Connectivity In Sensor Networks Research Articles

Improving Connectivity in Urban IoT-Based Wireless Sensor Networks: A Nonorthogonal Cognitive-Based Power Allocation

Improving Connectivity in Urban IoT-Based Wireless Sensor Networks: A Nonorthogonal Cognitive-Based Power Allocation

Coverage Optimization Strategy of UWSNs Based on Improved Sparrow Search Algorithm

Randomly distributed nodes lead to low coverage rate and low connectivity in the underwater wireless sensor networks (UWSNs). An UWSNs node coverage optimization strategy based on the Improved Sparrow Search Algorithm (ISSA) is designed to improve coverage rate and connectivity. The ISSA algorithm effectively balances the global and local optimization capabilities of nodes by introducing nonlinear inertia weights and Levy flight strategy, thereby avoiding the algorithm from getting trapped in local optima. Simulation experiments demonstrate that ISSA achieves optimal deployment results in a short time. It not only improves network coverage but also maintains good network connectivity and reduces node redundancy, thereby e ffectively reducing network energy consumption. Compared to other common algorithms , ISSA can increase network coverage up to 8.4% and improve network utilization up to11.8%, and meets the deployment requirements.

DAO2: Overcoming Overall Storage Overflow in Intermittently Connected Sensor Networks

Many emerging sensor network applications operate in challenging environments wherein the base station is unavailable. Data generated from such intermittently connected sensor networks (ICSNs) must be stored inside the network for some unpredictable time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{DAO}^2$</tex-math> </inline-formula> ). Utilizing spatial data correlation that commonly exists among sensory data, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{DAO}^2$</tex-math> </inline-formula> employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> ) and quota traveling salesmen placement problem (Q- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> ). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> and Q- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in energy consumption.

Modeling and Analyzing Urban Sensor Network Connectivity Based on Open Data

The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices’ location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.

Node Deployment Strategy Based on Improved Particle Swarm Algorithm in Three-dimensional Underwater Sensor Networks

To enhance the monitoring quality of the underwater three-dimensional target area and address issues such as uneven node distribution, inadequate coverage, and poor connectivity in traditional deployment strategies, a new node deployment strategy based on grid deployment with a variable weight particle swarm algorithm has been proposed in this research paper. Its goal is to minimize the quantity of deployed nodes while maintaining the coverage and connectivity of the three-dimensional wireless sensor networks (UWSNs). Simulation results demonstrate that this deployment strategy leads to good performance of the sensor network, and our approach ensures maximum coverage with the same number of nodes.

Technology Roadmap for Flexible Sensors.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Synthesis of Large-Scale Instant IoT Networks

While most networks have long lifetimes, temporary network infrastructure is often useful for special events, pop-up retail, or disaster response. An <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instant IoT</i> network is one that is rapidly constructed, used for a few days, then dismantled. We consider the synthesis of instant IoT networks in urban settings. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scaling</i> to large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems using satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks. To scale to problem sizes beyond what these frameworks are capable of, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hierarchical synthesis</i> technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.

Analysis of Life Time Improvement Energy Efficient Risk-Free Protocol (ERP) for Data Transmission in Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have come to be seen as the fundamental architecture that paved the way for the development of the Internet of Things (IoT). However, a challenging problem develops when WSNs are connected to the IoT because of their nodes' high energy consumption and low network lifetime. As a result, the core concepts of WSNs include energy limits in sensor nodes, sensor data exchange, and routing protocols. The enhanced smart-energy-efficient routing protocol (ESEERP) presented in this study addresses the aforementioned shortcomings by extending the network's lifespan and enhancing connectivity. Since the Internet of Things (IoT) demands a significant amount of energy for sensing, processing, and data transport, the IoT application in wireless sensor networks (WSNs) poses substantial issues in maintaining network durability. As a result, multiple traditional algorithms incorporate different optimization techniques to improve the performance of WSN networks. We utilize IPv4 in wireless sensor networks to transmit data (packets), however, this strategy prevents users from accessing big amounts of data over long distances since it may need a lot of power to transmit large amounts of data using IPv4. The Internet of Things (IoT) can be used to solve this problem because it connects small battery-operated devices like sensors as well as consumer electronics and home appliances. One of the most often utilised wireless methods for sensor network connectivity is the IEEE 802.11 standard.

Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme. Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors’ and clusters’ detection quality, heterogeneity in their communication opportunities, and non-convexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.

E-DRAFT: An Efficient Data Retrieval and Forwarding Technique for Named Data Network Based Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Network (WMSN) is a wirelessly connected sensor network featuring multimedia devices such as cameras and microphones that can retrieve video and audio streams, photographs, and scalar sensor data. Because of the low cost of cameras and microphones, in addition to significant advances in distributed signal processing and multimedia data coding approaches, WMSNs can offer multimedia content. Moreover, the most critical difficulty in the implementation of WMSN is to improve the efficiency and lifetime of the network. The Named-Data Network (NDN) technique is used in WMSNs to increase communication and transmission mechanisms efficiency. NDN is thought to be one of the most demanding Internet architectures in the future. Unlike host-centric techniques that focus on the location of the content, NDN focuses on providing the content. In this paper, we propose a novel accumulative Interest-based content-store architecture for NDN-based WMSNs to improve the efficiency of individual nodes as well as the overall network. Furthermore, the proposed solution also controls packet flooding. The proposed scheme is named "Efficient Data Retrieval and Forwarding (E-DRAFT)". Moreover, the ndnSIM simulator is used to run comprehensive simulations. Compared to existing methodologies, the simulation results reveal that the proposed approach stops additional packet flooding and effectively stores and retrieves Data packets with enhanced processing efficiency.

BIG DATA AND ARTIFICIAL INTELLIGENCE

The Big Data embodies a technology that permits the storage, processing, and management of broad and complex data sets in which traditional data processing applications are not applicable. These sets of data are usually characterized by a substantial volume of information that they carry, variety, versatility in terms of the format in which they are written, as well as a high-speed ingress which is often greater than the speed of processing. A particular challenge is the data which are coming from the Internet of Things (IoT) “world” that is constantly expanding and which already consists of several billion devices that can measure various parameters in the environment, communicate, process and transmit information. The data streams emanating from these devices are changing traditional approaches to data management and contribute to the emergence of the Big Data paradigm. This paper discusses the characteristics of the IoT infrastructure in terms of vast scale sensor applications and a possibility of connectivity of sensor networks, as well as various techniques of collection, storage, archiving and processing of the data on the cloud.

Efficient target monitoring with fault‐tolerant connectivity in wireless sensor networks

AbstractSensing coverage with fault‐tolerant connectivity is considered as a recent research challenge, which measures the Quality of Service (QoS) in wireless sensor networks. QoS is determined based on the set of deployed sensor nodes that monitor all available discrete targets lying at the given point of interest and at the same time have to retain reliable connectivity with the base station. In this article, we focus on two phases namely, monitoring targets in the given sensing area by splitting the sensor network into set covers, which is considered as a nondeterministic polynomial‐time complete problem and optimal deployment of intelligent nodes. At first, achieving full coverage with deployed sensors by covering all discrete targets to prolong the network lifetime. On the other hand, a novel S2 (sensors‐spine) node approach was used to place an optimal number of intelligent nodes using a nature‐inspired algorithm named moth flame optimization to overcome the fault among the disjoints sets such that the entire network was fully connected and ensure that sensed data from each target points are collected by the sink node. To evaluate our proposed design, extensive simulations were conducted to demonstrate the efficacy of our proposed mechanism that outperforms other existing techniques found in the literature in terms of prolonged network lifetime and there is no failure in the transmission path during data transmission to the sink node at every t s, such that the entire network was fully connected.

Fast Path Planning of Autonomous Vehicles in 3D Environments

Three dimensional path planner is crucial for the safe navigation of autonomous vehicles (AV), such as unmanned aerial vehicles or unmanned underwater vehicles, which operate in three dimensions. In this paper, we develop a novel 3D path planner, which is fast in generating a near-optimal solution path. The planner generates the 3D path considering the size of an AV so that as the AV traverses the constructed path, it does not collide with an obstacle. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to the goal utilizing the network. The virtual agent’s maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star algorithm.

Modified Floyd–Warshall’s Algorithm for Maximum Connectivity in Wireless Sensor Networks under Uncertainty

The main objective of this paper is to find the duration of maximum time connectivity of sensor nodes under uncertainty utilizing the prespecified voltage/power of each sensor node. Wireless sensor networks (WSNs) are composed of nodes that transmit data between each other over routing. A variety of routing protocols and algorithms exist, each related to a particular set of conditions. There are a variety of routing algorithms available, some of which can be used in WSNs for routing. The goal of the fastest distance routing algorithms in a WSN is to use the least amount of energy possible. In a WSN, Dijkstra’s algorithm is typically used for shortest path routing. The Floyd–Warshall’s algorithm is used to compute the shortest paths between distinct nodes in a regular graph, but due to the absence of a communication mode, this algorithm is not ideal for routing in wireless networks. In this research work, we have considered a WSN to find out the maximum connectivity time utilizing optimum voltage. On the other hand, duration of connectivity and energy/voltage are two vital parameters that are difficult to manage. Because of limited resources and safety concerns, safety implementation is limited. Also, due to the irregular/hazardous environmental situations, the distance between sensor nodes and its voltage to link up the nodes are totally unpredictable. In this work, we employ triangular fuzzy numbers to express unpredictability. Then, utilizing defuzzification of fuzzy numbers, the associated WSN problem was transformed into a crisp one. The widely used signed distance approach has been applied for the defuzzification of fuzzy numbers in this case. To determine the best outcome and to illustrate the usefulness of the suggested technique, a numerical example has been solved using the modified Floyd–Warshall’s algorithm. Finally, concluding remarks on the proposed approach as well as future studies have been provided.

Parallelized Active Information Gathering Using Multisensor Network for Environment Monitoring

Motivated by the application of environment monitoring, this article studies a novel algorithmic framework for solving, in a parallelized manner, the problem of multisensor active information gathering. Unlike the existing methods relying on the fully connected sensor network and sequential processing, our approach builds on the generic network topology and enables individual sensors to simultaneously make their decisions by communicating with the immediate neighbors. Leveraging the cooperation among the multisensor network, we show that the computational complexity of the proposed parallelized algorithm can be greatly reduced compared to the sequential updating schemes, and meanwhile, the suboptimality of obtained solutions is guaranteed. The interconnection between the algorithm complexity and solution quality is explicitly established with respect to the network topology. Based on such interconnection, we further provide the approach to design the optimal sensor network by the given time budget of the algorithm execution. Finally, numerical simulations on a methane emission monitoring scenario are presented to validate the effectiveness of our approach.

ДОСЛІДЖЕННЯ ЗАДАЧ ПОБУДОВИ БЕЗДРОТОВИХ СЕНСОРНИХ МЕРЕЖ

The research of problems of construction of wireless sensor networks is carried out in the work, the main stage of their decision is development of models of the description of a sensor network and the corresponding methods, technologies of maintenance of necessary parameters of quality of functioning of a sensor network. The study involves the consideration of basic technologies and standards in which there is an opportunity to implement the creation of wireless sensor networks. The basis of wireless sensor networks are communication channels organized between network elements using appropriate wireless technology. The main tasks of building wireless sensor networks: determining traffic parameters; determining the list of services; development or selection of a network model; determination of network functioning indicators; solving the problem of compromise between the quality of operation, traffic and resources. A number of wireless networking technologies are widespread in corporate networks, use an unlicensed frequency band, and in private networks, which allows their use in a number of tasks, due to the density of devices that support them and the availability of technology. A number of technologies do not support the functions of self-organization of the communication network. Such mechanisms can be implemented using software and related protocols at the network level. Existing technologies have the ability to organize communication from tens to tens of thousands of meters. For specific applications, it is one of the essential factors in choosing the appropriate technology. The probability of connectivity in wireless sensor networks depends on the structure and parameters that determine the network: characteristics of the service area (volume, flat, configuration, geometric dimensions), number of network nodes, characteristics of communication capabilities of network elements, antenna patterns, standards and protocols, communication range, transmitter power), features of the involved routing protocols. Features of providing connectivity in wireless sensor networks lie in the possibility of using gateways built using various technologies, which can significantly expand the capabilities of wireless networks in increasing the likelihood of connectivity. A feature of the construction of wireless sensor networks are significant differences in traffic servicing in the networks of different technologies used. Indicators of the quality of wireless networks, such as bandwidth, delay, probability of loss are interdependent and show how effectively the sensor network works. When you change one of the performance indicators of wireless sensor networks, it affects other quality indicators.

Low-Latency and High-Concurrency 5G Wireless Sensor Network Assists Innovation in Ideological and Political Education in Colleges and Universities

This paper first analyzes the existing optimization models and methods based on low-latency and high-concurrency 5G wireless sensor networks and focuses on some research results based on the physical layer, data link layer, and network layer. After that, the energy-constrained cross-layer optimization algorithm of the joint physical layer and network layer and the cross-layer optimization algorithm based on reliable transmission are analyzed and compared. According to the ideological and political education cycle and network connectivity of wireless sensor networks, the existing definition of the ideological and political education cycle of wireless sensor networks was questioned and its irrationality was demonstrated. Firstly, we introduce the system model, energy capture model, and energy consumption model of this scheme; then, calculate the energy consumption of the node sending and receiving data and analyze the maximum throughput that the node can provide for data transmission to perform mathematical modeling. In a balanced ideological and political education cycle, the optimal solution is given. Finally, it introduces distributed high-throughput routing for EH-WSNs based on low latency and high concurrency and gives a performance comparison with the shortest path routing scheme. The energy consumption of each part of the node is analyzed in a comprehensive and in-depth manner, and the direction of the energy optimization strategy of the network node and its sensor network is pointed out. On this basis, this article proposes a new definition of the network ideological and political education cycle: the ideological and political education cycle of the wireless sensor network is equal to the ideological and political education cycle of the first joint in the network that fails due to energy exhaustion. Based on this definition, the wireless sensor network is analyzed, and the cross-layer (physical layer, data link layer, and network layer) optimization mathematical model of the wireless sensor network ideological and political education cycle is established. The two data collection schemes proposed in this paper are simulated under the conditions of no-delay ideological and political education cycle and different time-delayed ideological and political education cycles. The simulation results show that the model is better than both in terms of network life and data integrity.

Memetic Algorithm based Energy Efficient Wake-up Scheduling Scheme for Maximizing the Network Lifetime, Coverage and Connectivity in Three-Dimensional Wireless Sensor Networks

Memetic Algorithm based Energy Efficient Wake-up Scheduling Scheme for Maximizing the Network Lifetime, Coverage and Connectivity in Three-Dimensional Wireless Sensor Networks

Lattice-Based 3-Dimensional Wireless Sensor Deployment

With the wide application of wireless sensor networks (WSNs) in real space, there are numerous studies on 3D sensor deployments. In this paper, the k -connectivity theoretical model of fixed and random nodes in regular lattice-based deployment was proposed to study the coverage and connectivity of sensor networks with regular lattice in 3D space. The full connectivity range and cost of the deployment with sensor nodes fixed in the centers of four regular lattices were quantitatively analyzed. The optimal single lattice coverage model and the ratio of the communication range to the sensing range r c / r s were investigated when the deployment of random nodes satisfied the k -connectivity requirements for full coverage. In addition, based on the actual sensing model, the coverage, communication link quality, and reliability of different lattice-based deployment models were determined in this study.

Design of energy‐efficient intermittently connected sensor networks

This paper introduces the concept of intermittently connected sensor networks where sensors are sleep most of time and become active randomly and rarely. In the networks, packet collision probability and power consumption can be reduced. Meanwhile, the delivery delay increases with the use of the low activation values. In this paper, we design the energy‐efficient intermittently connected sensor networks, considering this trade‐off of activation control. We first provide the algorithm that computes the optimal packet forwarding rule achieving the minimum delivery delay, which can be calculated using the activation rates. We then propose the activation rate control methods, which control the activation rates of the sensors based on the density of the sensors and/or the number of wake‐ups of sensors having relay packets. By doing this, we can improve the delay performance, while suppressing the increase in the packet collision probability and the power consumption of sensors. Through simulation experiments, we demonstrate the effectiveness of our activation control methods in the intermittently connected sensor networks. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.