation. Simulation results demonstrate that HCR protocol outperforms existing state-of-the-art clustering protocols in terms of network lifetime, balanced clustering, throughput and energy efficiency.

Optimal Bibd Extended Design Based Neighbour Discovery in Asynchronous Wsn

For asynchronous Wireless Sensor Networks (WSNs), this research suggests an ideal Balanced Incomplete Block Design (BIBD) extended design based neighbour finding technique. The algorithm takes use of the characteristics of BIBD extended designs to build a series of transmission plans that guarantee effective neighbour detection with little energy consumption and delay. Simulations are used to test the proposed algorithm, and the results show that it significantly outperforms previous approaches in terms of energy usage, latency, and scalability.
 The value of this study is in the innovative neighbour finding algorithm that it proposes, which overcomes the drawbacks of existing approaches and provides a scalable and energy-efficient solution for large-scale WSNs. The suggested technique may be used in a variety of settings, including environmental monitoring, industrial automation, and healthcare, where accurate and timely data gathering and processing depend on efficient neighbour detection.
 The suggested algorithm is implemented step by step in the technique section along with the relevant equations and tables. The simulation results for sample input data are shown in the results section along with pertinent tables and graphs. The results are outlined in the conclusion section, which also emphasises the importance of the suggested method.
 Overall, the suggested technique provides a viable option for rapid neighbour detection in WSNs, potentially having repercussions for a broad variety of applications.

Lifetime Enhancement of Sensor Networks by the Moth Flame Optimization

Advancements in wireless communication technologies have facilitated the deployment of large-scale Wireless Sensor Networks (WSNs). Due to the constraint of associated battery power, various optimization structures have been proposed to enhance the lifetime of WSNs. In this article, the concept of supernodes is used along with Moth Flame Optimization algorithm to improve the lifetime of the heterogeneous WSNs. The Moth Flame Optimization algorithm is used to achieve the energy-efficient clustering and energy-aware routing. The performance of Moth Flame Optimization algorithm is compared with the other existing protocol, including Genetic Algorithm and Particle Swarm Optimization algorithm. The effects of varying populations of supernodes and sensor nodes on the network metrics are also analyzed here. The influence of the number of hops on the lifetime is also investigated considering two different positions of the base-station in WSNs.

Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks

Thanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone’s maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new deep deterministic policy gradient based maneuver control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step. Numerical results demonstrate that the proposed DDPG-MC achieves 15.2 and 47.6 percent lower packet loss rate than deep Q-learning-based flight control and non-learning scheduling policies, respectively.

A Blockchain-Assisted Massive IoT Data Collection Intelligent Framework

Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this article, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework: 1) we design a large-scale heterogeneous WSNs collaborative identity verification protocol to ensure reliable data sources; 2) we build a hierarchical massive data aggregation scheme to collect massive IoT data efficiently and securely; and 3) we depict a blockchain-based massive IoT data management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.

WSN-SES/MB: System Entity Structure and Model Base Framework for Large-Scale Wireless Sensor Networks.

Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network’s environment configurations, such as the number of sensor nodes, after deployment of the nodes. Although several simulators are able to variously construct simulation models for sensor networks before their deployment, the configurations should be modified with extra human effort as the simulators cannot freely generate diverse models. In this paper, we propose a novel framework, called a system entity structure and model base for large-scale wireless sensor networks (WSN-SES/MB), which is based on discrete event system specification formalism. Our proposed framework synthesizes the structure and models for sensor networks through our modeling construction process. The proposed framework achieves time and cost savings in constructing discrete event simulation-based models. In addition, the framework increases the diversity of simulation models by the process’s pruning algorithm. The simulation results validate that the proposed framework provides up to 8% time savings and up to 23% cost savings as compared to the manual extra effort.

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

Large‐scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs’ lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs’ lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).

РАСПРЕДЕЛЕННОЕ ИЗВЛЕЧЕНИЕ ИНФОРМАЦИИ ИЗ КРУПНОМАСШТАБНЫХ БЕСПРОВОДНЫХ СЕНСОРНЫХ СЕТЕЙ

The article discusses distributed information extraction from large-scale wireless sensor networks. The first subsection identifies a family of applications that could benefit from a higher-level information retrieval and delivery mechanism integrated into the WSN, and discusses the state of the art of deployment in a particular application. The following describes the requirements for an integrated high-level information retrieval engine and provides an overview of the prior art in complex queries and processing on a network. The next subsection defines WSN topologies, architectures and protocols suitable for modern information systems; this is followed by a description of a new hybrid approach called a distributed complex query processor.

РАСПРЕДЕЛЕННОЕ ИЗВЛЕЧЕНИЕ ИНФОРМАЦИИ ИЗ КРУПНОМАСШТАБНЫХ БЕСПРОВОДНЫХ СЕНСОРНЫХ СЕТЕЙ

The article discusses distributed information extraction from large-scale wireless sensor networks. The focus is on agentbased approaches to information retrieval, query-based systems and macro programming approaches are considered, and a research question is raised regarding the usefulness of network processing from an information point of view. Examples of developments are given, in which those that were used in practical placements are highlighted. Where possible, the architecture and the way the information is processed (centralized or distributed) are defined.

A Multi-Featured Actor-Critic Relay Selection Scheme for Large-Scale Energy Harvesting WSNs

In this letter, we focus on the design of a relay selection scheme in large-scale energy-harvesting wireless sensor networks. Considering the dynamic nature required for practical networks, multiple features, including queuing state, energy level, channel quality, and location, are involved in modeling. By introducing reinforcement learning techniques, a novel relay selection scheme based on an actor-critic algorithm with linear function approximation is proposed to improve network reliability while also taking into account the transmission delay and energy efficiency. The proposed scheme can be implemented independently in each source to maximize the data delivery ratio. Such a distributed scheme is more stable and scalable than centralized structures. The Lagrangian formula is applied to satisfy the constraints on hops and energy efficiency. Compared with traditional timer-based and Q-learning-based schemes, our simulation results show that our proposed scheme achieves good performance in terms of network reliability and obtains higher energy efficiency and lower transmission delay.

Parametric Variational Sum-Product Algorithm for Cooperative Localization in Wireless Sensor Networks

Communication overhead is a key task for the large-scale wireless sensor networks (WSNs) in the cooperative localization problems. This paper is aimed to propose a low-overhead algorithm without sacrificing the performance of localization, which is more practical for energy-saving WSNs. In this paper, the cooperative localization problem is formulated as a variational reference problem on a factor graph (FG). Combining the high performance of particle-based algorithms and the overhead advantage of parametric ones, a parametric variational sum-product algorithm (PVSPA) is proposed for the cooperative localization of WSNs. In prediction operation, to ensure high accuracy, messages referenced inside sensor nodes are particle-based. In correction operation, Gaussian parametric representation is adopted to the observation message and the communication overhead is obviously decreased. Simulation results show that the localization accuracy of PVSPA equally matches the classic particle-based algorithms, while the communication overhead of it is far lower.

WSN Data Collection and Routing Protocol with Time Synchronization in Low-cost IoT Environment

The Internet of Things (IoT) is rapidly growing as a topic of research and conversation. IoT includes monitoring applications such as environment, healthcare, and military, requiring real-time gathering and data delivery. In dealing with time-sensitive applications, time synchronization is essential to collaborative wireless sensor network (WSN) nodes’ data collection and routing information sharing. This paper proposes an ad hoc routing and data collection protocol with a time synchronization function. A hybrid packet-based time synchronization is introduced, wherein reference time is sourced from the sink node (centralized) and sourced from the adjacent nodes (distributed). The time synchronization process is integrated into the routing and data transmissions in the network as it lessens overheads and transmission exchanges, which would be beneficial for large-scale WSN. Finally, we evaluate the protocol’s performance using a realistic low-cost WSN testbed. Our proposed protocol has achieved an averaged time offset of less than 100 milliseconds for a 15-minute test duration in multiple topology scenarios.

A decentralized algorithm to combine topology control with network coding

Network coding and topology control techniques have been widely used to increase throughput and improve the lifetime of Wireless Sensor Networks (WSNs). This paper considers the simultaneous utilization of these techniques in a WSN and proposes convex non-linear programming. Since solving the problem for a large-scale and dynamic WSN is impractical and almost impossible, Lagrangian, sub-gradient and the decomposition methods are employed to provide a decentralized algorithm. In the proposed algorithm, a node makes the computations by acquiring local knowledge and information from its neighbors. This paper provides a mathematical language to build an analytic foundation for the design of a modularized and decentralized algorithm that provides transmission ranges and routes for a WSN. The simulation results show that increasing the number of sources, sinks, sensors, and traffic load leads to improving the lifetime which is acquired by the proposed algorithm.

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.

SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs

Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve better fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network’s state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously.In this paper, we first formulate the controller placement problem as a multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the Cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs.

An efficient data collection path planning scheme for wireless sensor networks with mobile sinks

Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks.

A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (NMFMP-CDA)

Data aggregation is a promising solution for minimizing the communication overhead by merging redundant data thereby prolonging the lifetime of energy starving Wireless Sensor Network (WSN). Deployment of heterogeneous sensors for measuring different kinds of physical parameter requires the aggregator to combine diverse data in a smooth and secure manner. Supporting multi functional data aggregation can reduce the transmission cost wherein the base station can compute multiple statistical operations in one query. In this paper, we propose a novel secure energy efficient scheme for aggregating data of diverse parameters by representing sensed data as number of occurrences of different range value using binary encoded form thereby enabling the base station to compute multiple statistical functions over the obtained aggregate of each single parameter in one query. This also facilitates aggregation at every hop with less communication overhead and allows the network size to grow dynamically which in turn meets the need of large scale WSN. To support the recovery of parameter wise elaborated view from the multi parameter aggregate a novelty is employed in additive aggregation. End to end confidentiality of the data is secured by adopting elliptic curve based homomorphic encryption scheme. In addition, signature is attached with the cipher text to preserve the data integrity and authenticity of the node both at the base station and the aggregator which filters out false data at the earliest there by saving bandwidth. The efficiency of the proposed scheme is analyzed in terms of computation and communication overhead with respect to various schemes for various network sizes. This scheme is also validated against various attacks and proved to be efficient for aggregating more number of parameters. To the best of our understanding, our proposed scheme is the first to meet all of the above stated quality measures with a good performance.