Large-scale Wireless Sensor Networks Research Articles

APAP: An adaptive packet-reproduction and active packet-loss data collection protocol for WSNs

In lossy, large-scale wireless sensor networks (WSNs), data collection protocols face the challenge of balancing expected data transmission reliability and limited resources of sensors. Packet redundancy strategies have been widely employed to guarantee transmission reliability. However, these schemes often result in energy waste, especially in sink-neared hotspot districts. We propose the APAP to balance the two competing interests in energy-constrained WSNs. APAP mainly consists of two parts: adaptive packet-reproduction routing and active packet-loss mechanism. In the first part, we design a compensation function to compensate for packet losses and employ it in the packet-reproduction routing. Through available network parameters, such a function offers an optimized number of redundant packets. In the second part, we devise a novel distributed packet-loss mechanism to detect and intercept redundant packets in the hotspot area. Hotspot nodes record the simple traffic information in their vicinity on a local micro-table, these nodes transform their mode based on the records to reach a basic consensus with one-hop neighbors, they then collaboratively intercept excess packets from outside, thus the injection traffic can be mitigated. Such an interception area has the potential to be expanded and prolonged as the redundancy becomes more severe. Our mechanism imposes low-complexity requirements on general devices. Moreover, thorough mathematical analyses are offered for the performance of APAP. Simulation results indicate that with high reliability, the maximum node energy consumption is reduced by 44.7% to 66.3% compared to conventional protocols in one round. Besides, the network lifetime is prolonged by 91.1% to 200%.

Enhancing Packet Reliability in Wireless Multimedia Sensor Networks using a Proposed Distributed Dynamic Cooperative Protocol (DDCP) Routing Algorithm

Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as environmental monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Cooperation Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions. Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate, end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs. Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission.

Energy Efficient Data Aggregation with Dynamic Mobile Sink-Based Path Optimization in Large Scale WSNs Using Reinforcement Learning

Energy Efficient Data Aggregation with Dynamic Mobile Sink-Based Path Optimization in Large Scale WSNs Using Reinforcement Learning

An enhanced energy efficient protocol for large-scale IoT-based heterogeneous WSNs

There is increasing attention, recently, to optimizing energy consumption in IoT-based large-scale networks. Extending the lifetime of battery-powered nodes is a key challenge in such systems and their various application scenarios. This paper proposes a new zone-based and event-driven protocol for saving energy in large-scale heterogeneous WSNs called TESEES (Threshold Enabled Scalable and Energy Efficient Scheme). The proposed protocol is designed to support network scenarios deploying higher levels of heterogeneity with more than three types of sensor nodes (i.e., four, five, and more). TESEES is a reactive version of the proactive SEES protocol, in which we leverage a novel state-of-the-art thresholding model on the zone-based hierarchical deployments of heterogeneous nodes to regulate the data reporting process, avoiding unnecessary frequent data transmission and reducing the amount of energy dissipation of the sensing nodes and the entire system. With this model, we present a general technique for formulating distinct thresholds for network nodes in each established zone. This mechanism allows for individually configuring the nodes with transmission settings tailored to their respective roles, independent of the heterogeneity levels, total node count, or initial energy. This approach ensures that each node operates optimally within the network. In addition, we present an improved hybrid TMCCT (Threshold-based Minimum Cost Cross-layer Transmission) algorithm that operates at the node level and ensures effective data transmission control by considering current sensor values, heterogeneous event thresholds, and previous data records. Instead of periodical data transmission, this hybridization mechanism, integrated with a grid of energy-harvesting relay nodes, keeps the zone member nodes in the energy-saving mode for maximum time and allows for reactive data transmission only when necessary. This results in a reduced data-reporting frequency, less traffic load, minimized energy consumption, and thus a greater extension of the network’s lifetime. Moreover, unlike the traditional cluster-head election in the weighted probability-based protocols, TESEES relies on an efficient mechanism for zone aggregators’ election that runs at the zone level in multiple stages and employs various static and dynamic parameters based on their generated weights of importance. This leads to selecting the best candidate nodes for the aggregation task and, hence, fairly rotating the role among the zones’ alive nodes. The simulation results show significant improvements in the total energy saving, the lifetime extension, and the transmitted data reduction, reaching 29%, 68%, and 26% respectively, compared to the traditional SEES protocol. Also, the average energy consumption per single round has decreased by 36%.

Self-detection based fault diagnosis for wireless sensor networks

Wireless sensor network (WSN) is a collection of low-power and low-cost sensor nodes (SNs) deployed stochastically in the monitoring area to support various applications. The random occurrence of faulty sensor nodes affects the quality of service in WSN. Most of the existing fault detection algorithms are based on statistics, thresholds, majority voting, hypothetical tests, comparison, or machine learning and depend on the neighbor's sensing information to detect the fault status of SNs. Hence, these fault detection algorithms suffer from low detection accuracy (DA) and a high false alarm rate (FAR). Moreover, existing fault detection techniques are not scalable for large-scale WSNs, as they suffer from high energy overhead and detection latency. Furthermore, existing fault diagnosis algorithms burden an SN's resources while estimating the fault status of the WSN. This paper proposes a deep belief network (DBN) based self-detection algorithm to address these issues. The performance of the proposed DBN based self-detection algorithm has been evaluated using google colab, and MATLAB. The simulation results of the proposed algorithm has been compared with the existing fault detection algorithms. Simulation results show that the proposed algorithm performs better than the existing algorithms in terms of DA, FAR, and false positive rate. The performance of the proposed algorithm has also been evaluated in other network parameters such as energy consumption, and fault detection latency.

Integrity assured multi-functional multi-application secure data aggregation in wireless sensor networks (IAMFMA-SDA)

Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results.

Energy optimization routing for hierarchical cluster based WSN using artificial bee colony

Technology advancement in Wireless sensor network (WSNs) has attracted much attention in smart computing and potential usage across numerous applications fields. WSN composed of tiny and self-configured sensor nodes with battery operated. Sensors are restricted to limited energy and resources. Unbalanced nodes energy results in more energy consumption and affects network lifetime. Achieving energy efficiency remains a challenging issue in designing WSN routing. Approaches for minimising node energy usage through clustering techniques have offered optimal solutions. However, the existing clustering scheme fails to balance nodes energy without considering energy parameters, low energy node being chosen as cluster head, node density, and scalability. Existing scheme limits to global exploration possibilities and restricted to only specific search regions. In this paper, we propose an energy optimization routing using improved artificial bee colony (EOR-iABC) for cluster based WSN. The proposed EOR-iABC routing scheme intends to achieve energy optimization and prolong network's lifetime. The EOR-iABC adapts distinctive search policy using improved artificial bee colony algorithm for selection of energy efficient cluster heads (CH) in periodic time intervals through integration of crossover and mutation. The delay convergence is eliminated by associating employee and onlooker-bee stages, which helps to reform local search strategies. An optimal path from CH to base station (BS) is discovered through energy-efficient fitness node to improve network data collection efficiency. Grenade explosion method (GEM) and Cauchy operator are employed to extend search policies dynamically from one region to another for large-scale WSN. Simulation results show EOR-iABC outperforms in terms of energy efficiency by 27% compared to OCABC scheme and by 16% compared to IABCOCT scheme.

A Distributed Gradient Descent Method for Node Localization on Large-Scale Wireless Sensor Network

A distributed iterative method is proposed to solve the problem of node (sensor) localization for large-scale wireless sensor network (WSN), by leveraging the graph topology decomposition and gradient descent method. First, the undirected graph representing the WSN is divided into several overlapping subgraphs. Based on the decomposition subgraphs, the localization problem is splitting into a series of subproblems each of which reside on one subgraph. The iterative procedure is proceeded on the subgraphs and each iteration consists of two operators. The first operator is solving the subproblem in every subgraph by using the gradient descent method which possesses light computational cost, and the second operator is to fuse and average the local positions of nodes in the overlapping region of adjacent subgraphs. In order to enrich the available information of localization, the positions of the target nodes with high localization accuracy are used as the (pseudo) anchor nodes for the subsequent iteration. Owing to that the operators are accomplished on subgraphs with small sizes, the proposed distributed iterative method possesses low computational cost, making it suitable for large-scale WSN. Numerical results are included to demonstrate the effectiveness of the proposed localization method.

Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

The use of unmanned aerial vehicles (UAVs) is a promising solution to efficiently acquire data in large-scale wireless sensor networks (WSNs). In a wide-area WSN environment, UAV path planning is one of the challenging issues in solving the optimization problem to achieve complex and multiple objectives under various constraints related to UAV operation. In this paper, we propose a novel asynchronous UAV path planning mechanism for multi-objective UAV operation. In a three-dimensional sensor field, a grid-based sensor field and information gathering model is introduced. We define a UAV coverage area where line-of-sight communication is possible between a UAV and the sensors and propose a method to quickly find the grid-cells within the coverage area. We define a multi-purpose fitness function that maximizes the value of the acquired sensing information and at the same time minimizes the time and energy required for UAV operation. The value of the sensing information reflects the sensor density for each sensor type within UAV coverage, as well as changes in the sensing information values over time. In the proposed method, time and energy objective functions are learned by considering location-dependent communication link quality, sensor density, and the next UAV locations. The optimum UAV position and the movement schedule of each UAV are asynchronously derived using the proposed particle swarm optimization (PSO) algorithm with several UAV operational constraints. The experiment results demonstrate that the proposed method can maximize the utility of the objective function and achieve fast convergence in finding the optimal solution compared with other methods.

Deep Reinforcement Learning-Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network.

Wireless rechargeable sensor networks (WRSN) have been emerging as an effective solution to the energy constraint problem of wireless sensor networks (WSN). However, most of the existing charging schemes use Mobile Charging (MC) to charge nodes one-to-one and do not optimize MC scheduling from a more comprehensive perspective, leading to difficulties in meeting the huge energy demand of large-scale WSNs; therefore, one-to-multiple charging which can charge multiple nodes simultaneously may be a more reasonable choice. To achieve timely and efficient energy replenishment for large-scale WSN, we propose an online one-to-multiple charging scheme based on Deep Reinforcement Learning, which utilizes Double Dueling DQN (3DQN) to jointly optimize the scheduling of both the charging sequence of MC and the charging amount of nodes. The scheme cellularizes the whole network based on the effective charging distance of MC and uses 3DQN to determine the optimal charging cell sequence with the objective of minimizing dead nodes and adjusting the charging amount of each cell being recharged according to the nodes' energy demand in the cell, the network survival time, and MC's residual energy. To obtain better performance and timeliness to adapt to the varying environments, our scheme further utilizes Dueling DQN to improve the stability of training and uses Double DQN to reduce overestimation. Extensive simulation experiments show that our proposed scheme achieves better charging performance compared with several existing typical works, and it has significant advantages in terms of reducing node dead ratio and charging latency.

Fault Tolerance and Energy Efficient Multi-Hop Clustering with Dual Base Stations in Large Scale Wireless Sensor Network

Fault Tolerance and Energy Efficient Multi-Hop Clustering with Dual Base Stations in Large Scale Wireless Sensor Network

Optimizing Relay Sensors in Large-Scale Wireless Sensor Networks: A Biologically Inspired Approach

In recent years, tremendous advances in communication technologies coupled with the advent of the Internet of Things (IoT) have led to the emergence of the Big Data phenomenon. Big Data is one of the big IT challenges of the current decade. The amount of data produced is constantly increasing and makes it more and more difficult to process. Managing these masses of data requires the use of new data management systems with efficient access methods. Considered as one of the main sources of Big Data, wireless sensors used in networks offer a credible solution to the problem of Big Data management, especially its collection. Several solutions for Big Data collection based on large-scale wireless sensor networks (LS-WSN) are proposed, taking into account the nature of the applications. The hierarchical architecture is the one used for the deployment of these applications. In such an architecture, relay sensors play an important role in finding the balance of the network and maximizing its lifetime. In most LS-WSN applications, once deployed, the LS-WSN does not provide a mechanism to evaluate and improve the positions of the initially deployed relay sensors. This paper proposes, based on the growth model of physarum polycephalum and its ability to prune unnecessary links and retain only those deemed useful for food routing, a mechanism for evaluating and optimizing relay sensors in LS-WSNs. Simulation results indicate that the proposed approach significantly improves the network lifetime compared to the initial deployment and that can be a useful approach for LS-WSNs dedicated to Big Data collection. The effectiveness of the proposed technique is demonstrated by experimental results in terms of connectivity and network lifetime.

A detection probability guaranteed energy-efficient scheduling mechanism in large-scale WSN

In intrusion detection wireless sensor network (WSN), ensuring detection performance and prolonging network lifetime are two equally important but contradictory pursuits. In this paper, we propose a node scheduling scheme jointly considering the detection probability, energy consumption, and energy balance to ensure the detection performance and maximize the network lifetime. We mathematically transform the scheduling process into an optimization problem and further propose a novel mechanism called greedy-like step-by-step trimming heuristic algorithm (GLSTA) to obtain the optimal task nodes set. By gradually trimming unnecessary nodes, GLSTA intelligently eliminates redundancy and obtains a task set that can save energy and balance the residual energy of nodes. Moreover, we propose a dual cluster head (OCH-DCH, organization cluster head-data cluster head) scheme to realize the smooth update of the cluster due to the target movement. Simulation results show that the rounds of half nodes death of GLSTA are extended by at least 28.6% compared with existing mechanisms.

Deep Reinforcement Learning-Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks

Emergency event monitoring is a hot topic in wireless sensor networks (WSNs). Benefiting from the progress of Micro-Electro-Mechanical System (MEMS) technology, it is possible to process emergency events locally by using the computing capacities of redundant nodes in large-scale WSNs. However, it is challenging to design a resource scheduling and computation offloading strategy for a large number of nodes in an event-driven dynamic environment. In this paper, focusing on cooperative computing with a large number of nodes, we propose a set of solutions, including dynamic clustering, inter-cluster task assignment and intra-cluster one-to-multiple cooperative computing. Firstly, an equal-size K-means clustering algorithm is proposed, which activates the nodes around event location and then divides active nodes into several clusters. Then, through inter-cluster task assignment, every computation task of events is alternately assigned to the cluster heads. Next, in order to make each cluster efficiently complete the computation tasks within the deadline, a Deep Deterministic Policy Gradient (DDPG)-based intra-cluster one-to-multiple cooperative computing algorithm is proposed to obtain a computation offloading strategy. Simulation studies show that the performance of the proposed algorithm is close to that of the exhaustive algorithm and better than other classical algorithms and the Deep Q Network (DQN) algorithm.

Improved 3D localization algorithm for large scale wireless sensor networks

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

Optimal deployment of large-scale wireless sensor networks based on graph clustering and matrix factorization

It is undeniable that there are a large number of redundant nodes in a wireless sensor network. These redundant nodes cause a colossal waste of resources and seriously threaten the life of the sensor network. In this paper, we provide a sensor nodes optimization selection algorithm based on a graph for a large-scale wireless sensor network. Firstly, we propose a representation-clustering joint algorithm based on Graph Neural Network to partition the large-scale graph into several subgraphs. Then, we use Singular-Value-QR Decomposition for the node selection of each subgraph and achieve the optimal deployment for a large-scale wireless sensor network. We conduct the experiments on the CIMIS dataset. The results show that the mean square error between the reconstructed network and the original network is as low as 0.02433. Meanwhile, we also compare our algorithm with the classical optimization algorithm. The results imply that the mean square error of the proposed algorithm is lower and the distribution is more uniform. Further, we verify the scalability of the algorithm for the optimal deployment of the large-scale wireless sensor network.

Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.

Mobile Multiple Sink Path Planning for Large-Scale Sensor Networks Based on Hyper-Heuristic Artificial Bee Colony Algorithm

Large-scale wireless sensor networks consists a terrific amount of nodes, a wide range of deployment, extended data transmission time, and large network energy consumption. To solve the above problems, a mobile multiple Sink path planning based on hyper-heuristic artificial bee colony algorithm is proposed. The artificial bee colony algorithm is used as the high-level strategy. In view of the changes in the network operation process, namely the number of nodes and energy changes, the design of three stages of the artificial bee colony algorithm is used to choose and manipulate the low-level heuristic operator. The selected lower layer operator set plans the path of each mobile sink nodes. Compared with other famous meta-heuristic algorithms, it is proved that the proposed hyper-heuristic algorithm is an effective, efficient and robust algorithm, which can effectively solve the path planning problem in view of multiple sink nodes, reduce network delay and reduce network energy consumption.

CFMS: A Cluster-based Convergecast Framework for Dense Multi-Sink Wireless Sensor Networks

Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data collection process must be conducted while copying with packet collisions, nodes’ congestion or data redundancy. These issues always result in energy waste which is detrimental to network efficiency and lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise message overhead and packet losses. Simulation results show that this solution can mitigate delay while significantly increasing packet delivery and network lifetime.

RTM: Realistic Weight-Based Reliable Trust Model for Large Scale WSNs

RTM: Realistic Weight-Based Reliable Trust Model for Large Scale WSNs