Terrestrial Wireless Sensor Networks Research Articles

Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks.

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.

A review of routing algorithms for underwater wireless sensor networks

With the rise of the upsurge of exploiting marine resources and the rapid development of the research of terrestrial wireless sensor networks, the research of underwater wireless sensor networks (UWSNs,) has become a new research hotspot. In UWSNs, routing protocol plays a very important role as one of the important components. This paper introduces several classical routing algorithms from the following categories: location, sector, reinforcement learning, and intelligent optimization algorithms. Finally, the paper also summarizes and prospects the research direction of underwater wireless sensor network routing algorithms.

IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks

With the emerging technology in underwater wireless sensor networks (UWSN), many researchers are undergoing this field since it cannot maintain the batteries and recharge them manually. Network duration should be taken into account because they can easily be recharged by a non-conventional resource like solar energy. When coming to the data collection process, clustering is an effective method to construct vitality effective UWSNs. The clustering properties of UWSNs differ from those of terrestrial wireless sensor networks (TWSNs) due to the sparse deployment of nodes as well as the dynamic nature of the channel. This paper proposes improved efficient data aggregation in a Hexagonal grid with energy optimization (IEDA-HGEO) protocol for effective data transmission with an optimal clustering process. It is further compared with ERP2R n energy-efficient routing protocol and EGRC (Energy-efficiency Grid Routing based on 3D Cubes). The three techniques mentioned above are specifically examined for their applicability to underwater communication, and their performance is compared in terms of energy consumption, efficiency, throughput, packet delivery ratio, and delay. The proposed method achieved the following metrics: delay 41%, energy consumption 48%, efficiency 95%, throughput 95%, and PDR 92%.

Adaptive Data Dissemination Algorithm Based on Storing-Discarding Equilibrium for OUSNs

Opportunistic underwater sensor networks (OUSNs) are deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. The data dissemination in OUSNs differs significantly from those in terrestrial wireless sensor networks or delay-tolerant networks, due to the signal irregularity in underwater communications and the limited storage capacity of the nodes in OUSNs. To alleviate the storage overflows on nodes and make room for the newly arriving data packets, some stored data packets ought to be actively discarded by nodes. This research begins with the construction of a differential equation set to describe the propagation process of data packets in OUSNs, and the storing-discarding equilibrium is investigated such that each data packet is expected to propagate and disappear during the allowable dissemination time slots. After that, the optimal storing probabilities and discarding probabilities are obtained for the nodes with different in-degrees to maximize the delivery ratio of data packets. Then, we propose an Adaptive Data Dissemination Algorithm (ADDA) for the storage-limited OUSNs with signal irregularity, where at each time slot the newly arriving data packets are stored and the stored data packets are discarded by nodes according to the obtained storing probabilities and discarding probabilities, respectively. Simulation results demonstrate the excellent performance of ADDA, showing that it can enhance the delivery ratio of data packets and reduce the number of storage overflows.

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.

Floating Nodes Assisted Cluster-Based Routing for Efficient Data Collection in Underwater Acoustic Sensor Networks

Exhaust research work has been carried out in the field of Terrestrial Wireless Sensor Networks (TWSN). Now, research penetration is moving toward Underwater Acoustic Sensor Networks (UASNs). The communication of UASNs has dependencies on acoustic instead of radio. Efficient data collection by the UASN seems a tedious job in comparison with TWSN due to the peculiar features of underwater communication. However, there is a way to accomplish efficient data collection metrics through the design of routing protocols by considering the unique features of UASN communications. In this context, we propose a novel scheme and we call it Floating Nodes assisted Cluster-Based Routing (FNCBR) scheme for efficient data collection in UASNs. In FNCBR, the network space is divided into cuboids to form clusters. Then, every cuboid is consigned with a floating node (FN) at the surface layer and two fixed cluster heads (CHs) are suspended at different depth levels. All CHs of cuboids are supposed to be connected with a floating node via a wired connection, while source nodes are haphazardly distributed in the whole network region. In FNCBR, source nodes are liable to send the sensed data either to the FN or to the nearest CH. The data collected by the CHs is moved towards the FNs, which further disseminate the data to the on-shore monitoring center via a Radio Frequency (RF) link. We conduct the simulations in Network Simulator (NS-3) to appraise the proposed FNCBR protocol with other clustering protocols (e.g., CVBF, BS-CVBF, and ANCRP) in terms of different performance metrics. After describing the results, a quantitative comparison table is also given. The FNCBR scheme is also evaluated in terms of multiple FN densities. The simulation outcomes justify that the proposed FNCBR scheme has given better results in all performance metrics than the baseline schemes.

AI-Based Efficient WUGS Network Channel Modeling and Clustered Cooperative Communication

Wireless underground sensor networks (WUSNs) are sub-ground-surface sensor node networks designed to establish real-time tracking capacities in diverse underground ecosystems composed of soil, water, oil, and other materials. The contact medium is the key distinction between USNs and terrestrial wireless sensor networks. The communication properties of electromagnetic (EM) waves in underwater resources like mud, water, oil, and other materials, as well as major variations between propagation in air, make classification of the underground wireless channel difficult. From the transmission of data, the channels modelling is involved to remove the noise and reduce the energy. After that Multi-driven Clustering Algorithm (MCA) method is used to cluster the channel based on the EDT method (Energy, Distance, and Time). Completing the transmission, the data can be transmitted to the destination. The experimental portion of the proposed method is analyzed by varying parameters such as energy, throughput, channel response, and time duration of the data transmission. From the experimental analysis, it is observed that 55% and 45% more energy levels can be saved with the proposed MCA method than with the LEACH methods for DR and SW media methods, respectively. The throughput of the proposed method is proven with 45% and 42% improvement over the time horizon in the median under the frequency for soil of the LEACH and LEACH-CO2 methods, correspondingly.

A K-Means and Ant Colony Optimization-Based Routing in Underwater Sensor Networks

Reliable data transfer seems a quite challenging task in underwater sensor networks (UWSNs) in comparison with terrestrial wireless sensor networks due to the peculiar attributes of UWSN communication. Therefore, K-means and ant colony optimization-based routing (KACO) is proposed in this paper. In KACO, network area under water is divided into layers with regard to the depth level. And nodes of each layer are divided into clusters by the optimized K-means algorithm. The K-means algorithm is used to cluster nodes. Considering the shortcoming of K-means clustering, an improved K-means clustering is used to select the initial cluster center. In the stage of selecting cluster heads, the remaining energy of nodes and the distance from the sink node are used to calculate the competing factors of nodes, and then, the cluster heads are selected according to the competing factors. In the intercluster routing, the ant colony optimization (ACO) was improved by introducing the Gini coefficient, and the intercluster routing based on improved ACO is proposed. The simulation results show that the proposed KACO routing can effectively reduce the energy consumption of nodes and improve the efficiency of packet transmission.

An Intelligent Scheduling Model of Computer Human Resources in Complex Scenarios Based on Artificial Intelligence

Human resource allocation in any organization is still a challenging process as the candidate should meet the requirement of the organization’s needs and skills. During the traditional period of human resource allocation, the employer is able to meet the candidate and perform communication directly to analyze the candidature. Though this process is preferred by most organizations, the organizations were facing difficulties during the complex scenarios. In those circumstances, human resource allocation can be overcome by implementing artificial intelligence with the support of terrestrial wireless sensor network. The combination of these two technologies will make the easy communication among the employers and the employee, as like the traditional system. In this study, Fordful Carson Method (FCM) is implemented for the human resource allocation schedule by using intelligent networking system. The proposed model is compared with the existing Adam model for the resource scheduling and observed that the proposed model has obtained 99.12% of accuracy in resource scheduling.

РАЗРАБОТКА ИМИТАЦИОННОЙ МОДЕЛИ ИСПОЛЬЗОВАНИЯ РОЯ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ В СЕЛЬСКОМ ХОЗЯЙСТВЕ

Introduction: Due to the rapid technological development of communication and network technologies and the emergence of the Internet of Things technology, unmanned aerial vehicles (UAVs) have become used in almost all areas of society, from military to civilian. Agriculture is one area where UAVs will be used to monitor vast areas of crops. UAVs will be able to receive data on the state of the soil, improve agriculture and use plant protection products from insects and birds. Consequently, agriculture will be the largest market for UAVs. Objective. The article discusses the possibility of using a swarm of unmanned aerial vehicles in hard-to-reach agricultural areas to create a wide coverage area for transmitting information from ground-based wireless sensor networks. Architectural solutions for flying sensor networks are investigated and a simulation model is presented that integrates flying sensor networks (LSNs) and terrestrial wireless sensor networks (WSNs) for data transmission. The model studies the clustering of an UAV swarm using the K-means method and the search for the shortest path in routing using the Dijkstra algorithm. Result. Computer simulation of optimal routing for a swarm of 250 UAVs has been carried out. The results of UAV swarm clustering modeling are presented, a UAV network clustering model is presented using the machine learning method the K-means algorithm, and an algorithm for routing data through the UAV swarm network using Dijkstra's algorithm, which finds the shortest path through the formed clusters.

Adaptive Time Difference of Time of Arrival in Wireless Sensor Network Routing for Enhancing Quality of Service

Underwater wireless communications are critical in military and corporate operations such as environmental monitoring, underwater exploration, and scientific data collection. Existing protocols for terrestrial wireless sensor networks (TWSNs) perform poorly in terms of energy efficiency, dependability, and transmission. Because they have separate qualities, they cannot be used directly in the UWSN. The present challenges include developing an EDVR algorithm for determining the distance to each node and the variance in node depth in order to estimate energy consumption reductions. This technique takes the depth of the two-hop neighbors into account and calculates the time aid from the Adaptive Time Difference of Arrival (ATDoA), which is avoided by broadcasting information to its neighboring node, with farther forward nodes. To determine the time difference between the reception of two signals at a node, the adaptive time Difference of time of arrival (ATDoA) is easier to measure than the time at which the signal arrives. In the UWSN, the following transmission assigns higher node energy if the node is lower. It increases system performance, saves lives, and minimizes packet wait time at the destination. The results show that nodes have a longer lifetime, fewer dead nodes, use less energy, and take less time to propagate than techniques.

Development of Underwater Wireless Sensor Networks (UWSNs): Using MAC Protocol with Concurrent Transmission Based on Slotted FAMA

Dong, W.; Yang, Q.; Huang, X.; Chen, Y., and Sun, S., 2022. Development of underwater wireless sensor networks (UWSNs): Using MAC protocol with concurrent transmission based on slotted FAMA. Journal of Coastal Research, 38(1), 241–248. Coconut Creek (Florida), ISSN 0749-0208. With the continuous development of ocean research in recent years, as an extension of terrestrial wireless sensor networks, underwater wireless sensor networks play an increasingly important role in ocean and coastal research. The handshaking efficiency in the traditional slotted floor acquisition multiple access (FAMA) is low. After a successful handshake, only one node is allowed to send data. Moreover, there are unnecessary back-off problems; thus, the channel utilization is low. Aiming at improving the shortcomings of the traditional slotted FAMA, this paper proposes a Medium Access Control protocol with concurrent transmission based on slotted FAMA (SFAMA-CT). The sending node uses the long propagation delay of the network to maintain a sending matrix according to the position information of the nodes in the propagation range. The value of each element of the sending matrix is determined by whether two nodes can simultaneously send data without collision. Then, the sending node determines whether it can choose to send in the DATA phase according to the number of received X Clear-To-Send (XCTS) packets (where “X” indicates a CTS packet intended for another node) and element information of the sending matrix. The proposed protocol has higher handshake efficiency and improves the concurrent transmission of the network compared to that of other protocols. The simulation results show that, compared to slotted FAMA and SFAMA-data train (DT), the throughput of SFAMA-CT is increased by approximately 115% and 20%, respectively. SFAMA-CT also has good performance in the end-to-end delay.

Localization Algorithm for Underwater Sensor Network: A Review

As a significant component of ocean exploration, underwater localization has attracted extensive attention in both military and civil fields. Due to its low cost and convenience, underwater wireless sensor networks (UWSNs) is favored by related fields. As an important part of the Internet of Things (IoT), it can strengthen the trinity of land, sea, and air. The location of the underwater sensor node is the foundation of the UWSN application and is one of the research hotspots today. Many relevant research scholars have optimized the localization algorithm or introduced new methods to better locate the target nodes, thus promoting the development of related fields. In this article, the challenges of underwater acoustic communication and underwater positioning, the comparison between UWSNs and terrestrial wireless sensor networks (WSNs), the network structure, routing technology, and localization evaluation criteria are all introduced in detail. Moreover, we survey many cutting-edge underwater localization algorithms based on a new taxonomy (i.e., distance measurement, network scale, and anchor utilization). Moreover, these localization algorithms are compared and analyzed from various aspects. Meanwhile, localization discussion and research prospects are illustrated in this article.

A systematic literature review on mobility in terrestrial and underwater wireless sensor networks

SummaryWireless sensor network (WSN) is one of the most significant technologies that gets much attention from researchers due to its promising applications in various environments. Mobility in wireless sensor networks contributes significantly to improving the performance of terrestrial wireless sensor networks (TWSNs) and underwater wireless sensor networks (UWSNs). This research's primary goal is to highlight the roles of mobility and mobile element tasks in enhancing terrestrial and underwater wireless sensor networks. Our systematic literature review (SLR) analyzed studies published between the periods of 2003 and January 2020. Out of 1762 studies retrieved by searching six online digital libraries and other resources, only 107 of them were selected for primary studies based on selection criteria and relevant to three identified research questions. This was followed by data extraction from the primary studies for answering the three SLR questions. The study reveals that mobility plays an essential role in improving network performance in TWSNs and UWSNs, especially in the aspects of coverage, energy efficiency, throughput, and connectivity. The study also identifies the tasks of the mobile elements in TWSNs and UWSNs, including data collection, localization, and charging. Moreover, it demonstrates the parameters of the mobile elements that should be considered when building networks. Finally, this study concludes by highlighting some open research issues that can be considered for future research.

A Balanced Routing Protocol Based on Machine Learning for Underwater Sensor Networks

An underwater sensor network (UWSN) is a wireless network that is deployed in oceans, seas, and rivers for real-time exploration of environmental conditions. The network is used to measure temperature, pressure, water pollution, oxygen level, volcanic activity, floods, and water streams. Although radio frequency (RF) is widely utilized in wireless networks, it is incompatible with the UWSN environment; therefore, other communication mechanisms have been employed to manage the underwater wireless communication among sensors, such as acoustic channels, optical waves, or magnetic induction (MI). Unlike terrestrial wireless sensor networks, UWSNs are dynamic, and sensors move according to water activity. Therefore, the network topology changes rapidly. One of the most critical challenges in UWSNs is how to collect and route the sensed data from the distributed sensors to the sink node. Unfortunately, the direct application of efficient and well-established terrestrial routing protocols is not possible in UWSNs. In this work, a balanced routing protocol based on machine learning for underwater sensor networks (BRP-ML) is proposed that considers the UWSN environmental characteristics, such as power limitations and latency, while considering the void area issue. It is based on reinforcement learning (Q-learning), which aims to reduce the network latency and energy consumption of UWSNs. The communication technique in the proposed protocol is based on the MI technique, which has many advantages, such as steady and predictable channel response and low signal propagation delay. The simulation findings validated that BRP-ML reduced latency by 18% and increased energy efficiency by 16% compared to QELAR.

Survey on Recent Advancements in Energy-Efficient Routing Protocols for Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) have become highly efficient in conducting various operations in maritime environments. Compared to terrestrial wireless sensor networks, routing protocols in UWSNs are prone to high propagation delay, high energy consumption, low bandwidth, and low throughput. UWSNs are remotely located and operate without the need for human intervention. Most sensor batteries are energy restricted and very difficult to replace. One of the major challenges of UWSNs is the uneven utilization of energy resources, which reduces the network lifetime. Therefore, an energy-efficient routing mechanism is necessary to overcome the aforementioned problems. Many significant studies have attempted to realize this goal by designing energy-efficient routing protocols to provide efficient packet routing from source to destination. In this paper, we focus on discussing various energy-efficient routing protocols that are currently available for UWSNs, categorize them with a new taxonomy, and provide a comparative discussion. Finally, we present various research problems that remain open and challenges for future research.

Anchor Nodes Assisted Cluster-Based Routing Protocol for Reliable Data Transfer in Underwater Wireless Sensor Networks

Reliable data transfer seems a quite challenging task in Underwater Wireless Sensor Networks (UWSN) in comparison with Terrestrial Wireless Sensor Networks due to the peculiar attributes of UWSN communication. However, the reliable data transmission in UWSN is very limited. Yet, there is a way to achieve reliable data transfer metrics through the design of routing protocols by considering the exceptional features of UWSN communications. With this aim, we propose two schemes with multiple sinks-based network architecture: Anchor Nodes assisted Cluster-based Routing Protocol (ANCRP) to achieve reliable data transfer metrics and Void Handling technique in ANCRP (VH-ANCRP) to cope with the local maximum nodes. For which, the network space is divided into small cubes to form clusters. Then, each cube is assigned with an anchor node as a cluster head (CH). All cluster heads are supposed to be anchored at the centroid of a cube via a string, while source nodes are randomly distributed. In ANCRP, the source nodes are liable to send the sensed data to their designated CH. The CH transmits the sensed data to the next-hop CH and continues this procedure till the successful delivery of the data packets at the surface sinks. In VH-ANCRP, a void handling technique of making the ad-hoc CH is used by the void nodes to reconnect with the network operations. We perform extensive simulations in NS3 to validate our schemes. The simulation outcomes expel that both proposed schemes have improved the network performance when compared with the baseline schemes.

Security challenges of Internet of Underwater Things: A systematic literature review

AbstractWater covers approximately 71% of the earth surface, yet much of the underwater world remains unexplored due to technology limitations. Internet of Underwater Things (IoUT) is a network of underwater objects that enables monitoring subsea environment remotely. Underwater Wireless Sensor Network (UWSN) is the main enabling technology for IoUT. UWSNs are characterised by the limitations of the underlying acoustic communication medium, high energy consumption, lack of hardware resources to implement computationally intensive tasks and dynamic network topology due to node mobility. These characteristics render UNWSNs vulnerable to different attacks, such as Wormhole, Sybil, flooding, jamming, spoofing, and Denial of Service attacks. This article reviews peer‐reviewed literature that addresses the security challenges and attacks on UWSNs as well as possible mitigative solutions. Findings show that the biggest contributing factors to security threats in UWSNs are the limited energy supply, the limited communication medium and the harsh underwater communication conditions. Researchers in this field agree that the security measures of terrestrial wireless sensor networks are not directly applicable to UWSNs due to the unique nature of the underwater environment where resource management becomes a significant challenge. This article also outlines future research directions on security and privacy challenges of IoUT and UWSN.

SFM-MAC: Multichannel MAC Protocol for Underwater Sensor Networks Based on Underwater Passive Localization Mechanisms

Yang, Q.; Dong, W.; Xu, Z., and Huang, X., 2020. SFM-MAC: Multichannel MAC protocol for underwater sensor networks based on underwater passive localization mechanisms. Journal of Coastal Research, 36(6), 1332–1342. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of ocean research in recent years, underwater acoustic sensor networks, as the extension of terrestrial wireless sensor networks (WSNs), play an increasingly important role in ocean and coastal research. To solve the problem of spatial unfairness caused by spatiotemporal uncertainty of underwater acoustic signal propagation in underwater WSNs and improve the throughput of underwater networks, this paper proposes a slotted-floor multichannel medium access control (SFM-MAC) protocol based on a single transceiver. SFM-MAC uses an underwater passive localization algorithm to obtain location information and then transforms spatiotemporal information to avoid the synchronization of the whole network to reduce the spatiotemporal uncertainty of underwater networks; further, it uses request to send (RT)/channel announcement/channel listening/clear to send (CTS) handshaking mechanism to avoid multichannel hidden terminal problems and achieve fair access. In addition, the paper uses the Markov chain to construct the reservation model of control channels, and theoretically analyzes and calculates the theoretical throughput of the multichannel MAC protocol under the condition of reserved collision. Simulation experiments show that the network fairness index of SFM-MAC is 15% higher than that of traditional RTS/ CTS protocols. The effective network throughput of SFM-MAC in different business scenarios is 60–70% and 12–15% higher than the single-channel underwater MAC protocol SF acquisition multiple access and the multichannel underwater MAC protocol, respectively.

A synchronous duty-cycled reservation based MAC protocol for underwater wireless sensor networks

To design an energy-efficient Medium Access Control (MAC) protocol for the Underwater Wireless Sensor Networks (UWSNs) is an urgent research issue since depleted batteries cannot be recharged or replaced in the underwater environment. Moreover, the underwater acoustic channels are affected by hindrances such as long propagation delay and limited bandwidth, which appear in the design of the MAC protocol for the UWSNs. The available MAC protocols for the terrestrial wireless sensor networks exhibit low performance in energy efficiency, throughput and reliability in the UWSNs, and cannot be used in the UWSNs directly because of their unique characteristics. This paper proposes a synchronous duty-cycled reservation-based MAC protocol named Ordered Contention MAC (OCMAC) protocol. The basic mechanism of this protocol is to schedule data transmission by transmitters through the scheduling of Ready To Send (RTS) frames. The protocol eliminates the possible collision during data transmission and improves communication efficiency. The paper analyzes the performance in energy efficiency, throughput and reliability of the protocol by modeling the queuing behavior of OCMAC with a Markov Chain process. Furthermore, the analytical model is validated through a simulation study. The analysis results demonstrated that while providing good throughput and reliability, OCMAC can achieve energy saving.