Stability Of Wireless Sensor Networks Research Articles

Joint User Scheduling, Power Configuration and Trajectory Planning Strategy for UAV-Aided WSNs

The future sixth-generation (6G) communication system is promising to provide differentiated communication services for massive users worldwide. To achieve this goal, wireless sensor networks (WSNs), regarded as the critical component of the 6G system, will be widely deployed in large-scale non-urban areas such as mountain villages, forests, and oceans. To ensure the high efficiency and stability of WSNs without the support of ground facilities, unmanned aerial vehicles (UAVs) are dispatched to provide assistance for WSNs. Nevertheless, considering the limitations of both the mobility and communication capability of UAVs, the diversified communication demands pose huge challenges for UAV-aided WSNs in large-scale application scenarios, such as how to schedule the SNs served in each time slot, how to optimize their working parameters, and how to plan the UAV trajectory. Therefore, a joint user scheduling, power configuration, and trajectory planning strategy is proposed in this article, which is formulated as an optimization problem suffering from non-convexity and complication. The successive optimization scheme is adopted here to efficiently solve this problem, which divides the original problem into three sub-problems and solves them iteratively. The simulation results prove the effectiveness and efficiency of the proposed strategy.

Distributed energy-efficient clustering routing protocol for wireless sensor networks using affinity propagation and fuzzy logic

Organizing nodes into clusters and forwarding data to the base station (BS) in clustering routing protocols have been widely utilized to improve the energy efficiency, scalability and stability of wireless sensor networks. Making decisions on how many clusters are formed, which nodes are selected as cluster heads (CHs) and who become the relay nodes significantly impact the network performance. Therefore, a distributed clustering routing protocol combined affinity propagation (AP) with fuzzy logic called DAPFL is proposed in this paper, which considers not only energy efficiency but also energy balance to extend the network lifetime. In DAPFL, AP is firstly used to determine the number of clusters and select the best CHs simultaneously based on residual energy, distance between nodes. Then, the optimal next-hop CHs are chosen by using fuzzy logic system with residual energy, data length and distance to BS as descriptors. Simulations in different scenarios are carried out to verify the effectiveness of DAPFL, and the results show that DAPFL exhibits the promising performance in terms of network energy consumption, standard deviation of residual energy, network throughput and lifetime, compared with the up-to-date distributed clustering routing protocols EEFUC, EEFRP, LEACH-AP and APSA.

Smart Spider Monkey Optimization (SSMO) for Energy-Based Cluster-Head Selection Adapted for Biomedical Engineering Applications.

Using energy efficiency to increase the life and sustainability of wireless sensor networks (WSNs) for biomedical applications is still a challenge. Clustering has boosted energy productivity by allowing cluster heads to be categorized, but its implementation is still a challenge. Existing cluster head selection criteria start with determining acceptable cluster head locations. The cluster heads are picked from the nodes that are most closely connected with these places. This location-based paradigm incorporates challenges such as faster processing, less precise selection, and redundant node selection. The development of the sampling-based smart spider monkey optimization (SSMO) approach is addressed in this paper. If the sample population's nodes are varied, network nodes are picked from among them. The problems with distributed nodes and cluster heads are no longer a concern. This article shows how to use an SSMO and smart CH selection to increase the lifetime and stability of WSNs. The goal of this study is to look at how cluster heads are chosen using standard SMO and sampling-based SMO for biomed applications. Low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SSMO improved routing protocol measurements are compared to those obtained in homogeneous and heterogeneous settings using equivalent methodologies. In these implementations, SSMO boosts network longevity and stability periods by an estimated 12.22%, 6.92%, 32.652%, and 1.22%.

A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

The energy efficiency and stability of wireless sensor networks (WSNs) have always been a hot issue in the research. Clustering is a typical architecture for WSNs, and cluster heads (CHs) play a vital role. Unreasonable CH selection causes a lot of energy consumption. In this paper, we propose a competition-based unequal clustering multihop approach (CUCMA). CHs are selected by competition. First, the cluster radius (CR) of a node is calculated according to the distance to base station (BS). Then, CR is resized based on the number of around nodes. Only the nodes with high residual energy and appropriate distances to the selected CHs maybe become CHs, which are usually closer to the surrounding nodes. CUCMA and four related approaches are simulated in different scenarios. The results are analyzed, and it is proved that CUCMA balances the energy consumption of the CHs and reduces the energy consumption of the whole networks, thus leading to prolong the lifetime of WSNs.

Data-Driven Anomaly Detection Approach for Time-Series Streaming Data.

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization.

Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.

Long-Term Routing Stability of Wireless Sensor Networks in a Real-World Environment

The reliability of a wireless sensor network (WSN) is often assessed on node-to-node communication performance through link characterization. Long-term routing stability is an aspect of a WSN that is often overlooked in routing protocol implementations. In this paper, we investigate the routing stability of ZigBee PRO implemented WSN nodes that are deployed in a real-world environment. Frequent changes in next hops along routing paths between source and destination nodes can result in an increase in undesired energy consumption of the WSN. Hence, the relative routing path usage count, usage rate of unique next hop and switching frequency count are proposed as routing stability indicators. Our findings show that routing stability is subjected to not only the quality of a link but also to the implemented routing protocols, deployed environment and routing options available. More importantly, next hops with low usage rates are shown to experience a higher probability of disconnection from the Neighbor Table of respective source nodes, causing them to be short-lived. The need to avoid these links shows the importance of evaluating routing stability and identifying network bottlenecks.

An Active Defense Model with Low Power Consumption and Deviation for Wireless Sensor Networks Utilizing Evolutionary Game Theory

In wireless sensors networks, nodes may be easily captured and act non-cooperatively, for example by not defending forwarding packets in response to their own limited resources. If most of these nodes are obtained by attackers, and an attack by an internal malicious node occurs, the entire network will be paralyzed and not be able to provide normal service. Low power consumption indicates that the rational sensor nodes tend to be very close to the mean; high power consumption indicates that the rational sensor nodes are spread out over a large range of values. This paper offers an active defense model for wireless sensor networks based on evolutionary game theory. We use evolutionary game theory to analyze the reliability and stability of a wireless sensor network with malicious nodes. Adding a defense model into the strategy space of the rational nodes and establishing a preventive mechanism forces the malicious node to abandon the attack and even switch to cooperative strategies. Thus, this paper argues that the stability and reliability of wireless sensor networks can be improved. Numerical experiments were conducted to evaluate the proposed defense model, and these results verified our conclusions based on a theoretical analysis that showed that, compared with the existing algorithms, our approach has lower energy consumption, lower deviation, and a higher probability to quickly switch each node to cooperative strategies.

Energy optimization of ant colony algorithm in wireless sensor network

An energy consumption optimization algorithm based on ant colony algorithm is proposed for wireless sensor network. The proposed algorithm allows each node in wireless sensor network to save the distance and residual energy of neighbor nodes. Furthermore, in terms of probability selection of the nodes and the pheromone update, this algorithm focuses on the next hop node through the comparison of distance between the nodes and the residual energy, which ensures less possibility of nodes with low energy selected as the next hop. Therefore, the proposed algorithm improves energy load balancing, stability of wireless sensor network and, eventually, extends the life span of the wireless sensor network. The simulation results show that the improved ant colony algorithm avoids too much energy consumption of a certain local node resulting in more uniform energy consumption for each node.